title: Early steps towards web scale information extraction with LODIE

abstract: Information extraction (IE) is the technique for transforming unstructured textual data into structured representation that can be understood by machines. The exponential growth of the Web generates an exceptional quantity of data for which automatic knowledge capture is essential. This work describes the methodology for web scale information extraction in the LODIE project (linked open data information extraction) and highlights results from the early experiments carried out in the initial phase of the project. LODIE aims to develop information extraction techniques able to scale at web level and adapt to user information needs. The core idea behind LODIE is the usage of linked open data, a very large-scale information resource, as a ground-breaking solution for IE, which provides invaluable annotated data on a growing number of domains. This article has two objectives. First, describing the LODIE project as a whole and depicting its general challenges and directions. Second, describing some initial steps taken towards the general solution, focusing on a specific IE subtask, wrapper induction.


Proposal of a shape adaptive gripper for robotic assembly tasks


title: Real-Time Indoor Staircase Detection on Mobile Devices

abstract: Staircases are among the most common architectural features in urban environments. In this paper, we describe a method for detecting indoor staircases from depth images. The objective of this development is to aid visually impaired people in perceiving the environment, especially in unfamiliar places. This method is based on the detection and clustering of patches that have the surface normal vectors oriented towards the "up" direction. A series of filters is applied in order to eliminate false-positive patches. In addition to the depth information, we use the camera's absolute orientation information, provided by an IMU sensor. This additional information is used to correct the orientation of the surface normal vectors in such a way that no matter how the camera moves, the surface normal vectors have the correct orientation. This method provides real-time performance on a mobile device with low computational capabilities and it was tested on real-case scenarios, showing promising results


title: Ku-band transition between microstrip and substrate integrated waveguide (SIW)

abstract: In this paper, a Ku-band planar transition between microstrip and substrate integrated waveguide at satellite communication band is discussed. The SIW is one of alternatives for packaging the microwave components on the same substrate. The transition structure suggested in this paper shows the insertion loss of 0.7 dB at 12 GHz including transmission line of full wavelength.


title: A guided tour of data-center networking

abstract: A good user experience depends on predictable performance within the data-center network.


title: High-speed low-power Single-Stage latched-comparator with improved gain and kickback noise rejection

abstract: This paper presents a high speed Single-Stage latched comparator which is scheduled in time for both amplification and latch operations. Small active area besides desired power consumption at high comparison rates qualifies the proposed comparator to be repeatedly employed in high speed flash A/D converters. A strategy of kickback noise elimination besides gain enhancement is also introduced. A low power holding Read-Out circuit is presented. Post-Layout simulation results confirm 500MS/s comparison rate with 5mv resolution for a 1.6v peak-to-peak input signal range and 600µw power consumption from a 3.3v power supply by using TSMC model of 0.35µm CMOS technology. Total active area of proposed comparator and Read-Out circuit is about 300µm2.


title: Moving towards the edge : matrix matters!

abstract: In this issue of the Journal of Vegetation Science, Chabrerie et al. use plant inventories and geographical data to investigate effects on species richness and turnover caused by management intensi ...


title: A soluble thymic stromal lymphopoietin (TSLP) antagonist, TSLPR-immunoglobulin, reduces the severity of allergic disease by regulating pulmonary dendritic cells.

abstract: Recent studies show that thymic stromal lymphopoietin (TSLP) plays a critical role in the upstream phase of the allergic cascade to induce T helper type 2 cell (Th2)-dominant allergic diseases. However, the effect of blocking TSLP signalling with the soluble TSLP receptor (TSLPR), TSLPR-immunoglobulin (Ig), on asthma development needs further investigation. Here, we examined the effects of TSLPR-Ig on asthmatic airway inflammation and dendritic cell (DC) function. TSLPR-Ig (comprising the extracellular domain of murine TSLPR and an IgG2a Fc tail) purified from transfected COS-7 cells reduced the expression of CD40, CD80 and CD86 on TSLP-activated DCs in vitro. We also investigated the mechanisms underlying TSLPR-Ig-mediated amelioration of allergic airway inflammation in a murine asthma model. When TSLP signalling was blocked by intratracheal administration of TSLPR-Ig prior to sensitization, allergen-specific serum IgE levels, airway tissue inflammation, inflammatory cell infiltration and Th2 cytokine levels in the bronchiolar lavage fluid (BALF) were reduced significantly. This was because of the TSLP-Ig-mediated down-regulation of co-stimulatory molecule expression on pulmonary DCs. We also transferred bone marrow-derived mature DCs (mDCs) into the airways of asthmatic mice. Intratracheal administration of TSLPR-Ig prior to the transfer of mDCs reduced eosinophilic airway inflammation and Th2 differentiation significantly. Collectively, these data suggest that local use of TSLPR-Ig prevents airway inflammation, at least in part, by regulating DC function, and that blocking TSLP signalling using TSLPR-Ig may be a novel strategy for the treatment of asthma bronchiale.


Privacy by BlockChain Design: A BlockChain-enabled GDPR-compliant Approach for Handling Personal Data


Personalized Learning for Cyberbullying Detection


Aberration hubs in protein interaction networks highlight actionable targets in cancer


Building the data warehouse


A Blockchain Grand Challenge: Smart Financial Derivatives


title: An efficient P300-based brain-computer interface for disabled subjects

abstract: A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. In this paper, we present a BCI that achieves high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is based on the P300 evoked potential and is tested with five severely disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25 bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested. Further factors that are possibly important for obtaining good classification accuracy in P300-based BCI systems for disabled subjects are discussed.


title: Detecting Offensive Language in Social Media to Protect Adolescent Online Safety

abstract: Since the textual contents on online social media are highly unstructured, informal, and often misspelled, existing research on message-level offensive language detection cannot accurately detect offensive content. Meanwhile, user-level offensiveness detection seems a more feasible approach but it is an under researched area. To bridge this gap, we propose the Lexical Syntactic Feature (LSF) architecture to detect offensive content and identify potential offensive users in social media. We distinguish the contribution of pejoratives/profanities and obscenities in determining offensive content, and introduce hand-authoring syntactic rules in identifying name-calling harassments. In particular, we incorporate a user's writing style, structure and specific cyber bullying content as features to predict the user's potentiality to send out offensive content. Results from experiments showed that our LSF framework performed significantly better than existing methods in offensive content detection. It achieves precision of 98.24% and recall of 94.34% in sentence offensive detection, as well as precision of 77.9% and recall of 77.8% in user offensive detection. Meanwhile, the processing speed of LSF is approximately 10msec per sentence, suggesting the potential for effective deployment in social media.


title: Development of packaging technologies for SiC power module

abstract: In recent years, SiC power semiconductor devices are emerging as alternatives that outperform Si devices by reduce power loss and high operation temperature (>200°C). However, commercial packaging technologies for Si devices are within limited operation temperature (<150°C). The development of packaging technologies for SiC power module is a critical issue. In this paper, the full SiC power module (SiC MOSFET+ SiC Schottky diode) is developed. Thermal, thermomechanical and electrical characteristics of power module are analyzed. For increase reliability, Cu-Al heavy wire bonding and die bonding with high temperature solder are studied. Finally, the performance of full SiC power module is measured and compared with Si IGBT power module.


title: An interleaving and load sharing method for multiphase LLC converters

abstract: Interleaving frequency-controlled LLC resonant converters will encounter load-sharing problem due to the tolerance of the resonant components. In this paper, full-wave and half-wave switch-controlled capacitors (SCCs) are used in LLC stages to solve this problem. By using resonant capacitance as a control variable, the output current can be modulated even when all the LLC stages are synchronized at the same switching frequency. A design procedure is developed. A 600W prototype is built to verify the feasibility.


title: Bandwidth Enhancement of Novel Compact Single and Dual Band-Notched Printed Monopole Antenna With a Pair of L-Shaped Slots

abstract: A dual band-notched printed ultrawideband monopole antenna is presented, with a modified ground plane. By using this modified element including a pair of variable L-shaped slots, cut in the ground plane, additional resonances are excited and hence the bandwidth is increased up to 130%. To generate single and dual band-notched characteristics, we use inverted U- and fork-shaped parasitic structures, respectively, instead of changing the patch or feedline shapes. By properly adjusting the dimensions of these capacitive-coupled elements, not only one or two controllable notch resonances are achieved, but also the lower-edge frequency of the band is decreased. The measured results show that the proposed dual band-notched monopole antenna offers a very wide bandwidth from 2.2 to 13.4 GHz (143%), defined by 10-dB return loss, with two notched bands, covering all the 5.2/5.8-GHz WLAN, 3.5/5.5-GHz WiMax and 4-GHz C bands. Impedance, radiation, efficiency and gain characteristics of the antenna are also discussed.


title: A DHT-based Infrastructure for Content-based Publish/Subscribe Services

abstract: Publish/subscribe model has become a prevalent paradigm for building distributed event delivering systems. Content-based publish/subscribe allows high expresses in subscriptions and thus is more appropriate for content dissemination. However, the scalability has remained a challenge in the design of distributed content-based publish/subscribe systems due to the expensive matching and delivering cost of content-based events. In this paper we propose an infrastructure built on top of distributed hash table for efficient content-based data distribution. Based on efficient subscription installation, event publication and event delivery mechanisms, the proposed infrastructure can simultaneously support any numbers of pub/sub schemas with different number of attributes. There are three key features in our design: (1) a locality-preserving hashing mechanism which partitions and maps the content space to nodes. Subscriptions and events are mapped to the corresponding node for efficiently matching; (2) an efficient event delivery algorithm which exploits the embedded trees in the underlying DHT to deliver events to the corresponding subscribers; (3) light-weighted load balancing mechanisms to adjust the load among peers and ensure that no peer is unduly loaded.


