The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform
Vitor R Carvalho - One of the best experts on this subject based on the ideXlab platform.
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Email Leaks and Recipient Suggestions: A User Study with Mozilla Thunderbird
2016Co-Authors: Vitor R Carvalho, Ramnath Balasubramanyan, William W. CohenAbstract:People often make terrible mistakes when addressing email messages. One type of costly mistake is an email leak, i.e., accidentally sending a message to an Unintended Recipient — a widespread problem that can severely harm individuals and corporations. An-other type of addressing error is forgetting to add an intended collaborator as Recipient, a likely source of costly misunderstandings and communication delays. Several methods to address these problems have been recently proposed in the literature [2, 3]. In this pa-per we describe the implementation of some of these methods in a popular email client (Mozilla Thunder-bird), and conduct a 4-week long user study based on this implementation. Results showed that more than 15 % of the human subjects reported that the client prevented real cases of email leaks, and more than 47% of them utilized the provided Recipient recommenda-tions. Overall the study shows that methods to prevent email addressing mistakes can be valuable additions to email communication, with more than 80 % of the subjects reporting that they would permanently use these methods if a few interface/optimization changes were included in our Thunderbird implementation
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Modeling Intention in Email - Modeling Intention in Email
Studies in Computational Intelligence, 2011Co-Authors: Vitor R CarvalhoAbstract:Email management has a fundamental role in modern work productivity. In this thesis we present evidence that email management can be potentially improved by the effective use of machine learning techniques to model different aspects of user intention. We initially propose a taxonomy of user intentions in terms of Speech Acts applied to email communication, or “email acts”, and show that email act classification can be largely automated, potentially leading to better email prioritization and management. We then describe how machine learning can be used to reduce the chances of costly email addressing errors. One type of costly error is an “email leak”, i.e., mistakenly sending a message to an Unintended Recipient — a widespread problem that can severely harm individuals and corporations. Another type of addressing error is forgetting to add an intended collaborator as Recipient, a likely source of costly misunderstandings and communication delays that can be potentially addressed with intelligent Recipient recommendation. We propose several different approaches to address these problems, and show very positive experimental results in a large email collection. In addition, we describe a 4-week long user study based on the implementation of some of the proposed models in a popular email client (Mozilla Thunderbird). More than 15% of the human subjects reported that it prevented real email leaks, and more than 47% of them utilized Recipient recommendations. Overall the study shows that Recipient recommendation and email leak detection can be valuable additions to real email clients, with more than 80% of the subjects reporting that they would permanently use these models if a few interface/optimization changes were implemented. Finally, we introduce a new robust rank learning algorithm to further improve Recipient recommendation predictions. The algorithm is essentially a non-convex optimization procedure over a sigmoidal loss function, in which any linear baseline ranking model can be used as starting point. This new learning method provides substantial rank performance improvements on Recipient recommendation tasks, outperforming all previously introduced models, including well-known state-of-the-art ranking algorithms.
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Information Leaks and Suggestions: A Case Study using Mozilla Thunderbird ABSTRACT
2009Co-Authors: Vitor R CarvalhoAbstract:People often make serious mistakes when addressing email messages. One type of costly mistake is an“email leak”, i.e., accidentally sending a message to an Unintended Recipient — a widespread problem that can severely harm individuals and corporations. Another type of addressing error is forgetting to add an intended collaborator as Recipient, a likely source of costly misunderstandings and communication delays in large corporations. To address these problems, various data mining techniques have been proposed recently [3, 4]. In this paper we describe the deployment of some of these techniques in a popular email client (Mozilla Thunderbird), and report how users responded to such data mining techniques in their everyday lives. In spite of interface, privacy and speed constraints, results were fairly positive. More than 15 % of the users reported that the client prevented real cases of email leaks, and more than 47 % of them accepted recommendations provided by the data mining techniques. We then conclude by presenting a few lessons learned from this deployment, and discussing costs and benefits of making these techniques permanent additions to email clients
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Modeling Intention in Email
2009Co-Authors: Vitor R Carvalho, Tom M. Mitchell, Robert E. KrautAbstract:Email management has a fundamental role in modern work productivity. In this thesis we present evidence that email management can be potentially improved by the effective use of machine learning techniques to model different aspects of user intention. We initially propose a taxonomy of user intentions in terms of Speech Acts applied to email communication, or “email acts”, and show that email act classification can be largely automated, potentially leading to better email prioritization and management. We then describe how machine learning can be used to reduce the chances of costly email addressing errors. One type of costly error is an “email leak”, i.e., mistakenly sending a message to an Unintended Recipient — a widespread problem that can severely harm individuals and corporations. Another type of addressing error is forgetting to add an intended collaborator as Recipient, a likely source of costly misunderstandings and communication delays that can be potentially addressed with intelligent Recipient recommendation. We propose several different approaches to address these problems, and show ver
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information leaks and suggestions a case study using mozilla thunderbird
2009Co-Authors: Vitor R Carvalho, Ramnath Balasubramanyan, William W. CohenAbstract:People often make serious mistakes when addressing email messages. One type of costly mistake is an“email leak”, i.e., accidentally sending a message to an Unintended Recipient — a widespread problem that can severely harm individuals and corporations. Another type of addressing error is forgetting to add an intended collaborator as Recipient, a likely source of costly misunderstandings and communication delays in large corporations. To address these problems, various data mining techniques have been proposed recently [3, 4]. In this paper we describe the deployment of some of these techniques in a popular email client ( Mozilla Thunderbird ), and report how users responded to such data mining techniques in their everyday lives. In spite of interface, privacy and speed constraints, results were fairly positive. More than 15% of the users reported that the client prevented real cases of email leaks, and more than 47% of them accepted recommendations provided by the data mining techniques. We then conclude by presenting a few lessons learned from this deployment, and discussing costs and benefits of making these techniques permanent additions to email clients.
