431 resultados para Online Grocery Shopping
Resumo:
Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline.
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Online social networking has become one of the most popular Internet applications in the modern era. They have given the Internet users, access to information that other Internet based applications are unable to. Although many of the popular online social networking web sites are focused towards entertainment purposes, sharing information can benefit the healthcare industry in terms of both efficiency and effectiveness. But the capability to share personal information; the factor which has made online social networks so popular, is itself a major obstacle when considering information security and privacy aspects. Healthcare can benefit from online social networking if they are implemented such that sensitive patient information can be safeguarded from ill exposure. But in an industry such as healthcare where the availability of information is crucial for better decision making, information must be made available to the appropriate parties when they require it. Hence the traditional mechanisms for information security and privacy protection may not be suitable for healthcare. In this paper we propose a solution to privacy enhancement in online healthcare social networks through the use of an information accountability mechanism.
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Universities continue to struggle with the need to combine the pedagogical benefits of collaborative learning with large scale, interactive and technologically sophisticated learning and teaching arrproaches and support systems. This challenge requires imaginative approaches if the outcome is not to the 'worst of both worlds' that results in confusion and disillusionism amongst students. This paper presents three case studies that use online technologies to provide collaborative teaching solutions arguably much superior to that possible without an online intervention.
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Simulating passenger flows within airports is very important as it can provide an indication of queue lengths, bottlenecks, system capacity and overall level of service. To date, visual simulation tools such as agent based models have focused on processing formalities such as check-in, and not incorporate discretionary activities such as duty-free shopping. As airport retail contributes greatly to airport revenue generation, but also has potentially detrimental effects on facilitation efficiency benchmarks, this study developed a simplistic simulation model which captures common duty-free purchasing opportunities, as well as high-level behaviours of passengers. It is argued that such a model enables more realistic simulation of passenger facilitation, and provides a platform for simulating real-time revenue generation as well as more complex passenger behaviours within the airport. Simulations are conducted to verify the suitability of the model for inclusion in the international arrivals process for assessing passenger flow and infrastructure utilization.
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People with a physical disability are a population who for a number of reasons may be vulnerable to social isolation. Research into Internet-based support sites has found that social support and an online sense of community can be developed through computer mediated communication channels. This study aims to gain an understanding of the benefits that membership of disability-specific online communities may have for people with a physical disability. An online survey was administered to a sample of users of such sites (N = 160). Results indicated that users did receive moral support and personal advice through participating in such online communities. Further, results indicated that online social support and feeling a sense of community online were positively associated with participants' well-being in the areas of personal relations and personal growth.
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This chapter sets out to identify related issues surrounding the use of Information and Computer Technology (ICT) in developing relationships between local food producers and consumers (both individuals and businesses). Three surveys were conducted in South- East Wales to consider the overlapping issues. The first concerned the role of ICT in relationships between farmers’ market (FMs) vendors and their traditional customers. The second survey examined potential new markets for farmers in the propensity of restaurants and hotels to buy locally, the types and sources of purchases made and the modes of advertising of these businesses. The final survey focused on the potential to expand local web- based selling of farmers’ produce in the future, by examining the potential market of high ICT- use small hotels. Despite the development of tailored ICT facilities, farmers’ market vendors and current individual customers are antipathetic to them. In addition, whilst there is a desire for more local produce particularly amongst independent local restaurants and hotels, this has not been capitalised upon and there is much work to be done even amongst high ICT-use small hotels, to expand the range and scope of farmers’ markets. This raises the need for creation and utilisation of enhanced logistics, payment and marketing management capacity available through a web- based presence, linked to promotion of FMs in business- to- business (B2B) links with local restaurants and hotels. This linked quantitative research highlights the potential value in substantial development of both web portals and supporting logistics to exploit this potential in the future.
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This thesis investigates the place of online moderation in supporting teachers to work in a system of standards-based assessment. The participants of the study were fifty middle school teachers who met online with the aim of developing consistency in their judgement decisions. Data were gathered through observation of the online meetings, interviews, surveys and the collection of artefacts. The data were viewed and analysed through sociocultural theories of learning and sociocultural theories of technology, and demonstrates how utilising these theories can add depth to understanding the added complexity of developing shared meaning of standards in an online context. The findings contribute to current understanding of standards-based assessment by examining the social moderation process as it acts to increase the reliability of judgements that are made within a standards framework. Specifically, the study investigates the opportunities afforded by conducting social moderation practices in a synchronous online context. The study explicates how the technology affects the negotiation of judgements and the development of shared meanings of assessment standards, while demonstrating how involvement in online moderation discussions can support teachers to become and belong within a practice of standards-based assessment. This research responds to a growing international interest in standards-based assessment and the use of social moderation to develop consistency in judgement decisions. Online moderation is a new practice to address these concerns on a systemic basis.
