264 resultados para User reviews


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With the widespread of social media websites in the internet, and the huge number of users participating and generating infinite number of contents in these websites, the need for personalisation increases dramatically to become a necessity. One of the major issues in personalisation is building users’ profiles, which depend on many elements; such as the used data, the application domain they aim to serve, the representation method and the construction methodology. Recently, this area of research has been a focus for many researchers, and hence, the proposed methods are increasing very quickly. This survey aims to discuss the available user modelling techniques for social media websites, and to highlight the weakness and strength of these methods and to provide a vision for future work in user modelling in social media websites.

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The selection of optimal camera configurations (camera locations, orientations, etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we propose a statistical framework of the problem as well as propose a trans-dimensional simulated annealing algorithm to effectively deal with it. We compare our approach with a state-of-the-art method based on binary integer programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than two alternative heuristics designed to deal with the scalability issue of BIP. Last, we show the versatility of our approach using a number of specific scenarios.

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The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

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Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.

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In the space of the past decade, the technologies, business models, everyday uses and public understandings of social media have co-evolved rapidly. In the early to mid 2000s, websites like MySpace, Facebook or Twitter were garnering interest in both the press and academia as places for amateur creativity, political subversion or trivial time-wasting on the behalf of subcultures of geeks or ‘digital natives’, but such websites were not seen as legitimate, mainstream media organisations, nor were they generally understood as respectable places for professionals (other than new media professionals) to conduct business. By late 2011, online marketing company Comscore was reporting that social networking was “the most popular online activity worldwide accounting for nearly 1 in every 5 minutes spent online”, reaching 82 percent of the world’s Internet population, or 1.2 billion users (Comscore, 2011). Today, social media is firmly established as an industry sector in its own right, and is deeply entangled with and embedded in the practices and everyday lives of media professionals, celebrities and ordinary users. We might now think of it as an embedded communications infrastructure extending across culture, society and the economy – ranging from local government Facebook pages alerting us to kerbside collection, to Tumblr blogs providing humorous cultural commentary by curating animated .gifs, to Telstra Twitter accounts responding to user requests for tech help, and to Yelp reviews helping us find somewhere to grab dinner in a strange town. As well as at least appearing to be near-ubiquitous, social media is increasingly seen as highly significant by scholars researching issues as diverse as journalistic practice (Hermida, 2012), the coordination of government and community responses to natural disasters (Bruns & Burgess, 2012), and the activities of global social and political protest movements (Howard & Hussain, 2013)...

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Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining strategies have been adapted to reduce noisy information from extracted features; however, text-mining techniques suffer from low frequency. The key issue is how to discover relevance features in text documents to fulfil user information needs. To address this issue, we propose a new method to extract specific features from user relevance feedback. The proposed approach includes two stages. The first stage extracts topics (or patterns) from text documents to focus on interesting topics. In the second stage, topics are deployed to lower level terms to address the low-frequency problem and find specific terms. The specific terms are determined based on their appearances in relevance feedback and their distribution in topics or high-level patterns. We test our proposed method with extensive experiments in the Reuters Corpus Volume 1 dataset and TREC topics. Results show that our proposed approach significantly outperforms the state-of-the-art models.

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Scenario 1 A buys a two storey commercial building built along the only street frontage to the property. Vehicles cannot reach the rear of the property as the building extends across the entire width of the land. A bought the building with full knowledge that vehicular access to the rest of the property had been compromised by a desire to obtain maximum street frontage for the building which was occupied by a commercial tenant. On street parking is scarce in the surrounding area. A (to the knowledge of the adjoining owner B) constructs a carpark at the rear of the building. The employees of A’s tenant have been using the carpark obtaining access via a driveway on B’s land. To formalise this arrangement, A seeks a right of way for vehicles to travel down B’s driveway to access the carpark...

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We propose a new kind of asymmetric mutual authentication from passwords with stronger privacy against malicious servers, lest they be tempted to engage in “cross-site user impersonation” to each other. It enables a person to authenticate (with) arbitrarily many independent servers, over adversarial channels, using a memorable and reusable single short password. Beside the usual PAKE security guarantees, our framework goes to lengths to secure the password against brute-force cracking from privileged server information.

