Improving the freshman electrical and computer engineering lab

This thesis covers the challenges of creating and maintaining an introductory engineering laboratory. The history of the University of Illinois Electrical and Computer Engineering department’s introductory course, ECE 110, is recounted. The current state of the course, as of Fall 2008, is discussed along with current challenges arising from the use of a hand-wired prototyping board with logic gates. A plan for overcoming these issues using a new microcontroller-based board with a pseudo hardware description language is discussed. The new microcontroller based system implementation is extensively detailed along with its new accompanying description language. This new system was tried in several sections of the Fall 2008 semester alongside the old system; the students’ final performances with the two different approaches are compared in terms of design, performance, complexity, and enjoyment. The system in its first run shows great promise, increasing the students’ enjoyment, and improving the performance of their designs.

SES-RELATED DIFFERENCES IN WORD LEARNING: EFFECTS OF COGNITIVE INHIBITION AND WORD LEARNING

Socioeconomic status (SES) influences language and cognitive development, with discrepancies particularly noticeable in vocabulary development. This study examines how SES-related differences impact the development of syntactic processing, cognitive inhibition, and word learning. 38 4-5-year-olds from higher- and lower-SES backgrounds completed a word-learning task, in which novel words were embedded in active and passive sentences. Critically, unlike the active sentences, all passive sentences required a syntactic revision. Measures of cognitive inhibition were obtained through a modified Stroop task. Results indicate that lower-SES participants had more difficulty using inhibitory functions to resolve conflict compared to their higher-SES counterparts. However, SES did not impact language processing, as the language outcomes were similar across SES background. Additionally, stronger inhibitory processes were related to better language outcomes in the passive sentence condition. These results suggest that cognitive inhibition impact language processing, but this function may vary across children from different SES backgrounds

Privacy in data publishing for tailored recommendation scenarios

Personal information is increasingly gathered and used for providing services tailored to user preferences, but the datasets used to provide such functionality can represent serious privacy threats if not appropriately protected. Work in privacy-preserving data publishing targeted privacy guarantees that protect against record re-identification, by making records indistinguishable, or sensitive attribute value disclosure, by introducing diversity or noise in the sensitive values. However, most approaches fail in the high-dimensional case, and the ones that don’t introduce a utility cost incompatible with tailored recommendation scenarios. This paper aims at a sensible trade-off between privacy and the benefits of tailored recommendations, in the context of privacy-preserving data publishing. We empirically demonstrate that significant privacy improvements can be achieved at a utility cost compatible with tailored recommendation scenarios, using a simple partition-based sanitization method.

A flexible bio-inspired hierarchical model for analyzing musical timbre

A flexible and multipurpose bio-inspired hierarchical model for analyzing musical timbre is presented in this paper. Inspired by findings in the fields of neuroscience, computational neuroscience, and psychoacoustics, not only does the model extract spectral and temporal characteristics of a signal, but it also analyzes amplitude modulations on different timescales. It uses a cochlear filter bank to resolve the spectral components of a sound, lateral inhibition to enhance spectral resolution, and a modulation filter bank to extract the global temporal envelope and roughness of the sound from amplitude modulations. The model was evaluated in three applications. First, it was used to simulate subjective data from two roughness experiments. Second, it was used for musical instrument classification using the k-NN algorithm and a Bayesian network. Third, it was applied to find the features that characterize sounds whose timbres were labeled in an audiovisual experiment. The successful application of the proposed model in these diverse tasks revealed its potential in capturing timbral information.

Potencialidades de cultivo de macroalgas no litoral do Rio Grande do Norte (NE, Brasil): uma perspectiva de integração de dados ambientais em sistemas de informação geográfica (SIG)

Since centuries ago, the Asians use seaweed as an important source of feeding and are their greatest world-wide consumers. The migration of these peoples for other countries, made the demand for seaweed to increase. This increasing demand prompted an industry with annual values of around US$ 6 billion. The algal biomass used for the industry is collected in natural reservoirs or cultivated. The market necessity for products of the seaweed base promotes an unsustainable exploration of the natural banks, compromising its associated biological balance. In this context, seaweed culture appears as a viable alternative to prevent the depletion of these natural supplies. Geographic Information Systems (GIS) provide space and produce information that can facilitate the evaluation of important physical and socio-economic characteristics for the planning of seaweed culture. This objective of this study is to identify potential coastal areas for seaweed culture in the state of Rio Grande do Norte, from the integration of social-environmental data in the SIG. In order to achieve this objective, a geo-referred database composed of geographical maps, nautical maps and orbital digital images was assembled; and a bank of attributes including physical and oceanographical variables (winds, chains, bathymetry, operational distance from the culture) and social and environmental factors (main income, experience with seaweed harvesting, demographic density, proximity of the sheltered coast and distance of the banks) was produced. In the modeling of the data, the integration of the space database with the bank of attributes for the attainment of the map of potentiality of seaweed culture was carried out. Of a total of 2,011 ha analyzed by the GIS for the culture of seaweed, around 34% or 682 ha were indicated as high potential, 55% or 1,101 ha as medium potential, and 11% or 228 ha as low potential. The good indices of potentiality obtained in the localities studied demonstrate that there are adequate conditions for the installation of seaweed culture in the state of Rio Grande do Norte

European primary pupils show their life to African peers

Since primary school pupils lack a common language, primary school pupils from Germany and Africa show a piece of their origin and of their daily live through simple drawings to their peers in a other, distant land. The teachers accompanying the exchange of these drawings communicated in natural language, but helped to transform what their pupils wanted to show by their drawing. Five students drawings are presented in order to explain and illustrate this exchange method.

