Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary information such as product reviews and news titles, leading to hybrid filtering methods. Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods. In real-world life, no single service can satisfy a user's all information needs. Thus it motivates us to exploit both auxiliary and source information for RSs in this paper. We propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH) methods for cross-domain recommendation with unstructured text in an end-to-end manner. TMH attentively extracts useful content from unstructured text via a memory module and selectively transfers knowledge from a source domain via a transfer network. On two real-world datasets, TMH shows better performance in terms of three ranking metrics by comparing with various baselines. We conduct thorough analyses to understand how the text content and transferred knowledge help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape

A haze feature extraction and pollution level identification pre-warning algorithm

Copyright © 2020 The Author(s). The prediction of particles less than 2.5 micrometers in diameter (PM2.5) in fog and haze has been paid more and more attention, but the prediction accuracy of the results is not ideal. Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze. In order to improve the effects of prediction, this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning. Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze, and deep confidence network is utilized to extract high-level features. eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features, as well as predict haze. Establish PM2.5 concentration pollution grade classification index, and grade the forecast data. The expert experience knowledge is utilized to assist the optimization of the pre-warning results. The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine (SVM) and Back Propagation (BP) widely used at present, the accuracy has greatly improved compared with SVM and BP.National Natural Science Fund of China (Grant Nos. 61371143, 61662033)

Empirical study of multi-label classification methods for image annotation and retrieval

This paper presents an empirical study of multi-label classification methods, and gives suggestions for multi-label classification that are effective for automatic image annotation applications. The study shows that triple random ensemble multi-label classification algorithm (TREMLC) outperforms among its counterparts, especially on scene image dataset. Multi-label k-nearest neighbor (ML-kNN) and binary relevance (BR) learning algorithms perform well on Corel image dataset. Based on the overall evaluation results, examples are given to show label prediction performance for the algorithms using selected image examples. This provides an indication of the suitability of different multi-label classification methods for automatic image annotation under different problem settings.<br /

On the semantic information in zero-shot action recognition

Orientador: Dr. David MenottiCoorientador: Dr. Hélio PedriniTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 14/04/2023Inclui referências: p. 117-132Área de concentração: Ciência da ComputaçãoResumo: Os avanços da última década em modelos de aprendizagem profunda aliados à alta disponibilidade de exemplos em plataformas como o YouTube foram responsáveis por notáveis progressos no problema de Reconhecimento de Ações Humanas (RAH) em vídeos. Esses avanços trouxeram o desafio da inclusão de novas classes aos modelos existentes, pois incluí-las é uma tarefa que demanda tempo e recursos computacionais. Além disso, novas classes de ações são frequentemente criadas pelo uso de novos objetos ou novas formas de interação entre humanos. Esse cenário é o que motiva o problema Zero-Shot Action Recognition (ZSAR), definido como classificar instâncias pertencentes a classes não disponíveis na fase de treinamento dos modelos. Métodos ZSAR objetivam aprender funções de projeção que relacionem as representações dos vídeos com as representações semânticas dos rótulos das classes conhecidas. Trata-se, portanto, de um problema de representação multi-modal. Nesta tese, investigamos o problema do semantic gap em ZSAR, ou seja, as propriedades dos espaços vetoriais das representações dos vídeos e dos rótulos não são coincidentes e, muitas vezes, as funções de projeção aprendidas são insuficientes para corrigir distorções. Nós defendemos que o semantic gap deriva do que chamamos semantic lack, ou falta de semântica, que ocorre em ambos os lados do problema (i.e., vídeos e rótulos) e não é suficientemente investigada na literatura. Apresentamos três abordagens ao problema investigando diferentes informações semânticas e formas de representação para vídeos e rótulos. Mostramos que uma forma eficiente de representar vídeos é transformando-os em sentenças descritivas utilizando métodos de video captioning. Essa abordagem permite descrever cenários, objetos e interações espaciais e temporais entre humanos. Nós mostramos que sua adoção gera modelos de alta eficácia comparados à literatura. Também propusemos incluir informações descritivas sobre os objetos presentes nas cenas a partir do uso de métodos treinados em reconhecimento de objetos. Mostramos que a representação dos rótulos de classes apresenta melhores resultados com o uso de sentenças extraídas de textos descritivos coletados da Internet. Ao usar apenas textos, nós nos valemos de modelos de redes neurais profundas pré-treinados na tarefa de paráfrase para codificar a informação e realizar a classificação ZSAR com reduzido semantic gap. Finalmente, mostramos como condicionar a representação dos quadros de um vídeo à sua correspondente descrição texual, produzindo um modelo capaz de representar em um espaço vetorial conjunto tanto vídeos quanto textos. As abordagens apresentadas nesta tese mostraram efetiva redução do semantic gap a partir das contribuições tanto em acréscimo de informação quanto em formas de codificação.Abstract: The advancements of the last decade in deep learning models and the high availability of examples on platforms such as YouTube were responsible for notable progress in the problem of Human Action Recognition (HAR) in videos. These advancements brought the challenge of adding new classes to existing models, since including them takes time and computational resources. In addition, new classes of actions are frequently created, either by using new objects or new forms of interaction between humans. This scenario motivates the Zero-Shot Action Recognition (ZSAR) problem, defined as classifying instances belonging to classes not available for the model training phase. ZSAR methods aim to learn projection functions associating video representations with semantic label representations of known classes. Therefore, it is a multi-modal representation problem. In this thesis, we investigate the semantic gap problem in ZSAR. The properties of vector spaces are not coincident, and, often, the projection functions learned are insufficient to correct distortions. We argue that the semantic gap derives from what we call semantic lack, which occurs on both sides of the problem (i.e., videos and labels) and is not sufficiently investigated in the literature. We present three approaches to the problem, investigating different information and representation strategies for videos and labels. We show an efficient way to represent videos by transforming them into descriptive sentences using video captioning methods. This approach enables us to produce high-performance models compared to the literature. We also proposed including descriptive information about objects present in the scenes using object recognition methods. We showed that the representation of class labels presents better results using sentences extracted from descriptive texts collected on the Internet. Using only texts, we employ deep neural network models pre-trained in the paraphrasing task to encode the information and perform the ZSAR classification with a reduced semantic gap. Finally, we show how conditioning the representation of video frames to their corresponding textual description produces a model capable of representing both videos and texts in a joint vector space. The approaches presented in this thesis showed an effective reduction of the semantic gap based on contributions in addition to information and representation ways

