Supporting aspect-based video browsing - analysis of a user study

In this paper, we present a novel video search interface based on the concept of aspect browsing. The proposed strategy is to assist the user in exploratory video search by actively suggesting new query terms and video shots. Our approach has the potential to narrow the "Semantic Gap" issue by allowing users to explore the data collection. First, we describe a clustering technique to identify potential aspects of a search. Then, we use the results to propose suggestions to the user to help them in their search task. Finally, we analyse this approach by exploiting the log files and the feedbacks of a user study

User Review-Based Change File Localization for Mobile Applications

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table

Recommendation System for News Reader

Recommendation Systems help users to find information and make decisions where they lack the required knowledge to judge a particular product. Also, the information dataset available can be huge and recommendation systems help in filtering this data according to users‟ needs. Recommendation systems can be used in various different ways to facilitate its users with effective information sorting. For a person who loves reading, this paper presents the research and implementation of a Recommendation System for a NewsReader Application using Android Platform. The NewsReader Application proactively recommends news articles as per the reading habits of the user, recorded over a period of time and also recommends the currently trending articles. Recommendation systems and their implementations using various algorithms is the primary area of study for this project. This research paper compares and details popular recommendation algorithms viz. Content based recommendation systems, Collaborative recommendation systems etc. Moreover, it also presents a more efficient Hybrid approach that absorbs the best aspects from both the algorithms mentioned above, while trying to eliminate all the potential drawbacks observed

Taste and the algorithm

Today, a consistent part of our everyday interaction with art and aesthetic artefacts occurs through digital media, and our preferences and choices are systematically tracked and analyzed by algorithms in ways that are far from transparent. Our consumption is constantly documented, and then, we are fed back through tailored information. We are therefore witnessing the emergence of a complex interrelation between our aesthetic choices, their digital elaboration, and also the production of content and the dynamics of creative processes. All are involved in a process of mutual influences, and are partially determined by the invisible guiding hand of algorithms. With regard to this topic, this paper will introduce some key issues concerning the role of algorithms in aesthetic domains, such as taste detection and formation, cultural consumption and production, and showing how aesthetics can contribute to the ongoing debate about the impact of today’s “algorithmic culture”

Website evaluation measures, website credibility and user engagement for municipal website

This paper attempts to explore website evaluation measures specifically for information driven website such Municipal electronic government website toward website credibility and user engagement. Despite overwhelming of information source in online environment, the role of government website as a prominent government information provider becomes less preferred. Even, rapid development and continuous assessment been done by the government bodies to enhance and make utilize their website by the users, issues such usability problem, low popularity ranking and less user engagement still been reported. Therefore, the first part of this article reviews on existing assessment measures for websites done by scholars and also by practitioners. Then, in the second part of this article presents some finding on self evaluation of ten municipal website around Klang valley, Malaysia in term of popularity ranking and user engagement measure (bounce rate, Daily Pageviews per Visitor, and Daily Time on Site). Through related literatures reviewed, less study done previously includes overall or multiple measures for evaluation of information driven website. Estimation result of popularity ranking and user engagement percentage among municipal website also shows that there is still need some improvement to make the gateway of Malaysia electronic government become more favorable and engaging

Affective Music Information Retrieval

Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this article, we present a novel generative approach to music emotion modeling, with a specific focus on the valence-arousal (VA) dimension model of emotion. The presented generative model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the subjectivity of emotion perception by the use of probability distributions. Specifically, it learns from the emotion annotations of multiple subjects a Gaussian mixture model in the VA space with prior constraints on the corresponding acoustic features of the training music pieces. Such a computational framework is technically sound, capable of learning in an online fashion, and thus applicable to a variety of applications, including user-independent (general) and user-dependent (personalized) emotion recognition and emotion-based music retrieval. We report evaluations of the aforementioned applications of AEG on a larger-scale emotion-annotated corpora, AMG1608, to demonstrate the effectiveness of AEG and to showcase how evaluations are conducted for research on emotion-based MIR. Directions of future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Individual Tariffs for Mobile Communication Services

This paper introduces a conceptual framework and a computational model for individual tariffs for mobile communication services. The purpose is to provide guidance for implementation by communication service suppliers or user groups alike. The paper first examines the sociological and economic incentives for personalized services and individual tariffs. Then it introduces a framework for individual tariffs which is centered on user and supplier behaviours. The user, instead of being fully rational, has "bounded rationality" and his behaviours are subject to economic constraints and influenced by social needs. The supplier can belong to different types of entities such as firms and communities; each has his own goals which lead to different behaviors. Individual tariffs are decided through interactions between the user and the supplier and can be analyzed in a structured way using game theory. A numerical case in mobile music training is developed to illustrate the concepts.risks;mobile communication services;Individual tariffs;computational games

The Hitchhiker\u27s Guide to the Long Tail: The Influence of Online-Reviews and Product Recommendations on Book Sales - Evidence from German Online Retailing

Exploring the long tail phenomenon, we empirically analyze whether online reviews, discussion forums, and product recommendations help to reduce search costs and actually alter the sales distribution in online book retailing. We have collected a data set containing 320,248 observations for 40,031 different books at Amazon.de, each assigned to one of 111 different product categories in our sample. By adopting an innovative approach, we provide the first long tail conversion model for the German online market, based on publicly available sales data. Our results indicate that online reviews and automated product recommendations reduce search costs by facilitating the identification of adequate books and the assessment of their quality. This highlights the relevance of information technology implementation as vital part of the marketing strategy

Managing by data: algorithmic categories and organizing

Data and data management techniques increasingly permeate organizations and the contexts in which they are embedded. We conduct an empirical investigation of Last.fm, an online music discovery platform, with a view to unpacking the work of data and algorithms in the process of categorization. Drawing on Eleanor Rosch and her colleagues, we link the making of categories with the construction of basic objects that function as key filters or registers for perceiving and organizing the world and interacting with it. In contexts such as the ones we have studied, basic objects are made out of data rather than expert or community-based knowledge. In such settings, basic objects work as pervasive reality filters and as the entities on which other organizational objects and categories are built. As they diffuse, such objects and the categories they instantiate become naturalized, increasingly reconfiguring the social order of organizations and their environments as a data order. Once key organizational activities such as the making of objects and categorizing are rearranged by data and algorithms, organizations can no longer be framed as separate from the technologies they deploy