3,557 research outputs found

    Email at Work

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    Presents findings from a survey conducted in April and May 2002, to examine the role of email in mainstream work situations. Documents how workers utilize, value, and are affected by email, and looks at the future of email use in the workplace

    Machine Learning and Law

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    Part I of this Article explains the basic concepts underlying machine learning. Part II will convey a more general principle: non-intelligent computer algorithms can sometimes produce intelligent results in complex tasks through the use of suitable proxies detected in data. Part III will explore how certain legal tasks might be amenable to partial automation under this principle by employing machine learning techniques. This Part will also emphasize the significant limitations of these automated methods as compared to the capabilities of similarly situated attorneys

    An Information Systems Teaching Case: Bayesian Probability Applied to Spam eMail Filters

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    Information Systems professionals can participate in the strategic planning and policy development of the business organization by applying sound techniques for rational decision making. Decision Support Systems often utilize inferential techniques to provide analysis and knowledge creation for business and its information systems. One common method of reasoning under uncertainty is the application of the Bayesian probability model. This teaching case can be used in an Information Systems program to teach one method of inferential reasoning as applied to policy and business rules for spam email filters

    Heckerthoughts

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    This manuscript is technical memoir about my work at Stanford and Microsoft Research. Included are fundamental concepts central to machine learning and artificial intelligence, applications of these concepts, and stories behind their creation

    A reputation framework for behavioural history: developing and sharing reputations from behavioural history of network clients

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    The open architecture of the Internet has enabled its massive growth and success by facilitating easy connectivity between hosts. At the same time, the Internet has also opened itself up to abuse, e.g. arising out of unsolicited communication, both intentional and unintentional. It remains an open question as to how best servers should protect themselves from malicious clients whilst offering good service to innocent clients. There has been research on behavioural profiling and reputation of clients, mostly at the network level and also for email as an application, to detect malicious clients. However, this area continues to pose open research challenges. This thesis is motivated by the need for a generalised framework capable of aiding efficient detection of malicious clients while being able to reward clients with behaviour profiles conforming to the acceptable use and other relevant policies. The main contribution of this thesis is a novel, generalised, context-aware, policy independent, privacy preserving framework for developing and sharing client reputation based on behavioural history. The framework, augmenting existing protocols, allows fitting in of policies at various stages, thus keeping itself open and flexible to implementation. Locally recorded behavioural history of clients with known identities are translated to client reputations, which are then shared globally. The reputations enable privacy for clients by not exposing the details of their behaviour during interactions with the servers. The local and globally shared reputations facilitate servers in selecting service levels, including restricting access to malicious clients. We present results and analyses of simulations, with synthetic data and some proposed example policies, of client-server interactions and of attacks on our model. Suggestions presented for possible future extensions are drawn from our experiences with simulation
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