title: Implementing fault-tolerant services using the state machine approach: a tutorial

abstract: The state machine approach is a general method for implementing fault-tolerant services in distributed systems. This paper reviews the approach and describes protocols for two different failure models—Byzantine and fail stop. Systems reconfiguration techniques for removing faulty components and integrating repaired components are also discussed.


title: Remote Sensing Image Scene Classification Using Bag of Convolutional Features

abstract: More recently, remote sensing image classification has been moving from pixel-level interpretation to scene-level semantic understanding, which aims to label each scene image with a specific semantic class. While significant efforts have been made in developing various methods for remote sensing image scene classification, most of them rely on handcrafted features. In this letter, we propose a novel feature representation method for scene classification, named bag of convolutional features (BoCF). Different from the traditional bag of visual words-based methods in which the visual words are usually obtained by using handcrafted feature descriptors, the proposed BoCF generates visual words from deep convolutional features using off-the-shelf convolutional neural networks. Extensive evaluations on a publicly available remote sensing image scene classification benchmark and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed BoCF method for remote sensing image scene classification.


title: From Crowdsourced Mapping to Community Mapping: The Post-earthquake Work of OpenStreetMap Haiti

abstract: The earthquake that struck Haiti on January 12, 2010 catalyzed a nascent set of efforts in then-emergent “volunteer technology communities.” Among these was the response from OpenStreetMap, a volunteer-driven project that makes geospatial data free and openly available. Following the earthquake, remotely located volunteers rapidly mapped the affected areas to support the aid effort in a remarkable display of crowdsourced work. However, some within that effort believed that the impact and import of open and collaborative mapping techniques could provide much richer value to humanitarian aid work and the long-term development needs of the country. They launched an ambitious project that trialed methods for how to create sustainable and locally-owned community-mapping ecosystems in at-risk regions of the world. This paper describes how an organization that emerged out of the response—the Humanitarian OpenStreetMap Team—formalized their practices in relation to many different stakeholder needs with the aim for setting a model for how the potential of participatory, community mapping could be realized in Haiti and beyond.


title: Domestic Violence Crisis Identification From Facebook Posts Based on Deep Learning

abstract: Domestic violence (DV) is a cause of concern due to the threat it poses toward public health and human rights. There is a need for quick identification of the victims of this condition so that DV crisis service (DVCS) can offer necessary support in a timely manner. The availability of social media has allowed DV victims to share their stories and receive support from the community, which opens an opportunity for DVCS to actively approach and support DV victims. However, it is time consuming and inefficient to manually browse through a massive number of available posts. This paper adopts deep learning as an approach for automatic identification of DV victims in critical need. Empirical evidence on a ground truth data set has achieved an accuracy of up to 94%, which outperforms traditional machine-learning techniques. The analysis of informative features helps to identify important words which might indicate critical posts in the classification process. The experimental results are helpful to researchers and practitioners in developing techniques for identifying and supporting DV victims.


title: Evolution of ventral hernia repair

abstract: Purpose ::: The aim of this review was to look at relevant data and research on the evolution of ventral hernia repair. ::: ::: Methods ::: Resources including books, research, guidelines, and online articles were reviewed to provide a concise history of and data on the evolution of ventral hernia repair. ::: ::: Results ::: The evolution of ventral hernia repair has a very long history, from the recognition of ventral hernias to its current management, with significant contributions from different authors. Advances in surgery have led to more cases of ventral hernia formation, and this has required the development of new techniques and new materials for ventral hernia management. The biocompatibility of prosthetic materials has been important in mesh development. The functional anatomy and physiology of the abdominal wall has become important in ventral hernia management. New techniques in abdominal wall closure may prevent or reduce the incidence of ventral hernia in the future. ::: ::: Conclusion ::: The management of ventral hernia is continuously evolving as it responds to new demands and new technology in surgery.


title: Dual-Band Circularly Polarized Cavity-Backed Crossed-Dipole Antennas

abstract: In this letter, a novel dual-band circularly polarized cavity-backed crossed-dipole antenna is proposed. Complementary split-ring resonators (CSRRs) are loaded along both the arms of double-sided printed crossed dipoles along with vacant quarter-printed rings to achieve dual-band circularly polarized operation. Cavity-backed reflector is employed in the antenna to achieve unidirectional pattern with high front-to-back-lobe ratio ( $> 25~\hbox{dB}$ ), wide circularly polarized radiation beamwidth, and high gain in both frequency bands. The impedance and axial-ratio bandwidths in both bands are also considerably improved by the cavity backing. The proposed dual-band crossed-dipole antenna has two operating bands centered at 1.81 GHz (impedance bandwidth 22.4%, axial-ratio bandwidth 6.6%) and 2.67 GHz (impedance bandwidth 33%, axial-ratio bandwidth 10.1%), respectively .


Human Computer Interaction in Smart Homes


title: Model-driven assessment of learners in open-ended learning environments

abstract: Open-ended learning environments (OELEs) provide students with opportunities to take part in authentic and complex problem-solving tasks. However, many students struggle to succeed in such complex learning endeavors. Without support, these students often use system tools incorrectly and adopt suboptimal learning strategies. However, providing adaptive support to students in OELEs poses significant challenges, and relatively few OELEs provide students with adaptive support. This paper presents the initial development of a systematic approach for interpreting and evaluating learner behaviors in OELEs called model-driven assessments, which uses a model of the cognitive and metacognitive processes important for completing the open-ended learning task. The model provides a means for both classifying and assessing students' learning behaviors while using the system. An evaluation of the analysis technique is presented in the context of Betty's Brain, an OELE designed to help middle school students learn about science.


title: Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers

abstract: Layer-wise relevance propagation is a framework which allows to decompose the prediction of a deep neural network computed over a sample, e.g. an image, down to relevance scores for the single input dimensions of the sample such as subpixels of an image. While this approach can be applied directly to generalized linear mappings, product type non-linearities are not covered. This paper proposes an approach to extend layer-wise relevance propagation to neural networks with local renormalization layers, which is a very common product-type non-linearity in convolutional neural networks. We evaluate the proposed method for local renormalization layers on the CIFAR-10, Imagenet and MIT Places datasets.


title: Putting the face in context: Body expressions impact facial emotion processing in human infants

abstract: Body expressions exert strong contextual effects on facial emotion perception in adults. Specifically, conflicting body cues hamper the recognition of emotion from faces, as evident on both the behavioral and neural level. We examined the developmental origins of the neural processes involved in emotion perception across body and face in 8-month-old infants by measuring event-related brain potentials (ERPs). We primed infants with body postures (fearful, happy) that were followed by either congruent or incongruent facial expressions. Our results revealed that body expressions impact facial emotion processing and that incongruent body cues impair the neural discrimination of emotional facial expressions. Priming effects were associated with attentional and recognition memory processes, as reflected in a modulation of the Nc and Pc evoked at anterior electrodes. These findings demonstrate that 8-month-old infants possess neural mechanisms that allow for the integration of emotion across body and face, providing evidence for the early developmental emergence of context-sensitive facial emotion perception.


title: Assistive infrared sensor based smart stick for blind people

abstract: Blind people need some aid to feel safe while moving. Smart stick comes as a proposed solution to improve the mobility of both blind and visually impaired people. Stick solution use different technologies like ultrasonic, infrared and laser but they still have drawbacks. In this paper we propose, light weight, cheap, user friendly, fast response and low power consumption, smart stick based on infrared technology. A pair of infrared sensors can detect stair-cases and other obstacles presence in the user path, within a range of two meters. The experimental results achieve good accuracy and the stick is able to detect all of obstacles.


title: Attestation & Authentication for USB Communications

abstract: In this paper, we present the design, implementation, and software testing of USBSec, a secure extension of the vanilla USB protocol that incorporates host authentication to defend against software threats. Specifically, we force the USB host to supply authentication information to the peripheral device \emph{before} enumerating the device. The peripheral validates the authentication information against its own list of \emph{authorized host keys}. If both sides can validate each other, standard USB enumeration continues otherwise the connection is terminated. We have implemented a fully working prototype of USBSec based on USB implementation in Linux kernel and our experimental results demonstrate its practicality and effectiveness.


