21 research outputs found

    Decoding Information from noisy, redundant, and intentionally-distorted sources

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    Advances in information technology reduce barriers to information propagation, but at the same time they also induce the information overload problem. For the making of various decisions, mere digestion of the relevant information has become a daunting task due to the massive amount of information available. This information, such as that generated by evaluation systems developed by various web sites, is in general useful but may be noisy and may also contain biased entries. In this study, we establish a framework to systematically tackle the challenging problem of information decoding in the presence of massive and redundant data. When applied to a voting system, our method simultaneously ranks the raters and the ratees using only the evaluation data, consisting of an array of scores each of which represents the rating of a ratee by a rater. Not only is our appraoch effective in decoding information, it is also shown to be robust against various hypothetical types of noise as well as intentional abuses.Comment: 19 pages, 5 figures, accepted for publication in Physica

    Sulindac Enhances the Killing of Cancer Cells Exposed to Oxidative Stress

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    BACKGROUND:Sulindac is an FDA-approved non-steroidal anti-inflammatory drug (NSAID) that affects prostaglandin production by inhibiting cyclooxygenases (COX) 1 and 2. Sulindac has also been of interest for more than decade as a chemopreventive for adenomatous colorectal polyps and colon cancer. PRINCIPAL FINDINGS:Pretreatment of human colon and lung cancer cells with sulindac enhances killing by an oxidizing agent such as tert-butyl hydroperoxide (TBHP) or hydrogen peroxide. This effect does not involve cyclooxygenase (COX) inhibition. However, under the conditions used, there is a significant increase in reactive oxygen species (ROS) within the cancer cells and a loss of mitochondrial membrane potential, suggesting that cell death is due to apoptosis, which was confirmed by Tunel assay. In contrast, this enhanced killing was not observed with normal lung or colon cells. SIGNIFICANCE:These results indicate that normal and cancer cells handle oxidative stress in different ways and sulindac can enhance this difference. The combination of sulindac and an oxidizing agent could have therapeutic value

    Improving the accuracy of Business-to-Business (B2B) reputation systems through rater expertise prediction

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    International audienceDigital ecosystems rely on reputation systems in order to build trust and to foster collaborations among users. Reputation systems are commonplace in the C2C and B2C contexts, however, they have not yet found mainstream acceptance in B2B environments. Our first contribution in this paper is to identify the particularities of feedback collection in B2B reputation systems. An issue that we identify is that the reputation target in the B2B context is a business, which requires evaluation on a large number of criteria. We observe that due to the wide variation in user expertise, feedback forms that require users to evaluate all criteria have significant negative consequences for rating accuracy. Our second contribution is to propose an expertise prediction algorithm for B2B reputation systems, which filters the criteria describing the target business such that each user rates only on those criteria that he has expertise in. Experiments based on our real dataset show that the algorithm accurately predicts the expertise of users in given criteria. The algorithm may also increase the motivation of users to submit feedback as well as the confidence of users in B2B reputation systems
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