The big four skills: Teachers’ assumptions on measurement of non-native students cognition

The four-skills on tests for young native speakers commonly do not generate correlation incongruency concerning the cognitive strategies frequently reported. Considering the non-native speakers there are parse evidence to determine which tasks are important to assess properly the cognitive and academic language proficiency (Cummins, 1980; 2012). Research questions: It is of high probability that young students with origin in immigration significantly differ on their communication strategies and skills in a second language processing context (1); attached to this first assumption, it is supposed that teachers significantly differ depending on their scientific area and previous training (2). Purpose: This study intends to examine whether school teachers (K-12) as having different origin in scientific domain of teaching and training perceive differently an adapted four-skills scale, in European Portuguese. Research methods: 77 teachers of five areas scientific areas, mean of teaching year service = 32 (SD= 2,7), 57 males and 46 females (from basic and high school levels). Main findings: ANOVA (Effect size and Post-hoc Tukey tests) and linear regression analysis (stepwise method) revealed statistically significant differences among teachers of different areas, mainly between language teachers and science teachers. Language teachers perceive more accurately tasks in a multiple manner to the broad skills that require to be measured in non-native students. Conclusion: If teachers perceive differently the importance of the big-four tasks, there would be incongruence on skills measurement that teachers select for immigrant puppils. Non-balanced tasks and the teachers’ perceptions on evaluation and toward competence of students would likely determine limitations for academic and cognitive development of non-native students. Furthermore, results showed sufficient evidence to conclude that tasks are perceived differently by teachers toward importance of specific skills subareas. Reading skills are best considered compared to oral comphreension skills in non-native students.

Collaborative Filtering with Semantic Neighbour Discovery

Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items – the subset of items co-rated by both users – typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process – a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.

Ruchy gałek ocznych i gesty jako korelaty przetwarzania języka w tłumaczeniu konsekutywnym i symultanicznym

Wydział Anglistyki

Context Models for Understanding Images and Videos

A computer vision system that has to interact in natural language needs to understand the visual appearance of interactions between objects along with the appearance of objects themselves. Relationships between objects are frequently mentioned in queries of tasks like semantic image retrieval, image captioning, visual question answering and natural language object detection. Hence, it is essential to model context between objects for solving these tasks. In the first part of this thesis, we present a technique for detecting an object mentioned in a natural language query. Specifically, we work with referring expressions which are sentences that identify a particular object instance in an image. In many referring expressions, an object is described in relation to another object using prepositions, comparative adjectives, action verbs etc. Our proposed technique can identify both the referred object and the context object mentioned in such expressions. Context is also useful for incrementally understanding scenes and videos. In the second part of this thesis, we propose techniques for searching for objects in an image and events in a video. Our proposed incremental algorithms use the context from previously explored regions to prioritize the regions to explore next. The advantage of incremental understanding is restricting the amount of computation time and/or resources spent for various detection tasks. Our first proposed technique shows how to learn context in indoor scenes in an implicit manner and use it for searching for objects. The second technique shows how explicitly written context rules of one-on-one basketball can be used to sequentially detect events in a game.

A tradução de textos económico-financeiros de português para inglês: potencialidades da Plataforma ISTRION

Este Trabalho de Projeto tem como objetivo primordial analisar a tradução, de português para inglês, de textos económico-financeiros, utilizando a plataforma de Tradução Automática (TA) ISTRION. A tradução de conteúdos selecionados da Newsletter Económico-Financeira Maximus Report é efetuada com base na referida plataforma, complementada com outras ferramentas de apoio ao processamento linguístico que sejam consideradas relevantes. Visa-se igualmente com este Trabalho de Projeto analisar as potencialidades desta plataforma, bem como medir os resultados da tradução. Por último pretende-se enquadrar, testar, estudar e medir quais os critérios em que se poderá tornar mais eficiente a tradução destes textos.

Potencialidades de cultivo de macroalgas no litoral do Rio Grande do Norte (NE, Brasil): uma perspectiva de integração de dados ambientais em sistemas de informação geográfica (SIG)

Since centuries ago, the Asians use seaweed as an important source of feeding and are their greatest world-wide consumers. The migration of these peoples for other countries, made the demand for seaweed to increase. This increasing demand prompted an industry with annual values of around US$ 6 billion. The algal biomass used for the industry is collected in natural reservoirs or cultivated. The market necessity for products of the seaweed base promotes an unsustainable exploration of the natural banks, compromising its associated biological balance. In this context, seaweed culture appears as a viable alternative to prevent the depletion of these natural supplies. Geographic Information Systems (GIS) provide space and produce information that can facilitate the evaluation of important physical and socio-economic characteristics for the planning of seaweed culture. This objective of this study is to identify potential coastal areas for seaweed culture in the state of Rio Grande do Norte, from the integration of social-environmental data in the SIG. In order to achieve this objective, a geo-referred database composed of geographical maps, nautical maps and orbital digital images was assembled; and a bank of attributes including physical and oceanographical variables (winds, chains, bathymetry, operational distance from the culture) and social and environmental factors (main income, experience with seaweed harvesting, demographic density, proximity of the sheltered coast and distance of the banks) was produced. In the modeling of the data, the integration of the space database with the bank of attributes for the attainment of the map of potentiality of seaweed culture was carried out. Of a total of 2,011 ha analyzed by the GIS for the culture of seaweed, around 34% or 682 ha were indicated as high potential, 55% or 1,101 ha as medium potential, and 11% or 228 ha as low potential. The good indices of potentiality obtained in the localities studied demonstrate that there are adequate conditions for the installation of seaweed culture in the state of Rio Grande do Norte

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.