title: Rubinstein Taybi Syndrome in an Indian Child due to EP300 Gene Mutation

abstract: To the Editor : A 10-y-old girl was diagnosed with Rubinstein Taybi Syndrome (RTS) based on distinctive facial features (low hanging columella, high palate, grimacing smile, talon cusps), broad, angulated thumbs and great toes, short stature and moderate intellectual disability (Fig. 1). Her younger sister, who died at 11 mo of age due to acute myeloid leukemia, did not have clinical features of RTS. Complete sequencing of CREBBP and EP300 genes was done for the proband as previously described [1, 2]. No pathogenic mutation was noted in CREBBP gene. She was found to be heterozygous for c.1282C > T or p.P428S mutation in exon five of EP300 gene (Fig. 1). The mutation is predicted to affect the donor splicing [3]. The patient’s mother was heterozygous for the same mutation. Prenatal diagnosis was offered to the couple and the fetus did not carry this mutation, and was confirmed to be unaffected postnatally. This mutation was not identified in 100 normal unrelated individuals. Deletion/duplication analysis of CREBBP and EP300 gene was not available. Our patient had horizontal palpebral fissures as described with EP300 related RTS, compared with CREBBP related RTS, who have anti-mongoloid slant [4]. Although patients with EP300 mutation have broad thumbs or toes, radial deviation has not been reported [2] (Fig. 1). This mutation was inherited from her mother who had mild hypertelorism, broad nasal bridge, normal intelligence and hands/feet. Negri et al. reported a patient with mutation in EP300 gene (p.N1511T) that was inherited from a healthy mother. Low penetrance in EP300 related RTS may present a challenge during genetic counseling and prenatal diagnosis [2]. EP300 gene somatic mutations are reported in acute leukemia and RTS patients have an increased incidence of cancer [2, 5]. Thus, we report an Indian child with Rubinstein Taybi Syndrome with familial EP300 gene mutation.


title: Experimental analysis on a 1:2 scale model of the double light pipe, an innovative technological device for daylight transmission

abstract: Abstract In this paper the authors present the double light pipe, an innovative technological device, designed as an evolution of a traditional light pipe, which distributes daylight to underground areas of a building, illuminating, at the same time, the passage areas thanks to a larger collector and a second transparent pipe attached to the first one. Unlike the traditional light pipe, thanks to this double illuminating function it can be located in the middle of a room, despite its encumbrance. In this paper the technological design of the double light pipe is presented and the results of an experimental analysis on a reduced scale (1:2) model are shown. Internal illuminance data over horizontal and vertical work-planes were measured in various sky conditions with or without direct solar radiation. Being this innovative device obtained by a light pipe integrated with a second pipe, it performs like a traditional light pipe for the final room and, at the same time, illuminates the intermediate room giving it uniform and high quality light, particularly indicated for wide plant areas, such as show-rooms or museums.


title: Preparing for and conducting focus groups in nursing research: part 2.

abstract: Focus group interviews are widely used in health research to explore phenomena and are accepted as a legitimate qualitative methodology. They are used to draw out interaction data from discussions among participants; researchers running these groups need to be skilled in interviewing and in managing groups, group dynamics and group discussions. This article follows Doody et al's (2013) article on the theory of focus group research; it addresses the preparation for focus groups relating to the research environment, interview process, duration, participation of group members and the role of the moderator. The article aims to assist researchers to prepare and plan for focus groups and to develop an understanding of them, so information from the groups can be used for academic studies or as part of a research proposal.


title: In-season strength maintenance training increases well-trained cyclists’ performance

abstract: We investigated the effects of strength maintenance training on thigh muscle cross-sectional area (CSA), leg strength, determinants of cycling performance, and cycling performance. Well-trained cyclists completed either (1) usual endurance training supplemented with heavy strength training twice a week during a 12-week preparatory period followed by strength maintenance training once a week during the first 13 weeks of a competition period (E + S; n = 6 [♂ = 6]), or (2) usual endurance training during the whole intervention period (E; n = 6 [♂ = 5, ♀ = 1]). Following the preparatory period, E + S increased thigh muscle CSA and 1RM (p < 0.05), while no changes were observed in E. Both groups increased maximal oxygen consumption and mean power output in the 40-min all-out trial (p < 0.05). At 13 weeks into the competition period, E + S had preserved the increase in CSA and strength from the preparatory period. From the beginning of the preparatory period to 13 weeks into the competition period, E + S increased peak power output in the Wingate test, power output at 2 mmol l−1 [la−], maximal aerobic power output (W max), and mean power output in the 40-min all-out trial (p < 0.05). The relative improvements in the last two measurements were larger than in E (p < 0.05). For E, W max and power output at 2 mmol l−1 [la−] remained unchanged. In conclusion, in well-trained cyclists, strength maintenance training in a competition period preserved increases in thigh muscle CSA and leg strength attained in a preceding preparatory period and further improved cycling performance determinants and performance.


title: A Secure Time Synchronization Protocol Against Fake Timestamps for Large-Scale Internet of Things

abstract: For large-scale Internet of Things (IoT), which located in the hostile environment where exists malicious nodes (MNs), the security of time synchronization is a critical and challenging issue. The malicious sensor nodes could decrease the accuracy of the whole network by broadcasting fake timestamp messages. In this paper, we propose a secure time synchronization model for large-scale IoT. In this model, a node utilizes its father node and grandfather node to detect the MN. By employing the model, a spanning tree topology which synchronizes to the reference nodes can be constructed hop by hop. Then a secure time synchronization protocol is developed to against fake timestamps, which adopts the secure model. We use NS2 as the simulation tool to evaluate our protocol, and compare the impact of fake timestamps in various circumstances with the pervious protocols TPSN and STETS. The experiment results show that our protocol is effective to prevent attacks from MNs.


title: State of the Art in Stereoscopic and Autostereoscopic Displays

abstract: Underlying principles of stereoscopic direct-view displays, binocular head-mounted displays, and autostereoscopic direct-view displays are explained and some early work as well as the state of the art in those technologies are reviewed. Stereoscopic displays require eyewear and can be categorized based on the multiplexing scheme as: 1) color multiplexed (old technology but there are some recent developments; low-quality due to color reproduction and crosstalk issues; simple and does not require additional electronics hardware); 2) polarization multiplexed (requires polarized light output and polarization-based passive eyewear; high-resolution and high-quality displays available); and 3) time multiplexed (requires faster display hardware and active glasses synchronized with the display; high-resolution commercial products available). Binocular head-mounted displays can readily provide 3-D, virtual images, immersive experience, and more possibilities for interactive displays. However, the bulk of the optics, matching of the left and right ocular images and obtaining a large field of view make the designs quite challenging. Some of the recent developments using unconventional optical relays allow for thin form factors and open up new possibilities. Autostereoscopic displays are very attractive as they do not require any eyewear. There are many possibilities in this category including: two-view (the simplest implementations are with a parallax barrier or a lenticular screen), multiview, head tracked (requires active optics to redirect the rays to a moving viewer), and super multiview (potentially can solve the accommodation-convergence mismatch problem). Earlier 3-D booms did not last long mainly due to the unavailability of enabling technologies and the content. Current developments in the hardware technologies provide a renewed interest in 3-D displays both from the consumers and the display manufacturers, which is evidenced by the recent commercial products and new research results in this field.


A I ] 1 3 N ov 2 01 8 Interpretable Credit Application Predictions With Counterfactual Explanations


title: Unsupervised convolutional neural networks for motion estimation

abstract: Traditional methods for motion estimation estimate the motion field F between a pair of images as the one that minimizes a predesigned cost function. In this paper, we propose a direct method and train a Convolutional Neural Network (CNN) that when, at test time, is given a pair of images as input it produces a dense motion field F at its output layer. In the absence of large datasets with ground truth motion that would allow classical supervised training, we propose to train the network in an unsupervised manner. The proposed cost function that is optimized during training, is based on the classical optical flow constraint. The latter is differentiable with respect to the motion field and, therefore, allows backpropagation of the error to previous layers of the network. Our method is tested on both synthetic and real image sequences and performs similarly to the state-of-the-art methods.


title: Image Matters: Visually Modeling User Behaviors Using Advanced Model Server

abstract: In Taobao, the largest e-commerce platform in China, billions of items are provided and typically displayed with their images.For better user experience and business effectiveness, Click Through Rate (CTR) prediction in online advertising system exploits abundant user historical behaviors to identify whether a user is interested in a candidate ad. Enhancing behavior representations with user behavior images will help understand user's visual preference and improve the accuracy of CTR prediction greatly. So we propose to model user preference jointly with user behavior ID features and behavior images. However, training with user behavior images brings tens to hundreds of images in one sample, giving rise to a great challenge in both communication and computation. To handle these challenges, we propose a novel and efficient distributed machine learning paradigm called Advanced Model Server (AMS). With the well-known Parameter Server (PS) framework, each server node handles a separate part of parameters and updates them independently. AMS goes beyond this and is designed to be capable of learning a unified image descriptor model shared by all server nodes which embeds large images into low dimensional high level features before transmitting images to worker nodes. AMS thus dramatically reduces the communication load and enables the arduous joint training process. Based on AMS, the methods of effectively combining the images and ID features are carefully studied, and then we propose a Deep Image CTR Model. Our approach is shown to achieve significant improvements in both online and offline evaluations, and has been deployed in Taobao display advertising system serving the main traffic.


title: Group invariance principles for causal generative models

abstract: The postulate of independence of cause and mechanism (ICM) has recently led to several new causal discovery algorithms. The interpretation of independence and the way it is utilized, however, varies across these methods. Our aim in this paper is to propose a group theoretic framework for ICM to unify and generalize these approaches. In our setting, the cause-mechanism relationship is assessed by comparing it against a null hypothesis through the application of random generic group transformations. We show that the group theoretic view provides a very general tool to study the structure of data generating mechanisms with direct applications to machine learning.