Assessing performance overhead of Virtual Machine Introspection and its suitability for malware analysis

Virtual Machine Introspection is the process of introspecting guest VM’s memory and reconstructing the state of the guest operating system. Due to its isolation, stealth and full visibility of the monitored target, VMI lends itself well for security monitoring and malware analysis. The topics covered in this thesis include operating system and hypervisor concepts, the semantic gap issue, VMI techniques and implementations, applying VMI for malware analysis, and analysis of the performance overhead. The behaviour and magnitude of the performance overhead associated with doing virtual machine introspection is analysed with five different empirical test cases. The intention of the tests is to estimate the costs of a single trapped event, determine the feasibility of various monitoring sensors from usability and stealth perspective, and analyse the behaviour of performance overhead. Various VMI-based tools were considered for the measurement, but DRAKVUF was chosen as it is the most advanced tool available. The test cases go as follows. The chosen load is first executed without any monitoring to determine the baseline execution time. Then a DRAKVUF monitoring plugin is turned on and the load is executed again. After both measurements have been made, the difference between the two execution times is the time spent executing monitoring code. The execution overhead is then determined by calculating the difference between the two execution times and dividing it by the baseline execution time. The disc consumption and execution overhead of a sensor, which captures removed files is small enough to be deployed as a monitoring solution. The performance overhead of system call monitoring sensor is dependant on the number of issued system calls. Loads which issue large numbers of system calls cause high performance overhead. The performance overhead of such loads can be limited by monitoring a subset of all system calls

Dual Attention on Pyramid Feature Maps for Image Captioning

Generating natural sentences from images is a fundamental learning task for visual-semantic understanding in multimedia. In this paper, we propose to apply dual attention on pyramid image feature maps to fully explore the visual-semantic correlations and improve the quality of generated sentences. Specifically, with the full consideration of the contextual information provided by the hidden state of the RNN controller, the pyramid attention can better localize the visually indicative and semantically consistent regions in images. On the other hand, the contextual information can help re-calibrate the importance of feature components by learning the channel-wise dependencies, to improve the discriminative power of visual features for better content description. We conducted comprehensive experiments on three well-known datasets: Flickr8K, Flickr30K and MS COCO, which achieved impressive results in generating descriptive and smooth natural sentences from images. Using either convolution visual features or more informative bottom-up attention features, our composite captioning model achieves very promising performance in a single-model mode. The proposed pyramid attention and dual attention methods are highly modular, which can be inserted into various image captioning modules to further improve the performance.Comment: in IEEE Transactions on Multimedia, 202

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications