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E-commerce giants like Amazon rely on consumer reviews to allow buyers to inform other potential buyers about a product’s pros and cons. While these reviews can be useful, they are less so when the number of reviews is large; no consumer can be expected to read hundreds or thousands of reviews in order to gain better understanding about a product. In an effort to provide an aggregate representation of reviews, Amazon offers an average user rating represented by a 1- to 5-star score. This score only represents how reviewers feel about a product without providing insight into why they feel that way. In this work, we propose an AI technique that generates an easy-to-read, concise summary of a product based on its reviews. It provides an overview of the different aspects reviewers emphasize in their reviews and, crucially, how they feel about those aspects. Our methodology generates a list of the topics most-mentioned by reviewers, conveys reviewer sentiment for each topic and calculates an overall summary score that reflects reviewers’ overall sentiment about the product. These sentiment scores adapt the same 1- to 5-star scoring scale in order to remain familiar to Amazon users. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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A multi-stage biometric verification system serially activates its verifiers and improves performance-cost trade-off by allowing users to submit a subset of the available biometrics. In the heart of a verifier in multi-stage systems lies the concept of ‘reject option’ where a reject region is used to identify a bad quality test sample. If the match-score falls inside the reject region, no binary (genuine/impostor) decision is made in the current stage and the verifier in the next stage is activated. Recent studies have demonstrated a significant promise of the ‘symmetric rejection method’ in choosing a suitable reject region for multi-stage verification systems. In this paper, we delve into the symmetric rejection method to gain more insights into its error reduction capabilities. Specifically, we develop a theory which mathematically proves that the symmetric rejection method reduces the false accept rate and false reject rate. Then, we empirically validate our theory. Results show that the symmetric rejection method significantly reduces the error rates, both the false accept rate and false reject rate. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
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Traditional keyboards remain the input device of choice for typing-heavy environments. When attached to sensitive data, security is a major concern. To continuously authenticate users in these environments, use of keystroke dynamics can be a preferred choice. An integral part of user enrollment in a keystroke based continuous authentication system is the writing instruction (prompt) given to the users, to use as a basis for their improvised writing. There are many prompts possible, and they directly impact the performance of authentication systems. Hence, prompts should be designed carefully, and with purpose. In this paper, we bridge the gap between cognitive psychology and computer science and attempt to influence the mental state of the users to acquire a better authentication performance. We compare two kinds of writing prompts, creative and factual, for generating reference samples. In addition, we perform two robustness tests: robustness to dissimilar writing style (e.g., creative reference and factual test) and robustness to surface (e.g., hard surface reference and soft surface test). We collect data from thirty participants in four weekly sessions. We experiment with three features: key interval, key press, and key hold latencies. We use Relative (R) measure to generate the match score between the reference and test samples. Results show that creative writing consistently performs better than the factual one. Both writing prompts perform well with dissimilar style in testing, i.e., continuous authentication is found robust to writing style. Also, we find that the surface (hard or soft) used in testing need not match that used for the reference, thus continuous authentication is also surface robust. © 2020 IEEE.
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