Cyberbullying is a highly relevant phenomenon in adolescence. Making available sound cyberbullying scales in different countries represents an important research issue. The present study aimed at extending previous findings on the Cyberbullying Experience Scale (CES), assessing its psychometric properties in a sample of community-dwelling Italian adolescents (N = 2,112). Results showed that a bifactor model was the best fitting model for both CES Perpetration and Victimisation scale items being provided also with measurement invariance across male and female adolescents. Joint CFA results demonstrated the dissociability of the CES Perpetration and Victimisation scales. Both CES dimensions were provided with adequate internal consistency reliability. Both CES Perpetration and Victimisation scales yielded meaningful relationships with theoretically relevant external variables (e.g. school/behaviour grades, online activities, general aggression measures, and dysfunctional personality dimensions), suggesting also possible gender differences concerning the negative psychological and behavioural outcomes of cyberbullying. This study provides further support for the reliability and validity of CES Perpetration and Victimisation scales, extending its applicability to a different time period (i.e. adolescents) and a different cultural context (i.e. Italy).

In molecular biology, it is a general assumption that the ensemble of expressed molecules, their activities and interactions determine biological function, cellular states and phenotypes. Stable protein complexes-or macromolecular machines-are, in turn, the key functional entities mediating and modulating most biological processes. Although identifying protein complexes and their subunit composition can now be done inexpensively and at scale, determining their function remains challenging and labor intensive. This study describes Protein Complex Function predictor (PCfun), the first computational framework for the systematic annotation of protein complex functions using Gene Ontology (GO) terms. PCfun is built upon a word embedding using natural language processing techniques based on 1 million open access PubMed Central articles. Specifically, PCfun leverages two approaches for accurately identifying protein complex function, including: (i) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector and (ii) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing a hypergeometric statistical test to enrich the top NN GO terms within the child terms of the GO terms predicted by the RF models. The documentation and implementation of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

Existing depth-based approaches to predicting anthropometric measurements, such as body height, arm span and hip circumference, either directly compute the measurements on 3D point clouds, and thus are sensitive to noise, or fit a model to the observed depth values, which typically is time-consuming. In this paper, we rely on the intuition that, to predict a specific anthropometric measurement, one does not need to have detailed information about the entire body shape. We therefore introduce an approach to anthropometry based on a random regression forest trained from local depth cues. The local predictions are then accumulated into one global, image-level anthropometric measurement prediction. We introduce a forest refinement scheme, whose objective function directly relies on both the image-level prediction, as well as on the local predictions' reliability. The resulting approach has the advantage of being both computationally highly efficient and accurate.

In this work, we describe a novel method for creating 3D models of persons freely moving in front of a consumer depth sensor and we show how they can be used for long-term person re-identification. For overcoming the problem of the different poses a person can assume, we exploit the information provided by skeletal tracking algorithms for warping every point cloud frame to a standard pose in real time. Then, the warped point clouds are merged together to compose the model. Re-identification is performed by matching body shapes in terms of whole point clouds warped to a standard pose with the described method. We compare this technique with a classification method based on a descriptor of skeleton features and with a mixed approach which exploits both skeleton and shape features. We report experiments on two datasets we acquired for RGB-D re-identification which use different skeletal tracking algorithms and which are made publicly available to foster research in this new research branch.

We present a novel approach for detecting social interactions in a crowded scene by employing solely visual cues. The detection of social interactions in unconstrained scenarios is a valuable and important task, especially for surveillance purposes. Our proposal is inspired by the social signaling literature, and in particular it considers the sociological notion of F-formation. An F-formation is a set of possible configurations in space that people may assume while participating in a social interaction. Our system takes as input the positions of the people in a scene and their (head) orientations; then, employing a voting strategy based on the Hough transform, it recognizes F-formations and the individuals associated with them. Experiments on simulations and real data promote our idea.

Unsupervised categorization of objects is a fundamental problem in computer vision. While appearance-based methods have become popular recently, other important cues like functionality are largely neglected. Motivated by psycho logical studies giving evidence that human demonstration has a facilitative effect on categorization in infancy, we pro pose an approach for object categorization from depth video streams. To this end, we have developed a method for cap turing human motion in real-time. The captured data is then used to temporally segment the depth streams into actions. The set of segmented actions are then categorized in an un supervised manner, through a novel descriptor for motion capture data that is robust to subject variations. Further more, we automatically localize the object that is manipulated within a video segment, and categorize it using the corresponding action. For evaluation, we have recorded a dataset that comprises depth data with registered video sequences for 6 subjects, 13 action classes, and 174 object manipulations.

We combine detection and tracking techniques to achieve robust 3D motion recovery of people seen from arbitrary viewpoints by a single and potentially moving camera. We rely on detecting key postures, which can be done reliably, using a motion model to infer 3D poses between consecutive detections, and finally refining them over the whole sequence using a generative model. We demonstrate our approach in the cases of golf motions filmed using a static camera and walking motions acquired using a potentially moving one. We will show that our approach, although monocular, is both metrically accurate because it integrates information over many frames and robust because it can recover from a few misdetections.

The articulated body models used to represent human motion typically have many degrees of freedom, usually expressed as joint angles that are highly correlated. The true range of motion can therefore be represented by latent variables that span a low-dimensional space. This has often been used to make motion tracking easier. However, learning the latent space in a problem- independent way makes it non trivial to initialize the tracking process by picking appropriate initial values for the latent variables, and thus for the pose. In this paper, we show that by directly using observable quantities as our latent variables, we eliminate this problem and achieve full automation given only modest amounts of training data. More specifically, we exploit the fact that the trajectory of a person's feet or hands strongly constrains body pose in motions such as skating, skiing, or golfing. These trajectories are easy to compute and to parameterize using a few variables. We treat these as our latent variables and learn a mapping between them and sequences of body poses. In this manner, by simply tracking the feet or the hands, we can reliably guess initial poses over whole sequences and, then, refine them.

We combine detection and tracking techniques to achieve robust 3-D motion recovery of people seen from arbitrary viewpoints by a single and potentially moving camera. We rely on detecting key postures, which can be done reliably, using a motion model to infer 3-D poses between consecutive detections, and finally refining them over the whole sequence using a generative model. We demonstrate our approach in the case of people walking against cluttered backgrounds and filmed using a moving camera, which precludes the use of simple background subtraction techniques. In this case, the easy-to-detect posture is the one that occurs at the end of each step when people have their legs furthest apart.