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Newspapers and, if to a lesser extent as yet, linear broadcast news providers on TV and radio are in the process of being replaced as the dominant carrier media of journalism by an emerging network of online outlets.
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In late 2009, Sandra Haukka secured funding from the auDA Foundation to explore what older Australians who never or rarely use the Internet (referred to as ‘non-users’) know about the types of online products and services available to them, and how they might use these products and services to improve their daily life. This project aims to support current and future strategies and initiatives by: 1) exploring the extent to which non-users are aware of the types and benefits of online products and services, (such as e-shopping, e-banking, e-health, social networking, and general browsing and research) as well as their interest in them b) identifying how the Internet can improve the daily life of older Australians c) reviewing the effectiveness of support and services designed to educate and encourage older people to engage with the Internet d) recommending strategies that aim to raise non-user awareness of current and emerging online products and services, and provide non-users with the skills and knowledge needed to use those products and services that they believe can improve their daily life. The Productive Ageing Centre at National Seniors Australia, and Professor Trevor Barr from Swinburne University provided the project with in-kind support.
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The advent of e-learning has seen the adaptation and use of a plethora of educational techniques. Of these, online discussion forums have met with success and been used widely in both undergraduate and postgraduate education. The authors of this paper, having previously used online discussion forums in the postgraduate arena with success, adopted this approach for the design and subsequent delivery of a learning and teaching subject. This learning and teaching subject, however, was part of an international collaboration and designed for nurse academics in another country – Vietnam. With the nursing curriculum in Vietnam currently moving to adopt a competency based approach, two learning and teaching subjects were designed by an Australian university for Vietnamese nurse academics. Subject materials constituted a DVD which arrived by post and access to an online platform. Assessment for the subject included (but was not limited to) mandatory participation in online discussion with the other nurse academics enrolled in the subject. The purpose behind the online discussion was to generate discourse between the Vietnamese nurse academics located across Vietnam. Consequently the online discussions occurred in both Vietnamese and English; the Australian academic moderating the discussion did so in Australia with a Vietnamese translator. For the Australian University delivering this subject the difference between this and past online discussions were twofold: delivery was in a foreign language; and the teaching experience of the Vietnamese nurse teachers was mixed and frequently very limited. This paper will provide a discussion addressing the design of an online learning environment for foreign correspondents, the resources and translation required to maximise the success of the online discussion, the lessons learnt and consequent changes made, as well as the rationale of delivering complex content in a foreign language. While specifically addressing the first iteration of the first learning module designed, this paper will also address subsequent changes made for the second iteration of the first module and comment on their success. While a translator is clearly a key component of success, the elements of simplicity and clarity in hand with supportive online moderation must not be overlooked.
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In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.
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In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.
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We consider the problem of choosing, sequentially, a map which assigns elements of a set A to a few elements of a set B. On each round, the algorithm suffers some cost associated with the chosen assignment, and the goal is to minimize the cumulative loss of these choices relative to the best map on the entire sequence. Even though the offline problem of finding the best map is provably hard, we show that there is an equivalent online approximation algorithm, Randomized Map Prediction (RMP), that is efficient and performs nearly as well. While drawing upon results from the "Online Prediction with Expert Advice" setting, we show how RMP can be utilized as an online approach to several standard batch problems. We apply RMP to online clustering as well as online feature selection and, surprisingly, RMP often outperforms the standard batch algorithms on these problems.
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Online learning algorithms have recently risen to prominence due to their strong theoretical guarantees and an increasing number of practical applications for large-scale data analysis problems. In this paper, we analyze a class of online learning algorithms based on fixed potentials and nonlinearized losses, which yields algorithms with implicit update rules. We show how to efficiently compute these updates, and we prove regret bounds for the algorithms. We apply our formulation to several special cases where our approach has benefits over existing online learning methods. In particular, we provide improved algorithms and bounds for the online metric learning problem, and show improved robustness for online linear prediction problems. Results over a variety of data sets demonstrate the advantages of our framework.
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A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a prediction x from a convex set, the environment plays a loss function f, and the learner’s long-term goal is to minimize regret. Algorithms have been proposed by Zinkevich, when f is assumed to be convex, and Hazan et al., when f is assumed to be strongly convex, that have provably low regret. We consider these two settings and analyze such games from a minimax perspective, proving minimax strategies and lower bounds in each case. These results prove that the existing algorithms are essentially optimal.