title: Pneumatic Networks for Soft Robotics that Actuate Rapidly

abstract: Soft robots actuated by infl ation of a pneumatic network (a “pneu-net”) of small channels in elastomeric materials are appealing for producing sophisticated motions with simple controls. Although current designs of pneu-nets achieve motion with large amplitudes, they do so relatively slowly (over seconds). This paper describes a new design for pneu-nets that reduces the amount of gas needed for infl ation of the pneu-net, and thus increases its speed of actuation. A simple actuator can bend from a linear to a quasicircular shape in 50 ms when pressurized at Δ P = 345 kPa. At high rates of pressurization, the path along which the actuator bends depends on this rate. When infl ated fully, the chambers of this new design experience only one-tenth the change in volume of that required for the previous design. This small change in volume requires comparably low levels of strain in the material at maximum amplitudes of actuation, and commensurately low rates of fatigue and failure. This actuator can operate over a million cycles without signifi cant degradation of performance. This design for soft robotic actuators combines high rates of actuation with high reliability of the actuator, and opens new areas of application for them.


title: A framework for detection and classification of plant leaf and stem diseases

abstract: We propose and evaluate a framework for detection of plant leaf/stem diseases. Studies show that relying on pure naked-eye observation of experts to detect such diseases can be prohibitively expensive, especially in developing countries. Providing fast, automatic, cheap and accurate image-processing-based solutions for that task can be of great realistic significance. The proposed framework is image-processing-based and is composed of the following main steps; in the first step the images at hand are segmented using the K-Means technique, in the second step the segmented images are passed through a pre-trained neural network. As a testbed, we use a set of leaf images taken from Al-Ghor area in Jordan. Our experimental results indicate that the proposed approach can significantly support accurate and automatic detection of leaf diseases. The developed Neural Network classifier that is based on statistical classification perform well and could successfully detect and classify the tested diseases with a precision of around 93%.


title: SVM Based Speaker Selection Using GMM Supervector for Rapid Speaker Adaptation

abstract: In this paper, we propose a novel method for rapid speaker adaptation called speaker support vector selection (SSVS). By taking gaussian mixture model (GMM) as speaker model, the speakers acoustically close to the test speaker are selected.Different from other selection method, just computing the likelihood between models, we utilizing support vector machines (SVM) to obtain a 'more optimal speaker subset'. Such selection is dynamically determined according to the distribution of reference speakers close the test. Furthermore, a single-pass re-estimation procedure conditioned on the selected speakers is shown. This adaptation strategy was evaluated in a large vocabulary speech recognition task. The presented method improves the relative accuracy rates by 13% compared to the baseline system.


Risk Management Framework for Cloud Computing : A Critical Review


title: Automated detection of exudates in retinal images using a split-and-merge algorithm

abstract: Retinal image analysis is commonly used for the diagnosis and monitoring of diseases. In fundus photographs, bright lesions representing hard and soft exudates are the earliest signs of diabetic retinopathy. In this paper, an automated method for the detection of these exudates in retinal images is presented. Candidates are detected using a combination of coarse and fine segmentation. The coarse segmentation is based on a local variation operation to outline the boundaries of all candidates which have clear borders. The fine segmentation is based on an adaptive thresholding and a new split-and-merge technique to segment all bright candidates locally. Using a clinician's reference for ground truth exudates were detected from a database with 89.7% sensitivity, 99.3% specificity and 99.4% accuracy. Due to its distinctive performance measures, the proposed method may be successfully applied to images of variable quality.


title: Predictive modelling of football injuries

abstract: The goal of this thesis is to investigate the potential of predictive modelling for football injuries. This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation of Wolverhampton Wanderers (WW). Three investigations were conducted: 1. Predicting the recovery time of football injuries using the UEFA injury recordings: The UEFA recordings is a common standard for recording injuries in professional football. For this investigation, three datasets of UEFA injury recordings were available. Different machine learning algorithms were used in order to build a predictive model. The performance of the machine learning models is then improved by using feature selection conducted through correlation-based subset feature selection and random forests. 2. Predicting injuries in professional football using exposure records: The relationship between exposure (in training hours and match hours) in professional football athletes and injury incidence was studied. A common problem in football is understanding how the training schedule of an athlete can affect the chance of him getting injured. The task was to predict the number of days a player can train before he gets injured. 3. Predicting intrinsic injury incidence using in-training GPS measurements: A significant percentage of football injuries can be attributed to overtraining and fatigue. GPS data collected during training sessions might provide indicators of fatigue, or might be used to detect very intense training sessions which can lead to overtraining. This research used GPS data gathered during training sessions of the first team of THFC, in order to predict whether an injury would take place during a week.


title: A Taxonomy of Serious Games for Dementia

abstract: Serious games for dementia (SG4D) hold their own, unique and significant space within the Games for Health domain. However, the SG4D field still has not been fully mapped out and classified. In this work, we present a generic taxonomy of serious games for dementia, based on the health functions and the health purposes they serve. Firstly, we classify dementia games based on the health function they serve, in: cognitive, physical and social-emotional games. Each of these functions serves a variety of health purposes, leading us to a second, lower level of classification in: preventative, rehabilitative, assessing and educative games. Furthermore, we provide an ex-post evaluation of the proposed taxonomy by exploring whether the existing serious games for dementia can be validly classified, based on the proposed taxonomic characters. To this end, we collect and analyse a set of dementia-related serious games (e.g. WiiSports, Big Brain Academy, Cognifit, MinWii, et al.) by performing a literature review. The results show that the taxonomical system covers a sub-field of “games for health” and indicates areas which are under-explored by current games.


MoSIFT: Recognizing Human Actions in


OnTarget : An Electronic Archery Scoring System


title: Convergent multi-view geometric error correction with pseudo-inverse projection homography

abstract: The paper presents a geometric error correction method for convergent multi-view images, reducing the vertical parallax and non-uniform horizontal disparities over the set of input camera views. The global optimization method using inter-camera geometric relations on an arc is described. Camera orientations and optical centers are adjusted towards a uniform, circular distribution closest to the actual camera setup. Experimental validation shows up to 20% improvements over state-of-the-art w.r.t. reducing geometric errors and reaching better scalability/robustness to large rotation angles between adjacent camera views. The corrected images can be readily used for visualization on autostereoscopic 3D displays, and/or for further processing in an advanced 3D-TV imaging pipeline.


title: Integrated Spatio‐temporal Data Mining for Forest Fire Prediction

abstract: Forests play a critical role in sustaining the human environment. Most forest fires not only destroy the natural environment and ecological balance, but also seriously threaten the security of life and property. The early discovery and forecasting of forest fires are both urgent and necessary for forest fire control. This article explores the possible applications of Spatio-temporal Data Mining for forest fire prevention. The research pays special attention to the spatio-temporal forecasting of forest fire areas based upon historic observations. An integrated spatio-temporal forecasting framework – ISTFF – is proposed: it uses a dynamic recurrent neural network for spatial forecasting. The principle and algorithm of ISTFF are presented, and are then illustrated by a case study of forest fire area prediction in Canada. Comparative analysis of ISTFF with other methods shows its high accuracy in short-term prediction. The effect of spatial correlations on the prediction accuracy of spatial forecasting is also explored.


title: A Privacy-Preserving Framework for Integrating Person-Specific Databases

abstract: Many organizations capture personal information, but the quantity of records needed to detect statistically significant patterns is often beyond the grasp of a single data collector. In the biomedical realm, this problem has pressed regulatory agencies to require funded investigators to share research-derived data to public repositories. The challenge; however, is that shared records must not reveal the identity of the subjects. In this paper, we extend a secure framework in which data holders contribute and query encrypted person-specific data stored on a third party's server. Specifically, we develop protocols that enable data holders to merge personal records, thus creating larger profiles and diminishing duplication. The repository administrator can merge records via encrypted identifiers without decrypting or inferring the contents of the joined records. Our model is more practical than prior secure join methods because each data holder needs only a single interaction with the central repository. We further present an extension to the protocol that permits the revelation of k-anonymous demographics, such that the administrator can perform joins more efficiently with the guarantee that each record can be linked to no less than k individuals in the population. We prove the privacy preserving features of our protocols and experimentally evaluate their efficiency in a real world Census dataset.


title: Understanding physiological responses to stressors during physical activity

abstract: With advances in physiological sensors, we are able to understand people's physiological status and recognize stress to provide beneficial services. Despite the great potential in physiological stress recognition, there are some critical issues that need to be addressed such as the sensitivity and variability of physiology to many factors other than stress (e.g., physical activity). To resolve these issues, in this paper, we focus on the understanding of physiological responses to both stressor and physical activity and perform stress recognition, particularly in situations having multiple stimuli: physical activity and stressors. We construct stress models that correspond to individual situations, and we validate our stress modeling in the presence of physical activity. Analysis of our experiments provides an understanding on how physiological responses change with different stressors and how physical activity confounds stress recognition with physiological responses. In both objective and subjective settings, the accuracy of stress recognition drops by more than 14% when physical activity is performed. However, by modularizing stress models with respect to physical activity, we can recognize stress with accuracies of 82% (objective stress) and 87% (subjective stress), achieving more than a 5-10% improvement from approaches that do not take physical activity into account.


