Local and territorial determinants in the realization of public–private–partnerships: an empirical analysis for Italian provinces

ABSTRACT Relational networks and intangible factors are crucial elements for the competitiveness of a territory. Public–Private–Partnerships (PPPs), in particular, allow for the provision of goods and services that favour the exploitation of complementarities between public and private resources. They aim at promoting an increase in the overall efficiency of investment projects through a complex mechanism that distributes risk and revenues among stakeholders. This paper examines the local and territorial determinants of PPPs through an econometric analysis based upon Italian municipal data, grouped at the provincial level. Using a tobit model, we analyse the relationship between the realization of successful PPP initiatives and different sets of factors, including less analysed local and territorial determinants. We stress the role of the local management of infrastructure assets, the administrative efficiency of local authorities and the diffusion of previous local development initiatives. Local management and territorial context factors explain most of the occurrence of successful PPP initiatives in the pre-crisis period while usual determinants (infrastructure endowment and financial distress) display a weaker effect

Copy-Move Forgery Detection by Matching Triangles of Keypoints

Copy-move forgery is one of the most common types of tampering for digital images. Detection methods generally use block-matching approaches, which first divide the image into overlapping blocks and then extract and compare features to find similar ones, or point-based approaches, in which relevant keypoints are extracted and matched to each other to find similar areas. In this paper, we present a very novel hybrid approach, which compares triangles rather than blocks, or single points. Interest points are extracted from the image, and objects are modeled as a set of connected triangles built onto these points. Triangles are matched according to their shapes (inner angles), their content (color information), and the local feature vectors extracted onto the vertices of the triangles. Our methods are designed to be robust to geometric transformations. Results are compared with a state-of-the-art block matching method and a point-based method. Furthermore, our data set is available for use by academic researchers

Scale detection via keypoint density maps in regular or near-regular textures

In this paper we propose a new method to detect the global scale of images with regular, near regular, or homogenous textures. We define texture ‘‘scale’’ as the size of the basic elements (texels or textons) that most frequently occur into the image. We study the distribution of the interest points into the image, at different scale, by using our Keypoint Density Maps (KDMs) tool. A ‘‘mode’’ vector is built computing the most frequent values (modes) of the KDMs, at different scales. We observed that the mode vector is quasi linear with the scale. The mode vector is properly subsampled, depending on the scale of observation, and compared with a linear model. Texture scale is estimated as the one which minimizes an error function between the related subsampled vector and the linear model. Results, compared with a state of the art method, are very encouraging

Copy-move Forgery Detection via Texture Description

Copy-move forgery is one of the most common type of tampering in digital images. Copy-moves are parts of the image that are copied and pasted onto another part of the same image. Detection methods in general use block-matching methods, which first divide the image into overlapping blocks and then extract features from each block, assuming similar blocks will yield similar features. In this paper we present a block-based approach which exploits texture as feature to be extracted from blocks. Our goal is to study if texture is well suited for the specific application, and to compare performance of several texture descriptors. Tests have been made on both uncompressed and JPEG compressed images

Saliency Based Image Cropping

Image cropping is a technique that is used to select the most relevant areas of an image and discarding the useless parts. Handmade selection, especially in case of large photo collections, is a time consuming task. Automatic image cropping techniques may help users, suggesting to them which part of the image is the most relevant, according to specific criteria. In this paper we suppose that the most visually salient areas of a photo are also the most relevant ones to the users. We compare three different saliency detection methods within an automatic image cropping system, to study the effectiveness of the related saliency maps for this task. We furthermore extended one of the three methods (our previous work), which is based on the extraction of keypoints from the image. Tests have been conducted onto an online available dataset, made of 5000 images which have been manually labeled by 9 users

Detecting Multiple Copies in Tampered Images

Copy-move forgeries are parts of the image that are duplicated elsewhere into the same image, often after being modified by geometrical transformations. In this paper we present a method to detect these image alterations, using a SIFT-based approach. First we describe a state of the art SIFT-point matching method, which inspired our algorithm, then we compare it with our SIFT-based approach, which consists of three parts: keypoint clustering, cluster matching, and texture analysis. The goal is to find copies of the same object, i.e. clusters of points, rather than points that match. Cluster matching proves to give better results than single point matching, since it returns a complete and coherent comparison between copied objects. At last, textures of matching areas are analyzed and compared to validate results and to eliminate false positives

« I like it but that’s not what we need ». A critical analysis of participatory music projects in refugee reception centres.

The 2015-18 refugee reception crisis featured strong humanitarian engagement and political mobilization of the European civil society. Organizations and engaged citizens played a key role in integrating the field practices of the state-mandated actors in charge of the reception and accommodation of asylum seekers. In many instances, the civil society represented the sole provider of services oriented towards the sociocultrual integration and the physical and psychological wellbeing of newcomers asylum seekers. Artistic practices were often employed by civil society actors to establish ties and relationships with newcomers. Practitioners, artists and culture professionals, indeed, designed and carried out participatory projects based on performing arts involving asylum seekers. The principles that motivated such activity revolved around the idea that being involved in the arts would help participants to relieve from post-traumatic stress, cope with gruelling wait and uncertainty for their asylum application, and ultimately get back to a normal life. I draw on ethnographic fieldwork contucted during the refugee reception crisis to critically discuss the impact of music-based participatory projects involving the residents of collective reception centres in Belgium. I argue that the principles motivating these projects, as well as the initial and final outcomes intended by practitioners (non-migrants), are often not shared by participants (migrants) who have different priorities, motivations and objectives. Although often positive, the impact of such projects is thus unrelated to the nature of the proposed activities, and springs from random contextual factors. I conclude by providing some recommendations to be adopted in the design and implementation of art-based projects in the specific context of collective reception centres

Toward an Integrated System for Surveillance and Behaviour Analysis of Groups and People

Security and INTelligence SYStem is an Italian research project which aims to create an integrated system for the analysis of multi-modal data sources (text, images, video, audio), to assist operators in homeland security applications. Within this project the Scientific Research Unit of the University of Palermo is responsible of the image and video analysis activity. The SRU of Palermo developed a web service based architecture that provides image and video analysis capabilities to the integrated analysis system. The developed architecture uses both state of the art techniques, adapted to cope with the particular problem at hand, and new algorithms to provide the following services: image cropping, image forgery detection, face and people detection, weapon detection and classification, and terrorist logo recognition. In the last phase of the project we plan to include in our system new services, mainly oriented to the video analysis, to study and understand the behaviour of individuals, either alone or in a group

Solidarity with vulnerable migrants during and beyond the state of crisis

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