Opinion Mining

In this thesis we study the field of opinion mining by giving a comprehensive review of the available research that has been done in this topic. Also using this available knowledge we present a case study of a multilevel opinion mining system for a student organization's sales management system. We describe the field of opinion mining by discussing its historical roots, its motivations and applications as well as the different scientific approaches that have been used to solve this challenging problem of mining opinions. To deal with this huge subfield of natural language processing, we first give an abstraction of the problem of opinion mining and describe the theoretical frameworks that are available for dealing with appraisal language. Then we discuss the relation between opinion mining and computational linguistics which is a crucial pre-processing step for the accuracy of the subsequent steps of opinion mining. The second part of our thesis deals with the semantics of opinions where we describe the different ways used to collect lists of opinion words as well as the methods and techniques available for extracting knowledge from opinions present in unstructured textual data. In the part about collecting lists of opinion words we describe manual, semi manual and automatic ways to do so and give a review of the available lists that are used as gold standards in opinion mining research. For the methods and techniques of opinion mining we divide the task into three levels that are the document, sentence and feature level. The techniques that are presented in the document and sentence level are divided into supervised and unsupervised approaches that are used to determine the subjectivity and polarity of texts and sentences at these levels of analysis. At the feature level we give a description of the techniques available for finding the opinion targets, the polarity of the opinions about these opinion targets and the opinion holders. Also at the feature level we discuss the various ways to summarize and visualize the results of this level of analysis. In the third part of our thesis we present a case study of a sales management system that uses free form text and that can benefit from an opinion mining system. Using the knowledge gathered in the review of this field we provide a theoretical multi level opinion mining system (MLOM) that can perform most of the tasks needed from an opinion mining system. Based on the previous research we give some hints that many of the laborious market research tasks that are done by the sales force, which uses this sales management system, can improve their insight about their partners and by that increase the quality of their sales services and their overall results.

Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Network virtualisation is considerably gaining attentionas a solution to ossification of the Internet. However, thesuccess of network virtualisation will depend in part on how efficientlythe virtual networks utilise substrate network resources.In this paper, we propose a machine learning-based approachto virtual network resource management. We propose to modelthe substrate network as a decentralised system and introducea learning algorithm in each substrate node and substrate link,providing self-organization capabilities. We propose a multiagentlearning algorithm that carries out the substrate network resourcemanagement in a coordinated and decentralised way. The taskof these agents is to use evaluative feedback to learn an optimalpolicy so as to dynamically allocate network resources to virtualnodes and links. The agents ensure that while the virtual networkshave the resources they need at any given time, only the requiredresources are reserved for this purpose. Simulations show thatour dynamic approach significantly improves the virtual networkacceptance ratio and the maximum number of accepted virtualnetwork requests at any time while ensuring that virtual networkquality of service requirements such as packet drop rate andvirtual link delay are not affected.

Entornos e-learning como soporte de aprendizaje a la asignatura educación musical en infancia

The music is a compulsory subject in the first stage of primary education. We detected several teachers from different educational areas, including the area of music, using e-learning platforms and web tools for teaching the curriculum that marks the “Department of Education of the Generalitat de Catalunya”. From the body of analysis has drawn the picture in e-learning platforms, analyzing the types and uses. Through the sample of e-learning platforms in music education, have identified four schools with e-learning platforms in advanced stage. We performed a case study on one of these platforms for content analysis and validate the interview format used; this has served to create a model that can be used in other centers with e-learning platform for music subject.

Learning Grasp Affordances from Vision

Monimutkaisissa ja muuttuvissa ympäristöissä työskentelevät robotit tarvitsevat kykyä manipuloida ja tarttua esineisiin. Tämä työ tutkii robottitarttumisen ja robottitartuntapis-teiden koneoppimisen aiempaa tutkimusta ja nykytilaa. Nykyaikaiset menetelmät käydään läpi, ja Le:n koneoppimiseen pohjautuva luokitin toteutetaan, koska se tarjoaa parhaan onnistumisprosentin tutkituista menetelmistä ja on muokattavissa sopivaksi käytettävissä olevalle robotille. Toteutettu menetelmä käyttää intensititeettikuvaan ja syvyyskuvaan po-hjautuvia ominaisuuksi luokitellakseen potentiaaliset tartuntapisteet. Tämän toteutuksen tulokset esitellään.

Deep generative models for facial keypoints detection

A new area of machine learning research called deep learning, has moved machine learning closer to one of its original goals: artificial intelligence and general learning algorithm. The key idea is to pretrain models in completely unsupervised way and finally they can be fine-tuned for the task at hand using supervised learning. In this thesis, a general introduction to deep learning models and algorithms are given and these methods are applied to facial keypoints detection. The task is to predict the positions of 15 keypoints on grayscale face images. Each predicted keypoint is specified by an (x,y) real-valued pair in the space of pixel indices. In experiments, we pretrained deep belief networks (DBN) and finally performed a discriminative fine-tuning. We varied the depth and size of an architecture. We tested both deterministic and sampled hidden activations and the effect of additional unlabeled data on pretraining. The experimental results show that our model provides better results than publicly available benchmarks for the dataset.

