Query: SKILL: A System for Skill Identification and Normalization

Relevant passages:
Efficient Estimation of Word Representations in Vector Space <=SEP=> We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.

Large-Scale Named Entity Disambiguation Based on Wikipedia Data <=SEP=> This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disambiguation paradigm employed and the information extraction process from Wikipedia. Through a process of maximizing the agreement between the contextual information extracted from Wikipedia and the context of a document, as well as the agreement among the category tags associated with the candidate entities, the implemented system shows high disambiguation accuracy on both news stories and Wikipedia articles.

Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors <=SEP=> Context-predicting models (more commonly known as embeddings or neural language models) are the new kids on the distributional semantics block. Despite the buzz surrounding these models, the literature is still lacking a systematic comparison of the predictive models with classic, count-vector-based distributional semantic approaches. In this paper, we perform such an extensive evaluation, on a wide range of lexical semantics tasks and across many parameter settings. The results, to our own surprise, show that the buzz is fully justified, as the context-predicting models obtain a thorough and resounding victory against their count-based counterparts.

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

Topics in semantic representation. <=SEP=> Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language-processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure.

Performance Counters to Rescue: A Machine Learning based safeguard against Micro-architectural Side-Channel-Attacks <=SEP=> Micro-architectural side-channel-attacks are presently daunting threats to most mathematically elegant encryption algorithms. Even though there exist various defense mechanisms, most of them come with the extra overhead of implementation. Recent studies have prevented some particular categories of these attacks but fail to address the detection of other classes. This paper presents a generic machine learning based multi-layer detection approach targeting these micro-architectural side-channel-attacks, without concentrating on a single category. The proposed approach work by profiling low-level hardware events using Linux perf event API and then by analyzing these data with some appropriate machine learning techniques. This paper also presents a novel approach, using time-series data, to correlate the execution trace of the adversary with the secret key of encryption for dealing with false-positives and unknown attacks. The experimental results and performance of the proposed approach suggest its superiority with high detection accuracy and low performance overhead.

Crohn's disease of the vulva in a 10-year-old girl. <=SEP=> Crohn's disease may involve all parts of the gastrointestinal tract and may often involve other organs as well. These non-intestinal affections are termed extraintestinal manifestations. Vulval involvement is an uncommon extraintestinal manifestation of Crohn's disease, and it is very rare in children. Patients with vulval CD typically present with erythema and edema of the labia majora, which progresses to extensive ulcer formation. Vulval Crohn's disease can appear before or after intestinal problems or it may occur simultaneously. We present a 10-year-old girl with intestinal Crohn's disease complicated with perianal skin tags and asymptomatic unilateral labial hypertrophy. The course of her lesion was independent of the intestinal disease and responded significantly to medical treatment including azathioprine and topical steroid. We emphasize that although vulval involvement in childhood is uncommon, Crohn's disease must be considered in the differential diagnosis of nontender, red, edematous lesions of the genital area.

Magic Mirror: A virtual handbag shopping system <=SEP=> We present an augmented reality system based on Kinect for on-line handbag shopping. The users can virtually try on different handbags on a TV screen at home. They can interact with the virtual handbags naturally, such as sliding a handbag to different positions on their arms and rotating a handbag to see it from different angles. The users can also see how the handbags fit them in different virtual environments other than the current real background. We describe the technical details for building such a system and demonstrate the experimental results.

Population genetic structure and ecological niche modelling of the leafhopper Hishimonus phycitis <=SEP=> Witches’ broom disease of lime, caused by ‘Candidatus Phytoplasma aurantifolia’, is responsible for major losses of Mexican lime trees in Southern Iran, Oman and the United Arab Emirates. The causative phytoplasma is transmitted by the leafhopper, Hishimonus phycitis. We combined ecological niche modelling with environmental and genetic data for six populations of H. phycitis from Iran and one from Oman. The mitochondrial cytochrome c oxidase I (COI) gene and nine microsatellite DNA markers were used for the genetic analyses. Although the Oman population had specific haplotypes, the COI sequences were highly conserved among all populations studied. In contrast, the microsatellite data divided the populations from Iran and Oman into two separate clades. An analysis of molecular variance indicated a high level of variation within populations. The Mantel test showed no correlation between genetic and geographical distances. Gene flow values were small between the populations from Iran and north of Oman but significantly higher among the Iranian populations supporting the differentiation between Iran and Oman. In addition, we found that patterns of genetic divergence within Iranian populations were associated strongly with divergence in terms of their ecological niches. Data on six climatic variables, including elevation, were used to create ecological niche models. Our results suggest that the genetic differentiation of H. phycitis may be attributable to climatic conditions and/or geographical barriers.

