Collaborative Feature Learning from Social Media

Image feature representation plays an essential role in image recognition and related tasks. The current state-of-the-art feature learning paradigm is supervised learning from labeled data. However, this paradigm requires large-scale category labels, which limits its applicability to domains where labels are hard to obtain. In this paper, we propose a new data-driven feature learning paradigm which does not rely on category labels. Instead, we learn from user behavior data collected on social media. Concretely, we use the image relationship discovered in the latent space from the user behavior data to guide the image feature learning. We collect a large-scale image and user behavior dataset from Behance.net. The dataset consists of 1.9 million images and over 300 million view records from 1.9 million users. We validate our feature learning paradigm on this dataset and find that the learned feature significantly outperforms the state-of-the-art image features in learning better image similarities. We also show that the learned feature performs competitively on various recognition benchmarks

Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks

Quantized Neural Networks (QNNs), which use low bitwidth numbers for representing parameters and performing computations, have been proposed to reduce the computation complexity, storage size and memory usage. In QNNs, parameters and activations are uniformly quantized, such that the multiplications and additions can be accelerated by bitwise operations. However, distributions of parameters in Neural Networks are often imbalanced, such that the uniform quantization determined from extremal values may under utilize available bitwidth. In this paper, we propose a novel quantization method that can ensure the balance of distributions of quantized values. Our method first recursively partitions the parameters by percentiles into balanced bins, and then applies uniform quantization. We also introduce computationally cheaper approximations of percentiles to reduce the computation overhead introduced. Overall, our method improves the prediction accuracies of QNNs without introducing extra computation during inference, has negligible impact on training speed, and is applicable to both Convolutional Neural Networks and Recurrent Neural Networks. Experiments on standard datasets including ImageNet and Penn Treebank confirm the effectiveness of our method. On ImageNet, the top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7\%, which is superior to the state-of-the-arts of QNNs

Translationese and post-editese : how comparable is comparable quality?

Whereas post-edited texts have been shown to be either of comparable quality to human translations or better, one study shows that people still seem to prefer human-translated texts. The idea of texts being inherently different despite being of high quality is not new. Translated texts, for example,are also different from original texts, a phenomenon referred to as ‘Translationese’. Research into Translationese has shown that, whereas humans cannot distinguish between translated and original text,computers have been trained to detect Translationesesuccessfully. It remains to be seen whether the same can be done for what we call Post-editese. We first establish whether humans are capable of distinguishing post-edited texts from human translations, and then establish whether it is possible to build a supervised machine-learning model that can distinguish between translated and post-edited text

The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits

Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)

Multi-agent evolutionary systems for the generation of complex virtual worlds

Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex models is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive Evolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This workflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer's intent through interaction, and encourages playful discovery

A comparison of drone imagery and groundbased methods for estimating the extent of habitat destruction by lesser snow geese (\u3ci\u3eAnser caerulescens caerulescens\u3c/i\u3e) in La Pérouse Bay

Lesser snow goose (Anser caerulescens caerulescens) populations have dramatically altered vegetation communities through increased foraging pressure. In remote regions, regular habitat assessments are logistically challenging and time consuming. Drones are increasingly being used by ecologists to conduct habitat assessments, but reliance on georeferenced data as ground truth may not always be feasible. We estimated goose habitat degradation using photointerpretation of drone imagery and compared estimates to those made with ground-based linear transects. In July 2016, we surveyed five study plots in La Pérouse Bay, Manitoba, to evaluate the effectiveness of a fixed-wing drone with simple Red Green Blue (RGB) imagery for evaluating habitat degradation by snow geese. Ground-based land cover data was collected and grouped into barren, shrub, or non-shrub categories. We compared estimates between ground-based transects and those made from unsupervised classification of drone imagery collected at altitudes of 75, 100, and 120 m above ground level (ground sampling distances of 2.4, 3.2, and 3.8 cm respectively). We found large time savings during the data collection step of drone surveys, but these savings were ultimately lost during imagery processing. Based on photointerpretation, overall accuracy of drone imagery was generally high (88.8% to 92.0%) and Kappa coefficients were similar to previously published habitat assessments from drone imagery. Mixed model estimates indicated 75m drone imagery overestimated barren (F2,182 = 100.03, P \u3c 0.0001) and shrub classes (F2,182 = 160.16, P \u3c 0.0001) compared to ground estimates. Inconspicuous graminoid and forb species (non-shrubs) were difficult to detect from drone imagery and were underestimated compared to ground-based transects (F2,182 = 843.77, P \u3c 0.0001). Our findings corroborate previous findings, and that simple RGB imagery is useful for evaluating broad scale goose damage, and may play an important role in measuring habitat destruction by geese and other agents of environmental change