Effect of 5-HT1B receptor agonists injected into the prefrontal cortex on maternal aggression in rats

Serotonin (5-HT1B) receptors play an essential role in the inhibition of aggressive behavior in rodents. CP-94,253, a 5-HT1B receptor agonist, can reduce aggression in male mice when administered directly into the ventro-orbitofrontal (VO) prefrontal cortex (PFC). The objective of the current study was to assess the effects of two selective 5-HT1B receptor agonists (CP-94,253 and CP-93,129), microinjected into the VO PFC, on maternal aggressive behavior after social instigation in rats. CP-94,253 (0.56 µg/0.2 µL, N = 8, and 1.0 µg/0.2 µL, N = 8) or CP-93,129 (1.0 µg/0.2 µL, N = 9) was microinjected into the VO PFC of Wistar rats on the 9th day postpartum and 15 min thereafter the aggressive behavior by the resident female against a male intruder was recorded for 10 min. The frequency and duration of aggressive and non-aggressive behaviors were analyzed using ANOVA and post hoc tests. CP-93,129 significantly decreased maternal aggression. The frequency of lateral attacks, bites and pinnings was reduced compared to control, while the non-aggressive behaviors and maternal care were largely unaffected by this treatment. CP-94,253 had no significant effects on aggressive or non-aggressive behaviors when microinjected into the same area of female rats. CP-93,129, a specific 5-HT1B receptor agonist, administered into the VO PFC reduced maternal aggressive behavior, while the CP-94,253 agonist did not significantly affect this behavior after social instigation in female rats. We conclude that only the 5-HT1B receptor agonist CP-93,129 administered into the VO PFC decreased aggression in female rats postpartum after social instigation.

Anxiety and depression in parents of a Brazilian non-clinical sample of attention-deficit/ hyperactivity disorder (ADHD) students

Higher prevalence rates of anxiety and depression have been reported in parents of children with attention-deficit/hyperactivity disorder (ADHD). The interaction between the burden of ADHD in offspring, a higher prevalence rate of this highly inherited disorder in parents, and comorbidities may explain this finding. Our objective was to investigate levels of ADHD, anxious and depressive symptomatology, and their relationship in parents of ADHD children from a non-clinical sample using a dimensional approach. The sample included 396 students enrolled in all eight grades of a public school who were screened for ADHD using the SNAP IV rating scale. Positive cases were confirmed through a semi-structured interview. Parents of all 26 ADHD students and 31 paired controls were enrolled. A sample of 36 parents of ADHD children (21 mothers, 15 fathers) and 30 parents of control children (18 mothers, 12 fathers) completed the Adult Self Report Scale, State-Trait Anxiety Inventory, and Beck Depression Inventory in order to investigate anxious and depressive symptomatology. Probands' mothers presented a higher level of ADHD symptomatology (with only inattention being a significant cluster). Again, mothers of ADHD children presented higher depressive and anxiety levels; however, these did not correlate with their own ADHD symptomatology. Only trait-anxiety levels were higher in ADHD mothers. Our findings suggest that: 1) anxious and depressive symptoms might be more prevalent in mothers of ADHD students; 2) anxious and depressive symptomatology might be independent of impairment associated with ADHD symptoms; 3) anxious and depressive symptoms are independent of the presence of ADHD.

A Bayesian Approach for Forecasting Heat Load in a District Heating System

The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Predictors of academic achievement and social competence in adolescence : facilitation, inhibition and general cognitive processing /

