CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

Recommender systems (RSs) are systems that produce individualized recommendations as output or drive the user in a personalized way to interesting or useful objects in a space of possible options. Recently, RSs emerged as an effective support for decision making. However, when people make decisions, they usually take into account different and often conicting information such as preferences, long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to provide an effective decision-making support, a RS should be ``holistic'', i.e., it should rely on a complete representation of the user, encoding heterogeneous user features (such as personal interests, psychological traits, health data, social connections) that may come from multiple data sources. However, to obtain such holistic recommendations some steps are necessary: rst, we need to identify the goal of the decision-making process; then, we have to exploit common-sense and domain knowledge to provide the user with the most suitable suggestions that best t the recommendation scenario. In this article, we present a methodological framework that can drive researchers and developers during the design process of this kind of ``holistic'' RS. We also provide evidence of the framework validity by presenting the design process and the evaluation of a food RS based on holistic principles

A Social Framework for Set Recommendation in Group Recommender Systems

This research article presents a study about the background in Group Recommender Systems and how social factors are directly related to these applications. Some important group recommender systems in academia are described to exemplify their contribution in different domains. Besides, a framework that is intended to improve group recommender systems is proposed. The main idea of the framework is to enhance social cognition to help the group members agree and make a decision. Its structure includes a process where an influential group is detected among the target groups of people to recommend to. Social influence detection uses the knowledge behind online social connections and interactions. Trying to understand human behavior and ties among groups in a social network and how to use this to improve group recommender systems is considered the main challenge for future research. Combining this with the kind of item recommendation which involves a temporal sequence of ordered elements will present a novel and original path in Group Recommender Systems design. &nbsp

An MPEG-7 scheme for semantic content modelling and filtering of digital video

Abstract Part 5 of the MPEG-7 standard specifies Multimedia Description Schemes (MDS); that is, the format multimedia content models should conform to in order to ensure interoperability across multiple platforms and applications. However, the standard does not specify how the content or the associated model may be filtered. This paper proposes an MPEG-7 scheme which can be deployed for digital video content modelling and filtering. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user. We present details of the scheme, front-end systems used for content modelling and filtering and experiences with a number of users

Assisted Viewpoint Interaction for 3D Visualization

Many three-dimensional visualizations are characterized by the use of a mobile viewpoint that offers multiple perspectives on a set of visual information. To effectively control the viewpoint, the viewer must simultaneously manage the cognitive tasks of understanding the layout of the environment, and knowing where to look to find relevant information, along with mastering the physical interaction required to position the viewpoint in meaningful locations. Numerous systems attempt to address these problems by catering to two extremes: simplified controls or direct presentation. This research attempts to promote hybrid interfaces that offer a supportive, yet unscripted exploration of a virtual environment.Attentive navigation is a specific technique designed to actively redirect viewers' attention while accommodating their independence. User-evaluation shows that this technique effectively facilitates several visualization tasks including landmark recognition, survey knowledge acquisition, and search sensitivity. Unfortunately, it also proves to be excessively intrusive, leading viewers to occasionally struggle for control of the viewpoint. Additional design iterations suggest that formalized coordination protocols between the viewer and the automation can mute the shortcomings and enhance the effectiveness of the initial attentive navigation design.The implications of this research generalize to inform the broader requirements for Human-Automation interaction through the visual channel. Potential applications span a number of fields, including visual representations of abstract information, 3D modeling, virtual environments, and teleoperation experiences

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

A Framework for Exploiting Internet of Things for Context-Aware Trust-based Personalized Services

In the last years, we have witnessed the introduction of Internet of Things as an integral part of the Internet with billions of interconnected and addressable everyday objects. On the one hand, these objects generate massive volume of data that can be exploited to gain useful insights into our day-to-day needs. On the other hand, context-aware recommender systems (CARSs) are intelligent systems that assist users to make service consumption choices that satisfy their preferences based on their contextual situations. However, one of the major challenges in developing CARSs is the lack of functionality providing dynamic and reliable context information required by the recommendation decision process based on the objects that users interact with in their environments. Thus, contextual information obtained from IoT objects and other sources can be exploited to build CARSs that satisfy users’ preferences, improve quality of experience and recommendation accuracy. This article describes various components of a conceptual IoT based framework for context-aware personalized recommendations. The framework addresses the weakness whereby CARSs rely on static and limited contextual information from user’s mobile phone, by providing additional components for reliable and dynamic contextual information, using IoT context sources. The core of the framework consists of context recognition and reasoning management, dynamic user profile model incorporating trust to improve accuracy of context-aware personalized recommendations. Experimental evaluations show that incorporating context and trust in personalized recommendations can improve its accuracy

Supporting decision making process with "Ideal" software agents: what do business executives want?

According to Simon’s (1977) decision making theory, intelligence is the first and most important phase in the decision making process. With the escalation of information resources available to business executives, it is becoming imperative to explore the potential and challenges of using agent-based systems to support the intelligence phase of decision-making. This research examines UK executives’ perceptions of using agent-based support systems and the criteria for design and development of their “ideal” intelligent software agents. The study adopted an inductive approach using focus groups to generate a preliminary set of design criteria of “ideal” agents. It then followed a deductive approach using semi-structured interviews to validate and enhance the criteria. This qualitative research has generated unique insights into executives’ perceptions of the design and use of agent-based support systems. The systematic content analysis of qualitative data led to the proposal and validation of design criteria at three levels. The findings revealed the most desirable criteria for agent based support systems from the end users’ point view. The design criteria can be used not only to guide intelligent agent system design but also system evaluation