Data Driven Ontology Evaluation <=SEP=> The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the ‘fit’ between an ontology and a domain of knowledge. We consider a number of methods for measuring this ‘fit’ and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology.

HRM as a motivator to share knowledge-The importance of seeing the whole picture <=SEP=> Connecting Human Resource Management (HRM) and knowledge transfer through motivation is a new research area. Out of the few existing studies there is a predominance of quantitative studies, which are showing inconclusive results. As a response, this study uses a qualitative micro perspective to investigate how HRM practises influence intrinsicand extrinsic motivation to share knowledge. It is important to divide motivation into intrinsic and extrinsic, as it impacts knowledge sharing differently. Former studies have identified a need to study the whole HRM system, therefore, to capture differences in motivation among employees exposed to the same system, this thesis takes on a single case study approach. Qualitative interviews were held with employees at an MNC that relies on knowledge intensive activities. The findings showed that employees were motivated intrinsically through career development and extrinsically by the performance management system. The supportive climate showed to influence motivation to share knowledge, both directly and indirectly. Job design was shown to work well in combination with other practises. Finally, a key finding was the importance of having an aligned HRM system.

Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals <=SEP=> This study applies clustering analysis for data mining and machine learning to predict trends in technology professional turnover rates, including the hybrid artificial neural network and clustering analysis known as the self-organizing map (SOM). This hybrid clustering method was used to study the individual characteristics of turnover trend clusters. Using a transaction questionnaire, we studied the period of peak turnover, which occurs after the Chinese New Year, for individuals divided into various age groups. The turnover trend of technology professionals was examined in well-known Taiwanese companies. The results indicate that the high outstanding turnover trend circle was primarily caused by a lack of inner fidelity identification, leadership and management. Based on cross-verification, the clustering accuracy rate was 92.7%. This study addressed problems related to the rapid loss of key human resources and should help organizations learn how to enhance competitiveness and efficiency. 2012 Elsevier Ltd. All rights reserved.

Digital Business Strategy and Value Creation: Framing the Dynamic Cycle of Control Points <=SEP=> Within changing value networks, the profits and competitive advantages of participation reside dynamically at control points that are the positions of greatest value and/or power. The enterprises that hold these positions have a great deal of control over how the network operates, how the benefits are redistributed, and how this influences the execution of a digital business strategy. This article is based on a field study that provides preliminary, yet promising, empirical evidence that sheds light on the dynamic cycle of value creation and value capture points in digitally enabled networks in response to triggers related to technology and business strategy. We use the context of the European and U.S. broadcasting industry. Specifically, we illustrate how incremental innovations may shift value networks from static, vertically integrated networks to more loosely coupled networks, and how cross-boundary industry disruptions may then, in turn, shift those to two-sided markets. Based on our analysis we provide insights and implications for digital business strategy research and practice.

Collaborative Online Examinations: Impacts on Interaction, Learning, and Student Satisfaction <=SEP=> This paper presents the results of a field experiment on online examinations facilitated by collaboration support systems. In particular, it examines collaborative learning and virtual teams through online examinations as an assessment procedure, compared to traditional examinations. Assessment is increasingly regarded as an important part of the learning process. Applying constructivism and collaborative-learning theories, the collaborative examination process features students' active participation in various phases of the exam process through small group activities online. A 1 times 3 field experiment evaluated the collaborative online exam compared with the traditional in-class exam, and the participatory exam, where students participated in the online exam processes without groups. Data analysis using results from 485 students indicates that collaborative examinations significantly enhance interactions and the sense of an online learning community and result in significantly higher levels of perceived learning

Mining heterogeneous information networks: a structural analysis approach <=SEP=> Most objects and data in the real world are of multiple types, interconnected, forming complex, heterogeneous but often semi-structured information networks. However, most network science researchers are focused on homogeneous networks, without distinguishing different types of objects and links in the networks. We view interconnected, multityped data, including the typical relational database data, as heterogeneous information networks, study how to leverage the rich semantic meaning of structural types of objects and links in the networks, and develop a structural analysis approach on mining semi-structured, multi-typed heterogeneous information networks. In this article, we summarize a set of methodologies that can effectively and efficiently mine useful knowledge from such information networks, and point out some promising research directions.

