Your search

Macula fovea detection is a crucial prerequisite towards screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither accuracy nor effectiveness of the diagnose process could be guaranteed. In this project, we proposed a deep learning approach on ultra-widefield fundus (UWF) images for macula fovea detection. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. Methods based on U-shape network (Unet) and Fully Convolutional Networks (FCN) are implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. Results are measured by calculating the Euclidean distance between proposed approaches and the accurate grounded standard, which is detected by Ultra-widefield swept-source optical coherence tomograph (UWF-OCT) approach. Through a comparation of proposed methods, we conclude that, deep learning approach of Unet outperformed other methods on macula fovea detection tasks, by which outcomes obtained are comparable to grounded standard method.

Macau, Macau Business, MAG, MB, MB Featured, Opinion | The late psychologist, business and management consultant Edward de Bono gained a worldwide reputation for ‘lateral thinking’, which included his ‘six thinking hats’ and ‘tools for thinking’. Though his work is arguably only plausible pseudoscience, his ‘tools for thinking’ remain interesting. Consider some of these from his Cognitive Research Trust (CoRT), in approaching planning, e.g.: CAF (Consider All Factors); EBS (Examine Both Sides); and OPV (consider Other People’s Views). Here I apply them to Macau.

Convolutional neural network (CNN) model based on deep learning has excellent performance for target detection. However, the detection effect is poor when the object is circular or tubular because most of the existing object detection methods are based on the traditional rectangular box to detect and recognize objects. To solve the problem, we propose the circular representation structure and RepVGG module on the basis of CenterNet and expand the network prediction structure, thus proposing a high-precision and high-efficiency lightweight circular object detection method RebarDet. Specifically, circular tubular type objects will be optimized by replacing the traditional rectangular box with a circular box. Second, we improve the resolution of the network feature map and the upper limit of the number of objects detected in a single detect to achieve the expansion of the network prediction structure, optimized for the dense phenomenon that often occurs in circular tubular objects. Finally, the multibranch topology of RepVGG is introduced to sum the feature information extracted by different convolution modules, which improves the ability of the convolution module to extract information. We conducted extensive experiments on rebar datasets and used AB-Score as a new evaluation method to evaluate RebarDet. The experimental results show that RebarDet can achieve a detection accuracy of up to 0.8114 and a model inference speed of 6.9 fps while maintaining a moderate amount of parameters, which is superior to other mainstream object detection models and verifies the effectiveness of our proposed method. At the same time, RebarDet’s high precision detection of round tubular objects facilitates enterprise intelligent manufacturing processes.

Traditional text classification models have some drawbacks, such as the inability of the model to focus on important parts of the text contextual information in text processing. To solve this problem, we fuse the long and short-term memory network BiGRU with a convolutional neural network to receive text sequence input to reduce the dimensionality of the input sequence and to reduce the loss of text features based on the length and context dependency of the input text sequence. Considering the extraction of important features of the text, we choose the long and short-term memory network BiLSTM to capture the main features of the text and thus reduce the loss of features. Finally, we propose a BiGRU-CNN-BiLSTM model (DCRC model) based on CNN, GRU and LSTM, which is trained and validated on the THUCNews and Toutiao News datasets. The model outperformed the traditional model in terms of accuracy, recall and F1 score after experimental comparison.

Artists are increasingly using blockchain as a tool for trading digital artwork as non-fungible tokens (NFTs); however, some are also beginning to experiment with the blockchain as a medium for generative art, using it as a seed for a generative process or to continuously modify an evolving piece. This paper surveys, reviews, and classifies the state-of-the-art in blockchain-interactive NFTs and presents a liberal-arts critique of the opportunities and threats posed by this technology, whilst addressing existing criticism on the broader topic of art-related NFTs. The paper examines some of the most experimental pieces minted on the Hic et Nunc (HEN) and Teia NFT marketplaces, for which a purpose-built research tool was developed. The survey reveals some reliance on centralised infrastructure, namely blockchain indexers, placing undesired trust on third parties which undermines the potential longevity of the artwork. The paper concludes with recommendations for artists and NFT platform designers for developing more resilient and economically sustainable architectures.

