Context-based Grouping and Recommendation in MANETs

International audienceWe propose in this chapter a context grouping mechanism for context distribution over MANETs. Context distribution is becoming a key aspect for successful context-aware applications in mobile and ubiquitous computing environments. Such applications need, for adaptation purposes, context information that is acquired by multiple context sensors distributed over the environment. Nevertheless, applications are not interested in all available context information. Context distribution mechanisms have to cope with the dynamicity that characterizes MANETs and also prevent context information to be delivered to nodes (and applications) that are not interested in it. Our grouping mechanism organizes the distribution of context information in groups whose definition is context based: each context group is defined based on a criteria set (e.g. the shared location and interest) and has a dissemination set, which controls the information that can be shared in the group. We propose a personalized and dynamic way of defining and joining groups by providing a lattice-based classification and recommendation mechanism that analyzes the interrelations between groups and users, and recommend new groups to users, based on the interests and preferences of the user

The Web as an Adaptive Network: Coevolution of Web Behavior and Web Structure

Much is known about the complex network structure of the Web, and about behavioral dynamics on the Web. A number of studies address how behaviors on the Web are affected by different network topologies, whilst others address how the behavior of users on the Web alters network topology. These represent complementary directions of influence, but they are generally not combined within any one study. In network science, the study of the coupled interaction between topology and behavior, or state-topology coevolution, is known as 'adaptive networks', and is a rapidly developing area of research. In this paper, we review the case for considering the Web as an adaptive network and several examples of state-topology coevolution on the Web. We also review some abstract results from recent literature in adaptive networks and discuss their implications for Web Science. We conclude that adaptive networks provide a formal framework for characterizing processes acting 'on' and 'of' the Web, and offers potential for identifying general organizing principles that seem otherwise illusive in Web Scienc

Processing and Linking Audio Events in Large Multimedia Archives: The EU inEvent Project

In the inEvent EU project [1], we aim at structuring, retrieving, and sharing large archives of networked, and dynamically changing, multimedia recordings, mainly consisting of meetings, videoconferences, and lectures. More specifically, we are developing an integrated system that performs audiovisual processing of multimedia recordings, and labels them in terms of interconnected “hyper-events ” (a notion inspired from hyper-texts). Each hyper-event is composed of simpler facets, including audio-video recordings and metadata, which are then easier to search, retrieve and share. In the present paper, we mainly cover the audio processing aspects of the system, including speech recognition, speaker diarization and linking (across recordings), the use of these features for hyper-event indexing and recommendation, and the search portal. We present initial results for feature extraction from lecture recordings using the TED talks. Index Terms: Networked multimedia events; audio processing: speech recognition; speaker diarization and linking; multimedia indexing and searching; hyper-events. 1

On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis

Multimodal-aware recommender systems (MRSs) exploit multimodal content (e.g., product images or descriptions) as items' side information to improve recommendation accuracy. While most of such methods rely on factorization models (e.g., MFBPR) as base architecture, it has been shown that MFBPR may be affected by popularity bias, meaning that it inherently tends to boost the recommendation of popular (i.e., short-head) items at the detriment of niche (i.e., long-tail) items from the catalog. Motivated by this assumption, in this work, we provide one of the first analyses on how multimodality in recommendation could further amplify popularity bias. Concretely, we evaluate the performance of four state-of-the-art MRSs algorithms (i.e., VBPR, MMGCN, GRCN, LATTICE) on three datasets from Amazon by assessing, along with recommendation accuracy metrics, performance measures accounting for the diversity of recommended items and the portion of retrieved niche items. To better investigate this aspect, we decide to study the separate influence of each modality (i.e., visual and textual) on popularity bias in different evaluation dimensions. Results, which demonstrate how the single modality may augment the negative effect of popularity bias, shed light on the importance to provide a more rigorous analysis of the performance of such models

A scalable mining of frequent quadratic concepts in d-folksonomies

Folksonomy mining is grasping the interest of web 2.0 community since it represents the core data of social resource sharing systems. However, a scrutiny of the related works interested in mining folksonomies unveils that the time stamp dimension has not been considered. For example, the wealthy number of works dedicated to mining tri-concepts from folksonomies did not take into account time dimension. In this paper, we will consider a folksonomy commonly composed of triples and we shall consider the time as a new dimension. We motivate our approach by highlighting the battery of potential applications. Then, we present the foundations for mining quadri-concepts, provide a formal definition of the problem and introduce a new efficient algorithm, called QUADRICONS for its solution to allow for mining folksonomies in time, i.e., d-folksonomies. We also introduce a new closure operator that splits the induced search space into equivalence classes whose smallest elements are the quadri-minimal generators. Carried out experiments on large-scale real-world datasets highlight good performances of our algorithm

Report on the Second Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE2)

This technical report records and discusses the Second Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE2). The report includes a description of the alternative, experimental submission and review process, two workshop keynote presentations, a series of lightning talks, a discussion on sustainability, and five discussions from the topic areas of exploring sustainability; software development experiences; credit & incentives; reproducibility & reuse & sharing; and code testing & code review. For each topic, the report includes a list of tangible actions that were proposed and that would lead to potential change. The workshop recognized that reliance on scientific software is pervasive in all areas of world-leading research today. The workshop participants then proceeded to explore different perspectives on the concept of sustainability. Key enablers and barriers of sustainable scientific software were identified from their experiences. In addition, recommendations with new requirements such as software credit files and software prize frameworks were outlined for improving practices in sustainable software engineering. There was also broad consensus that formal training in software development or engineering was rare among the practitioners. Significant strides need to be made in building a sense of community via training in software and technical practices, on increasing their size and scope, and on better integrating them directly into graduate education programs. Finally, journals can define and publish policies to improve reproducibility, whereas reviewers can insist that authors provide sufficient information and access to data and software to allow them reproduce the results in the paper. Hence a list of criteria is compiled for journals to provide to reviewers so as to make it easier to review software submitted for publication as a “Software Paper.

Visualizing internetworked argumentation

In this chapter, we outline a project which traces its source of inspiration back to the grand visions of Vannevar Bush (scholarly trails of linked concepts), Doug Engelbart (highly interactive intellectual tools, particularly for argumentation), and Ted Nelson (large scale internet publishing with recognised intellectual property). In essence, we are tackling the age-old question of how to organise distributed, collective knowledge. Specifically, we pose the following question as a foil: In 2010, will scholarly knowledge still be published solely in prose, or can we imagine a complementary infrastructure that is ‘native’ to the emerging semantic, collaborative web, enabling more effective dissemination and analysis of ideas

Formalizing Multimedia Recommendation through Multimodal Deep Learning

Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains. Multimodality can help tap into richer information sources and construct more refined user/item profiles for recommendations. However, existing literature lacks a shared and universal schema for modeling and solving the recommendation problem through the lens of multimodality. This work aims to formalize a general multimodal schema for multimedia recommendation. It provides a comprehensive literature review of multimodal approaches for multimedia recommendation from the last eight years, outlines the theoretical foundations of a multimodal pipeline, and demonstrates its rationale by applying it to selected state-of-the-art approaches. The work also conducts a benchmarking analysis of recent algorithms for multimedia recommendation within Elliot, a rigorous framework for evaluating recommender systems. The main aim is to provide guidelines for designing and implementing the next generation of multimodal approaches in multimedia recommendation