Autonomous Vehicles for Smart and Sustainable Cities: An In-Depth Exploration of Privacy and Cybersecurity Implications <=SEP=> Amidst rapid urban development, sustainable transportation solutions are required to meet the increasing demands for mobility whilst mitigating the potentially negative social, economic, and environmental impacts. This study analyses autonomous vehicles (AVs) as a potential transportation solution for smart and sustainable development. We identified privacy and cybersecurity risks of AVs as crucial to the development of smart and sustainable cities and examined the steps taken by governments around the world to address these risks. We highlight the literature that supports why AVs are essential for smart and sustainable development. We then identify the aspects of privacy and cybersecurity in AVs that are important for smart and sustainable development. Lastly, we review the efforts taken by federal governments in the US, the UK, China, Australia, Japan, Singapore, South Korea, Germany, France, and the EU, and by US state governments to address AV-related privacy and cybersecurity risks in-depth. Overall, the actions taken by governments to address privacy risks are mainly in the form of regulations or voluntary guidelines. To address cybersecurity risks, governments have mostly resorted to regulations that are not specific to AVs and are conducting research and fostering research collaborations with the private sector.

Parallel AES algorithm for fast Data Encryption on GPU <=SEP=> With the improvement of cryptanalysis, More and more applications are starting to use Advanced Encryption Standard (AES) instead of Data Encryption Standard (DES) to protect their information security. However, current implementations of AES algorithm suffer from huge CPU resource consumption and low throughput. In this paper, we studied the technologies of GPU parallel computing and its optimized design for cryptography. Then, we proposed a new algorithm for AES parallel encryption, and designed and implemented a fast data encryption system based on GPU. The test proves that our approach can accelerate the speed of AES encryption significantly.

Ranking outlier nodes in subspaces of attributed graphs <=SEP=> Outlier analysis is an important data mining task that aims to detect unexpected, rare, and suspicious objects. Outlier ranking enables enhanced outlier exploration, which assists the user-driven outlier analysis. It overcomes the binary detection of outliers vs. regular objects, which is not adequate for many applications. Traditional outlier ranking techniques focus on either vector data or on graph structures. However, many of today's databases store both, multi dimensional numeric information and relations between objects in attributed graphs. An open challenge is how outlier ranking should cope with these different data types in a unified fashion. In this work, we propose a first approach for outlier ranking in subspaces of attributed graphs. We rank graph nodes according to their degree of deviation in both graph and attribute properties. We describe novel challenges induced by this combination of data types and propose subspace analysis as important method for outlier ranking on attributed graphs. Subspace clustering provides a selected subset of nodes and its relevant attributes in which deviation of nodes can be observed. Our graph outlier ranking (GOutRank) introduces scoring functions based on these selected subgraphs and subspaces. In addition to this technical contribution, we provide an attributed graph extracted from the Amazon marketplace. It includes a ground truth of real outliers labeled in a user experiment. In order to enable sustainable and comparable research results, we publish this database on our website1 as benchmark for future publications. Our experiments on this graph demonstrate the potential and the capabilities of outlier ranking in subspaces of attributed graphs.

High aspect-ratio 3D microstructures via near-field electrospinning for energy storage applications <=SEP=> High-aspect-ratio, three dimensional (3D) microstructures have been constructed by a direct-write method via self-aligned near-field electrospinning process. Using papers as the collectors, the process enables both design and fabrication of a variety of 3D structures, such as walls, grids and other configurations based on layer-by-layer deposition of electrospun micro/nano fibers. Afterwards, these fabricated polymeric structures can be mechanically separated from the paper substrate for various applications. In this work, we have successfully fabricated 20-layer, ca. 100 μm in thickness, 2 × 2 cm2 3D grid-structures made of PVDF (Polyvinylidene fluoride) electrospun fibers. After a coating process using the dip-and-dry process in the CNT (carbon nanotube) ink, conductive and flexible electrodes made of CNT coated polymer grid-structures have been demonstrated and assembled as a micro-supercapacitor.

