Optimizing Crowd Counting in Dense Environments Through Curriculum Learning Training Strategy

Counting individuals in highly crowded environments, characterized by thousands of people, has garnered significant attention in recent years, due to the high number of vertical markets wherein such algorithms can prove beneficial, ranging from smart city and transportation to retail sectors, among others. Within this context, in this paper we introduce a novel training methodology tailored for estimating the number of people, ensuring precise counting accuracy in both moderately and highly crowded scenarios. The proposed approach exploits a formulation of the problem based on point detection, where each point represents an individual’s head. Our innovative contributions center around the designing of a novel training strategy employing Curriculum Learning (CL), which aims to replicate the gradual learning process observed in human cognition, training on simpler tasks at the beginning and tackling more complex tasks as the training evolves. In order to evaluate the complexity of each sample image, we propose a novel indicator taking into account both the number of people and their distribution within the image. The experimentation phase encompassed 18 publicly available datasets; the obtained results validate the effectiveness of the proposed approach, surpassing the baseline state-of-the-art point detection by 71% and 70% in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), respectively

La dimensione Testuale del Videogioco. Classificazione dei transcript dei videogiochi basata sul lessico

In this work, we explore the textual dimension of video games. Despite their pronounced visual and interactive characteristics, video games can be regarded as documents due to their narrative and communicative elements. Our research delves into this textual dimension to automatically generate rating tags associated with offensive language, violence, and the presence of drugs. We utilized a dictionary of English slang, compiled from various online sources and manually annotated with four categories: Slang, Violence, Drugs, and Discrimination. The resulting electronic dictionary facilitated the automatic assignment of the three rating tags with high precision. It has also been employed to classify video games based on their lexical content. The two classification tasks – by rating tags and by lexical dimension – could pave the way for an automatic warning system capable of analyzing the full textual dimension of a video game

Sociodemographic and psychological factors affecting motor vehicle crashes (MVCs): a classification analysis based on the contextual-mediated model of traffic-accident involvement

The study aimed to determine the sociodemographic and psychological profiles of drivers with a history of motor vehicle crashes (MVCs), following the contextual-mediated model of crash involvement, and trying to define similarities and differences with drivers without MVCs. Although road trauma prevention has become a central public health issue, the study of psychological determinants of MVCs does not have consistent results due to methodological and theoretical weaknesses. Three-hundred and forty-five active drivers (20% females) completed an extensive office-based fitness-to-drive evaluation including measures of cognition, personality, self-reported driving-related behaviors, attitudes, as well as computerized measures of driving performance. The Classification and Regression Tree method (CART) was used to identify discriminant predictors. The classification identified several relevant predictors; the personality trait of Discostraint (as a distal context variable; cut-point: 50 T points) and motor speed (as a proximal context variable; cut-point: 64 percentile ranks). The global classification model increased approximately 3 times the probability of identifying people with a history of MVC involvement, starting from an estimated prevalence of being involved in an MVC in a period of five years in the population of active drivers. Consistent with the 'contextual-mediated model of traffic accident involvement', the results of the present study suggest that road trauma analysis should focus on both distal and proximal driver-related factors by paying attention to their association in determining MVCs. These results represent a valuable source of knowledge for researchers and practitioners for preventing road trauma

Flatness‐based control in successive loops for mechatronic motion transmission systems

Mechatronic systems with nonlinear dynamics are met in motion transmission applications for vehicles and robots. In this article, the control problem for the nonlinear dynamics of mechatronic motion transmission systems is solved with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of these systems is separated into a series of subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system, and control about it can be performed with inversion of its dynamics as in the case of input-output linearized flat systems. In this chain of i = 1, 2, ... , N subsystems, the state variables of the subsequent (i+1)-th subsystem become virtual control inputs for the preceding i-th subsystem and so on. In turn, exogenous control inputs are applied to the last subsystem and are computed by tracing backwards the virtual control inputs of the preceding N - 1 subsystems. The whole control method is implemented in successive loops, and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in the following two case studies: (a) control of a permanent magnet linear synchronous motor (PMLSM)-actuated vehicle's clutch and (ii) control of a multi-Degrees of Freedom (multi-DOF) flexible joint robot

Optimal Placement of Damage-Free Self-centring Links in Steel EBFs

Low-damage self-centring steel structures have been proposed over the last few decades as seismic-resistant frames capable of achieving seismic resilience during strong earthquakes. Recently, the authors proposed a damage-free self-centring link as an innovative seismic link in steel eccentrically braced frames and proved its effectiveness through experimental and numerical analyses. However, there is a lack of generalised design recommendations for self-centring structures, and follow-up studies showed that their peak deformations are increased compared to conventional structures. This study investigates the opportunity to optimise the location of SC-links in EBFs to enhance the seismic performance of structures in terms of both peak and residual deformations. A 6-story, 3-bay case-study structure has been designed and successively upgraded with SC-links. Numerical models have been developed in OpenSees, and Incremental Dynamic Analyses have been performed to investigate and compare their seismic performance. The seismic responses of the case-study structures equipped with different SC-links layouts have been successively evaluated, providing some considerations regarding their optimal distributions. A Genetic Algorithm implemented in Matlab, interfacing with OpenSees for Non-Linear Time-History Analyses, has been validated using a Brute-Force Approach. The results demonstrate that integrating a limited number of SC-links in steel EBFs can enhance their seismic performance, highlighting the efficiency of the proposed GA

The Budgeted Labeled Minimum Spanning Tree Problem

In order to reduce complexity when designing multi-media communication networks, researchers often consider spanning tree problems defined on edge-labeled graphs. The earliest setting addressed in the literature aims to minimize the number of different media types, i.e., distinct labels, used in the network. Despite being extensively addressed, such a setting completely ignores edge costs. This led to the definition of more realistic versions, where budgets for the total cost, or the number of distinct labels allowed, were introduced. In this paper, we introduce and prove the NP-hardness of the Budgeted Labeled Minimum Spanning Tree problem, consisting in minimizing the cost of a spanning tree while satisfying specified budget constraints for each label type. This problem combines the challenges of cost efficiency and label diversity within a fixed budgetary framework, providing a more realistic and practical approach to network design. We provide three distinct mathematical programming formulations of the problem and design a Lagrangian approach to derive tighter lower bounds for the optimal solution of the problem. The performances of the proposed methods are assessed by conducting a series of computational experiments on a variety of randomly generated instances, which showed how the complexity of the problem increases as the size of the network, as well as the number of labels, increase and the budget restrictions are tightened