472 research outputs found
Automated Social Hierarchy Detection through Email Network Analysis
We present our work on automatically extracting social hierarchies from electronic communication data. Data mining based on user behavior can be leveraged to analyze and catalog patterns of communications between entities to rank relationships. The advantage is that the analysis can be done in an automatic fashion and can adopt itself to organizational changes over time. We illustrate the algorithms over real world data using the Enron corporation's email archive. The results show great promise when compared to the corporations work chart and judicial proceeding analyzing the major players
Structural Change in EU Agriculture and the Supply of Social Attributes
The social attributes that agriculture is assumed to provide in its multifunctional role are analysed. Links with structural characteristics are examined and questions raised on the extent to which these are dependent on sustaining the present structure of EU agriculture. The nature of an efficient policy to provide these attributes is explored, with pointers for the next round of rural development policy. Our conclusion is that non-agricultural policies may be far more significant to the supply of social attributes than those conventionally seen as agricultural and rural developmental, suggesting that general community regeneration policies and "rural proofing" of general policies will be important for the future.multifunctionality, social attributes, sustainability, rural development, CAP, Agricultural and Food Policy,
An approach to preventing spam using Access Codes with a combination of anti-spam mechanisms
Spam is becoming a more and more severe problem for individuals, networks,
organisations and businesses. The losses caused by spam are billions of dollars every
year. Research shows that spam contributes more than 80% of e-mails with an increased
in its growth rate every year. Spam is not limited to emails; it has started affecting other
technologies like VoIP, cellular and traditional telephony, and instant messaging services.
None of the approaches (including legislative, collaborative, social awareness and
technological) separately or in combination with other approaches, can prevent sufficient
of the spam to be deemed a solution to the spam problem.
The severity of the spam problem and the limitations of the state-of-the-Art solutions
create a strong need for an efficient anti-spam mechanism that can prevent significant
volumes of spam without showing any false positives. This can be achieved by an
efficient anti-spam mechanism such as the proposed anti-spam mechanism known as
"Spam Prevention using Access Codes", SPAC. SPAC targets spam from two angles i.e.
to prevent/block spam and to discourage spammers by making the infrastructure
environment very unpleasant for them.
In addition to the idea of Access Codes, SPAC combines the ideas behind some of the
key current technological anti-spam measures to increase effectiveness. The difference in
this work is that SPAC uses those ideas effectively and combines them in a unique way
which enables SPAC to acquire the good features of a number of technological anti-spam
approaches without showing any of the drawbacks of these approaches. Sybil attacks,
Dictionary attacks and address spoofing have no impact on the performance of SPAC. In
fact SPAC functions in a similar way (i.e. as for unknown persons) for these sorts of
attacks.
An application known as the "SPAC application" has been developed to test the
performance of the SPAC mechanism. The results obtained from various tests on the
SPAC application show that SPAC has a clear edge over the existing anti-spam
technological approaches
Personal Email Spam Filtering with Minimal User Interaction
This thesis investigates ways to reduce or eliminate the necessity of user input to
learning-based personal email spam filters. Personal spam filters have been shown in
previous studies to yield superior effectiveness, at the cost of requiring extensive user training which may be burdensome or impossible.
This work describes new approaches to solve the problem of building a personal
spam filter that requires minimal user feedback. An initial study investigates how well a personal filter can learn from different sources of data, as opposed to userâs messages. Our initial studies show that inter-user training yields substantially inferior results to
intra-user training using the best known methods. Moreover, contrary to previous
literature, it is found that transfer learning degrades the performance of spam filters when the source of training and test sets belong to two different users or different times.
We also adapt and modify a graph-based semi-supervising learning algorithm to
build a filter that can classify an entire inbox trained on twenty or fewer user judgments.
Our experiments show that this approach compares well with previous techniques when
trained on as few as two training examples.
We also present the toolkit we developed to perform privacy-preserving user studies
on spam filters. This toolkit allows researchers to evaluate any spam filter that conforms to a standard interface defined by TREC, on real usersâ email boxes. Researchers have access only to the TREC-style result file, and not to any content of a userâs email
stream.
To eliminate the necessity of feedback from the user, we build a personal autonomous filter that learns exclusively on the result of a global spam filter. Our laboratory experiments show that learning filters with no user input can substantially
improve the results of open-source and industry-leading commercial filters that employ no user-specific training. We use our toolkit to validate the performance of the
autonomous filter in a user study
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Email Thread Reassembly Using Similarity Matching
Email thread reassembly is the task of linking messages by parent-child relationships. In this paper, we present two approaches to address this problem. One exploits previously undocumented header information from the Microsoft Exchange Protocol. The other uses string similarity metrics and a heuristic algorithm to reassemble threads in the absence of header information. The pros and cons of both methods are discussed. The similarity matching method is evaluated using the Enron email corpus and found to perform well
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