Feature Selection using Multiobjective Optimization for Aspect based Sentiment Analysis <=SEP=> In this paper, we propose a system for aspect-based sentiment analysis (ABSA) by incorporating the concepts of multi-objective optimization (MOO), distributional thesaurus (DT) and unsupervised lexical induction. The task can be thought of as a sequence of processes such as aspect term extraction, opinion target expression identification and sentiment classification. We use MOO for selecting the most relevant features, and demonstrate that classification with the resulting feature set can improve classification accuracy on many datasets. As base learning algorithms we make use of Support Vector Machines (SVM) for sentiment classification and Conditional Random Fields (CRF) for aspect term and opinion target expression extraction tasks. Distributional thesaurus and unsupervised DT prove to be effective with enhanced performance. Experiments on benchmark setups of SemEval-2014 and SemEval-2016 shared tasks show that we achieve the state of the art on aspect-based sentiment analysis for several languages.

A hybridization of cuckoo search and particle swarm optimization for solving optimization problems <=SEP=> A new hybrid optimization algorithm, a hybridization of cuckoo search and particle swarm optimization (CSPSO), is proposed in this paper for the optimization of continuous functions and engineering design problems. This algorithm can be regarded as some modifications of the recently developed cuckoo search (CS). These modifications involve the construction of initial population, the dynamic adjustment of the parameter of the cuckoo search, and the incorporation of the particle swarm optimization (PSO). To cover search space with balance dispersion and neat comparability, the initial positions of cuckoo nests are constructed by using the principle of orthogonal Lation squares. To reduce the influence of fixed step size of the CS, the step size is dynamically adjusted according to the evolutionary generations. To increase the diversity of the solutions, PSO is incorporated into CS using a hybrid strategy. The proposed algorithm is tested on 20 standard benchmarking functions and 2 engineering optimization problems. The performance of the CSPSO is compared with that of several meta-heuristic algorithms based on the best solution, worst solution, average solution, standard deviation, and convergence rate. Results show that in most cases, the proposed hybrid optimization algorithm performs better than, or as well as CS, PSO, and some other exiting meta-heuristic algorithms. That means that the proposed hybrid optimization algorithm is competitive to other optimization algorithms.

Generalized Barycentric Coordinates on Irregular Polygons <=SEP=> In this paper we present an easy computation of a generalized form of barycentric coordinates for irregular, convex n-sided polygons. Triangular barycentric coordinates have had many classical applications in computer graphics, from texture mapping to ray-tracing. Our new equations preserve many of the familiar properties of the triangular barycentric coordinates with an equally simple calculation, contrary to previous formulations. We illustrate the properties and behavior of these new generalized barycentric coordinates through several example applications.

Neural basis of speech perception. <=SEP=> The functional neuroanatomy of speech processing has been difficult to characterize. One major impediment to progress has been the failure to consider task effects when mapping speech-related processing systems. We summarize a dual-stream model of speech processing that addresses this situation. In this model, a ventral stream processes speech signals for comprehension, and a dorsal stream maps acoustic speech signals to parietal and frontal-lobe articulatory networks. The model assumes that the ventral stream is largely bilaterally organized, although there are important computational differences between the left- and right-hemisphere systems, whereas the dorsal stream is strongly left-hemisphere-dominant.

Advances in Knowledge Discovery and Data Mining <=SEP=> Similarity in people to people (P2P) recommendation in social networks is not symmetric, where both entities of a relationship are involved in the reciprocal process of determining the success of the relationship. The widely used memory-based collaborative filtering (CF) has advantages of effectiveness and efficiency in traditional item to people recommendation. However, the critical step of computation of similarity between the subjects or objects of recommendation in memory-based CF is typically based on a heuristically symmetric relationship, which may be flawed in P2P recommendation. In this paper, we show that memory-based CF can be significantly improved by using a novel asymmetric model of similarity that considers the probabilities of both positive and negative behaviours, for example, in accepting or rejecting a recommended relationship. We present also a unified model of the fundamental principles of collaborative recommender systems that subsumes both user-based and item-based CF. Our experiments evaluate the proposed approach in P2P recommendation in the real world online dating application, showing significantly improved performance over traditional memory-based methods.

Fast and Accurate Detection and Classification of Plant Diseases <=SEP=> We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in [1] as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in [1]. The following two steps are added successively after the segmentation phase. In the first step we identify the mostlygreen colored pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm‟s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in [1].