title: Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds

abstract: We study optimization of finite sums of geodesically smooth functions on Riemannian manifolds. Although variance reduction techniques for optimizing finite-sums have witnessed tremendous attention in the recent years, existing work is limited to vector space problems. We introduce Riemannian SVRG (RSVRG), a new variance reduced Riemannian optimization method. We analyze RSVRG for both geodesically convex and nonconvex (smooth) functions. Our analysis reveals that RSVRG inherits advantages of the usual SVRG method, but with factors depending on curvature of the manifold that influence its convergence. To our knowledge, RSVRG is the first provably fast stochastic Riemannian method. Moreover, our paper presents the first non-asymptotic complexity analysis (novel even for the batch setting) for nonconvex Riemannian optimization. Our results have several implications; for instance, they offer a Riemannian perspective on variance reduced PCA, which promises a short, transparent convergence analysis.


title: Practical Formal Verification of Domain-Specific Language Applications

abstract: An application developer’s primary task is to produce performant systems that meet their specifications. Formal methods techniques allow engineers to create models and implementations that have a high assurance of satisfying a specification. In this experience report, we take a model-based approach to software development that adds the assurance of formal methods to software construction while automating over 90% of the formal modeling. We discuss a software development methodology and two specific examples that illustrate how to integrate formal methods and their benefits into a traditional (testing-based) software development process.


title: African Swine Fever Epidemic, Poland, 2014–2015

abstract: In Poland, African swine fever (ASF) emerged in February 2014; by August 2015, the virus had been detected in >130 wild boar and in pigs in 3 backyard holdings. We evaluated ASF spread in Poland during these 18 months. Phylogenetic analysis indicated repeated incursions of genetically distinct ASF viruses of genotype II; the number of cases positively correlated wild boar density; and disease spread was very slow. More cases were reported during summer than autumn. The 18-month prevalence of ASF in areas under various animal movement restrictions was 18.6% among wild boar found dead or killed by vehicles and only 0.2% in hunted wild boar. Repeated introductions of the virus into the country, the primary role of wild boar in virus maintenance, and the slow spread of the disease indicate a need for enhanced biosecurity at pig holdings and continuous and intensive surveillance for fast detection of ASF.


title: LaSTUS/TALN @ CLSciSumm-17: Cross-document Sentence Matching and Scientific Text Summarization Systems

abstract: Comunicacio presentada a: CL-SciSumm 2017, organitzat com a part de The 2nd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017) i conjuntament amb The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017)


title: A Teacher-Student Framework for Zero-Resource Neural Machine Translation

abstract: While end-to-end neural machine translation (NMT) has made remarkable progress recently, it still suffers from the data scarcity problem for low-resource language pairs and domains. In this paper, we propose a method for zero-resource NMT by assuming that parallel sentences have close probabilities of generating a sentence in a third language. Based on this assumption, our method is able to train a source-to-target NMT model ("student") without parallel corpora available, guided by an existing pivot-to-target NMT model ("teacher") on a source-pivot parallel corpus. Experimental results show that the proposed method significantly improves over a baseline pivot-based model by +3.0 BLEU points across various language pairs.


title: On the concrete hardness of Learning with Errors

abstract: The Learning with Errors (LWE) problem has become a central building block of modern cryptographic constructions. This work collects and presents hardness results for concrete instances of LWE. In particular, we discuss algorithms proposed in the literature and give the expected resources required to run them. We consider both generic instances of LWE as well as small secret variants. Since for several methods of solving LWE we require a lattice reduction step, we also review lattice reduction algorithms and use a refined model for estimating their running times. We also give concrete estimates for various families of LWE instances, provide a Sage module for computing these estimates and highlight gaps in the knowledge about algorithms for solving the Learning with Errors problem.


title: A survey of face detection, extraction and recognition

abstract: The goal of this paper is to present a critical survey of existing lite- ratures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important.


title: From Online to Mobile: Linking Consumers’ Online Purchase Behaviors with Mobile Commerce Adoption

abstract: With the growing popularity of mobile commerce (m-commerce), it becomes vital for both researchers and practitioners to understand consumers’ mobile commerce adoption behavior. In this study, we empirically investigate the drivers of consumers’ mobile commerce adoption behavior based on a cost and benefit framework. Based on consumers' browsing and purchase behaviors at the e-commerce site before the addition of mobile commerce channel, we constructed behavioral proxy variables which capture the underlying cost and benefit of mobile commerce channel relative to the pre-existing e-commerce channel. We collected two large datasets from of a large e-marketplace in South Korea that introduced mcommerce to its existing e-commerce offering in 2011. Based on the analysis of browsing and purchase behaviors of 29,283 subjects over a period of 28 months, we find that the need for ubiquity plays a significant role in the m-commerce adoption decision. The two proxies for ubiquity need— Purchase frequency and Purchase time irregularity—were found to have a positive impact on mcommerce adoption. The results also suggest that search cost influences the decision to adopt mcommerce. Specifically, we find that the consumers who search multi-item or categories at a time, engage in active search, and conduct thorough search, are less likely to adopt m-commerce. Finally, the results show that the risk preference of the consumer is related to the adoption decision. Risk aversion, as measured by the two proxies—Reliance on secure log-in system, and Need for receiving confirmations—lowers the likelihood of m-commerce adoption. These results highlight the importance of the unique features of mobile platform in influencing the consumers’ adoption of m-commerce. We discuss the implications of our findings for academics and practitioners.


title: Epididymitis: revelations at the convergence of clinical and basic sciences

abstract: Acute epididymitis represents a common medical condition in the urological outpatient clinic. Mostly, epididymitis is caused by bacterial ascent through the urogenital tract, with pathogens originating either from sexually transmitted diseases or urinary tract infections. Although conservative antimicrobial therapy is possible in the majority of patients and is usually sufficient to eradicate the pathogen, studies have shown persistent oligozoospermia and azoospermia in up to 40% of these patients. Animal models of epididymitis are created to delineate the underlying reasons for this observation and the additional impairment of sperm function that is often associated with the disease. Accumulated data provide evidence of a differential expression of immune cells, immunoregulatory genes and pathogen-sensing molecules along the length of the epididymal duct. The evidence suggests that a tolerogenic environment exists in the caput epididymidis, but that inflammatory responses are most intense toward the cauda epididymidis. This is consistent with the need to provide protection for the neo-antigens of spermatozoa emerging from the testis, without compromising the ability to respond to ascending infections. However, severe inflammatory responses, particularly in the cauda, may lead to collateral damage to the structure and function of the epididymis. Convergence of the clinical observations with appropriate animal studies should lead to better understanding of the immunological environment throughout the epididymis, the parameters underlying susceptibility to epididymitis, and to therapeutic approaches that can mitigate epididymal damage and subsequent fertility problems.


title: Continuous software engineering: A roadmap and agenda

abstract: Abstract Throughout its short history, software development has been characterized by harmful disconnects between important activities such as planning, development and implementation. The problem is further exacerbated by the episodic and infrequent performance of activities such as planning, testing, integration and releases. Several emerging phenomena reflect attempts to address these problems. For example, Continuous Integration is a practice which has emerged to eliminate discontinuities between development and deployment. In a similar vein, the recent emphasis on DevOps recognizes that the integration between software development and its operational deployment needs to be a continuous one. We argue a similar continuity is required between business strategy and development, BizDev being the term we coin for this. These disconnects are even more problematic given the need for reliability and resilience in the complex and data-intensive systems being developed today. We identify a number of continuous activities which together we label as ‘Continuous * ’ (i.e. Continuous Star ) which we present as part of an overall roadmap for Continuous Software engineering. We argue for a continuous (but not necessarily rapid) software engineering delivery pipeline. We conclude the paper with a research agenda.


title: Motivation to learn: an overview of contemporary theories

abstract: OBJECTIVE ::: To succinctly summarise five contemporary theories about motivation to learn, articulate key intersections and distinctions among these theories, and identify important considerations for future research. ::: ::: ::: RESULTS ::: Motivation has been defined as the process whereby goal-directed activities are initiated and sustained. In expectancy-value theory, motivation is a function of the expectation of success and perceived value. Attribution theory focuses on the causal attributions learners create to explain the results of an activity, and classifies these in terms of their locus, stability and controllability. Social- cognitive theory emphasises self-efficacy as the primary driver of motivated action, and also identifies cues that influence future self-efficacy and support self-regulated learning. Goal orientation theory suggests that learners tend to engage in tasks with concerns about mastering the content (mastery goal, arising from a 'growth' mindset regarding intelligence and learning) or about doing better than others or avoiding failure (performance goals, arising from a 'fixed' mindset). Finally, self-determination theory proposes that optimal performance results from actions motivated by intrinsic interests or by extrinsic values that have become integrated and internalised. Satisfying basic psychosocial needs of autonomy, competence and relatedness promotes such motivation. Looking across all five theories, we note recurrent themes of competence, value, attributions, and interactions between individuals and the learning context. ::: ::: ::: CONCLUSIONS ::: To avoid conceptual confusion, and perhaps more importantly to maximise the theory-building potential of their work, researchers must be careful (and precise) in how they define, operationalise and measure different motivational constructs. We suggest that motivation research continue to build theory and extend it to health professions domains, identify key outcomes and outcome measures, and test practical educational applications of the principles thus derived.


