ICCSA 2022

Producción CientíficaThe process of economic, social, and cultural development leads to relevant changes in urban areas. Urban transformations usually generate a series of public and private real estate compounds which constitute real obstacles to urban walkability. The growing attention towards the sustainable development goals established on a global scale introduced new contents in urban redevelopment policies, aimed at favoring higher levels of accessibility in the consolidated fabric, particularly that of the pedestrian type. In addition, the recent pandemic has recently reassessed the role of pedestrian mobility as a primary way of moving instead of using other means of transport. As a result, urban walkability has moved at the core of the sustainable city paradigm. More precisely, issues related to accessibility and walkability should be considered when addressing the obstacle generated by those sites that can be properly defined ‘urban enclaves’, especially when abandoned or under redevelopment. These conditions may encourage the gradual reopening of these areas for citizens. Within this framework, the Sustainable Urban Mobility Plan (SUMP) can represent a strategic tool for identifying the critical aspects to face for the creation of a new network of pedestrian routes aimed at improving urban walkability. The objective of this study is to define a set of principles and criteria, both tangible and intangible, for calculating the proximity index (PI). The PI may consequently drive urban regeneration projects also through the design of new paths for crossing the enclaves to improve urban permeability and, therefore, the level of walkabilitySardinia Foundation (CUP F74I19001040007

Information Buried in B2B Contracts: Towards Identifying Interdependencies in IT Service Processes

A key aspect of Information Technology Service Management (ITSM) is the monitoring and evaluation of service performance – a task that is complicated by the presence of interrelationships among different service processes in a multiservice contract. While success in the service arrangement requires participant organizations’ knowledge about the nature of service dependency and their subsequent effect on performance measures; such information is often tacitly present in the service level agreement/contract documents. In this context, the aim of our research is extracting information that might be hidden in the service contracts to assist in better process management and contract (re)negotiation. We propose an information extraction driven framework for analyzing Service Level Agreements (SLA) for IT services. Our framework consists of three stages – 1) Service Entity Recognition, 2) Service Entity Context Recognition, and 3) Service Interdependency Analysis. In this article the focus is on stage 1, where we identify interrelationships by using domain ontology on a set of annotated industry-standard SLAs. Our ongoing research is aimed towards the creation and subsequent validation of process models from the information extracted from SLAs that will help both customer and service provider organizations in contract and compensation formulation, resource allocation, and SLA life cycle management

Winnowing ontologies based on application use

The requirements of specific applications and services are often over estimated when ontologies are reused or built. This sometimes results in many ontologies being too large for their intended purposes. It is not uncommon that when applications and services are deployed over an ontology, only a few parts of the ontology are queried and used. Identifying which parts of an ontology are being used could be helpful to winnow the ontology, i.e., simplify or shrink the ontology to smaller, more fit for purpose size. Some approaches to handle this problem have already been suggested in the literature. However, none of that work showed how ontology-based applications can be used in the ontology-resizing process, or how they might be affected by it. This paper presents a study on the use of the AKT Reference Ontology by a number of applications and services,and investigates the possibility of relying on this usage information to winnow that ontology

Ascertaining the Ideality of Photometric Stereo Datasets under Unknown Lighting

The standard photometric stereo model makes several assumptions that are rarely verified in experimental datasets. In particular, the observed object should behave as a Lambertian reflector, and the light sources should be positioned at an infinite distance from it, along a known direction. Even when Lambert’s law is approximately fulfilled, an accurate assessment of the relative position between the light source and the target is often unavailable in real situations. The Hayakawa procedure is a computational method for estimating such information directly from data images. It occasionally breaks down when some of the available images excessively deviate from ideality. This is generally due to observing a non-Lambertian surface, or illuminating it from a close distance, or both. Indeed, in narrow shooting scenarios, typical, e.g., of archaeological excavation sites, it is impossible to position a flashlight at a sufficient distance from the observed surface. It is then necessary to understand if a given dataset is reliable and which images should be selected to better reconstruct the target. In this paper, we propose some algorithms to perform this task and explore their effectiveness

Semantic model for mining e-learning usage with ontology and meaningful learning characteristics

The use of e-learning in higher education institutions is a necessity in the learning process. E-learning accumulates vast amount of usage data which could produce a new knowledge and useful for educators. The demand to gain knowledge from e-learning usage data requires a correct mechanism to extract exact information. Current models for mining e-learning usage have focused on the activities usage but ignored the actions usage. In addition, the models lack the ability to incorporate learning pedagogy, leading to a semantic gap to annotate mining data towards education domain. The other issue raised is the absence of usage recommendation that refers to result of data mining task. This research proposes a semantic model for mining e-learning usage with ontology and meaningful learning characteristics. The model starts by preparing data including activity and action hits. The next step is to calculate meaningful hits which categorized into five namely active, cooperative, constructive, authentic, and intentional. The process continues to apply K-means clustering analysis to group usage data into three clusters. Lastly, the usage data is mapped into ontology and the ontology manager generates the meaningful usage cluster and usage recommendation. The model was experimented with three datasets of distinct courses and evaluated by mapping against the student learning outcomes of the courses. The results showed that there is a positive relationship between meaningful hits and learning outcomes, and there is a positive relationship between meaningful usage cluster and learning outcomes. It can be concluded that the proposed semantic model is valid with 95% of confidence level. This model is capable to mine and gain insight into e-learning usage data and to provide usage recommendation