Stefan-mario Kasper - One of the best experts on this subject based on the ideXlab platform.
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Human error: the persisting risk of blood transfusion: a report of five cases.
Anesthesia and analgesia, 2002Co-Authors: Jens Krombach, Sandra Kampe, Birgit S. Gathof, Christoph Diefenbach, Stefan-mario KasperAbstract:UNLABELLED: It is common experience that virus transmission, particularly transmission of the human immunodeficiency virus (HIV), is a principal concern of patients and physicians regarding blood transfusion (1). Many physicians are probably unaware that transfusion-transmitted HIV infection is approximately 50 to 100 times less likely to occur than transfusion error (2-4). This misconception may have been encouraged by the scarcity of reports on transfusion error relative to the tremendous public attention focused on HIV infection. We present five cases illustrating how anesthesiologists, intensivists, and emergency physicians are particularly vulnerable to the risk of administering blood to the wrong Recipient. All five cases were collected during a 4-yr period. Transfused units of packed red cells totaled approximately 50,000 U during this period in our department. IMPLICATIONS: Human error leading to the transfusion of blood to an Unintended Recipient is a major source of transfusion-related fatalities. We report five cases that highlight some specific areas in which transfusion error is likely to occur.
Matthieu Bloch - One of the best experts on this subject based on the ideXlab platform.
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Embedding Covert Information in Broadcast Communications
IEEE Transactions on Information Forensics and Security, 2019Co-Authors: Keerthi Suria Kumar Arumugam, Matthieu BlochAbstract:We analyze a two-receiver binary-input discrete memoryless broadcast channel, in which the transmitter communicates a common message simultaneously to both receivers and a covert message to only one of them. The Unintended Recipient of the covert message is treated as an adversary who attempts to detect the covert transmission. This model captures the problem of embedding covert messages in an innocent codebook and generalizes previous covert communication models in which innocent behavior corresponds to the absence of communication between legitimate users. We identify the exact asymptotic behavior of the number of covert bits that can be transmitted when the rate of the innocent codebook is close to the capacity of the channel to the adversary. Our results also identify the dependence of the number of covert bits on the channel parameters and the characteristics of the innocent codebook.
Jens Krombach - One of the best experts on this subject based on the ideXlab platform.
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Human error: the persisting risk of blood transfusion: a report of five cases.
Anesthesia and analgesia, 2002Co-Authors: Jens Krombach, Sandra Kampe, Birgit S. Gathof, Christoph Diefenbach, Stefan-mario KasperAbstract:UNLABELLED: It is common experience that virus transmission, particularly transmission of the human immunodeficiency virus (HIV), is a principal concern of patients and physicians regarding blood transfusion (1). Many physicians are probably unaware that transfusion-transmitted HIV infection is approximately 50 to 100 times less likely to occur than transfusion error (2-4). This misconception may have been encouraged by the scarcity of reports on transfusion error relative to the tremendous public attention focused on HIV infection. We present five cases illustrating how anesthesiologists, intensivists, and emergency physicians are particularly vulnerable to the risk of administering blood to the wrong Recipient. All five cases were collected during a 4-yr period. Transfused units of packed red cells totaled approximately 50,000 U during this period in our department. IMPLICATIONS: Human error leading to the transfusion of blood to an Unintended Recipient is a major source of transfusion-related fatalities. We report five cases that highlight some specific areas in which transfusion error is likely to occur.
Yuanxiang Jin - One of the best experts on this subject based on the ideXlab platform.
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Gut microbiota: An underestimated and Unintended Recipient for pesticide-induced toxicity.
Chemosphere, 2019Co-Authors: Xianling Yuan, Zihong Pan, Cuiyuan Jin, Yuanxiang JinAbstract:Pesticide pollution residues have become increasingly common health hazards over the last several decades because of the wide use of pesticides. The gastrointestinal tract is the first physical and biological barrier to contaminated food and is therefore the first exposure site. Interestingly, a number of studies have shown that the gut microbiota plays a key role in the toxicity of pesticides and has a profound relationship with environmental animal and human health. For instance, intake of the pesticide of chlorpyrifos can promote obesity and insulin resistance through influencing gut and gut microbiota of mice. In this review, we discussed the possible effects of different kinds of widely used pesticides on the gut microbiota in different experimental models and analyzed their possible subsequent effects on the health of the host. More and more studies indicated that the gut microbiota of animals played a very important role in pesticides-induced toxicity, suggesting that gut micriobita was also the Unintended Recipient of pesticides. We hope that more attention can focus on the relationship between pesticides, gut microbiota and environmental health risk assessment in near future.