title: Novel pattern of editing regions in mitochondrial transcripts of the cryptobiid Trypanoplasma borreli.

abstract: In mitochondria of Kinetoplastida belonging to the suborder Trypanosomatina, the nucleotide sequence of transcripts is post-transcriptionally edited via insertion and deletion of uridylate residues. In order to shed more light on the evolutionary history of this process we have searched for editing in mitochondrial RNAs of Trypanoplasma borreli, an organism belonging to the suborder Bodonina. We have cloned and sequenced a 5.3 kb fragment derived from a 37 kb mitochondrial DNA molecule which does not appear to be a part of a network structure and have found genes encoding cytochrome c oxidase (cox) subunit 1, cox 2 and apocytochrome (cyt) b, and genes encoding the small and large subunit mitoribosomal RNAs. The order in which these genes occur is completely different from that of trypanosomatid maxicircle genes. The 5' and 3' termini of both the cytb and cox1 gene are cryptic, the protein coding sequences being created by extensive insertion/deletion of Us in the corresponding mRNA sections. Phylogenetic analyses of the protein and ribosomal RNA sequences demonstrated that the separation between T.borreli and Trypanosomatina was an early event, implying that U-insertion/deletion processes are ancient. Different patterns of editing have persisted in different lineages, however, since editing of cox1 RNA and of relatively small 3'-terminal RNA sections is not found in trypanosomatids. In contrast, cox2 RNA which is edited in trypanosomatids by the insertion of four Us, is unedited in T.borreli.


title: Characterization and Antimicrobial Activity of Nanocurcumin and Curcumin

abstract: Curcumin is the principal curcuminoid of the popular Indian spice Curcuma longa (Turmeric), which is a member of ginger family (Zingiberaceae). Inspite of showing extraordinary medicinal properties its commercialized formulation is still a challenge because of its poor solubility, bioavailability and rapid plasma clearance. In our work, different shapes of nanocurcumin are prepared in alcohol-water solutions using sonication assisted solvent worsening method. The nanocrcumin in the suspension have been characterized using Dynamic and Static Light Scattering (DLS and SLS) and Transmission Electron Microscopy methods. The DLS data revealed that the curcumin nanoparticles in the suspension had asymmetric shape with effective hydrodynamic radius in the range of 120-160nm. With increase in water concentration in the solvent medium the shape of the nanocurcumin was seen to undergo a gradual transition from isotropic to anisotropic state which was supported by TEM study. The in vitro antimicrobial activity (Inhibition) of the nanocurcumin has been compared with that of the normal curcumin using Broth dilution and Kirby-Bauer methods against both Gram positive and Gram negative bacteria. The efficacy of nanocurcumin is marginally better than curcurmin per se. We observed that nanocurcumin had better dispersibility and enhanced bioavailability in hydrophilic environment as compared to normal curcumin.


title: The correlation between cluster hypothesis tests and the effectiveness of cluster-based retrieval

abstract: We present a study of the correlation between the extent to which the cluster hypothesis holds, as measured by various tests, and the relative effectiveness of cluster-based retrieval with respect to document-based retrieval. We show that the correlation can be affected by several factors, such as the size of the result list of the most highly ranked documents that is analyzed. We further show that some cluster hypothesis tests are often negatively correlated with one another. Moreover, in several settings, some of the tests are also negatively correlated with the relative effectiveness of cluster-based retrieval.


Reimagining Auditing in a Wired World


Raising Champions - A Parents Perspective


title: Language Production in Trilingual Children: Insights on Code Switching and Code Mixing

abstract: The present study looked at language switching – both code mixes and code switches as language events which shed light on the "vulnerability" of a language structure. This vulnerability becomes the "trigger" for language switching. An analysis of these switches showed that irrespective of age, input or task, language switching are systematic and regular if the structure is susceptible and permeable. Furthermore, we report on unique data of trilingual code switching and code mixing as features which distinguish between bilingual and multilingual language processing in general and language switching in particular. We conclude that code switches and code mixes in TFLA (trilingual first language acquisition) as well as BFLA (bilingual first language acquisition) are driven by three factors: a) the individual characteristic of the trilingual and his/her language development; b) the impact input may or may not have on such productions; and c) the "susceptibility" of language structures to undergo switches. Yet, what distinguishes switches in trilinguals versus bilinguals is the additional language system involved making the switches more explicable as a universal multilingual processing rather than a language specific multilingual processing.


Effectiveness of Front-Of-Pack Nutrition Labels in French Adults: Results from the NutriNet-Santé Cohort Study


title: A Note on Quantum Geometric Langlands Duality, Gauge Theory, and Quantization of the Moduli Space of Flat Connections

abstract: Montonen-Olive duality implies that the categories of A-branes on the moduli spaces of Higgs bundles on a Riemann surface C for a pair of Langlands-dual groups are equivalent. We reformulate this as a statement about categories of B-branes on the quantized moduli spaces of flat connections for these groups. We show that it implies the statement of the Quantum Geometric Langlands duality with a purely imaginary ``quantum parameter'' which is proportional to the inverse of the Planck constant of the gauge theory. The ramified version of the story is also considered.


title: Supporting Diverse Notations in MPS' Projectional Editor

abstract: To be able to build effective DSLs, these DSLs must not just use language concepts that are aligned with their respective domain, but also use notations that correspond closely to established domain notations – and those are often not purely textual or graphical. The underlying language workbench must support these notations, and combining different notations in a single editor must be supported as well in order to support the coherent definitions of systems that use several DSLs. In this paper we provide an overview over the notations supported by JetBrains MPS. MPS is a language workbench that uses a projectional editor, which, by its very nature, can deal with many different notational styles, including text, prose, math tables and graphics. The various supported notations are illustrated with examples from real-world systems.


Controlling prejudice and stereotyping: Antecedents, mechanisms, and contexts


title: Mining subtopics from different aspects for diversifying search results

abstract: User queries to the Web tend to have more than one interpretation due to their ambiguity and other characteristics. How to diversify the ranking results to meet users' various potential information needs has attracted considerable attention recently. This paper is aimed at mining the subtopics of a query either indirectly from the returned results of retrieval systems or directly from the query itself to diversify the search results. For the indirect subtopic mining approach, clustering the retrieval results and summarizing the content of clusters is investigated. In addition, labeling topic categories and concept tags on each returned document is explored. For the direct subtopic mining approach, several external resources, such as Wikipedia, Open Directory Project, search query logs, and the related search services of search engines, are consulted. Furthermore, we propose a diversified retrieval model to rank documents with respect to the mined subtopics for balancing relevance and diversity. Experiments are conducted on the ClueWeb09 dataset with the topics of the TREC09 and TREC10 Web Track diversity tasks. Experimental results show that the proposed subtopic-based diversification algorithm significantly outperforms the state-of-the-art models in the TREC09 and TREC10 Web Track diversity tasks. The best performance our proposed algorithm achieves is ?-nDCG@5 0.307, IA-P@5 0.121, and ?#-nDCG@5 0.214 on the TREC09, as well as ?-nDCG@10 0.421, IA-P@10 0.201, and ?#-nDCG@10 0.311 on the TREC10. The results conclude that the subtopic mining technique with the up-to-date users' search query logs is the most effective way to generate the subtopics of a query, and the proposed subtopic-based diversification algorithm can select the documents covering various subtopics.


title: SPONGE: A generalized eigenproblem for clustering signed networks

abstract: We introduce a principled and theoretically sound spectral method for $k$-way clustering in signed graphs, where the affinity measure between nodes takes either positive or negative values. Our approach is motivated by social balance theory, where the task of clustering aims to decompose the network into disjoint groups, such that individuals within the same group are connected by as many positive edges as possible, while individuals from different groups are connected by as many negative edges as possible. Our algorithm relies on a generalized eigenproblem formulation inspired by recent work on constrained clustering. We provide theoretical guarantees for our approach in the setting of a signed stochastic block model, by leveraging tools from matrix perturbation theory and random matrix theory. An extensive set of numerical experiments on both synthetic and real data shows that our approach compares favorably with state-of-the-art methods for signed clustering, especially for large number of clusters and sparse measurement graphs.


title: Local, Fine Co-Registration of SAR Interferometry Using the Number of Singular Points for the Evaluation

abstract: Synthetic aperture radar (SAR) has great advantage of being able to observe large area accurately in any weather. It can measure various properties of the earth Boerner (2003), e.g., the topography, the vegetation Hajinsek et al. (2009), and the landscape changes caused by earthquakes or volcanoes Gabriel et al. (1989). One of the common usages of SAR is Interferometric synthetic aperture radar (InSAR), which can measure the landscape with an interferogram of SAR images. The SAR interferogram is made from two complex-valued maps obtained by observing identical place, named "master" and "slave." The phase information of the interferogram corresponds to the ground topography. To generate digital elevation model (DEM) from interferogram, phase unwrapping process is required. However, an unwrapping process is disturbed seriously by singular points (SPs), the rotational points existing in the phase map.


