Ricerc@Sapienza

In the Attentional Boost Effect (ABE), images or words encoded with unrelated to-be-responded targets are later remembered better than images or words encoded with to-be-ignored distractors. In the realm of short-term memory, the ABE has been previously shown to enhance the short-term recognition of single-feature stimuli. The present study replicated this finding and extended it to a condition requiring the encoding and retention of colour-shape associations.

Conscientious objections against same sex unions raise
delicate issues concerning the relation between freedom of conscience and religion and
the prohibition of discrimination on grounds of sexual orientation. The Chapter analyzes
the main international and comparative experiences concerning conscientious objection
to same sex unions, focusing more in detail on the US and UK experience, as
well as on ECtHR case law and the main cases decided in other European countries.

In the Attentional Boost Effect (ABE), images or words presented with to-be-responded target squares are later recognized more accurately than images or words presented with to-be-ignored distractor squares. Surprisingly, previous studies investigating the ABE have always examined young participants: thus, the question of whether this memory facilitation can be also observed in older adults has never been tested.

The present study examined predictions of the early-phase-elevated-attention hypothesis of the attentional boost effect (ABE), which suggests that transient increases in attention at encoding, as instantiated in the ABE paradigm, should enhance the recognition of neutral and positive items (whose encoding is mostly based on controlled processes), while having small or null effects on the recognition of negative items (whose encoding is primarily based on automatic processes).

Over 3 million work-related injuries and illnesses occur annually. This symposium presents five empirically, contextually, and methodologically diverse studies that provide insight on how to enact effective safety interventions given different national, industry, and organizational features. The session opens with empirical findings from a large scale study undertaken in hospitals in China and India, providing important evidence on the role of national and organizational labor practices in safety management.

Person search has recently gained attention as the novel task of finding a person, provided as a cropped sample, from a gallery of non-cropped images, whereby several other people are also visible. We believe that i. person detection and re-identification should be pursued in a joint optimization framework and that ii. the person search should leverage the query image extensively (e.g. emphasizing unique query patterns). However, so far, no prior art realizes this. We introduce a novel query-guided end-to-end person search network (QEEPS) to address both aspects.

A desirable smart traffic-monitoring and street-safety system can elicit and support the intervention of law enforcement agencies or medical staff. Recently, there has been a dramatically higher demand for such smart systems. To this end, the International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S) was organized in conjunction with the 15th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS 2018).

Lighting design and modelling or industrial applications like luminaire planning and commissioning rely heavily on time-consuming manual measurements or on physically coherent computational simulations. Regarding the latter, standard approaches are based on CAD modeling simulations and offline rendering, with long processing times and therefore inflexible workflows. Thus, in this paper we propose a computer vision based system to measure lighting with just a single RGBD camera.

Faster R-CNN has established itself as the de-facto best object detector but it remains strongly limited in two aspects: (i) it is sensitive to background clutter and its classification performance decreases when it is confronted with more noisy proposals; (ii) it suffers when the objects vary largely in scale and specifically for the small objects. We address both issues with our geometric-proposals for Faster R-CNN (GP-FRCNN), whereby we re-rank the generic object proposals with an approximate geometric estimate of the scene.

Human activity recognition and forecasting can be used as a primary cue for scene understanding. Acquiring details from the scene has vast applications in different fields such as computer vision, robotics and more recently smart lighting. This work brings together advanced research in computer vision and the most modern technology in lighting. The goal of this work is to eliminate the need for any switches for lighting, which means that each person in the office perceives the entire office as all lit, while lights, which are not visible by the person, are switched off by the system.