This contribution to the special issue is an historical account of Paulo Freire’s pedagogical and administrative praxis before his forced exile in 1964. It relies on interviews collected during a field trip in 1976, a conversation with Paulo Freire in Geneva one year later and on the secondary literature up to date. Being the head of the first Extension Service of a major Brazilian university in the early 1960s gave Freire and his collaborators the space and time to experiment with the today world famous literacy method bearing his name. The concept of ‘Field of Cultural Production’ (Bourdieu) is used to elucidate better Freire and his team’s avant-gardist production within the spaces opened up by Brazil’s popular movements in the early sixties. The contribution shows how the ‘Paulo Freire System’ developed in the praxis of a cultural movement and received its academic consecration in an incremental and eclectic style.

Information and communication technologies (ICTs) are highly associated with the study of e-government, and many scholars believe that within the coming decades, government operation and policy decision-making cannot persist without the use of ICTs (Van Dijk, 2018). This thesis aims to generate a conceptual framework of the behavioral factors that could contribute to the acceleration of the implementation of e-government services in Macao SAR. Rather than regarding e-government services as a goal to be realized in traditional practice through evaluating the outcome, a process-oriented study was conducted. The e-government services are regarded as advanced tools in the 21st century to transform Macao into a smart city. The design of the process-oriented approach and the comparative study of four groups of Macao citizens' behavioral intentions are solidly supported by the research gaps identified in the literature review of e-government studies in an international perspective and the actual context of local Macao studies. Under the framework of the Theory of Planned Behavior (Ajzen, 1985, 1991), the behavioral factors of the general public and civil servants are investigated through a qualitative approach, and the findings are triangulated from various aspects. Firstly, a systematic literature review of TPB was conducted thoroughly to better understand the current study of e-government around the world. Secondly, a content and thematic analysis of the official documents and articles from local press media and research institutes related to the topic of e-government services was carried out to demonstrate a more comprehensive picture of the current problems of implementing iv and adopting e-services in Macao SAR. Observations in some government premises that provided e-services and 40 in-depth interviews were conducted to generate detailed and first-hand data. Key issues were extracted from the interviewees’ narratives and daily actual usages. Different conceptual models for different age groups and civil servant group were formed. Special attention was paid to analyzing the "hard-to-reach" groups' behavioral intentions. Research limitations identified from the previous literature were overcome partially in this study too. After comparing the similarities and differences, a new conceptual model of significant behavioral factors that affect the behavioral intentions in adopting e-government services was built. Results and findings from the analysis could be used to develop effective interventions by the government policymakers in responding to the behavioral change of the general public in the aspect of e-government services acceptance and adoption

Sentiment analysis technologies have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors. The objective of this paper is to evaluate the current state of the art and synthesize the published literature related to the financial sentiment analysis, especially in investor sentiment for prediction of stock price. Starting from this overview the paper provides answers to the questions about how and to what extent research on investor sentiment analysis and stock price trend forecasting in the financial markets has developed and which tools are used for these purposes remains largely unexplored. This paper represents the comprehensive literature-based study on the fields of the investors sentiment analytics and machine learning applied to analyzing the sentiment of investors and its influencing stock market and predicting stock price.

"Semantic studies of the Biblical Hebrew verb "whole" have been influenced by those of its most invoked nominal form "whole". In this volume Andrew Chin Hei Leong shows that the concepts of balance, alliance, and completeness form the basic semantic structure of "whole". Previous studies on "whole" employed either historical or textual methodology, which has been dominant in biblical lexical studies. In addition to these methods, in Leong develops a systematic semantic methodology from Cognitive Semantics and Frame Semantics, to demonstrate that it is balance, rather than completeness, that is the most central concept in holding the semantic network together"--

Macau, Macau Business, MAG, MB, MB Featured, Opinion | When the casino resorts applied for the first licenses to operate in Macau, one of the commitments that they made was to serve the Macau society. Many of them have honoured those commitments outstandingly well, and continue to do so, and in ways too many and diverse to list here.