Effect of Multisensory Training on Writing Skills of Children with Dysgraphia


title: Compact Reconfigurable UWB Slot Antenna for Cognitive Radio Applications

abstract: A very compact reconfigurable ultrawideband (UWB) stepped-slot antenna for cognitive radio (CR) applications is presented in this letter. The proposed antenna operates in two modes: UWB mode for sensing application and communicating mode for different narrow/wideband communication applications. The UWB mode of antenna is obtained by the creation of three resonances that lie in UWB spectrum. The narrow/wide communicating bands are obtained by different switching states of five p-i-n diodes. The impedance bandwidth of the proposed antenna in UWB mode is 2.8-10.7 GHz. The communicating bands (3.2-4.5, 4.3-7.8, and 7.9-11.2 GHz) cover the entire UWB spectrum. The proposed antenna has the compact size of 21 × 9 mm ::: 2 ::: . A prototype of the designed antenna is fabricated and measured. The measured results are in good agreement with the simulated results that validate the proposed approach of reconfigurable UWB antenna design for CR applications.


title: A Generic Framework for Context-Aware Routing and its Implementation in Wireless Sensor Networks

abstract: As networks become more dynamic, the awareness of the network members’ current context is of growing importance. This can be said especially for communication networks, but also for other types of networks, e.g. logistic networks. Context-aware routing takes this context information into account when selecting routes in such networks. This paper introduces a novel generic framework for context-aware routing, including the message flow as well as the route decision method. The framework is not limited to a single application domain, but it is a generalized approach that can be adapted and specialized for various application domains. As an example, a wireless sensor network scenario is presented where this routing framework is applied.


title: Semantic graph reduction approach for abstractive Text Summarization

abstract: One of the important Natural Language Processing applications is Text Summarization, which helps users to manage the vast amount of information available, by condensing documents' content and extracting the most relevant facts or topics included. Text Summarization can be classified according to the type of summary: extractive, and abstractive. Extractive summary is the procedure of identifying important sections of the text and producing them verbatim while abstractive summary aims to produce important material in a new generalized form. In this paper, a novel approach is presented to create an abstractive summary for a single document using a rich semantic graph reducing technique. The approach summaries the input document by creating a rich semantic graph for the original document, reducing the generated graph, and then generating the abstractive summary from the reduced graph. Besides, a simulated case study is presented to show how the original text was minimized to fifty percent.


title: Gas Sensors Based on One Dimensional Nanostructured Metal-Oxides: A Review

abstract: Recently one dimensional (1-D) nanostructured metal-oxides have attracted much attention because of their potential applications in gas sensors. 1-D nanostructured metal-oxides provide high surface to volume ratio, while maintaining good chemical and thermal stabilities with minimal power consumption and low weight. In recent years, various processing routes have been developed for the synthesis of 1-D nanostructured metal-oxides such as hydrothermal, ultrasonic irradiation, electrospinning, anodization, sol-gel, molten-salt, carbothermal reduction, solid-state chemical reaction, thermal evaporation, vapor-phase transport, aerosol, RF sputtering, molecular beam epitaxy, chemical vapor deposition, gas-phase assisted nanocarving, UV lithography and dry plasma etching. A variety of sensor fabrication processing routes have also been developed. Depending on the materials, morphology and fabrication process the performance of the sensor towards a specific gas shows a varying degree of success. This article reviews and evaluates the performance of 1-D nanostructured metal-oxide gas sensors based on ZnO, SnO2, TiO2, In2O3, WOx, AgVO3, CdO, MoO3, CuO, TeO2 and Fe2O3. Advantages and disadvantages of each sensor are summarized, along with the associated sensing mechanism. Finally, the article concludes with some future directions of research.


title: Computing the Euler Number of a Binary Image Based on a Vertex Codification

abstract: We describe a method to compute the Euler number of a binary digital image based on a codification of contour pixelsof the image’s shapes. The overall procedure evolves from a set of lemmas and theorems, their demonstration andtheir numerical validation. The method is supported through an experimental set which analyzes some digital imagesand their outcome to demonstrate the applicability of the procedure. The paper also includes a discussion aboutpresent and futures steps on this research.


title: Automatic Secret Keys From Reciprocal MIMO Wireless Channels: Measurement and Analysis

abstract: Information theoretic limits for random key generation in multiple-input multiple-output (MIMO) wireless systems exhibiting a reciprocal channel response are investigated experimentally with a new three-node MIMO measurement campaign. As background, simple expressions are presented for the number of available key bits, as well as the number of bits that are secure from a close eavesdropper. Two methods for generating secret keys are analyzed in the context of MIMO channels and their mismatch rate and efficiency are derived. A new wideband indoor MIMO measurement campaign in the 2.51- to 2.59-GHz band is presented, whose purpose is to study the number of available key bits in both line-of-sight and nonline-of-sight environments. Application of the key generation methods to measured propagation channels indicates key generation rates that can be obtained in practice for four-element arrays.


title: From Papercraft to Paper Mechatronics: Exploring a New Medium and Developing a Computational Design Tool

abstract: Paper Mechatronics is a novel interdisciplinary design medium, enabled by recent advances in craft technologies: the term refers to a reappraisal of traditional papercraft in combination with accessible mechanical, electronic, and computational elements. I am investigating the design space of paper mechatronics as a new hands-on medium by developing a series of examples and building a computational tool, FoldMecha, to support non-experts to design and construct their own paper mechatronics models. This paper describes how I used the tool to create two kinds of paper mechatronics models: walkers and flowers and discuss next steps.


title: AMAL: High-fidelity, behavior-based automated malware analysis and classification

abstract: Abstract This paper introduces AMAL, an automated and behavior-based malware analysis and labeling system that addresses shortcomings of the existing systems. AMAL consists of two sub-systems, AutoMal and MaLabel. AutoMal provides tools to collect low granularity behavioral artifacts that characterize malware usage of the file system, memory, network, and registry, and does that by running malware samples in virtualized environments. On the other hand, MaLabel uses those artifacts to create representative features, use them for building classifiers trained by manually vetted training samples, and use those classifiers to classify malware samples into families similar in behavior. AutoMal also enables unsupervised learning, by implementing multiple clustering algorithms for samples grouping. An evaluation of both AutoMal and MaLabel based on medium-scale (4000 samples) and large-scale datasets (more than 115,000 samples)—collected and analyzed by AutoMal over 13 months—shows AMAL's effectiveness in accurately characterizing, classifying, and grouping malware samples. MaLabel achieves a precision of 99.5% and recall of 99.6% for certain families' classification, and more than 98% of precision and recall for unsupervised clustering. Several benchmarks, cost estimates and measurements highlight the merits of AMAL.


title: Color-based image segmentation on UAV reconnaissance videos with Matlab

abstract: K-means clustering algorithm is applied to process the colorful frames of UAV reconnaissance videos. After conversing the RGB color space into L*a*b* color space for reducing the dimensions, the appointed distinct segment can be partitioned from the colorful image with K-means clustering algorithm. This method can avoid the questions of poor precision to image segments with the algorithms of background subtraction, optical flows and feature matching about the complicated locomotive and shaking backgrounds. The experimental result shows that the adoption of the color-based segmentation algorithm can really produce high image segmentation precision, in UAV's civil actual application. At last, a GUI is designed to carry out the image segmentation and video processing expediently.


title: Cyber military strategy for cyberspace superiority in cyber warfare

abstract: In this paper, we proposed robust and operational cyber military strategy for cyberspace superiority in cyber warfare. We considered cyber forces manpower, cyber intelligence capability, and an organization of cyber forces for improving cyber military strategy. In cyber forces power field, we should cultivated personals that can perform network computer operations and hold cyber security technology such as cyber intelligence collection, cyber-attack, cyber defence, and cyber forensics. Cyber intelligence capability includes cyber surveillance/reconnaissance, cyber order of battle, pre-CTO, and cyber damage assessment. An organization of cyber forces has to change tree structure to network structure and has to organize task or functional centric organizations. Our proposed cyber military strategy provides prior decision of action to operate desired effects in cyberspace.


title: Automatic latent value determination

abstract: Latent fingerprints are the most frequently encountered and reliable crime scene evidence used in forensics investigations. Automatic methods for quantitative assessment of a latent in terms of (i) value for individualization (VID), (ii) value for exclusion only (VEO), and (iii) no value (NV), are needed to minimize the workload of latent examiners so that they can pay more attention to challenging prints (VID and NV latents). Current value determination is either made by examiners or predicted given manually annotated features. Because both of these approaches depend on human markup, they are subjective and time consuming. We propose a fully automatic method for latent value determination based on the number, reliability, and compactness of the minutiae, ridge quality, ridge flow, and the number of core and delta points. Given the small number of latents with VEO and NV labels in two latent databases available to us (NIST SD27 and WVU), only a two-class value determination is considered, namely VID and VID, where the VID class contains VEO and NV latents. Experimental results show that the value determination by the proposed method (i) obviates the need for examiner markup while maintaining the accuracy of value determination and (ii) can predict the AFIS performance better than examiners.


