Relation discovery from web data for competency management

In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (COmmunity Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.

In silico prediction of peptide-MHC binding affinity using SVRMHC

The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide-MHC binding affinities based on support rector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.

Event trigger identification for biomedical events extraction using domain knowledge

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. © 2014 The Author 2014. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.

The effect of the use of computer assisted instruction (CAI) on attitudes and computational scores of developmental mathematics students at two inner city schools with predominantly Black enrollment

This study examined the effects of computer assisted instruction (CAI) 1 hour per week for 18 weeks on changes in computational scores and attitudes of developmental mathematics students at schools with predominantly Black enrollment. Comparisons were made between students using CAI with differing software--PLATO, CSR or both together--and students using traditional instruction (TI) only.^ This study was conducted in the Dade County Public School System from February through June 1991, at two senior high schools. The dependent variables, the State Student Assessment Test (SSAT), and the School Subjects Attitude Scales (SSAS), measured students' computational scores and attitudes toward mathematics in 3 categories: interest, usefulness, and difficulty, respectively.^ Univariate analyses of variance were performed on the least squares mean differences from pretest to posttest for testing main effects and interactions. A t-test measured significant main effects and interactions. Results were interpreted at the.01 level of significance.^ Null hypotheses 1, 2, and 3 compared versions of CAI with the control group, for changes in mathematical computation scores measured with the SSAT. It could not be concluded that changes in standardized mathematics test scores of students using CAI with differing software 1 hour per week for 18 class hours combined with TI were significantly higher than changes in test scores for students receiving TI only.^ Null hypotheses 4, 5, and 6 tested the effects of CAI for attitudes toward mathematics for experimental groups against control groups measured with the SSAS. Changes in attitudes toward mathematics of students using CAI with differing software 1 hour per week for 18 class hours combined with TI were not significantly higher than attitude changes for students receiving TI only.^ Teacher effect on students' computational scores was a more influential variable than CAI. No interaction was found between gender and learning method on standardized mathematics test scores (null hypothesis 7). ^

The impact of income and social status on population health: a scenario based simulation for nursing students

The development of critical thinking and communication skills is an essential part of Baccalaureate and Practical Nursing education. Scenario-based simulation, a form of experiential learning, directly engages students in the learning process. This teaching learning method has been shown to increase students’ understanding of the influence of their personal beliefs and values when working with clients and to improve therapeutic communication and critical thinking skills. Students in both the BN (Collaborative) and PN Programs at the Centre for Nursing Studies demonstrate a strong theoretical understanding of the impact of income and social status on population health but often experience difficulty applying this knowledge to the clinical situations involving clients and families. The purpose of the project was to develop a scenario-based simulation activity to provide nursing students with first-hand experiences of the impact of income and social status on health service accessibility. A literature review and stakeholder consultations were conducted to inform the project. The findings of these initiatives and Kolb’s Experiential Learning Theory were used to guide all aspects of the project. This report is an account of how the income and social status simulation and its accompanying materials were developed. This project provided an excellent learning opportunity that demonstrated the use of advanced nursing competencies.

Identifying player profiles in video games

Tämä diplomityö tarkastelee pelaajatyyppien ja pelaajamotivaatioiden tunnistamista videopeleissä. Aiempi tutkimus tuntee monia pelaajatyyppien malleja, mutta niitä ei ole liiemmin sovellettu käytäntöön peleissä. Tässä työssä suoritetaan systemaattinen kirjallisuuskartoitus erilaisista pelaajatyyppien malleista, jonka pohjalta esitetään useita pelaajien luokittelutapoja. Lisäksi toteutetaan tapaustutkimus, jossa kirjallisuuden pohjalta valitaan pelaajien luokittelumalli ja testataan mallia käytännössä tunnistamalla pelaajatyyppejä data-analytiikan avulla reaaliaikaisessa strategiapelissä.

Was ist aus dem Integrierten Handlungsmodell geworden?

In diesem Beitrag wird das von Thomas Martens und Jürgen Rost entwickelte Integrierte Handlungsmodell reflektiert, auf Lernprozesse übertragen, theoretische erweitert und beispielhaft empirisch überprüft. Zunächst werden die theoretischen Potenziale des Modells aufgezeigt, indem es auf Lernprozesse angewendet wird. Danach werden die typischen Prozesse der drei Handlungsphasen dargestellt, insbesondere die funktionslogische Integration von bewussten und unbewussten sowie kognitiven und affektiven Prozessen. Abschließend wird eine empirische Studie vorgestellt, die das Lernen von Statistik im Hochschulbereich untersucht. Mit Hilfe von Mischverteilungsmodellen werden sieben Lerntypen identifiziert, die den entsprechenden motivationalen, intentionalen und volitionalen Mustern der Lern- und Handlungsgenese entsprechen. (DIPF/Orig.)

Translation – The fifth language skill? A comparison of the role of translation in Finnish lukio and the International Baccalaureate Diploma Programme