title: Step negotiation with wheel traction: a strategy for a wheel-legged robot

abstract: This paper presents a quasi-static step climbing behaviour for a minimal sensing wheel-legged quadruped robot called PAW. In the quasi-static climbing maneuver, the robot benefits from wheel traction and uses its legs to reconfigure itself with respect to the step during the climb. The control methodology with the corresponding controller parameters is determined and the state machine for the maneuver is developed. With this controller, PAW is able to climb steps higher than its body clearance. Furthermore, any step height up to this maximum achievable height can be negotiated autonomously with a single set of controller parameters, without knowledge of the step height or distance to the step.


title: Unobtrusive and Wearable Systems for Automatic Dietary Monitoring

abstract: The threat of obesity, diabetes, anorexia, and bulimia in our society today has motivated extensive research on dietary monitoring. Standard self-report methods such as 24-h recall and food frequency questionnaires are expensive, burdensome, and unreliable to handle the growing health crisis. Long-term activity monitoring in daily living is a promising approach to provide individuals with quantitative feedback that can encourage healthier habits. Although several studies have attempted automating dietary monitoring using wearable, handheld, smart-object, and environmental systems, it remains an open research problem. This paper aims to provide a comprehensive review of wearable and hand-held approaches from 2004 to 2016. Emphasis is placed on sensor types used, signal analysis and machine learning methods, as well as a benchmark of state-of-the art work in this field. Key issues, challenges, and gaps are highlighted to motivate future work toward development of effective, reliable, and robust dietary monitoring systems.


Lead-210 and its environmental and sedimentological applications


title: A Unified Perspective on Multi-Domain and Multi-Task Learning

abstract: In this paper, we provide a new neural-network based perspective on multi-task learning (MTL) and multi-domain learning (MDL). By introducing the concept of a semantic descriptor, this framework unifies MDL and MTL as well as encompassing various classic and recent MTL/MDL algorithms by interpreting them as different ways of constructing semantic descriptors. Our interpretation provides an alternative pipeline for zero-shot learning (ZSL), where a model for a novel class can be constructed without training data. Moreover, it leads to a new and practically relevant problem setting of zero-shot domain adaptation (ZSDA), which is the analogous to ZSL but for novel domains: A model for an unseen domain can be generated by its semantic descriptor. Experiments across this range of problems demonstrate that our framework outperforms a variety of alternatives.


title: Humor-based online positive psychology interventions: A randomized placebo-controlled long-term trial

abstract: While correlational evidence exists that humor is positively associated with well-being, only few studies addressed causality. We tested the effects of five humor-based activities on happiness and depression in a placebo-controlled, self-administered online positive psychology intervention study (N = 632 adults). All of the five one-week interventions enhanced happiness, three for up to six months (i.e. three funny things, applying humor, and counting funny things), whereas there were only short-term effects on depression (all were effective directly after the intervention). Additionally, we tested the moderating role of indicators of a person × intervention-fit and identified early changes in well-being and preference (liking of the intervention) as the most potent indicators for changes six months after the intervention. Overall, we were able to replicate existing work, but also extend knowledge in the field by testing newly developed interventions for the first time. Findings are discussed with respect to the current literature.


title: Features of apoptotic cells measured by flow cytometry

abstract: The present review describes several methods to characterize and differentiate between two different mechanisms of cell death, apoptosis and necrosis. Most of these methods were applied to studies of apoptosis triggered in the human leukemic HL-60 cell line by DNA topoisomerase I or II inhibitors, and in rat thymocytes by either topoisomerase inhibitors or prednisolone. In most cases, apoptosis was selective to cells in a particular phase of the cell cycle: only Sphase HL-60 cells and G0 thymocytes were mainly affected. Necrosis was induced by excessively high concentrations of these drugs. The following cell features were found useful to characterize the mode of cell death: (a) Activation of an endonuclease in apoptotic cells resulted in extraction of the low molecular weight DNA following cell permeabilization, which, in turn, led to their decreased stainability with DNA-specific fluorochromes. Measurements of DNA content made it possible to identify apoptotic cells and to recognize the cell cycle phase specificity oaf the apoptotic process. (b) Plasma membrane integrity, which is lost in necrotic but not in apoptotic cells, was probed by the exclusion of propidium iodide (PI). The combination of PI followed by Hoechst 33342 proved to be an excellent probe to distinguish live, necrotic, early- and late-apoptotic cells. (c) Mitochondrial transmembrane potential, assayed by retention of rhodamine 123 was preserved in apoptotic but not necrotic cells. (d) The ATP-dependent lysosomal proton pump, tested by the supravital uptake of acridine orange (AO) was also preserved in apoptotic but not necrotic cells. (e) Bivariate analysis of cells stained for DNA and protein revealed markedly diminished protein content in apoptotic cells, most likely due to activation of endogenous proteases. Necrotic cells, having leaky membranes, had minimal protein content. (f) Staining of RNA allowed for the discrimination of G0 from G1 cells and thus made it possible to reveal that apoptosis was selective to G0 thymocytes. (g) The decrease in forward light shatter, paralleled either by no change (HL-60 cells) or an increase (thymocytes) of right angle scatter, were early changes during apoptosis. (h) The sensitivity of DNA in situ to denaturation, was increased in apoptotic and necrotic cells. This feature, probed by staining with AO at love pH, provided a sensitive and early assay to discriminate between live, apoptotic and necrotic cells, and to evaluate the cell cycle phase specificity of these processes. (i) The in situ nick translation assay employing labeled triphosphonucleotides can be used to reveal DNA strand breaks, to detect the very early stages of apoptosis. The data presented indicate that flow cytometry can be applied in basic research on molecular and biochemical mechanisms of apoptosis, as well as in the clinic, where the ability to monitor early signs of apoptosis in samples from patients' tumors may be predictive of the outcome of some treatment protocols. © 1992 Wiley-Liss, Inc.


title: Micronewton force-controlled manipulation of biomaterials using a monolithic MEMS microgripper with two-axis force feedback

abstract: This paper presents the first demonstration of force-controlled micrograsping at the microNewton force level. The system manipulates highly deformable biomaterials (hydrogel microcapsules and biological cells) in an aqueous environment using a MEMS-based microgripper with integrated force feedback along two axes. The microgripper integrates an electrothermal V-beam microactuator and two capacitive force sensors, one for contact detection (force resolution: 38.5 nN) and the other for gripping force measurements (force resolution: 19.9 nN). The MEMS-based microgripper and the force control system experimentally demonstrate the capability of rapid contact detection and reliable force-controlled micrograsping to accommodate variations in sizes and mechanical properties of objects with a high reproducibility. Cell viability testing validated that the temperature at gripping arm tips does not exceed 50degC.


title: K-means clustering via principal component analysis

abstract: Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for performing unsupervised learning tasks. Here we prove that principal components are the continuous solutions to the discrete cluster membership indicators for K-means clustering. New lower bounds for K-means objective function are derived, which is the total variance minus the eigenvalues of the data covariance matrix. These results indicate that unsupervised dimension reduction is closely related to unsupervised learning. Several implications are discussed. On dimension reduction, the result provides new insights to the observed effectiveness of PCA-based data reductions, beyond the conventional noise-reduction explanation that PCA, via singular value decomposition, provides the best low-dimensional linear approximation of the data. On learning, the result suggests effective techniques for K-means data clustering. DNA gene expression and Internet newsgroups are analyzed to illustrate our results. Experiments indicate that the new bounds are within 0.5-1.5% of the optimal values.


title: A general geometric construction of coordinates in a convex simplicial polytope

abstract: Barycentric coordinates are a fundamental concept in computer graphics and geometric modeling. We extend the geometric construction of Floater's mean value coordinates [Floater, M.S., Kos, G., Reimers, M., 2005. Mean value coordinates in 3d. Computer Aided Geometric Design 22 (7) (2005) 623-631; Ju, T., Schaefer, S., Warren, J., 2005a. Mean value coordinates for closed triangular meshes. In: Proceedings of ACM SIGGRAPH 2005] to a general form that is capable of constructing a family of coordinates in a convex 2D polygon, 3D triangular polyhedron, or a higher-dimensional simplicial polytope. This family unifies previously known coordinates, including Wachspress coordinates, mean value coordinates and discrete harmonic coordinates, in a simple geometric framework. Using the construction, we are able to create a new set of coordinates in 3D and higher dimensions and study its relation with known coordinates. We show that our general construction is complete, that is, the resulting family includes all possible coordinates in any convex simplicial polytope.


title: ELECTROMAGNETIC DESIGN AND ANALYSIS OF A NOVEL MAGNETIC-GEAR-INTEGRATED WIND POWER GENERATOR USING TIME-STEPPING FINITE ELEMENT METHOD

abstract: This paper presents a novel permanent-magnet (PM) machine for wind power generation. In order to achieve high power/torque density as well as get rid of the nuisances aroused by the mechanical gearbox, a coaxial magnetic gear (CMG) is engaged. Difierent from the existing integrated machine in which armature windings are deployed in the inner bore of the CMG as an individual part, stator windings are directly inserted among the slots between the ferromagnetic segments in this proposed machine. Thus, it can ofier several merits, such as simpler mechanical structure, better utilization of PM materials and lower manufacturing cost. Moreover, by artfully designing the connection of the armature windings, the electromagnetic coupling between the windings and the outer rotor PMs can be dramatically decreased, and the electromechanical energy conversion can be achieved by the fleld interaction between the inner rotor PMs and the armature windings.


title: Rice yield prediction model using data mining

abstract: Food is perhaps the most important aspect of life, for, without it, humankind can't survive. Hence, storing food becomes an absolute compulsion for countries all over the world, especially the developing ones. The critical aspect the governments of such countries want to fulfill is storing enough food for the long term, particularly at a time of natural disaster. Bangladesh is a low-lying region, hence the estimation of food is very demanding. In this paper, we attempt to forecast the yield of rice with astute analysis.