title: Optimizing search engines using clickthrough data

abstract: This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches to learning retrieval functions from examples exist, they typically require training data generated from relevance judgments by experts. This makes them difficult and expensive to apply. The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Machine (SVM) approach, this paper presents a method for learning retrieval functions. From a theoretical perspective, this method is shown to be well-founded in a risk minimization framework. Furthermore, it is shown to be feasible even for large sets of queries and features. The theoretical results are verified in a controlled experiment. It shows that the method can effectively adapt the retrieval function of a meta-search engine to a particular group of users, outperforming Google in terms of retrieval quality after only a couple of hundred training examples.


title: ConvAE: A New Channel Autoencoder Based on Convolutional Layers and Residual Connections

abstract: In this letter, we propose ConvAE, a new channel autoencoder structure. ConvAE uses residual blocks with convolutional layers. This configuration increases performance while decreasing computational complexity at run-time compared with conventional channel autoencoders. The simulations using both conventional and proposed autoencoders for a 2-by-2 multiple-input multiple-output (MIMO) system under Rayleigh and Nakagami-m fading show that the ConvAE is able to attain a lower bit error rate and higher achievable rate relative to the conventional channel autoencoder schemes.


title: A survey on data stream clustering and classification

abstract: Nowadays, with the advance of technology, many applications generate huge amounts of data streams at very high speed. Examples include network traffic, web click streams, video surveillance, and sensor networks. Data stream mining has become a hot research topic. Its goal is to extract hidden knowledge/patterns from continuous data streams. Unlike traditional data mining where the dataset is static and can be repeatedly read many times, data stream mining algorithms face many challenges and have to satisfy constraints such as bounded memory, single-pass, real-time response, and concept-drift detection. This paper presents a comprehensive survey of the state-of-the-art data stream mining algorithms with a focus on clustering and classification because of their ubiquitous usage. It identifies mining constraints, proposes a general model for data stream mining, and depicts the relationship between traditional data mining and data stream mining. Furthermore, it analyzes the advantages as well as limitations of data stream algorithms and suggests potential areas for future research.


title: The Recognition of Human Movement Using Temporal Templates

abstract: A view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms.


title: Temporal Embedding in Convolutional Neural Networks for Robust Learning of Abstract Snippets

abstract: The prediction of periodical time-series remains challenging due to various types of data distortions and misalignments. Here, we propose a novel model called Temporal embedding-enhanced convolutional neural Network (TeNet) to learn repeatedly-occurring-yet-hidden structural elements in periodical time-series, called abstract snippets, for predicting future changes. Our model uses convolutional neural networks and embeds a time-series with its potential neighbors in the temporal domain for aligning it to the dominant patterns in the dataset. The model is robust to distortions and misalignments in the temporal domain and demonstrates strong prediction power for periodical time-series. ::: We conduct extensive experiments and discover that the proposed model shows significant and consistent advantages over existing methods on a variety of data modalities ranging from human mobility to household power consumption records. Empirical results indicate that the model is robust to various factors such as number of samples, variance of data, numerical ranges of data etc. The experiments also verify that the intuition behind the model can be generalized to multiple data types and applications and promises significant improvement in prediction performances across the datasets studied.


title: Combining the Benefits of CCA and SVMs for SSVEP-based BCIs in Real-world Conditions

abstract: In this paper we propose a novel method for SSVEP classification that combines the benefits of the inherently multi-channel CCA, the state-of-the-art method for detecting SSVEPs, with the robust SVMs, one of the most popular machine learning algorithms. The employment of SVMs, except for the benefit of robustness, provides us also with a confidence score allowing to dynamically trade-off the trial length with the accuracy of the classifier, and vice versa. By balancing this trade-off we are able to offer personalized self-paced BCIs that maximize the ITR of the system. Furthermore, we propose to perturb the template frequencies of CCA so as to accommodate with real world BCI applications requirements, where the environmental conditions may not be ideal compared to existing methods that rely on the assumption of soundproof and distraction-free environments.


title: Visual Madlibs: Fill in the Blank Description Generation and Question Answering

abstract: In this paper, we introduce a new dataset consisting of 360,001 focused natural language descriptions for 10,738 images. This dataset, the Visual Madlibs dataset, is collected using automatically produced fill-in-the-blank templates designed to gather targeted descriptions about: people and objects, their appearances, activities, and interactions, as well as inferences about the general scene or its broader context. We provide several analyses of the Visual Madlibs dataset and demonstrate its applicability to two new description generation tasks: focused description generation, and multiple-choice question-answering for images. Experiments using joint-embedding and deep learning methods show promising results on these tasks.


title: Understanding the Roots of Radicalisation on Twitter

abstract: In an increasingly digital world, identifying signs of online extremism sits at the top of the priority list for counter-extremist agencies. Researchers and governments are investing in the creation of advanced information technologies to identify and counter extremism through intelligent large-scale analysis of online data. However, to the best of our knowledge, these technologies are neither based on, nor do they take advantage of, the existing theories and studies of radicalisation. In this paper we propose a computational approach for detecting and predicting the radicalisation influence a user is exposed to, grounded on the notion of 'roots of radicalisation' from social science models. This approach has been applied to analyse and compare the radicalisation level of 112 pro-ISIS vs.112 "general" Twitter users. Our results show the effectiveness of our proposed algorithms in detecting and predicting radicalisation influence, obtaining up to 0.9 F-1 measure for detection and between 0.7 and 0.8 precision for prediction. While this is an initial attempt towards the effective combination of social and computational perspectives, more work is needed to bridge these disciplines, and to build on their strengths to target the problem of online radicalisation.


title: Design of Planar Dielectric Resonator Antenna Array at 28 GHz

abstract: This article presents a planar array of rectangular Dielectric Resonator Antenna operating for 28 GHz applications. The proposed antenna is formed through two stages of designs which are a single element and planar array. It is made up from a ceramic material with a dielectric constant of 10 and mounted on RT/Duroid 5880 with a relative permittivity of 2.2 and a thickness of 0.254 mm. A prospective study using three different configurations of three by three planar array is done in order to obtain the best performance in terms of bandwidth, gain, and cost reduction. Besides that, this study is also conducted for a beam steering capability of each configuration. Finally, the best configuration is proposed for 5G application.


title: Tweeting under pressure: analyzing trending topics and evolving word choice on sina weibo

abstract: In recent years, social media has risen to prominence in China, with sites like Sina Weibo and Renren each boasting hundreds of millions of users. Social media in China plays a profound role as a platform for breaking news and political commentary that is not available in the state-sanctioned news media. However, like all websites in China, Chinese social media is subject to censorship. Although several studies have identified censorship on Weibo and Chinese blogs, to date no studies have examined the overall impact of censorship on discourse in social media. In this study, we examine how censorship impacts discussions on Weibo, and how users adapt to avoid censorship. We gather tweets and comments from 280K politically active Weibo users for 44 days and use NLP techniques to identify trending topics. We observe that the magnitude of censorship varies dramatically across topics, with 82% of tweets in some topics being censored. However, we find that censorship of a topic correlates with high user engagement, suggesting that censorship does not stifle discussion of sensitive topics. Furthermore, we find that users adopt variants of words (known as morphs) to avoid keyword-based censorship. We analyze emergent morphs to learn how they are adopted and spread by the Weibo user community.


title: A 2GHz GaN Class-J power amplifier for base station applications

abstract: The design and implementation of a high efficiency Class-J power amplifier (PA) for basestation applications is reported. A commercially available 10W GaN HEMT device was used, for which a large-signal model and an extrinsic parasitic model were available. Following Class-J theory, the needed harmonic terminations at the output of the transistor were defined and realised. Experimental results show good agreement with simulations verifying the class of operation. Efficiency above 70% is demonstrated with an output power of 39.7dBm at an input drive of 29dBm. High efficiency is sustained over a bandwidth of 140MHz.


title: GASOLINE: a Greedy And Stochastic algorithm for Optimal Local multiple alignment of Interaction NEtworks

abstract: The analysis of structure and dynamics of biological networks plays a central role in understanding the intrinsic complexity of biological systems. Biological networks have been considered a suitable formalism to extend evolutionary and comparative biology. In this paper we present GASOLINE, an algorithm for multiple local network alignment based on statistical iterative sampling in connection to a greedy strategy. GASOLINE overcomes the limits of current approaches by producing biologically significant alignments within a feasible running time, even for very large input instances. The method has been extensively tested on a database of real and synthetic biological networks. A comprehensive comparison with state-of-the art algorithms clearly shows that GASOLINE yields the best results in terms of both reliability of alignments and running time on real biological networks and results comparable in terms of quality of alignments on synthetic networks. GASOLINE has been developed in Java, and is available, along with all the computed alignments, at the following URL: http://ferrolab.dmi.unict.it/gasoline/gasoline.html.