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Mitchell critiques Georges Perec's Life a User's Manual, which articulates compellingly the confluence of literature and architecture studies that emerged in the late twentieth century. He argues the Perec's novel diverges from this tradition, for, rather than being a search for origins and true expression, Life a User's Manual denies the very possibility of originality. He adds that Perec's architext is de-constructive and ironic.

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This paper describes the design and implementation of a wireless neural telemetry system that enables new experimental paradigms, such as neural recordings during rodent navigation in large outdoor environments. RoSco, short for Rodent Scope, is a small lightweight user-configurable module suitable for digital wireless recording from freely behaving small animals. Due to the digital transmission technology, RoSco has advantages over most other wireless modules of noise immunity and online user-configurable settings. RoSco digitally transmits entire neural waveforms for 14 of 16 channels at 20 kHz with 8-bit encoding which are streamed to the PC as standard USB audio packets. Up to 31 RoSco wireless modules can coexist in the same environment on non-overlapping independent channels. The design has spatial diversity reception via two antennas, which makes wireless communication resilient to fading and obstacles. In comparison with most existing wireless systems, this system has online user-selectable independent gain control of each channel in 8 factors from 500 to 32,000 times, two selectable ground references from a subset of channels, selectable channel grounding to disable noisy electrodes, and selectable bandwidth suitable for action potentials (300 Hz–3 kHz) and low frequency field potentials (4 Hz–3 kHz). Indoor and outdoor recordings taken from freely behaving rodents are shown to be comparable to a commercial wired system in sorting for neural populations. The module has low input referred noise, battery life of 1.5 hours and transmission losses of 0.1% up to a range of 10 m.

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As the Journal of Media Innovations comes into existence, this article reflects on the first and most obvious question: just what do we mean by “media innovations”? Drawing on the examples of a range of recent innovations in media technologies and practices, initiated by a variety of media audiences, users, professionals, and providers, it explores the interplay between the different drivers of innovation and the effects of such innovation on the complex frameworks of contemporary society and the media ecology which supports it. In doing so, this article makes a number of key observations: first, it notes that media innovation is an innovation in media practices at least as much as in media technologies, and that changes to the practices of media both reflect and promote societal changes as well – media innovations are never just media technology innovations. Second, it shows that the continuing mediatisation of society, and the shift towards a more widespread participation of ordinary users as active content creators and media innovators, make it all the more important to investigate in detail these interlinked, incremental, everyday processes of media and societal change – media innovations are almost always also user innovations. Finally, it suggests that a full understanding of these processes as they unfold across diverse interleaved media spaces and complex societal structures necessarily requires a holistic perspective on media innovations, which considers the contemporary media ecology as a crucial constitutive element of societal structures and seeks to trace the repercussions of innovations across both media and society – media innovations are inextricably interlinked with societal innovations (even if, at times, they may not be considered to be improvements to the status quo).

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At NDSS 2012, Yan et al. analyzed the security of several challenge-response type user authentication protocols against passive observers, and proposed a generic counting based statistical attack to recover the secret of some counting based protocols given a number of observed authentication sessions. Roughly speaking, the attack is based on the fact that secret (pass) objects appear in challenges with a different probability from non-secret (decoy) objects when the responses are taken into account. Although they mentioned that a protocol susceptible to this attack should minimize this difference, they did not give details as to how this can be achieved barring a few suggestions. In this paper, we attempt to fill this gap by generalizing the attack with a much more comprehensive theoretical analysis. Our treatment is more quantitative which enables us to describe a method to theoretically estimate a lower bound on the number of sessions a protocol can be safely used against the attack. Our results include 1) two proposed fixes to make counting protocols practically safe against the attack at the cost of usability, 2) the observation that the attack can be used on non-counting based protocols too as long as challenge generation is contrived, 3) and two main design principles for user authentication protocols which can be considered as extensions of the principles from Yan et al. This detailed theoretical treatment can be used as a guideline during the design of counting based protocols to determine their susceptibility to this attack. The Foxtail protocol, one of the protocols analyzed by Yan et al., is used as a representative to illustrate our theoretical and experimental results.