An Eight-Dimensional Systematic Evaluation of Optimized Search Algorithms on Modern Processors


title: The Epidemiology and Impact of Traumatic Brain Injury

abstract: Traumatic brain injury (TBI) is an important public health problem in the United States and worldwide. The estimated 5.3 million Americans living with TBI-related disability face numerous challenges in their efforts to return to a full and productive life. This article presents an overview of the epidemiology and impact of TBI.


title: Attention-deficit hyperactivity disorder (ADHD): an updated review of the essential facts

abstract: Attention-deficit hyperactivity disorder (ADHD) is a complex disorder that can affect individuals across the lifespan. It is associated with substantial heterogeneity in terms of aetiology, clinical presentation and treatment outcome and is the subject of extensive research. Because of this, it can be difficult for clinicians to stay up to date with the most relevant findings and know how best to respond to parents' questions and concerns about the disorder and interventions. This is a narrative review that aims to summarize key findings from recent research into ADHD and its treatment that clinicians can share with families in order to increase their knowledge about ADHD and intervention options. ADHD develops as a result of complex interplay between interdependent genetic and non-genetic factors. The disorder is associated with substantial impairments in functioning and poor long-term outcomes. Pharmacological and non-pharmacological treatment options are available for symptom management and to improve function, but functioning outcomes often fail to normalize in children with ADHD. Despite extensive advances in understanding this complex disorder, it is clear that there is still a long way to go. In particular, we address the need for future non-pharmacological interventions to be more specifically targeted for ADHD symptoms and its commonly associated functioning deficits in order to ensure the best long-term outcomes for children with ADHD.


Understanding Breast Health Awareness in the Kingdom of Saudi Arabia: A Systematic Review of the Literature


title: On the effectiveness of random jumps in an influence maximization algorithm for unknown graphs

abstract: In our previous work, we have proposed a problem called influence maximization for unknown graphs, and a heuristic algorithm for solving the problem. Influence maximization for unknown graphs is the problem of detecting a set of influential nodes in a social network using only the limited knowledge about its topological structure that is obtained from a limited amount of probing. In this paper, we extend the method of probing used in our previous algorithm, and propose an influence maximization algorithm for unknown graphs, which we call IMUG-RF (Influence Maximization for Unknown Graphs with Random Flight). IMUG-RF greedily probes the node with the highest expected degree and also periodically probes a randomly selected node in the network. Through simulation, we show that IMUG-RF can successfully identify influential seed nodes in real social networks.


title: The Coordination of Arm Movements: An Experimentally Confirmed Mathematical Model

abstract: This paper presents studies of the coordination of voluntary human arm movements. A mathematical model is formulated which is shown to predict both the qualitative features and the quantitative details observed experimentally in planar, multijoint arm movements. Coordination is modeled mathematically by defining an objective function, a measure of performance for any possible movement. The unique trajectory which yields the best performance is determined using dynamic optimization theory. In the work presented here, the objective function is the square of the magnitude of jerk (rate of change of acceleration) of the hand integrated over the entire movement. This is equivalent to assuming that a major goal of motor coordination is the production of the smoothest possible movement of the hand. Experimental observations of human subjects performing voluntary unconstrained movements in a horizontal plane are presented. They confirm the following predictions of the mathematical model: unconstrained point-to-point motions are approximately straight with bell-shaped tangential velocity profiles; curved motions (through an intermediate point or around an obstacle) have portions of low curvature joined by portions of high curvature; at points of high curvature, the tangential velocity is reduced; the durations of the low-curvature portions are approximately equal. The theoretical analysis is based solely on the kinematics of movement independent of the dynamics of the musculoskeletal system and is successful only when formulated in terms of the motion of the hand in extracorporal space. The implications with respect to movement organization are discussed.


title: Managing Cognitive Load in Adaptive ICT-Based Learning

abstract: The history of technological innovations in education has many examples of failed high expectations. To avoid becoming another one, current multimedia ICT tools need to be designed in accordance with how the human mind works. There are well established characteristics of its architecture that should be taken into account when evaluating, selecting, and using educational technology. This paper starts with a review of the most important features of human cognitive architecture and their implications for ICT-based learning. Expertise reversal effect relates to the interactions between levels of learner prior knowledge and effectiveness of different instructional techniques and procedures. Designs and techniques that are effective with low-knowledge learners can lose their effectiveness and even have negative consequences for more proficient learners. The paper describes recent empirical findings associated with the expertise reversal effect in multimedia and hypermedia learning environments, their interpretation within a cognitive load framework, and implications for the design of learner-tailored multimedia.


title: Uninterruptible power supply systems provide protection

abstract: Nowadays, uninterruptible power supply (UPS) systems are in use throughout the world, helping to supply a wide variety of critical loads, in situations of power outage or anomalies of the mains. This article describes the most common line problems and the relationship between these and the different existing kinds of UPS, showing their operation modes as well as the existent energy storage systems. It also addresses an overview of the control schemes applied to different distributed UPS configurations. Finally, it points out the applicability of such systems in distributed generation, microgrids, and renewable energy systems.


Clinical profile of a juvenile delinquent


title: Reliably Learning the ReLU in Polynomial Time

abstract: We give the first dimension-efficient algorithms for learning Rectified Linear Units (ReLUs), which are functions of the form $\mathbf{x} \mapsto \max(0, \mathbf{w} \cdot \mathbf{x})$ with $\mathbf{w} \in \mathbb{S}^{n-1}$. Our algorithm works in the challenging Reliable Agnostic learning model of Kalai, Kanade, and Mansour (2009) where the learner is given access to a distribution $\cal{D}$ on labeled examples but the labeling may be arbitrary. We construct a hypothesis that simultaneously minimizes the false-positive rate and the loss on inputs given positive labels by $\cal{D}$, for any convex, bounded, and Lipschitz loss function. The algorithm runs in polynomial-time (in $n$) with respect to any distribution on $\mathbb{S}^{n-1}$ (the unit sphere in $n$ dimensions) and for any error parameter $\epsilon = \Omega(1/\log n)$ (this yields a PTAS for a question raised by F. Bach on the complexity of maximizing ReLUs). These results are in contrast to known efficient algorithms for reliably learning linear threshold functions, where $\epsilon$ must be $\Omega(1)$ and strong assumptions are required on the marginal distribution. We can compose our results to obtain the first set of efficient algorithms for learning constant-depth networks of ReLUs. Our techniques combine kernel methods and polynomial approximations with a"dual-loss"approach to convex programming. As a byproduct we obtain a number of applications including the first set of efficient algorithms for"convex piecewise-linear fitting"and the first efficient algorithms for noisy polynomial reconstruction of low-weight polynomials on the unit sphere.


title: Distributed Reinforcement Learning based MAC protocols for autonomous cognitive secondary users

abstract: We consider a decentralized cognitive radio network in which autonomous secondary users seek spectrum opportunities in licensed spectrum bands. We assume that the primary users' channel occupancy follows a Markovian evolution, and formulate the spectrum sensing problem as a Decentralized Partially Observable Markov Decision Process (DEC-POMDP). We develop a distributed Reinforcement Learning (RL) algorithm that allows each autonomous cognitive radio to distributively learn its own spectrum sensing policy. The resulting decentralized sensing policy enables secondary users to non-cooperatively reach an equilibrium that leads to high utilization of idle channels while minimizing the collisions among secondary cognitive radios. Moreover, we propose a decentralized channel access policy that permits controlling, with high accuracy, the collision probability with primary users. Our numerical results validate the robustness of this collision probability control as the sensing noise changes. They also show the efficiency of the proposed learning algorithm in improving the spectrum utilization.


title: Deep neural networks for audio scene recognition

abstract: These last years, artificial neural networks (ANN) have known a renewed interest since efficient training procedures have emerged to learn the so called deep neural networks (DNN), i.e. ANN with at least two hidden layers. In the same time, the computational auditory scene recognition (CASR) problem which consists in estimating the environment around a device from the received audio signal has been investigated. Most of works which deal with the CASR problem have tried to ind well-adapted features for this problem. However, these features are generally combined with a classical classi-ier. In this paper, we introduce DNN in the CASR ield and we show that such networks can provide promising results and perform better than standard classiiers when the same features are used.


title: The modulation of conditional assertions and its effects on reasoning

abstract: The theory of mental models postulates that conditionals of the sort, if A then C, have a “core” meaning referring to three possibilities: A and C, not-A and C, and not-A and not-C. The meaning of a conditional's clauses and general knowledge can modulate this meaning, blocking certain possibilities or adding relations between the clauses. Four experiments investigated such interpretations in factual and deontic domains. In Experiment 1, the participants constructed instances of what was possible and what was impossible according to various conditionals. The results corroborated the general predictions of the model theory and also the occurrence of modulation. The resulting interpretations governed the conclusions that participants accepted in Experiment 2, which also yielded the predicted effects of a time limit on responding. In Experiment 3, the participants drew the predicted conclusions for themselves. In Experiment 4, modulation led to predicted temporal relations between A and C. We relate these re...


The OhioT1DM Dataset For Blood Glucose Level Prediction


title: Personalized input: improving ten-finger touchscreen typing through automatic adaptation

abstract: Although typing on touchscreens is slower than typing on physical keyboards, touchscreens offer a critical potential advantage: they are software-based, and, as such, the keyboard layout and classification models used to interpret key presses can dynamically adapt to suit each user's typing pattern. To explore this potential, we introduce and evaluate two novel personalized keyboard interfaces, both of which adapt their underlying key-press classification models. The first keyboard also visually adapts the location of keys while the second one always maintains a visually stable rectangular layout. A three-session user evaluation showed that the keyboard with the stable rectangular layout significantly improved typing speed compared to a control condition with no personalization. Although no similar benefit was found for the keyboard that also offered visual adaptation, overall subjective response to both new touchscreen keyboards was positive. As personalized keyboards are still an emerging area of research, we also outline a design space that includes dimensions of adaptation and key-press classification features.


title: Pain and depression : an interdisciplinary patient-centered approach

abstract: Preface Perspectives on Pain and Depression: Clark, M.R. Treisman, G.J. The Psychological Behaviorism Theory of Pain and the Placebo: Its Principles and Results of Research Application: Staats, P.S. Hekmat, H. Staats, A.W. Function, Disability, and Psychological Well-Being: Katz, P. Structural Models of Comorbidity among Common Mental Disorders: Connections to Chronic Pain: Krueger, R.F. Tackett, J.L. Markon, K.E. Neurobiology of Pain: Clark, M.R. Treisman, G.J. Complex Regional Pain Syndrome: Diagnostic Controversies, Psychological Dysfunction, and Emerging Concepts: Grabow, T.S. Christo, P.J. Raja, S.N. Can We Prevent a Second 'Gulf War Syndrome'? Population-Based Healthcare for Chronic Idiopathic Pain and Fatigue after War: Engel, C.C. Jaffer, A. Adkins, J. Riddle, J.R. Gibson R. Opioid Effectiveness, Addiction, and Depression in Chronic Pain: Christo, P.J. Grabow, T.S. Raja, S.N. Opioid Prescribing for Chronic Nonmalignant Pain in Primary Care: Challenges and Solutions: Olsen, Y. Daumit, G.L. To Help and Not to Harm: Ethical Issues in the Treatment of Chronic Pain in Patients with Substance Use Disorders: Geppert, C.M.A.


title: Patch-based fully convolutional neural network with skip connections for retinal blood vessel segmentation

abstract: Automated segmentation of retinal blood vessels plays an important role in the computer aided diagnosis of retinal diseases. The paper presents a new formulation of patch-based fully Convolutional Neural Networks (CNNs) that allows accurate segmentation of the retinal blood vessels. A major modification in this retinal blood vessel segmentation task is to improve and speed-up the patch-based fully CNN training by local entropy sampling and a skip CNN architecture with class-balancing loss. The proposed method is experimented on DRIVE dataset and achieves strong performance and significantly outperforms the-state-of-the-art for retinal blood vessel segmentation with 78.11% sensitivity, 98.39% specificity, 95.60% accuracy, 87.36% precision and 97.92% AUC score respectively.


title: Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications

abstract: Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.


title: Learning One-hidden-layer Neural Networks with Landscape Design

abstract: We consider the problem of learning a one-hidden-layer neural network: we assume the input $x\in \mathbb{R}^d$ is from Gaussian distribution and the label $y = a^\top \sigma(Bx) + \xi$, where $a$ is a nonnegative vector in $\mathbb{R}^m$ with $m\le d$, $B\in \mathbb{R}^{m\times d}$ is a full-rank weight matrix, and $\xi$ is a noise vector. We first give an analytic formula for the population risk of the standard squared loss and demonstrate that it implicitly attempts to decompose a sequence of low-rank tensors simultaneously. ::: Inspired by the formula, we design a non-convex objective function $G(\cdot)$ whose landscape is guaranteed to have the following properties: 1. All local minima of $G$ are also global minima. ::: 2. All global minima of $G$ correspond to the ground truth parameters. ::: 3. The value and gradient of $G$ can be estimated using samples. ::: With these properties, stochastic gradient descent on $G$ provably converges to the global minimum and learn the ground-truth parameters. We also prove finite sample complexity result and validate the results by simulations.


title: Recent advances in efficient computation of deep convolutional neural networks

abstract: Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems. At the same time, the computational complexity and resource consumption of these networks continue to increase. This poses a significant challenge to the deployment of such networks, especially in real-time applications or on resource-limited devices. Thus, network acceleration has become a hot topic within the deep learning community. As for hardware implementation of deep neural networks, a batch of accelerators based on a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) have been proposed in recent years. In this paper, we provide a comprehensive survey of recent advances in network acceleration, compression, and accelerator design from both algorithm and hardware points of view. Specifically, we provide a thorough analysis of each of the following topics: network pruning, low-rank approximation, network quantization, teacher–student networks, compact network design, and hardware accelerators. Finally, we introduce and discuss a few possible future directions.


title: Deep Visual Domain Adaptation: A Survey

abstract: Abstract Deep domain adaptation has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature subspaces or reuse important source instances with shallow representations, deep domain adaptation methods leverage deep networks to learn more transferable representations by embedding domain adaptation in the pipeline of deep learning. There have been comprehensive surveys for shallow domain adaptation, but few timely reviews the emerging deep learning based methods. In this paper, we provide a comprehensive survey of deep domain adaptation methods for computer vision applications with four major contributions. First, we present a taxonomy of different deep domain adaptation scenarios according to the properties of data that define how two domains are diverged. Second, we summarize deep domain adaptation approaches into several categories based on training loss, and analyze and compare briefly the state-of-the-art methods under these categories. Third, we overview the computer vision applications that go beyond image classification, such as face recognition, semantic segmentation and object detection. Fourth, some potential deficiencies of current methods and several future directions are highlighted.


title: Automatic Cartoon Colorization Based on Convolutional Neural Network

abstract: This paper deals with automatic cartoon colorization. This is a hard issue, since it is an ill-posed problem that usually requires user intervention to achieve high quality. Motivated by the recent successes in natural image colorization based on deep learning techniques, we investigate the colorization problem at the cartoon domain using Convolutional Neural Network. To our best knowledge, no existing papers or research studies address this problem using deep learning techniques. Here we investigate a deep Convolutional Neural Network based automatic color filling method for cartoons.


title: Foundations of Logic Programming

abstract: This is the second edition of an account of the mathematical foundations of logic programming. Its purpose is to collect, in a unified and comprehensive manner, the basic theoretical results of the field, which have previously only been available in widely scattered research papers. In addition to presenting the technical results, the book also contains many illustrative examples and problems. The text is intended to be self-contained, the only prerequisites being some familiarity with PROLOG and knowledge of some basic undergraduate mathematics. The material is suitable either as a reference book for researchers or as a textbook for a graduate course on the theoretical aspects of logic programming and deductive database systems.


title: Effective Regression Test Case Selection: A Systematic Literature Review

abstract: Regression test case selection techniques attempt to increase the testing effectiveness based on the measurement capabilities, such as cost, coverage, and fault detection. This systematic literature review presents state-of-the-art research in effective regression test case selection techniques. We examined 47 empirical studies published between 2007 and 2015. The selected studies are categorized according to the selection procedure, empirical study design, and adequacy criteria with respect to their effectiveness measurement capability and methods used to measure the validity of these results. ::: The results showed that mining and learning-based regression test case selection was reported in 39% of the studies, unit level testing was reported in 18% of the studies, and object-oriented environment (Java) was used in 26% of the studies. Structural faults, the most common target, was used in 55% of the studies. Overall, only 39% of the studies conducted followed experimental guidelines and are reproducible. ::: There are 7 different cost measures, 13 different coverage types, and 5 fault-detection metrics reported in these studies. It is also observed that 70% of the studies being analyzed used cost as the effectiveness measure compared to 31% that used fault-detection capability and 16% that used coverage.


title: Dual-Polarized Partially Reflective Surface Antenna With MEMS-Based Beamwidth Reconfiguration

abstract: A partially reflective surface (PRS) antenna design enabling 1-bit dynamic beamwidth control is presented. The antenna operates at X-band and is based on microelectromechanical systems (MEMS) technology. The reconfigurable PRS unit cell monolithically integrates MEMS elements, whose positions are chosen to reduce losses while allowing a considerable beamwidth variation. The combined use of the proposed PRS unit cell topology and MEMS technology allows achieving low loss in the reconfigurable PRS. In addition, the antenna operates in dual-linear polarization with independent beamwidth control of each polarization. An operative MEMS-based PRS unit cell is fabricated and measured upon reconfiguration, showing very good agreement with simulations. The complete antenna system performance is rigorously evaluated based on full-wave simulations and the unit cell measurements, demonstrating an 18° and 23° variation of the half-power beamwidth in the E-plane and the H-plane, respectively. The antenna radiation efficiency is better than 75% in all states of operation.


title: Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction

abstract: We address two shortcomings of the common spatial patterns (CSP) algorithm for spatial filtering in the context of brain--computer interfaces (BCIs) based on electroencephalography/magnetoencephalography (EEG/MEG): First, the question of optimality of CSP in terms of the minimal achievable classification error remains unsolved. Second, CSP has been initially proposed for two-class paradigms. Extensions to multiclass paradigms have been suggested, but are based on heuristics. We address these shortcomings in the framework of information theoretic feature extraction (ITFE). We show that for two-class paradigms, CSP maximizes an approximation of mutual information of extracted EEG/MEG components and class labels. This establishes a link between CSP and the minimal classification error. For multiclass paradigms, we point out that CSP by joint approximate diagonalization (JAD) is equivalent to independent component analysis (ICA), and provide a method to choose those independent components (ICs) that approximately maximize mutual information of ICs and class labels. This eliminates the need for heuristics in multiclass CSP, and allows incorporating prior class probabilities. The proposed method is applied to the dataset IIIa of the third BCI competition, and is shown to increase the mean classification accuracy by 23.4% in comparison to multiclass CSP.