The relevance of attentional measures to cognitive and social adaptive behaviour was examined in an adolescent sample. Unlike previous research, the influence of both inhibitory and facilitory aspects of attention were studied. In addition, contributions made by these attentional processes were compared with traditional psychometric measures of cognitive functioning. Data were gathered from 36 grade 10 and 1 1 high school students (20 male and 16 female students) with a variety of learning and attentional difficulties. Data collection was conducted in the course of two testing sessions. In the first session, students completed questionnaires regarding their medical history, and everyday behaviours (the Brock Adaptive Functioning Questionnaire), along with non-verbal problem solving tasks and motor speed tasks. In the second session, students performed working memory measures and computer-administered tasks assessing inhibitory and facilitory aspects of attention. Grades and teacher-rated measures of cognitive and social impulsivity were also gathered. Results indicate that attentional control has both cognitive and social/emotional implications. Performance on negative priming and facilitation trials from the Flanker task predicted grades in core courses, social functioning measures, and cognitive and social impulsivity ratings. However, beneficial effects for academic and social functioning associated with inhibition were less prevalent in those demonstrating a greater ability to respond to facilitory cues. There was also some evidence that high levels of facilitation were less beneficial to academic performance, and female students were more likely to exceed optimal levels of facilitory processing. Furthermore, lower negative priming was ''S'K 'i\':y-: -'*' - r " j«v ; ''*.' iij^y Inhibition, Facilitation and Social Competence 3 associated with classroom-rated distraction and hyperactivity, but the relationship between inhibition and social aspects of impulsivity was stronger for adolescents with learning or reading problems, and the relationship between inhibition and cognitive impulsivity was stronger for male students. In most cases, attentional measures were predictive of performance outcomes independent of traditional psychometric measures of cognitive functioning. >,, These findings provide support for neuropsychological models linking inhibition to control of interference and arousal, and emphasize the fundamental role of attention in everyday adolescent activities. The findings also warrant further investigation into the ways which inhibitory and facilitory attentional processes interact, and the contextdependent nature of attentional control.associated with classroom-rated distraction and hyperactivity, but the relationship between inhibition and social aspects of impulsivity was stronger for adolescents with learning or reading problems, and the relationship between inhibition and cognitive impulsivity was stronger for male students. In most cases, attentional measures were predictive of performance outcomes independent of traditional psychometric measures of cognitive functioning. >,, These findings provide support for neuropsychological models linking inhibition to control of interference and arousal, and emphasize the fundamental role of attention in everyday adolescent activities. The findings also warrant further investigation into the ways which inhibitory and facilitory attentional processes interact, and the contextdependent nature of attentional control.

Experiential learning instruments : an examination of the concept of the instrumental approach in experiential learning

Experiential Learning Instruments (ELls) are employed to modify the leamer's apprehension and / or comprehension in experiential learning situations, thereby improving the efficiency and effectiveness of those modalities in the learning process. They involve the learner in reciprocally interactive and determining transactions with his/her environment. Experiential Learning Instruments are used to keep experiential learning a process rather than an object. Their use is aimed at the continual refinement of the learner's knowledge and skill. Learning happens as the leamer's awareness, directed by the use of Ells, comes to experience, monitor and then use experiential feedback from living situations in a way that facilitates knmvledge/skill acquisition, self-correction and refinement. The thesis examined the literature relevant to the establishing of a theoretical experiential learning framework within which ELls can be understood. This framework included the concept that some learnings have intrinsic value-knowledge of necessary information-while others have instrumental value-knowledge of how to learn. The Kolb Learning Cycle and Kolb's six characteristics of experiential learning were used in analyzing three ELls from different fields of learning-saxophone tone production, body building and interpersonal communications. The ELls were examined to determine their learning objectives and how they work using experiential learning situations. It was noted that ELls do not transmit information but assist the learner in attending to and comprehending aspects of personal experience. Their function is to telescope the experiential learning process.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Towards Evaluating and Modelling the Impacts of Mobile-Based Augmented Reality Applications on Learning and Engagement

Mobile augmented reality applications are increasingly utilized as a medium for enhancing learning and engagement in history education. Although these digital devices facilitate learning through immersive and appealing experiences, their design should be driven by theories of learning and instruction. We provide an overview of an evidence-based approach to optimize the development of mobile augmented reality applications that teaches students about history. Our research aims to evaluate and model the impacts of design parameters towards learning and engagement. The research program is interdisciplinary in that we apply techniques derived from design-based experiments and educational data mining. We outline the methodological and analytical techniques as well as discuss the implications of the anticipated findings.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.

Credibility and Signaling in Disinflation- a Cross Country Examination

This paper develops a model of money demand where the opportunity cost of holding money is subject to regime changes. The regimes are fully characterized by the mean and variance of inflation and are assumed to be the result of alternative government policies. Agents are unable to directly observe whether government actions are indeed consistent with the inflation rate targeted as part of a stabilization program but can construct probability inferences on the basis of available observations of inflation and money growth. Government announcements are assumed to provide agents with additional, possibly truthful information regarding the regime. This specification is estimated and tested using data from the Israeli and Argentine high inflation periods. Results indicate the successful stabilization program implemented in Israel in July 1985 was more credible than either the earlier Israeli attempt in November 1984 or the Argentine programs. Government’s signaling might substantially simplify the inference problem and increase the speed of learning on the part of the agents. However, under certain conditions, it might increase the volatility of inflation. After the introduction of an inflation stabilization plan, the welfare gains from a temporary increase in real balances might be high enough to induce agents to raise their real balances in the short-term, even if they are uncertain about the nature of government policy and the eventual outcome of the stabilization attempt. Statistically, the model restrictions cannot be rejected at the 1% significance level.