title: Identifying and ranking cultural heritage resources on geotagged social media for smart cultural tourism services

abstract: With a large amount of data (e.g., ratings and feedbacks) obtained from social media (e.g., TripAdvisor), smart tourism applications and services have been studied to understand the contexts of users. More particularly, in this work, we have been focusing on "cultural tourism" service by automatically identifying and ranking cultural things (e.g., historical places). The main aims of this research are (1) to identify useful cultural heritage resources from geotagged social media (more precisely, spatial folksonomy), and (2) to rank them, with respect to the user context (e.g., location). Thus, the smart cultural tourism service can deliver smart interactions between the visitors of smart tourism environments by collecting and analyzing geotagged multimedia data (e.g., photos, tags, and comments) from available social media. In order to evaluate the proposed service, the system has been implemented with the real-world datasets related to cultural heritage sites (e.g., Hue, Hoi An, My Son in Vietnam, and Gyeongbokgung, Changdeokgung, Gyeongju in Korea).


title: Why Do People Regulate Their Emotions? A Taxonomy of Motives in Emotion Regulation

abstract: Emotion regulation involves the pursuit of desired emotional states (i.e., emotion goals) in the service of superordinate motives. The nature and consequences of emotion regulation, therefore, are likely to depend on the motives it is intended to serve. Nonetheless, limited attention has been devoted to studying what motivates emotion regulation. By mapping the potential benefits of emotion to key human motives, this review identifies key classes of motives in emotion regulation. The proposed taxonomy distinguishes between hedonic motives that target the immediate phenomenology of emotions, and instrumental motives that target other potential benefits of emotions. Instrumental motives include behavioral, epistemic, social, and eudaimonic motives. The proposed taxonomy offers important implications for understanding the mechanism of emotion regulation, variation across individuals and contexts, and psychological function and dysfunction, and points to novel research directions.


title: Design of the cmu sphinx-4 decoder

abstract: The decoder of the sphinx-4 speech recognition system incorporates several new design strategies which have not been used earlier in conventional decoders of HMM-based large vocabulary speech recognition systems. Some new design aspects include graph construction for multilevel parallel decoding with independent simultaneous feature streams without the use of compound HMMs, the incorporation of a generalized search algorithm that subsumes Viterbi and full-forward decoding as special cases, design of generalized language HMM graphs from grammars and language models of multiple standard formats, that toggles trivially from flat search structure to tree search structure etc. This paper describes some salient design aspects of the Sphinx-4 decoder and includes preliminary performance measures relating to speed and accuracy.


title: A.: An overview of research on passive brain-computer interfaces for implicit human-computer interaction

abstract: This paper surveys existing and past research on brain-computer interfaces (BCI) for implicit human-computer interaction. A novel way of using BCI has indeed emerged, which proposes to use BCI in a less explicit way : the so-called "passive" BCI. Implicit BCI or passive BCI refers to BCI in which the user does not try to control his brain activity. Thus the brain activity is assimilated to an input and can be used to adapt the application to the user's mental state. In this paper, we ﬁrst study "implicit interaction" in general and recall its main applications. Then, we make a survey of existing and past research on brain-computer interfaces for implicit human-computer interaction. It seems indeed that BCI can be used in many applications in an implicit way, such as for adaptive automation, affective computing, or for video games. In such applications, BCI based on implicit interaction was often reported to improve performance of either the system or the user, or to introduce novel capacities based on mental states.


title: Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model

abstract: In this paper, automatic motion control is investigated for wheeled inverted pendulum (WIP) models, which have been widely applied for modeling of a large range of two wheeled modern vehicles. First, the underactuated WIP model is decomposed into a fully actuated second-order subsystem Σ ::: a ::: consisting of planar movement of vehicle forward motion and yaw angular motions, and a passive (nonactuated) first-order subsystem Σ ::: b ::: of pendulum tilt motion. Due to the unknown dynamics of subsystem Σ ::: a ::: and universal approximation ability of neural network (NN), an adaptive NN scheme has been employed for motion control of subsystem Σ ::: a ::: . Model reference approach has been used, whereas the reference model is optimized by finite time linear quadratic regulation technique. Inspired by human control strategy of inverted pendulum, the tilt angular motion in the passive subsystem Σ ::: b ::: has been indirectly controlled using the dynamic coupling with planar forward motion of subsystem Σ ::: a ::: , such that the satisfactory tracking of set tilt angle can be guaranteed. Rigorous theoretic analysis has been established, and simulation studies have been performed to demonstrate the developed method.


title: Understanding the impact of entropy on policy optimization

abstract: Entropy regularization is commonly used to improve policy optimization in reinforcement learning. It is believed to help with exploration by encouraging the selection of more stochastic policies. In this work, we analyze this claim and, through new visualizations of the optimization landscape, we observe that incorporating entropy in policy optimization serves as a regularizer. We show that even with access to the exact gradient, policy optimization is difficult due to the geometry of the objective function. We qualitatively show that, in some environments, entropy regularization can make the optimization landscape smoother, thereby connecting local optima and enabling the use of larger learning rates. This manuscript presents new tools for understanding the underlying optimization landscape and highlights the challenge of designing general-purpose policy optimization algorithms in reinforcement learning.


title: The duval pentagon-a new complementary tool for the interpretation of dissolved gas analysis in transformers

abstract: Several methods are available for the interpretation of dissolved gas analysis (DGA) results in oil-filled electrical equipment. These methods use ratios of the five main hydrocarbon gases formed, namely H ::: 2 ::: , CH ::: 4 ::: , C ::: 2 ::: H ::: 6 ::: , C ::: 2 ::: H ::: 4 ::: , and C ::: 2 ::: H ::: 2 ::: , either 2-gas ratios, e.g., in IEEE [1] and IEC [2]; 3-gas ratios, in Duval Triangles 1 to 7 [3]; or 4-gas ratios in [4]. In this article a new method is presented using 5-gas ratios in a "pentagon" representation applicable to mineral oil-filled equipment. Carbon oxides CO and CO ::: 2 ::: are examined separately as in other methods.


title: Does SMS Text Messaging Help or Harm Adults' Knowledge of Standard Spelling?.

abstract: The recent increase in short messaging system (SMS) text messaging, often using abbreviated, non-conventional ‘textisms’ (e.g. ‘2nite’), in school-aged children has raised fears of negative consequences of such technology for literacy. The current research used a paradigm developed by Dixon and Kaminska, who showed that exposure to phonetically plausible misspellings (e.g. ‘recieve’) negatively affected subsequent spelling performance, though this was true only with adults, not children. The current research extends this work to directly investigate the effects of exposure to textisms, misspellings and correctly spelled words on adults' spelling. Spelling of a set of key words was assessed both before and after an exposure phase where participants read the same key words, presented either as textisms (e.g. ‘2nite’), correctly spelled (e.g. ‘tonight’) or misspelled (e.g. ‘tonite’) words. Analysis showed that scores decreased from pre- to post-test following exposure to misspellings, whereas performance improved following exposure to correctly spelled words and, interestingly, to textisms. Data suggest that exposure to textisms, unlike misspellings, had a positive effect on adults' spelling. These findings are interpreted in light of other recent research suggesting a positive relationship between texting and some literacy measures in school-aged children.


title: From recurrent choice to skill learning: A reinforcement-learning model

abstract: The authors propose a reinforcement-learning mechanism as a model for recurrent choice and extend it to account for skill learning. The model was inspired by recent research in neurophysiological studies of the basal ganglia and provides an integrated explanation of recurrent choice behavior and skill learning. The behavior includes effects of differential probabilities, magnitudes, variabilities, and delay of reinforcement. The model can also produce the violation of independence, preference reversals, and the goal gradient of reinforcement in maze learning. An experiment was conducted to study learning of action sequences in a multistep task. The fit of the model to the data demonstrated its ability to account for complex skill learning. The advantages of incorporating the mechanism into a larger cognitive architecture are discussed.


title: A microeconomic data mining problem: customer-oriented catalog segmentation

abstract: The microeconomic framework for data mining [7] assumes that an enterprise chooses a decision maximizing the overall utility over all customers where the contribution of a customer is a function of the data available on that customer. In Catalog Segmentation, the enterprise wants to design k product catalogs of size r that maximize the overall number of catalog products purchased. However, there are many applications where a customer, once attracted to an enterprise, would purchase more products beyond the ones contained in the catalog. Therefore, in this paper, we investigate an alternative problem formulation, that we call Customer-Oriented Catalog Segmentation, where the overall utility is measured by the number of customers that have at least a specified minimum interest t in the catalogs. We formally introduce the Customer-Oriented Catalog Segmentation problem and discuss its complexity. Then we investigate two different paradigms to design efficient, approximate algorithms for the Customer-Oriented Catalog Segmentation problem, greedy (deterministic) and randomized algorithms. Since greedy algorithms may be trapped in a local optimum and randomized algorithms crucially depend on a reasonable initial solution, we explore a combination of these two paradigms. Our experimental evaluation on synthetic and real data demonstrates that the new algorithms yield catalogs of significantly higher utility compared to classical Catalog Segmentation algorithms.