Applying Web Usage Mining Techniques to Design Effective Web Recommendation Systems : A Case Study


title: Aneurysmal bone cyst of the mandible affecting the articular condyle: a case report

abstract: Aneurysmal bone cyst (ABC) is a benign osteolytic lesion that is fast-growing, expansile, and locally destructive. The present case is of a young girl with facial asymmetry, which had become accentuated during the previous months. A conservative treatment was performed to reduce morbidity and affectation of the lower dental nerve.


title: Foot trajectory in human gait: a precise and multifactorial motor control task.

abstract: The trajectory of the heel and toe during the swing phase of human gait were analyzed on young adults. The magnitude and variability of minimum toe clearance and heel-contact velocity were documented on 10 repeat walking trials on 11 subjects. The energetics that controlled step length resulted from a separate study of 55 walking trials conducted on subjects walking at slow, natural, and fast cadences. A sensitivity analysis of the toe clearance and heel-contact velocity measures revealed the individual changes at each joint in the link-segment chain that could be responsible for changes in those measures. Toe clearance was very small (1.29 cm) and had low variability (about 4 mm). Heel-contact velocity was negligible vertically and small (0.87 m/s) horizontally. Six joints in the link-segment chain could, with very small changes (+/- 0.86 degrees - +/- 3.3 degrees), independently account for toe clearance variability. Only one muscle group in the chain (swing-phase hamstring muscles) could be responsible for altering the heel-contact velocity prior to heel contact. Four mechanical power phases in gait (ankle push-off, hip pull-off, knee extensor eccentric power at push-off, and knee flexor eccentric power prior to heel contact) could alter step length and cadence. These analyses demonstrate that the safe trajectory of the foot during swing is a precise endpoint control task that is under the multisegment motor control of both the stance and swing limbs.


title: Shallow Parsing With Conditional Random Fields

abstract: Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluation datasets and extensive comparison among methods. We show here how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task, and better than any reported single model. Improved training methods based on modern optimization algorithms were critical in achieving these results. We present extensive comparisons between models and training methods that confirm and strengthen previous results on shallow parsing and training methods for maximum-entropy models.


title: Extracting Synonyms from Dictionary Definitions

abstract: Automatic extraction of synonyms and/or semantically related words has various applications in Natural Language Processing (NLP). There are currently two mainstream extraction paradigms, namely, lexicon-based and distributional approaches. The former usually suffers from low coverage, while the latter is only able to capture general relatedness rather than strict synonymy. In this paper, two rule-based extraction methods are applied to definitions from a machine-readable dictionary. Extracted synonyms are evaluated in two experiments by solving TOEFL synonym questions and being compared against existing thesauri. The proposed approaches have achieved satisfactory results in both evaluations, comparable to published studies or even the state of the art.


Metacognition and Metamemory Concepts for AI Systems


title: Strong Intuitionistic Fuzzy Graphs

abstract: We introduce the notion of strong intuitionistic fuzzy graphs and investigate some of their properties. We discuss some propositions of self complementary and self weak complementary strong intuitionistic fuzzy graphs. We introduce the concept of intuitionistic fuzzy line graphs.


title: SirT1 fails to affect p53-mediated biological functions.

abstract: The SirT1 gene encodes a protein deacetylase that acts on a number of nuclear substrates. p53 was identified as a SirT1 substrate whose transcriptional activity was reported to be negatively regulated by SirT1-dependent deacetylation. We set out to determine whether developmental defects and perinatal lethality observed in SirT1-null mice were caused by p53 hyperactivity by creating mice deficient for both SirT1 and p53. Animals null for both proteins were smaller than normal at birth, had eyelid opening defects and died during the late prenatal and early postnatal periods, a phenotype indistinguishable from mice deficient for SirT1 alone. Upon re-examination of the role of SirT1 in modulating p53 activity, we found that while SirT1 interacts with p53, the SirT1 protein had little effect on p53-dependent transcription of transfected or endogenous genes and did not affect the sensitivity of thymocytes and splenocytes to radiation-induced apoptosis. These findings suggest that SirT1 does not affect many p53-mediated biological activities despite the fact that acetylated p53 has been shown to be a substrate for SirT1.


title: Survivin and apoptosis control

abstract: Survivin is a member of the inhibitor of apoptosis (IAP) gene family that exhibits differential expression in nearly all human cancers but not in most normal tissues. Recent progress identified a multifunctional survivin pathway positioned at the interface between mitotic progression and apoptosis inhibition, and required to preserve viability of dividing tumor cells ( Altieri, 2001 ; Andersen and Thor, 2002 ; Jaattela, 1999 ). The unique properties of survivin have recently found concrete applications for cancer detection, diagnosis, and outcome prediction. In addition, targeting the survivin pathway may offer new therapeutic prospects to lower a general survival threshold in cancer cells. This chapter will focus on the current developments in the field of survivin and its role in apoptosis regulation and mitotic progression. Current perspectives on exploiting the survivin pathway for cancer diagnosis and treatment will be highlighted.


title: Spectrum resource management and interference mitigation for D2D communications with awareness of BER constraint in mmWave 5G underlay network

abstract: The work presented in this paper deals with the issue of massive demands for higher capacity. For that matter, we investigate the spectrum resource management in outdoor mmWave cell for the uplink of cellular and D2D communications. Indeed, we provide a first insight how to optimize the system performance in terms of achievable throughput while realizing a compromise between the large number of admitted devices and the generated interference constraint. We propose a mathematical formulation of the optimization objective which falls in the mixed integer-real optimization scheme. To overcome its complexity, we apply a heuristic algorithm and test its efficiency through simulation results with a particular regard to the BER impact in the QoS.


title: Augmenting naive bayes for ranking

abstract: Naive Bayes is an effective and efficient learning algorithm in classification. In many applications, however, an accurate ranking of instances based on the class probability is more desirable. Unfortunately, naive Bayes has been found to produce poor probability estimates. Numerous techniques have been proposed to extend naive Bayes for better classification accuracy, of which selective Bayesian classifiers (SBC) (Langley & Sage, 1994), tree-augmented naive Bayes (TAN) (Friedman et al., 1997), NBTree (Kohavi, 1996), boosted naive Bayes (Elkan, 1997), and AODE (Webb et al., 2005) achieve remarkable improvement over naive Bayes in terms of classification accuracy. An interesting question is: Do these techniques also produce accurate ranking? In this paper, we first conduct a systematic experimental study on their efficacy for ranking. Then, we propose a new approach to augmenting naive Bayes for generating accurate ranking, called hidden naive Bayes (HNB). In an HNB, a hidden parent is created for each attribute to represent the influences from all other attributes, and thus a more accurate ranking is expected. HNB inherits the structural simplicity of naive Bayes and can be easily learned without structure learning. Our experiments show that HNB outperforms naive Bayes, SBC, boosted naive Bayes, NBTree, and TAN significantly, and performs slightly better than AODE in ranking.


title: Exploiting Similarities among Languages for Machine Translation

abstract: Dictionaries and phrase tables are the basis of modern statistical machine translation systems. This paper develops a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures based on large monolingual data and mapping between languages from small bilingual data. It uses distributed representation of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs.


title: Pragmatic leadership: A return to wisdom

abstract: Te Ao Maori, or the Maori world, locates Maori philosophy, ethics and knowledge (wisdom) in a central role, and is therefore foundational to Maori thinking and practice. In this article, we present an example of pragmatic leadership, using the epistemological and ontological space of kaupapa Maori, a perspective that represents practice guided by the wisdom of Te Ao Maori. To do so, we draw from narrative shared by 22 Maori practitioners involved in leadership roles in a wide range of organisations to illustrate key features of Maori leadership practice. We argue that this approach offers a unique contribution and deeper understanding to the contemporary and instrumental representation of pragmatic leadership. Such a perspective allows researchers to explore leadership practices, particularly those that may be heavily influenced by culturally specific behaviours. In regards to practitioners, it provides foundation for innovative leadership practices grounded on giving weight to more deeply contextualised processes and relations. The consequences are widespread not only for researchers and practitioners of leadership, but also for a perception of indigenous leadership that is true to the indigenous logics in which they are grounded and reflective of good practice.