28 resultados para Convolutional codes
Resumo:
This paper focuses on codes of practice in domestic (in-country) and international (out of country) philanthropic giving/grantmaking, their similarities and differences. Codes of principle and practice are interesting not so much because they accurately reflect differences in practice on the ground, but rather because they indicate what is considered important or relevant, as well as aspirational. Codes tell us what people are most concerned about – what is seen to be in need of regulation or reminder.
Resumo:
We propose to use a simple and effective way to achieve secure quantum direct secret sharing. The proposed scheme uses the properties of fountain codes to allow a realization of the physical conditions necessary for the implementation of no-cloning principle for eavesdropping-check and authentication. In our scheme, to achieve a variety of security purposes, nonorthogonal state particles are inserted in the transmitted sequence carrying the secret shares to disorder it. However, the positions of the inserted nonorthogonal state particles are not announced directly, but are obtained by sending degrees and positions of a sequence that are pre-shared between Alice and each Bob. Moreover, they can confirm that whether there exists an eavesdropper without exchanging classical messages. Most importantly, without knowing the positions of the inserted nonorthogonal state particles and the sequence constituted by the first particles from every EPR pair, the proposed scheme is shown to be secure.
Resumo:
We discuss algorithms for combining sequential prediction strategies, a task which can be viewed as a natural generalisation of the concept of universal coding. We describe a graphical language based on Hidden Markov Models for defining prediction strategies, and we provide both existing and new models as examples. The models include efficient, parameterless models for switching between the input strategies over time, including a model for the case where switches tend to occur in clusters, and finally a new model for the scenario where the prediction strategies have a known relationship, and where jumps are typically between strongly related ones. This last model is relevant for coding time series data where parameter drift is expected. As theoretical contributions we introduce an interpolation construction that is useful in the development and analysis of new algorithms, and we establish a new sophisticated lemma for analysing the individual sequence regret of parameterised models.
Resumo:
We observe that MDS codes have interesting properties that can be used to construct ideal threshold schemes. These schemes permit the combiner to detect cheating, identify cheaters and recover the correct secret. The construction is later generalised so the resulting secret sharing is resistant against the Tompa-Woll cheating.
Resumo:
Many educators are currently interested in using computer-mediated communications (CMCs) to support learning and creative practice. In my work I have been looking at how we might create drama through using cyberspaces, working with teachers and students in secondary school contexts. In trying to understand issues that have arisen and ways of working with the data I have found a number of frameworks helpful for analysing the online interactions. These frameworks draw from O'Toole's work on contexts negotiated in the creation of drama and other frameworks drawn from Wertsch, Bakhtin and Vygotsky's work on speech utterances, dialogic processes and internalisation of learning. The contexts and factors which must be negotiated in online communications within learning contexts are quite complex and educators may need to provide parameters and protocols to ensure appropriate languages, genres and utterances are utilised. The paper explores some of the types of languages, genres and utterances that emerged from a co-curricula drama project and issues that arose, including the importance of establishing processes for giving and receiving critical feedback This paper is of relevance to those whose research strategies may involve the use of computer-mediated communications as well as those utilising cyberspaces in educational contexts.
Resumo:
This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.
Resumo:
Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.
Resumo:
This paper describes the limitations of using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) to characterise patient harm in hospitals. Limitations were identified during a project to use diagnoses flagged by Victorian coders as hospital-acquired to devise a classification of 144 categories of hospital acquired diagnoses (the Classification of Hospital Acquired Diagnoses or CHADx). CHADx is a comprehensive data monitoring system designed to allow hospitals to monitor their complication rates month-to-month using a standard method. Difficulties in identifying a single event from linear sequences of codes due to the absence of code linkage were the major obstacles to developing the classification. Obstetric and perinatal episodes also presented challenges in distinguishing condition onset, that is, whether conditions were present on admission or arose after formal admission to hospital. Used in the appropriate way, the CHADx allows hospitals to identify areas for future patient safety and quality initiatives. The value of timing information and code linkage should be recognised in the planning stages of any future electronic systems.
Resumo:
We present an overview of the QUT plant classification system submitted to LifeCLEF 2014. This system uses generic features extracted from a convolutional neural network previously used to perform general object classification. We examine the effectiveness of these features to perform plant classification when used in combination with an extremely randomised forest. Using this system, with minimal tuning, we obtained relatively good results with a score of 0:249 on the test set of LifeCLEF 2014.
Resumo:
This paper provides a critical examination of the taken for granted nature of the codes/guidelines used towards the creation of designed spaces, their social relations with designers, and their agency in designing for people with disabilities. We conducted case studies at three national museums in Canada where we began by questioning societal representations of disability within and through material culture through the potential of actor-network theory where non-human actors have considerable agency. Specifically, our exploration looks into how representations of disability for designing, are interpreted through mediums such as codes, standards and guidelines. We accomplish this through: deep analyses of the museums’ built environments (outdoors and indoors); interviewed curators, architects and designers involved in the creation of the spaces/displays; completed dialoguing while in motion interviews with people who have disabilities within the spaces; and analyzed available documents relating to the creation of the museums. Through analyses of our rich data set involving the mapping of codes/guidelines in their ‘representation’ of disability and their contributions in ‘fixing’ disability, this paper takes an alternative approach to designing for/with disability by aiming to question societal representations of disability within and through material culture.
Resumo:
In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state-of-the art performance with several feature descriptors it still suffers from large quantization errors and reduces the overall performance. Recently proposed sparse representation methods have been shown to effectively represent features as a linear combination of an over complete dictionary by minimizing the reconstruction error. In contrast to most of the sparse representation methods which focus on Sparse-Reconstruction based Classification (SRC), this paper focuses on a discriminative classification using a SVM by constructing class-specific sparse codes for motion and appearance separately. Experimental results demonstrates that separate motion and appearance specific sparse coefficients provide the most effective and discriminative representation for each class compared to a single class-specific sparse coefficients.
Resumo:
Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.