Information Theory and Reliable Communication

Introducing a new hobby for other people may inspire them to join with you. Reading, as one of mutual hobby, is considered as the very easy hobby to do. But, many people are not interested in this hobby. Why? Boring is the reason of why. However, this feel actually can deal with the book and time of you reading. Yeah, one that we will refer to break the boredom in reading is choosing the myth of the paperless office as the reading material.

The Myth of the Paperless Office

A method for operating a voice trigger is provided. In some implementations, the method is performed at an electronic device including one or more processors and memory storing instructions for execution by the one or more processors. The method includes receiving a sound input. The sound input may correspond to a spoken word or phrase, or a portion thereof. The method includes determining whether at least a portion of the sound input corresponds to a predetermined type of sound, such as a human voice. The method includes, upon a determination that at least a portion of the sound input corresponds to the predetermined type, determining whether the sound input includes predetermined content, such as a predetermined trigger word or phrase. The method also includes, upon a determination that the sound input includes the predetermined content, initiating a speech-based service, such as a voice-based digital assistant.

Voice trigger for a digital assistant

The present disclosure pertains to an image-processing device and method that make it possible to suppress block noise. A βLUT_input calculator and a clip processor determine βLUT_input, which is a value inputted to an existing-β generator and an expanded-β generator. When the value of βLUT_input qp from the clip processor is 51 or less, the existing-β generator determines β using a LUT conforming to the HVEC standard, and supplies same to a filter process determination unit. When the value of βLUT_input qp from the clip processor is larger than 51, the expanded-β generator determines the expanded β and supplies same to the filter process determination part. This disclosure, for example, can be applied to an image processing device.

Image processing device and method

Automatic Text Summarization (ATS) is becoming much more important because of the huge amount of textual content that grows exponentially on the Internet and the various archives of news articles, scientific papers, legal documents, etc. Manual text summarization consumes a lot of time, effort, cost, and even becomes impractical with the gigantic amount of textual content. Researchers have been trying to improve ATS techniques since the 1950s. ATS approaches are either extractive, abstractive, or hybrid. The extractive approach selects the most important sentences in the input document(s) then concatenates them to form the summary. The abstractive approach represents the input document(s) in an intermediate representation then generates the summary with sentences that are different than the original sentences. The hybrid approach combines both the extractive and abstractive approaches. Despite all the proposed methods, the generated summaries are still far away from the human-generated summaries. Most researches focus on the extractive approach. It is required to focus more on the abstractive and hybrid approaches. This research provides a comprehensive survey for the researchers by presenting the different aspects of ATS: approaches, methods, building blocks, techniques, datasets, evaluation methods, and future research directions.

Automatic text summarization: A comprehensive survey

Automatic text summarization has been an active field of research for many years Several approaches have been proposed, ranging from simple position and word-frequency methods, to learning and graph based algorithms The advent of human-generated knowledge bases like Wikipedia offer a further possibility in text summarization – they can be used to understand the input text in terms of salient concepts from the knowledge base In this paper, we study a novel approach that leverages Wikipedia in conjunction with graph-based ranking Our approach is to first construct a bipartite sentence–concept graph, and then rank the input sentences using iterative updates on this graph We consider several models for the bipartite graph, and derive convergence properties under each model Then, we take up personalized and query-focused summarization, where the sentence ranks additionally depend on user interests and queries, respectively Finally, we present a Wikipedia-based multi-document summarization algorithm An important feature of the proposed algorithms is that they enable real-time incremental summarization – users can first view an initial summary, and then request additional content if interested We evaluate the performance of our proposed summarizer using the ROUGE metric, and the results show that leveraging Wikipedia can significantly improve summary quality We also present results from a user study, which suggests that using incremental summarization can help in better understanding news articles

Text summarization using Wikipedia

A method and apparatus for joint profiling for identifying one or more common interests of members of a group and recommending items accordingly. The method comprises receiving a user profile for each member, the user profile comprising a set of one or more topics of interest to that member; automatically mapping at least one topic from each of the profiles to a concept; deriving a group profile, in dependence upon the concepts and the topics; and generating a list of recommended items for the group based on the group profile. Also presented is a method and apparatus for joint profiling wherein a joint weight for each topic is calculated by: for each user, calculating the reciprocal of a weight associated with the topic for that user; calculating the sum of the resulting reciprocals; and calculating the joint weight as the reciprocal of the resulting sum.

Content recommendation for groups

Information-theoretic security offered by the wiretap channel model has been extensively studied for various scenarios recently. One scenario that has not received much attention is secrecy for systems with memory in the form of input constraints or inter-symbol interference (ISI). In this work, we consider finite state wiretap channels (FSWCs), which model the scenario of secrecy with memory. Using results on secrecy capacity for arbitrary wiretap channels, we first arrive at the secrecy capacity of a FSWC. Then, we develop a stochastic algorithm for computing tight lower bounds on the secrecy capacity of a less-noisy FSWC, and illustrate the computation through examples. Our results provide numerical comparisons between secrecy capacities with and without memory, and provide specific targets for code design.

Finite-state wiretap channels: Secrecy under memory constraints

Although most of the developing world is likely to first access the Internet through mobile phones, mobile devices are constrained by screen space, bandwidth and limited attention span. Single document summarization techniques have the potential to simplify information consumption on mobile phones by presenting only the most relevant information contained in the document. In this paper we present a language independent single-document summarization method. We map document sentences to semantic concepts in Wikipedia and select sentences for the summary based on the frequency of the mapped-to concepts. Our evaluation on English documents using the ROUGE package indicates our summarization method is competitive with the state of the art in single document summarization.

http://www.hpl.hp.com/techreports/2009/HPL-2009-39.pdf

Document Summarization using Wikipedia

System and method for adaptive content summarization is disclosed. In one embodiment, a summary size of content is computed based on a usability cost function and an information loss function. Further, a summary of the content is extracted based on the summary size. Furthermore, the extracted summary is displayed on a display device.

Yogesh Sankarasubramaniam

Papers

Text summarization using Wikipedia

Content recommendation for groups

Finite-state wiretap channels: Secrecy under memory constraints

Document Summarization using Wikipedia

System and method for adaptive content summarization