title: Masters of War: Rivals' Product Innovation and New Advertising in Mature Product Markets

abstract: We investigate the impact of rivals' product innovation and new advertising on a firm's financial market value in mature product markets. Our test bed is the carbonated soft drink market between 1999 and 2003, a period characterized by a near duopoly between Coca-Cola and Pespi. Empirically, we focus on new product announcements as a proxy of product innovation and on filed trademarks as a measure of new advertising. We find that rival product announcements decrease a firm's financial market value, and that rival filed trademarks increase it. Finally, we find that the effect of new advertising is channeled through market size dynamics, while that of product innovation operates through market share dynamics. Results are robust across different estimation techniques (event study, Tobin's q) and model specifications.


title: CoupleNet: Coupling Global Structure with Local Parts for Object Detection

abstract: The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly available.


title: Grasping novel objects with depth segmentation

abstract: We consider the task of grasping novel objects and cleaning fairly cluttered tables with many novel objects. Recent successful approaches employ machine learning algorithms to identify points on the scene that the robot should grasp. In this paper, we show that the task can be significantly simplified by using segmentation, especially with depth information. A supervised localization method is employed to select graspable segments. We also propose a shape completion and grasp planner method which takes partial 3D information and plans the most stable grasping strategy. Extensive experiments on our robot demonstrate the effectiveness of our approach.


title: A Multi-Modal Approach to Infer Image Affect

abstract: The group affect or emotion in an image of people can be inferred by extracting features about both the people in the picture and the overall makeup of the scene. The state-of-the-art on this problem investigates a combination of facial features, scene extraction and even audio tonality. This paper combines three additional modalities, namely, human pose, text-based tagging and CNN extracted features / predictions. To the best of our knowledge, this is the first time all of the modalities were extracted using deep neural networks. We evaluate the performance of our approach against baselines and identify insights throughout this paper.


title: The Contextual Loss for Image Transformation with Non-Aligned Data

abstract: Feed-forward CNNs trained for image transformation problems rely on loss functions that measure the similarity between the generated image and a target image. Most of the common loss functions assume that these images are spatially aligned and compare pixels at corresponding locations. However, for many tasks, aligned training pairs of images will not be available. We present an alternative loss function that does not require alignment, thus providing an effective and simple solution for a new space of problems. Our loss is based on both context and semantics -- it compares regions with similar semantic meaning, while considering the context of the entire image. Hence, for example, when transferring the style of one face to another, it will translate eyes-to-eyes and mouth-to-mouth. Our code can be found at https://www.github.com/roimehrez/contextualLoss


title: Bicultural identity, bilingualism, and psychological adjustment in multicultural societies: immigration-based and globalization-based acculturation.

abstract: ABSTRACT The present investigation examined the impact of bicultural identity, bilingualism, and social context on the psychological adjustment of multicultural individuals. Our studies targeted three distinct types of biculturals: Mainland Chinese immigrants in Hong Kong, Filipino domestic workers (i.e., sojourners) in Hong Kong, and Hong Kong and Mainland Chinese college students. Individual differences in Bicultural Identity Integration (BII; Benet-Martinez, Leu, Lee, & Morris, 2002) positively predicted psychological adjustment for all the samples except sojourners even after controlling for the personality traits of neuroticism and self-efficacy. Cultural identification and language abilities also predicted adjustment, although these associations varied across the samples in meaningful ways. We concluded that, in the process of managing multiple cultural environments and group loyalties, bilingual competence, and perceiving one's two cultural identities as integrated are important antecedents of beneficial psychological outcomes.


title: Mimo for millimeter-wave wireless communications: beamforming, spatial multiplexing, or both?

abstract: The use of mmWave frequencies for wireless communications offers channel bandwidths far greater than previously available, while enabling dozens or even hundreds of antenna elements to be used at the user equipment, base stations, and access points. To date, MIMO techniques, such as spatial multiplexing, beamforming, and diversity, have been widely deployed in lower-frequency systems such as IEEE 802.11n/ac (wireless local area networks) and 3GPP LTE 4G cellphone standards. Given the tiny wavelengths associated with mmWave, coupled with differences in the propagation and antennas used, it is unclear how well spatial multiplexing with multiple streams will be suited to future mmWave mobile communications. This tutorial explores the fundamental issues involved in selecting the best communications approaches for mmWave frequencies, and provides insights, challenges, and appropriate uses of each MIMO technique based on early knowledge of the mmWave propagation environment.


title: On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines

abstract: In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic objective function. Unlike most of previous approaches which typically decompose a multiclass problem into multiple independent binary classification tasks, our notion of margin yields a direct method for training multiclass predictors. By using the dual of the optimization problem we are able to incorporate kernels with a compact set of constraints and decompose the dual problem into multiple optimization problems of reduced size. We describe an efficient fixed-point algorithm for solving the reduced optimization problems and prove its convergence. We then discuss technical details that yield significant running time improvements for large datasets. Finally, we describe various experiments with our approach comparing it to previously studied kernel-based methods. Our experiments indicate that for multiclass problems we attain state-of-the-art accuracy.


Evaluating Attribute-Object Compositionality in Text-Image Multimodal Embeddings


title: Unsupervised Intuitive Physics from Visual Observations

abstract: While learning models of intuitive physics is an active area of research, current approaches fall short of natural intelligences in one important regard: they require external supervision, such as explicit access to physical states, at training and sometimes even at test time. Some approaches sidestep these requirements by building models on top of handcrafted physical simulators. In both cases, however, methods cannot learn automatically new physical environments and their laws as humans do. In this work, we successfully demonstrate, for the first time, learning unsupervised predictors of physical states, such as the position of objects in an environment, directly from raw visual observations and without relying on simulators. We do so in two steps: (i) we learn to track dynamically-salient objects in videos using causality and equivariance, two non-generative unsupervised learning principles that do not require manual or external supervision. (ii) we demonstrate that the extracted positions are sufficient to successfully train visual motion predictors that can take the underlying environment into account. We validate our predictors on synthetic datasets; then, we introduce a new dataset, Roll4Real, consisting of real objects rolling on complex terrains (pool table, elliptical bowl, and random height-field). We show that it is possible to learn reliable object trajectory extrapolators from raw videos alone, without any external supervision and with no more prior knowledge than the choice of a convolutional neural network architecture.


title: A Context-aware Collaborative Filtering Approach for Urban Black Holes Detection

abstract: Urban black hole, as a traffic anomaly, has caused lots of catastrophic accidents in many big cities nowadays. Traditional methods only depend on the single source data (e.g., taxi trajectories) to design blackhole detection algorithm from one point of view, which is rather incomplete to describe the regional crowd flow. In this paper, we model the urban black holes in each region of New York City (NYC) at different time intervals with a 3-dimensional tensor by fusing cross-domain data sources. Supplementing the missing entries of the tensor through a context-aware tensor decomposition approach, we leverage the knowledge from geographical features, 311 complaint features and human mobility features to recover the blackhole situation throughout NYC. The information can facilitate local residents and officials' decision making. We evaluate our model with five datasets related to NYC, diagnosing the urban black holes that cannot be identified (or earlier than those detected) by a single dataset. Experimental results demonstrate the advantages beyond four baseline methods.


title: A GPU accelerated algorithm for 3D Delaunay triangulation

abstract: We propose the first algorithm to compute the 3D Delaunay triangulation (DT) on the GPU. Our algorithm uses massively parallel point insertion followed by bilateral flipping, a powerful local operation in computational geometry. Although a flipping algorithm is very amenable to parallel processing and has been employed to construct the 2D DT and the 3D convex hull on the GPU, to our knowledge there is no such successful attempt for constructing the 3D DT. This is because in 3D when many points are inserted in parallel, flipping gets stuck long before reaching the DT, and thus any further correction to obtain the DT is costly. In contrast, we show that by alternating between parallel point insertion and flipping, together with picking an appropriate point insertion order, one can still obtain a triangulation very close to Delaunay. We further propose an adaptive star splaying approach to subsequently transform this result into the 3D DT efficiently. In addition, we introduce several GPU speedup techniques for our implementation, which are also useful for general computational geometry algorithms. On the whole, our hybrid approach, with the GPU accelerating the main work of constructing a near-Delaunay structure and the CPU transforming that into the 3D DT, outperforms all existing sequential CPU algorithms by up to an order of magnitude, in both synthetic and real-world inputs. We also adapt our approach to the 2D DT problem and obtain similar speedup over the best sequential CPU algorithms, and up to 2 times over previous GPU algorithms.


title: An Error Detection and Correction Framework for Connectomics

abstract: We define and study error detection and correction tasks that are useful for 3D reconstruction of neurons from electron microscopic imagery, and for image segmentation more generally. Both tasks take as input the raw image and a binary mask representing a candidate object. For the error detection task, the desired output is a map of split and merge errors in the object. For the error correction task, the desired output is the true object. We call this object mask pruning, because the candidate object mask is assumed to be a superset of the true object. We train multiscale 3D convolutional networks to perform both tasks. We find that the error-detecting net can achieve high accuracy. The accuracy of the error-correcting net is enhanced if its input object mask is "advice" (union of erroneous objects) from the error-detecting net.