Beef Cattle Instance Segmentation Using Mask R-Convolutional Neural Network

Maintaining the cattle farm along with the wellbeing of every heifer has been the major concern in dairy farm. A robust system is required which can tackle the problem of continuous monitoring of cows. the computer vision techniques provide a new way to understand the challenges related to the identification and welfare of the cows. This paper presents a state-of-art instance segmentation mask RCNN algorithm to train and build a model on a very challenging cow dataset that is captured during the winter season. The dataset poses many challenges such as overlapping of cows, partial occlusion, similarity between cows and background, and bad lightening. An attempt is made to improve the accuracy of the segmenter and the performance is measured after fine tuning the baseline model. The experiment result shows that fine tuning the mask RCNN algorithm helps in significantly improving the accuracy of instance segmentation of cows. this work is a contribution towards the real time monitoring of cows in cattle farm environment with the purpose of behavioural analysis of the cattle

Intelligent data analysis from the financial execution of research projects at University of Minho

Dissertação de mestrado em Engenharia InformáticaThe number of research and development (R&D) projects underway has increased substantially in recent years, which derives from the recognition of the importance of these projects for the future success of the University of Minho and its scientific partners, not only from a financial perspective but also innovation and search for knowledge. Any Higher Education Institution (HEI) needs a solid management base for many areas that are part of and complete its global organization, such as the area related to R&D projects. A large part of the financial man agement carried out by the University of Minho is intrinsically linked to project management, whose budgets are often in the thousands of euros. The data used by the most diverse entities and support centers at the University of Minho are available to those responsible for them in an unintuitive and dispersed way. This dispersion, besides making access to information very difficult, does not sympathize with the organization that a higher education unit needs. Therefore, getting detailed and reliable information is the key to success, both for researchers, who are directly responsible, and for the regulatory bodies that are implanted in the university. Thus, it was proposed to create a Data Visualization (DV) platform based on project execution data sources from the Financial and Patrimonial Services Unit (USFP) of the University of Minho to provide an organized and coherent data visualization platform, according to the needs of its stakeholders. With the creation of this platform, through an Intelligent Data Analysis System, using a temporal and detailed observation of the data, it is possible to draw conclusions about the investments made in research projects that have occurred until now and to help in future investment decisions crucial to the healthy functioning of the educational institution. Thus, this analysis seeks not only to improve the financial management of the area in question but also to understand the extent to which the use of Machine Learning techniques can be useful in analyzing data related to the financial execution of R&D projects. Furthermore, an area that is highly related to research projects, and cannot be ignored, is scientific production. The dissemination of scientific knowledge is an essential part of the research work carried out in any area, so this topic was also studied and introduced within the scope of this dissertation.O número de projetos de investigação e desenvolvimento (I&D) em execução tem vindo a aumentar substan cialmente nos últimos anos, o que deriva do reconhecimento da importância destes projetos para o sucesso futuro da Universidade do Minho e seus parceiros científicos, não só numa perspetiva financeira, mas também de inovação e procura pelo conhecimento. Qualquer instituição de ensino superior necessita de uma base sólida de gestão para todos os tipos de áreas que fazem parte e completam a sua organização global, como é o caso da área relacionada com os projetos de I&D. Uma grande parte da gestão financeira realizada pela Universidade do Minho está intrinsecamente ligada à gestão de projetos, cujos orçamentos rondam, muitas vezes, os milhares de euros. Os dados utilizados pelas mais diversas entidades e centros de apoio da Universidade do Minho encontram se à disposição dos responsáveis das mesmas de uma forma pouco intuitiva e dispersa. Esta dispersão, para além de dificultar bastante o acesso à informação, não se compadece com a organização que uma unidade de ensino superior necessita. Neste sentido, a obtenção de informação detalhada e fidedigna é a chave do sucesso, tanto para os in vestigadores, responsáveis diretos, como para as entidades reguladoras que se encontram implementadas na universidade. Assim, foi proposta a criação de uma plataforma de visualização de dados a partir de fontes de dados de execução de projetos provenientes da Unidade de Serviços Financeiro e Patrimonial (USFP) da Uni versidade do Minho com o intuito de fornecer uma plataforma de visualização de dados organizada e coerente, conforme as necessidades dos seus stakeholders. Com a criação desta plataforma, através de um sistema de Análise Inteligente de Dados, isto é, fazendo uso de uma observação temporal e detalhada dos dados, é possível retirar conclusões sobre os investimentos feitos nos projetos de investigação ocorridos até à data e ajudar nas futuras decisões de investimento cruciais ao funcionamento saudável da instituição de ensino. Assim, com esta análise procura-se, não só melhorar a gestão financeira da área em questão, mas também perceber até que ponto a utilização de técnicas de Machine Learning pode ser útil na análise de dados relativos à execução financeira de projetos de I&D. Para além disso, uma área que está altamente relacionada com os projetos de investigação, não podendo ficar alheia à mesma, é a produção científica. A disseminação de conhecimento científico é uma parte essencial do trabalho de investigação levado a cabo em qualquer área, pelo que é extremamente importante que também este tema seja estudado e introduzido no âmbito desta dissertação