Learning temporal features using LSTM-CNN architecture for face anti-spoofing <=SEP=> Temporal features is important for face anti-spoofing. Unfortunately existing methods have limitations to explore such temporal features. In this work, we propose a deep neural network architecture combining Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN). Our architecture works well for face anti-spoofing by utilizing the LSTM units' ability of finding long relation from its input sequences as well as extracting local and dense features through convolution operations. Our best model shows significant performance improvement over general CNN architecture (5.93% vs. 7.34%), and hand-crafted features (5.93% vs. 10.00%) on CASIA dataset.

Robust kernel density estimation <=SEP=> In this paper, we propose a method for robust kernel density estimation. We interpret a KDE with Gaussian kernel as the inner product between a mapped test point and the centroid of mapped training points in kernel feature space. Our robust KDE replaces the centroid with a robust estimate based on M-estimation (P. Huber, 1981), The iteratively re-weighted least squares (IRWLS) algorithm for M-estimation depends only on inner products, and can therefore be implemented using the kernel trick. We prove the IRWLS method monotonically decreases its objective value at every iteration for a broad class of robust loss functions. Our proposed method is applied to synthetic data and network traffic volumes, and the results compare favorably to the standard KDE.

Risk and maintenance factors for eating pathology: a meta-analytic review. <=SEP=> This meta-analytic review of prospective and experimental studies reveals that several accepted risk factors for eating pathology have not received empirical support (e.g., sexual abuse) or have received contradictory support (e.g.. dieting). There was consistent support for less-accepted risk factors(e.g., thin-ideal internalization) as well as emerging evidence for variables that potentiate and mitigate the effects of risk factors(e.g., social support) and factors that predict eating pathology maintenance(e.g., negative affect). In addition, certain multivariate etiologic and maintenance models received preliminary support. However, the predictive power of individual risk and maintenance factors was limited, suggesting it will be important to search for additional risk and maintenance factors, develop more comprehensive multivariate models, and address methodological limitations that attenuate effects.

Principal direction analysis-based real-time 3D human pose reconstruction from a single depth image <=SEP=> Human pose estimation in real-time is a challenging problem in computer vision. In this paper, we present a novel approach to recover a 3D human pose in real-time from a single depth human silhouette using Principal Direction Analysis (PDA) on each recognized body part. In our work, the human body parts are first recognized from a depth human body silhouette via the trained Random Forests (RFs). On each recognized body part which is presented as a set of 3D points cloud, PDA is applied to estimate the principal direction of the body part. Finally, a 3D human pose gets recovered by mapping the principal directional vector to each body part of a 3D human body model which is created with a set of super-quadrics linked by the kinematic chains. In our experiments, we have performed quantitative and qualitative evaluations of the proposed 3D human pose reconstruction methodology. Our evaluation results show that the proposed approach performs reliably on a sequence of unconstrained poses and achieves an average reconstruction error of 7.46 degree in a few key joint angles. Our 3D pose recovery methodology should be applicable to many areas such as human computer interactions and human activity recognition.

An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification <=SEP=> 0020-0255/$ see front matter 2009 Elsevier Inc doi:10.1016/j.ins.2008.12.029 * Corresponding author. Tel.: +34 985 456545; fa E-mail address: oscar.ibanez@softcomputing.es ( Photographic supra-projection is a forensic process that aims to identify a missing person from a photograph and a skull found. One of the crucial tasks throughout all this process is the craniofacial superimposition which tries to find a good fit between a 3D model of the skull and the 2D photo of the face. This photographic supra-projection stage is usually carried out manually by forensic anthropologists. It is thus very time consuming and presents several difficulties. In this paper, we aim to demonstrate that real-coded evolutionary algorithms are suitable approaches to tackle craniofacial superimposition. To do so, we first formulate this complex task in forensic identification as a numerical optimization problem. Then, we adapt three different evolutionary algorithms to solve it: two variants of a real-coded genetic algorithm and the state of the art evolution strategy CMA-ES. We also consider an existing binary-coded genetic algorithm as a baseline. Results on several superimposition problems of real-world identification cases solved by the Physical Anthropology lab at the University of Granada (Spain) are considered to test our proposals. 2009 Elsevier Inc. All rights reserved.