This study attempts to answer the question “Should translation be considered a fifth language skill?” by examining and comparing the use of translation as a language learning and assessment method in the national Finnish lukio curriculum and the curriculum of the International Baccalaureate Diploma Programme (IBDP). Furthermore, the students’ ability to translate and their opinions on the usefulness of translation in language learning will be examined. The students’ opinions were gathered through a questionnaire that was given to 156 students studying in either lukio or the IBDP in Turku and Rovaniemi. I present and compare the role of translation in selected language teaching and learning methods and approaches, and discuss the effectiveness of translation as a language learning method and an assessment method. The theoretical discussion provides the basis for examining the role of translation as a language learning method and an assessment method in the curricula and final examinations of both education programs. The analysis of the two curricula indicated that there is a significant difference in the use of translation, as translation is used as a language learning method and as an assessment method in lukio, but is not used in either form in the IB. The data obtained through the questionnaire indicated that there is a difference in the level of language competence between the lukio and IB students and suggested that the curriculum in which the student studies has some effect on his/her cognitive use of translation, ability to translate and opinions concerning the usefulness of translation in language learning. The results indicated that both groups of students used translation, along with their mother tongue, as a cognitive language learning method, and, contrary to the expectations set by the analysis of the two curricula, the IB students performed better in the translation exercises than lukio students. Both groups of students agreed that translation is a useful language learning method, and indicated that the most common dictionaries they use are bilingual Internet dictionaries. The results suggest that translation is a specific skill that requires teaching and practice, and that perhaps the translation exercises used in lukio should be developed from translating individual words and phrases to translating cultural elements. In addition, the results suggest that perhaps the IB curriculum should include the use of translation exercises (e.g., communicative translation exercises) in order to help students learn to mediate between languages and cultures rather than learn languages in isolation from each other.

Generating Discriminative Objective Proposals via Submodular Ranking

Object recognition has long been a core problem in computer vision. To improve object spatial support and speed up object localization for object recognition, generating high-quality category-independent object proposals as the input for object recognition system has drawn attention recently. Given an image, we generate a limited number of high-quality and category-independent object proposals in advance and used as inputs for many computer vision tasks. We present an efficient dictionary-based model for image classification task. We further extend the work to a discriminative dictionary learning method for tensor sparse coding. In the first part, a multi-scale greedy-based object proposal generation approach is presented. Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation. We first identify the representative and diverse exemplar clusters within each scale. Object proposals are obtained by selecting a subset from the multi-scale segment pool via maximizing a submodular objective function, which consists of a weighted coverage term, a single-scale diversity term and a multi-scale reward term. The weighted coverage term forces the selected set of object proposals to be representative and compact; the single-scale diversity term encourages choosing segments from different exemplar clusters so that they will cover as many object patterns as possible; the multi-scale reward term encourages the selected proposals to be discriminative and selected from multiple layers generated by the hierarchical image segmentation. The experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012 segmentation dataset demonstrate the accuracy and efficiency of our object proposal model. Additionally, we validate our object proposals in simultaneous segmentation and detection and outperform the state-of-art performance. To classify the object in the image, we design a discriminative, structural low-rank framework for image classification. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier.

O efeito dos exercícios excêntricos na tendinopatia do tendão de Aquiles em adultos saudáveis

Projeto de Graduação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Licenciada em Fisioterapia

Improving understandability in consumer health information search: Uevora @ 2016 fire chis

This paper presents our work at 2016 FIRE CHIS. Given a CHIS query and a document associated with that query, the task is to classify the sentences in the document as relevant to the query or not; and further classify the relevant sentences to be supporting, neutral or opposing to the claim made in the query. In this paper, we present two different approaches to do the classification. With the first approach, we implement two models to satisfy the task. We first implement an information retrieval model to retrieve the sentences that are relevant to the query; and then we use supervised learning method to train a classification model to classify the relevant sentences into support, oppose or neutral. With the second approach, we only use machine learning techniques to learn a model and classify the sentences into four classes (relevant & support, relevant & neutral, relevant & oppose, irrelevant & neutral). Our submission for CHIS uses the first approach.

Automatic detection of the inferior alveolar nerve in CT scans with CenterNet

The inferior alveolar nerve (IAN) lies within the mandibular canal, named inferior alveolar canal in literature. The detection of this nerve is important during maxillofacial surgeries or for creating dental implants. The poor quality of cone-beam computed tomography (CBCT) and computed tomography (CT) scans and/or bone gaps within the mandible increase the difficulty of this task, posing a challenge to human experts who are going to manually detect it and resulting in a time-consuming task.Therefore this thesis investigates two methods to automatically detect the IAN: a non-data driven technique and a deep-learning method. The latter tracks the IAN position at each frame leveraging detections obtained with the deep neural network CenterNet, fined-tuned for our task, and temporal and spatial information.

Synthetic generation of amyloid PET images by non-linear dimensionality reduction inversion

Privacy issues and data scarcity in PET field call for efficient methods to expand datasets via synthetic generation of new data that cannot be traced back to real patients and that are also realistic. In this thesis, machine learning techniques were applied to 1001 amyloid-beta PET images, which had undergone a diagnosis of Alzheimer’s disease: the evaluations were 540 positive, 457 negative and 4 unknown. Isomap algorithm was used as a manifold learning method to reduce the dimensions of the PET dataset; a numerical scale-free interpolation method was applied to invert the dimensionality reduction map. The interpolant was tested on the PET images via LOOCV, where the removed images were compared with the reconstructed ones with the mean SSIM index (MSSIM = 0.76 ± 0.06). The effectiveness of this measure is questioned, since it indicated slightly higher performance for a method of comparison using PCA (MSSIM = 0.79 ± 0.06), which gave clearly poor quality reconstructed images with respect to those recovered by the numerical inverse mapping. Ten synthetic PET images were generated and, after having been mixed with ten originals, were sent to a team of clinicians for the visual assessment of their realism; no significant agreements were found either between clinicians and the true image labels or among the clinicians, meaning that original and synthetic images were indistinguishable. The future perspective of this thesis points to the improvement of the amyloid-beta PET research field by increasing available data, overcoming the constraints of data acquisition and privacy issues. Potential improvements can be achieved via refinements of the manifold learning and the inverse mapping stages during the PET image analysis, by exploring different combinations in the choice of algorithm parameters and by applying other non-linear dimensionality reduction algorithms. A final prospect of this work is the search for new methods to assess image reconstruction quality.