Activity-driven content adaptation for effective video summarisation

In this paper, we present a novel method for content adaptation and video summarization fully implemented in compressed-domain. Firstly, summarization of generic videos is modeled as the process of extracted human objects under various activities/events. Accordingly, frames are classified into five categories via fuzzy decision including shot changes (cut and gradual transitions), motion activities (camera motion and object motion) and others by using two inter-frame measurements. Secondly, human objects are detected using Haar-like features. With the detected human objects and attained frame categories, activity levels for each frame are determined to adapt with video contents. Continuous frames belonging to same category are grouped to form one activity entry as content of interest (COI) which will convert the original video into a series of activities. An overall adjustable quota is used to control the size of generated summarization for efficient streaming purpose. Upon this quota, the frames selected for summarization are determined by evenly sampling the accumulated activity levels for content adaptation. Quantitative evaluations have proved the effectiveness and efficiency of our proposed approach, which provides a more flexible and general solution for this topic as domain-specific tasks such as accurate recognition of objects can be avoided

On multi-subjectivity in linguistic summarization of relational databases

We focus on one of the most powerful computing methods for natural-language-driven representation of data, i.e. on Yager’s concept of a linguistic summary of a relational database (1982). In particular, we introduce an original extension of that concept: new forms of linguistic summaries. The new forms are named Multi-Subject linguistic summaries, because they are constructed to handle more than one set of subjects, represented by related sets of records/objects collected in a database, like ”cars, bicycles and motorbikes” (within vehicles), ”male and female” (within people), e.g. More boys than girls play football well. Thanks to that, the generated linguistic summaries – quasi-natural language sentences – are more interesting and human-oriented. Moreover, they can be applied together with the classic forms od summaries, to enrich naturality of comments/ descriptions generated. Apart from traditional interpretions linguistic summaries in termsof fuzzy logic, we also introduce some higher-order fuzzy logic methods, to extend possibilities of representing too complex or too ill-defined linguistic terms used in generated messages. The new methods are applied to a computer system that generates natural language description of numeric data, that makes them possible to be clearly presented to an end-user

Towards Neural Numeric-To-Text Generation From Temporal Personal Health Data

With an increased interest in the production of personal health technologies designed to track user data (e.g., nutrient intake, step counts), there is now more opportunity than ever to surface meaningful behavioral insights to everyday users in the form of natural language. This knowledge can increase their behavioral awareness and allow them to take action to meet their health goals. It can also bridge the gap between the vast collection of personal health data and the summary generation required to describe an individual's behavioral tendencies. Previous work has focused on rule-based time-series data summarization methods designed to generate natural language summaries of interesting patterns found within temporal personal health data. We examine recurrent, convolutional, and Transformer-based encoder-decoder models to automatically generate natural language summaries from numeric temporal personal health data. We showcase the effectiveness of our models on real user health data logged in MyFitnessPal and show that we can automatically generate high-quality natural language summaries. Our work serves as a first step towards the ambitious goal of automatically generating novel and meaningful temporal summaries from personal health data.Comment: 5 pages, 2 figures, 1 tabl

APRIORI ALGORITHM APPROACH FOR AUTOMATIC TEXT PROCESSING AND GENERIC-BASED SUMMARIZATION SYSTEM

Text Processing has always existed in various forms. It makes voluminous text easily digestible, offers brief and quick overview of the subject contents and may provide critical context analysis to the reader. With the growth of digital articles in forms of news, blogs, wikis etc., there is serious need for a text processor which can adequately summarized an article or documents for the reader. This redirected and takes away the effort needed to read, assimilate and create summaries manually. This research paper proposed a system which provides unique opportunity for developing a core set text summarization system using Apriori Algorithm techniques to perform Binary Associated Rule on Data Mining. The system makes available a means of storing the automatic Generic-based summaries for future references and requirements

DATA MINING: A SEGMENTATION ANALYSIS OF U.S. GROCERY SHOPPERS

Consumers make choices about where to shop based on their preferences for a shopping environment and experience as well as the selection of products at a particular store. This study illustrates how retail firms and marketing analysts can utilize data mining techniques to better understand customer profiles and behavior. Among the key areas where data mining can produce new knowledge is the segmentation of customer data bases according to demographics, buying patterns, geographics, attitudes, and other variables. This paper builds profiles of grocery shoppers based on their preferences for 33 retail grocery store characteristics. The data are from a representative, nationwide sample of 900 supermarket shoppers collected in 1999. Six customer profiles are found to exist, including (1) "Time Pressed Meat Eaters", (2) "Back to Nature Shoppers", (3) "Discriminating Leisure Shoppers", (4) "No Nonsense Shoppers", (5) "The One Stop Socialites", and (6) "Middle of the Road Shoppers". Each of the customer profiles is described with respect to the underlying demographics and income. Consumer shopping segments cut across most demographic groups but are somewhat correlated with income. Hierarchical lists of preferences reveal that low price is not among the top five most important store characteristics. Experience and preferences for internet shopping shows that of the 44% who have access to the internet, only 3% had used it to order food.Consumer/Household Economics, Food Consumption/Nutrition/Food Safety,

"You Tube and I Find" - personalizing multimedia content access

Recent growth in broadband access and proliferation of small personal devices that capture images and videos has led to explosive growth of multimedia content available everywhereVfrom personal disks to the Web. While digital media capture and upload has become nearly universal with newer device technology, there is still a need for better tools and technologies to search large collections of multimedia data and to find and deliver the right content to a user according to her current needs and preferences. A renewed focus on the subjective dimension in the multimedia lifecycle, fromcreation, distribution, to delivery and consumption, is required to address this need beyond what is feasible today. Integration of the subjective aspects of the media itselfVits affective, perceptual, and physiological potential (both intended and achieved), together with those of the users themselves will allow for personalizing the content access, beyond today’s facility. This integration, transforming the traditional multimedia information retrieval (MIR) indexes to more effectively answer specific user needs, will allow a richer degree of personalization predicated on user intention and mode of interaction, relationship to the producer, content of the media, and their history and lifestyle. In this paper, we identify the challenges in achieving this integration, current approaches to interpreting content creation processes, to user modelling and profiling, and to personalized content selection, and we detail future directions. The structure of the paper is as follows: In Section I, we introduce the problem and present some definitions. In Section II, we present a review of the aspects of personalized content and current approaches for the same. Section III discusses the problem of obtaining metadata that is required for personalized media creation and present eMediate as a case study of an integrated media capture environment. Section IV presents the MAGIC system as a case study of capturing effective descriptive data and putting users first in distributed learning delivery. The aspects of modelling the user are presented as a case study in using user’s personality as a way to personalize summaries in Section V. Finally, Section VI concludes the paper with a discussion on the emerging challenges and the open problems

Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding

Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly summarized by a common abstractive sentence. This paper provides a novel approach to this task. We first introduce a neural contextual utterance encoder featuring three types of self-attention mechanisms. We then train it using the siamese and triplet energy-based meta-architectures. Experiments on the AMI corpus show that our system outperforms multiple energy-based and non-energy based baselines from the state-of-the-art. Code and data are publicly available.Comment: Update baseline