Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of topics associated with a set of content items. Next, the system obtains a first set of attributes associated with a first topic in the set of topics and a second set of attributes associated with a second topic in the set of topics. Next, the system calculates a similarity between the first and second sets of attributes and applies a threshold to the similarity to identify the first and second topics as synonymous when the similarity exceeds a threshold. The system then merges the first and second topics under a representative topic. Finally, the system displays the representative topic to a user to improve understanding of the set of content items by the user without requiring the user to manually analyze the set of content items.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of clauses in a first set of content items comprising unstructured data. Next, the system obtains a set of stop words comprising high-frequency words that occur in a second set of content items. The system then automatically extracts a set of topics from the set of clauses by generating a set of n-grams from the set of clauses and excluding a first n-gram in the set of n-grams from the set of topics when the first n-gram contains a word in the set of stop words in a pre-specified position of the first n-gram. Finally, the system displays the set of topics to a user to improve understanding of the first set of content items by the user without requiring the user to manually analyze the first set of content items.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of content items and a set of topics in the set of content items. For each topic in the set of topics, the system automatically extracts a set of attributes that provides a context for the topic from a subset of the content items containing the topic. The system then displays the set of attributes in the context of the topic to improve understanding of the set of content items by the user without requiring the user to manually analyze the set of content items.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of content items and a set of topics in the set of content items. For each topic in the set of topics, the system automatically extracts a set of attributes that provides a context for the topic from a subset of the content items containing the topic. The system then displays the set of attributes in the context of the topic to improve understanding of the set of content items by the user without requiring the user to manually analyze the set of content items.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of clauses in a first set of content items comprising unstructured data. Next, the system obtains a set of stop words comprising high-frequency words that occur in a second set of content items. The system then automatically extracts a set of topics from the set of clauses by generating a set of n-grams from the set of clauses and excluding a first n-gram in the set of n-grams from the set of topics when the first n-gram contains a word in the set of stop words in a pre-specified position of the first n-gram. Finally, the system displays the set of topics to a user to improve understanding of the first set of content items by the user without requiring the user to manually analyze the first set of content items.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of topics associated with a set of content items. Next, the system obtains a first set of attributes associated with a first topic in the set of topics and a second set of attributes associated with a second topic in the set of topics. Next, the system calculates a similarity between the first and second sets of attributes and applies a threshold to the similarity to identify the first and second topics as synonymous when the similarity exceeds a threshold. The system then merges the first and second topics under a representative topic. Finally, the system displays the representative topic to a user to improve understanding of the set of content items by the user without requiring the user to manually analyze the set of content items.

Abstract: We propose a multi user information theoretically secure scheme. Our scheme allows any two parties in a multi user system to exchange messages securely using encryption, and to sign messages. Our scheme achieves a significant saving in the number of total keys in the system and in the keys each user must store. The encryption, and signing algorithms proposed in the scheme are as efficient as possible. Our scheme is designed so that it is possible to easily and efficiently revoke and add membership of new users into the system. It is also designed so that authentication and security against man in the middle attacks can be added at low cost. In addition, we introduce a novel and efficient way to use steganography for key replenishment.