Beyond Findability: Search-Enhanced Information Architecture for Content-Intensive Rich Internet Applications

This paper details a way to extend classic information architecture for web-based applications. The goal is to enhance traditional user experiences, mainly based on navigation or search, to new ones (also relevant for stakeholders’ requirements). Examples are sense making, at a glance understanding, playful exploration, serendipitous browsing, and brand communication. These new experiences are often unmet by current information architecture solutions, which may be stiff and difficult to scale, especially in the case of large or very large websites. A heavy reliance upon search engines seems not to offer a viable solution: it supports, in fact, a limited range of user experiences. We propose to transform (parts of) websites into Rich Internet Applications (RIAs), based, beside other features, upon interaction-rich interfaces and semantic browsing across content. We introduce SEE-IA (SEarch-Enhanced Information Architecture), a coherent set of information architecture design strategies, which innovatively blend and extend IA and search paradigms. The key ingredients of SEE-IA are a seamless combination of structured hypertext-based information architectures, faceted search paradigms, and RIA-enabled visualization techniques. The paper elucidates and codifies these design strategies and their underlying principles, identifying also how they support a set of requirements which are often neglected by most current design approaches. A real case study of a complex RIA designed for a major institutional client in Italy is used to vividly showcase the design strategies and to provide ready-to-use examples that can be transferred to other IA contexts and domains

Analyzing the Reliability of Alternative Convolution Implementations for Deep Learning Applications

Convolution represents the core of Deep Learning (DL) applications, enabling the automatic extraction of features from raw input data. Several implementations of the convolution have been proposed. The impact of these different implementations on the performance of DL applications has been studied. However, no specific reliability-related analysis has been carried out. In this paper, we apply the CLASSES cross-layer reliability analysis methodology for an in-depth study aimed at: i) analyzing and characterizing the effects of Single Event Upsets occurring in Graphics Processing Units while executing the convolution operators; and ii) identifying whether a convolution implementation is more robust than others. The outcomes can then be exploited to tailor better hardening schemes for DL applications to improve reliability and reduce overhead

Data Modeling for Ambient Home Care Systems

Ambient assisted living (AAL) services are usually designed to work on the assumption that real-time context information about the user and his environment is available. Systems handling acquisition and context inference need to use a versatile data model, expressive and scalable enough to handle complex context and heterogeneous data sources. In this paper, we describe an ontology to be used in a system providing AAL services. The ontology reuses previous ontologies and models the partners in the value chain and their service offering. With our proposal, we aim at having an effective AAL data model, easily adaptable to specific domain needs and services

Dependability of Alternative Computing Paradigms for Machine Learning: hype or hope?

Today we observe amazing performance achieved by Machine Learning (ML); for specific tasks it even surpasses human capabilities. Unfortunately, nothing comes for free: the hidden cost behind ML performance stems from its high complexity in terms of operations to be computed and the involved amount of data. For this reasons, custom Artificial Intelligence hardware accelerators based on alternative computing paradigms are attracting large interest. Such dedicated devices support the energy-hungry data movement, speed of computation, and memory resources that MLs require to realize their full potential. However, when ML is deployed on safety-/mission-critical applications, dependability becomes a concern. This paper presents the state of the art of custom Artificial Intelligence hardware architectures for ML, here Spiking and Convolutional Neural Networks, and shows the best practices to evaluate their dependability

Exploring Interface Sign Ontologies for Web User Interface Design and Evaluation: A User Study

Part 2: Organizational Semiotics and ApplicationsInternational audienceThe aim of this paper is twofold: firstly, to find the set of ontologies (i.e., the set of concepts and skills) presupposed by users when interpreting the meaning of web interface signs (i.e., the smallest elements of web user interfaces), and secondly, to investigate users’ difficulties in interpreting the meanings of interface signs belonging to different kinds of ontologies. In order to achieve these aims an empirical user study was conducted with 26 test participants. The study data was gathered by semi-structured interviews and questionnaires. Following an empirical research approach, descriptive statistics and qualitative data analysis were used to analyze the data. The study results provide a total of twelve ontologies and reveal the users’ difficulties in interpreting the meanings of interface signs belonging to different kinds of ontologies

Context-aware access to heterogeneous resources through on-the-fly mashups

Current scenarios for app development are characterized by rich resources that often overwhelm the final users, especially in mobile app usage situations. It is therefore important to define design methods that enable dynamic filtering of the pertinent resources and appropriate tailoring of the retrieved content. This paper presents a design framework based on the specification of the possible contexts deemed relevant to a given application domain and on their mapping onto an integrated schema of the resources underlying the app. The context and the integrated schema enable the instantiation at runtime of templates of app pages in function of the context characterizing the user’s current situation of use

Sustainability marketing myopia: the lack of sustainability communication persuasiveness

Sustainability communication in accommodation businesses tends to be factual and descriptive, as companies are concerned with product-based messages that focus on what they do; they appear not to understand the potential benefits of constructing messages that would influence consumers to behave more sustainably, which is effectively sustainability marketing myopia. An analysis of 1,835 sustainability messages from award-winning businesses shows that messages communicate facts not emotions, and benefits for society as a whole rather than for the individual customer. The messages are explicit, but passive and not experiential hence they positively affect the cognitive but not the affective image of the business. The lack of message normalization and customer focus reinforces the image of sustainability being a niche concern. We reflect on the reasons for these shortcomings and highlight opportunities to improve persuasive communication, which we have now applied commercially in more than 400 website analyses and 60 training courses