Reading and Reasoning with Knowledge Graphs


title: Cognitive perspectives on SLA: The Associative-Cognitive CREED

abstract: This paper outlines current cognitive perspectives on second language acquisition (SLA). The Associative-Cognitive CREED holds that SLA is governed by the same principles of associative and cognitive learning that underpin the rest of human knowledge. The major principles of the framework are that SLA is Construction-based, Rational, Exemplar-driven, Emergent, and Dialectic. Language learning involves the acquisition of constructions that map linguistic form and function. Competence and performance both emerge from the dynamic system that is the frequency-tuned conspiracy of memorized exemplars of use of these constructions, with competence being the integrated sum of prior usage and performance being its dynamic contextualized activation. The system is rational in that it optimally reflects prior first language (L1) usage. The L1 tunes the ways in which learners attend to language. Learned-attention transfers to L2 and it is this L1 entrenchment that limits the endstate of usage-based SLA. But these limitations can be overcome by recruiting learner consciousness, putting them into a dialectic tension between the conflicting forces of their current stable states of interlanguage and the evidence of explicit form-focused feedback, either linguistic, pragmatic, or metalinguistic, that allows socially scaffolded development. The paper directs the reader to recent review articles in these key areas and weighs the implications of this framework.


title: The DIKW Hierarchy and Management Decision-Making

abstract: This paper reports on a mixed-method study in progress. The qualitative part has been completed and the quantitative part is underway. The findings of the qualitative study -- the theory of Integral Decision-Making (IDM) -- are introduced, and the research method to test IDM is discussed. It is expected that the integration of the qualitative and quantitative studies will provide insight into how data, information, and knowledge capacities can lead to more effective management decisions by incorporating more human inputs in the decision-and policy-making process. Implications for theory and practice will be suggested.


The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration: Agent-Based Models of Competition and Collaboration


title: Multiphase permanent magnet synchronous motor harmonic control base on Carrier-based PWM technology

abstract: Multiphase motor has many advantages compared with Three-phase motors, but the high harmonic content of multiphase motor phase current is a important factor restricting the development of the multiphase motor. This paper researched the PWM modulation method and vector control strategies and studied the harmonic problems of the multiphase motor control system, tried to find an effective way to suppress the harmonic current. Firstly, in the aspect of PWM technology, this paper studied the Carrier-based multiphase PWM technology, this kind of PWM technology has low harmonic content, simple algorithm, and very suitable for multiphase motor control system. Secondly, this paper presents a harmonic plane vector control strategy that can control the harmonic current. Finally, this paper toke the 15-phase permanent magnet synchronous motor for example, combined the Carrier-based multiphase PWM technology with the harmonic plane vector control strategy and simulated the system. Simulation and experiment result confirmed that the PMW technology and the harmonic plane vector control strategy that this paper present are principle feasible, the algorithm is simple, can effectively control the harmonic content of the current.


title: Security in Building Automation Systems

abstract: Building automation systems are traditionally concerned with the control of heating, ventilation, and air conditioning, as well as lighting and shading, systems. They have their origin in a time where security has been considered as a side issue at best. Nowadays, with the rising desire to integrate security-critical services that were formerly provided by isolated subsystems, security must no longer be neglected. Thus, the development of a comprehensive security concept is of utmost importance. This paper starts with a security threat analysis and identifies the challenges of providing security in the building automation domain. Afterward, the security mechanisms of available standards are thoroughly analyzed. Finally, two approaches that provide both secure communication and secure execution of possibly untrusted control applications are presented.


title: Throughput of the Magnetic Induction Based Wireless Underground Sensor Networks: Key Optimization Techniques

abstract: Wireless underground sensor networks (WUSNs) present a variety of new research challenges. Recently, a mag- netoinductive (MI) waveguide technique has been proposed to cope with the very harsh propagation conditions in WUSNs. This relay-based approach allows for an extension of the transmission range, which can be quite limited if relays are not deployed. In this paper, tree-based WUSNs are considered. The objective of our work is to determine the optimal system parameters, topology, and deployment strategy in order to avoid bottlenecks in the system and achieve optimal network throughput. We compare two different deployment schemes: MI waveguides and direct MI transmission (no relays deployed) based connections between sensors. The two schemes are different in nature and propaga- tion characteristics. Therefore, different optimization techniques are utilized. The optimal set of system parameters is chosen to maximize the channel capacity of the worst link and therefore optimize the available data rate. The bottleneck throughput of the direct MI transmission based network can be then compared with the respective results of the MI waveguides based network. In several cases, we observe a better performance of the direct MI transmission based networks.


title: OFDM SAR multiple targets image reconstruction using MUSIC-LSE algorithm

abstract: In Synthetic Aperture Radar (SAR) image reconstruction, the quality of the image depends on the range and cross range resolution to resolve multiple target positions. If the distance between the targets is less than both SAR resolutions, the radar imaged them as one single target. To overcome this challenge, Orthogonal Frequency Division Multiplexing (OFDM) SAR has been proposed to improve the image resolution. However the image quality degrades in lower boundary conditions. In this paper, we propose the use of Multiple Signal Classification (MUSIC) algorithm to estimate the signal Direction of Arrival (DoA) and Least Square Estimation (LSE) algorithm to estimate the phase history. The Cross-range Profile Reconstruction (CPR) is reconstructed using the phase history and combined with Range Profile Reconstruction (RPR) to form the image. Simulation results show that the proposed MUSIC-LSE SAR approach gives a higher image resolution compared to the LSE-SAR imaging. The approach also shows that by estimating the signal DoA and then apply LSE, the radar can separate the targets even at distances below the range and cross-range resolution without necessarily increasing the bandwidth.


title: A Modified Marchand Balun Configuration With Tunable Phase Balance

abstract: In this letter, a novel modified Marchand balun configuration with tunable phase balance is analyzed and verified experimentally. It is proposed to add a shunt susceptance in between the two couplers of the Marchand balun. This allows for a change in the phase balance which, to first order, is linear with the susceptance, while the magnitude balance is kept constant. To verify the proposed configuration, a lumped element Marchand balun has been fabricated using a SiGe BiCMOS technology. The balun design is centered around 9.4 GHz, with an insertion loss of 6.0 dB. The phase difference between the output ports can be changed from 175.8° to 183 ° whereas the magnitude imbalance is kept almost constant at 0.3 dB. The balun performs well in the range from 7 to 11 GHz, where it is possible to tune the phase to exactly 180°.


title: Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach

abstract: This paper presents a new solution for choosing the K parameter in the k-nearest neighbor (KNN) algorithm, the solution depending on the idea of ensemble learning, in which a weak KNN classifier is used each time with a different K, starting from one to the square root of the size of the training set. The results of the weak classifiers are combined using the weighted sum rule. The proposed solution was tested and compared to other solutions using a group of experiments in real life problems. The experimental results show that the proposed classifier outperforms the traditional KNN classifier that uses a different number of neighbors, is competitive with other classifiers, and is a promising classifier with strong potential for a wide range of applications.


title: Group multi-criteria supplier selection using combined grey systems theory and uncertainty theory

abstract: Developing framework for reducing purchasing risks associated with suppliers.Combination of grey system theory and uncertainty theory is used.It neither requires any probability distribution nor fuzzy membership function.It selects the most appropriate suppliers and allocates optimal purchase quantity. Supplier selection in supply chain is critical strategic decision for organization's success and has attracted much attention of both academic researchers and practitioners. Supplier selection problem consists of stochastic and recognitive uncertainties. However, the requirement of large sample size and strong subject knowledge to build suitable fuzzy membership function restrict the applicability of probability and fuzzy theories in supplier selection problem. In response, this study proposed a new tool for supplier selection. In this paper, we applied the combination of grey system theory and uncertainty theory which neither requires any probability distribution nor fuzzy membership function. The objective of this paper is to develop framework for reducing the purchasing risks associated with suppliers. The proposed supplier selection method not only selects the most appropriate supplier(s) but also allocate optimal purchase quantity under stochastic and recognitive uncertainties. An example is shown to highlight the procedure of the proposed model at the end of this paper.


title: Information Security Fortification by Ontological Mapping of the ISO/IEC 27001 Standard

abstract: This paper introduces an ontology-based framework to improve the preparation of ISO/IEC 27001 audits, and to strengthen the security state of the company respectively. Building on extensive previous work on security ontologies, we elaborate on how ISO/IEC 27001 artifacts can be integrated into this ontology. A basic introduction to security ontologies is given first. Specific examples show how certain ISO/IEC 27001 requirements are to be integrated into the ontology; moreover, our rule-based engine is used to query the knowledge base to check whether specific security requirements are fulfilled. The aim of this paper is to explain how security ontologies can be used for a tool to support the ISO/IEC 27001 certification, providing pivotal information for the preparation of audits and the creation and maintenance of security guidelines and policies.


title: A Model of a Social Chatbot

abstract: Traditional chatbots lack the capability to correctly manage conversations according to the social context. However a dialogue is a joint activity that must consider both individual and social processes. In this work we propose a model of a social chatbot able to choose the most suitable dialogue plans according to what in sociological literature is called a “social practice”. The proposed model is discussed considering a case study of a work in progress aimed at the development of a serious game for communicative skills learning.


Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection


title: A Design Methodology for Miniaturized 3-dB Branch-Line Hybrid Couplers Using Distributed Capacitors Printed in the Inner Area

abstract: This paper proposes a methodology to design and optimize the footprint of miniaturized 3-dB branch-line hybrid couplers, which consists of high-impedance transmission lines and distributed capacitors. To minimize the physical size of the coupler, the distributed capacitors are placed within the empty space of the hybrid. The proposed design methodology calls for the joint optimization of the length of the reduced high-impedance transmission lines and the area of the distributed capacitors. A prototype at S-band was designed and built to validate the approach. It showed a size reduction by 62% compared with the conventional 3-dB branch-line hybrid coupler while providing similar performance and bandwidth.


title: Antifungal activity of Boerhavia diffusa L. extract against Phytophthora spp. in tomato and pepper

abstract: Antifungal properties of Boerhavia diffusa L. (Nyctaginaceae) methanolic extract have been investigated against Phytophthora infestans and Phytophthora capsici, causal agents of late blight disease in tomato and pepper, respectively. The minimum inhibitory concentration of B. diffusa extract was 0.25 % for P. infestans and 0.5 % for P. capsici. The extract had fungitoxic effect on P. infestans and completely inhibited its mycelial growth at all tested concentrations (0.25, 0.5, and 1 %). P. capsici mycelial growth was inhibited of 90 % by the 1 % extract after five days of culture. B. diffusa extract, at the concentration of 0.25 % significantly reduced P. infestans disease symptoms also in young tomato plants. The plant extract significantly reduced disease symptoms of P. capsici in young pepper plants at all tested concentrations (0.25, 0.5, and 1 %). The efficacy of B. diffusa extract against P. capsici increased with the increasing extract concentration and decreased over time. The 1 % concentration showed the best efficacy in controlling this oomycete. As its effect was comparable to that of the synthetic fungicide, B. diffusa extract can be considered as a potential environmentally friendly alternative to chemical control of late blight in tomato and pepper.


Empirically Grounding the Evaluation of Creative Systems: Incorporating Interaction Design


title: How to Vote Privately Using Bitcoin

abstract: Bitcoin is the first decentralized crypto-currency that is currently by far the most popular one in use. The bitcoin transaction syntax is expressive enough to setup digital contracts whose fund transfer can be enforced automatically.


title: EvoSuite: On the Challenges of Test Case Generation in the Real World

abstract: Test case generation is an important but tedious task, such that researchers have devised many different prototypes that aim to automate it. As these are research prototypes, they are usually only evaluated on a few hand-selected case studies, such that despite great results there remains the question of usability in the “real world”. EVOSUITE is such a research prototype, which automatically generates unit test suites for classes written in the Java programming language. In our ongoing endeavour to achieve real-world usability, we recently passed the milestone success of applying EVOSUITE on hundred projects randomly selected from the SourceForge open source platform. This paper discusses the technical challenges that a testing tool like EVOSUITE needs to address when handling Java classes coming from real-world open source projects, and when producing JUnit test suites intended for real users.


Indoor Point Cloud Segmentation for Automatic Object Interpretation


title: A New Text Clustering Method Based on KSEP

abstract: Text clustering is one of the key research areas in data mining. k-medoids algorithm is a classical division algorithm, and can solve the problem of isolated points, But it often converges to local optimum. This article presents a improved social evolutionary programming(K-medoids Social Evolutionary Programming,KSEP). The algorithm is the k-medoids algorithm as the main cognitive reasoning algorithm. and Improved to learning of Paradigm、Optimal paradigm strengthening and attenuation and Cognitive agent betrayal of paradigm. This algorithm will increase the diversity of species group and enhance the optimization capability of social evolutionary programming, thus improve the accuracy of clustering and the capacity of acquiring isolated points.


How the Hough Transform Was Invented Drop cap


title: Opinion mining of movie reviews at document level

abstract: The whole world is changed rapidly and using the current technologies Internet becomes an essential need for everyone. Web is used in every field. Most of the people use web for a common purpose like online shopping, chatting etc. During an online shopping large number of reviews/opinions are given by the users that reflect whether the product is good or bad. These reviews need to be explored, analyse and organized for better decision making. Opinion Mining is a natural language processing task that deals with finding orientation of opinion in a piece of text with respect to a topic. In this paper a document based opinion mining system is proposed that classify the documents as positive, negative and neutral. Negation is also handled in the proposed system. Experimental results using reviews of movies show the effectiveness of the system.


title: Using the DOM Tree for Content Extraction

abstract: The main information of a webpage is usually mixed between menus, advertisements, panels, and other not necessarily related information; and it is often difficult to automatically isolate this information. This is precisely the objective of content extraction, a research area of widely interest due to its many applications. Content extraction is useful not only for the final human user, but it is also frequently used as a preprocessing stage of different systems that need to extract the main content in a web document to avoid the treatment and processing of other useless information. Other interesting application where content extraction is particularly used is displaying webpages in small screens such as mobile phones or PDAs. In this work we present a new technique for content extraction that uses the DOM tree of the webpage to analyze the hierarchical relations of the elements in the webpage. Thanks to this information, the technique achieves a considerable recall and precision. Using the DOM structure for content extraction gives us the benefits of other approaches based on the syntax of the webpage (such as characters, words and tags), but it also gives us a very precise information regarding the related components in a block, thus, producing very cohesive blocks.


title: Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

abstract: Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms - especially collaborative filtering (CF)-based approaches with shallow or deep models - usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the research focus on CF approaches. However, structured knowledge exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines.


title: Event Stream Processing Units in Business Processes

abstract: The Internet of Things and Cyber-physical Systems provide enormous amounts of real-time data in form of streams of events. Businesses can benefit from the integration of this real-world data; new services can be provided to customers, or existing business processes can be improved. Events are a well-known concept in business processes. However, there is no appropriate abstraction mechanism to encapsulate event stream processing in units that represent business functions in a coherent manner across the process modeling, process execution, and IT infrastructure layer. In this paper we present Event Stream Processing Units (SPUs) as such an abstraction mechanism. SPUs encapsulate application logic for event stream processing and enable a seamless transition between process models, executable process representations, and components at the IT layer. We derive requirements for SPUs and introduce a BPMN extension to model SPUs. We present a runtime infrastructure that executes SPUs and supports implicit invocation and completion semantics. We illustrate our approach using a logistics process as running example.


title: Analysis of Switched-capacitor DC-DC Converters in Soft-charging Operation

abstract: This paper formulates a formal analysis method for switched-capacitor (SC) dc-dc converters in soft-charging operation. Through soft-charging, the charging/discharging loss of a SC converter can be minimized or even eliminated by allowing the output voltage to vary to a greater extent. Usually a soft-charging SC converter is followed by a magnetic converter for ripple reduction and better regulation. For any given two-phase switched-capacitor topology, the proposed method can be used to determine whether soft-charging is at all possible and if so, the required capacitor sizes to achieve soft-charging. The proposed method also gives the resultant output voltage ripple due to soft-charging operation. A number of different topologies are analyzed, including Dickson, Series-parallel, Ladder, Fibonacci and Doubler configurations. The analysis gives insights regarding the expected improvement in performance when these topologies are cascaded by a magnetic converter. Through comparison to simulated results and existing work, the validity of the proposed method is confirmed.


title: Towards a functional neuroanatomy of pleasure and happiness

abstract: The pursuit of happiness is a preoccupation for many people. Yet only the pursuit can be promised, not happiness itself. Can science help? We focus on the most tractable ingredient, hedonia or positive affect. A step toward happiness might be gained by improving the pleasures and positive moods in daily life. The neuroscience of pleasure and reward provides relevant insights, and we discuss how specific hedonic mechanisms might relate to happiness or the lack thereof. Although the neuroscience of happiness is still in its infancy, further advances might be made through mapping overlap between brain networks of hedonic pleasure with others, such as the brain's default network, potentially involved in the other happiness ingredient, eudaimonia or life meaning and engagement.


title: End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures

abstract: We present a novel end-to-end neural model to extract entities and relations between them. Our recurrent neural network based model captures both word sequence and dependency tree substructure information by stacking bidirectional tree-structured LSTM-RNNs on bidirectional sequential LSTM-RNNs. This allows our model to jointly represent both entities and relations with shared parameters in a single model. We further encourage detection of entities during training and use of entity information in relation extraction via entity pretraining and scheduled sampling. Our model improves over the state-of-the-art feature-based model on end-to-end relation extraction, achieving 12.1% and 5.7% relative error reductions in F1-score on ACE2005 and ACE2004, respectively. We also show that our LSTM-RNN based model compares favorably to the state-of-the-art CNN based model (in F1-score) on nominal relation classification (SemEval-2010 Task 8). Finally, we present an extensive ablation analysis of several model components.