Abstract
With the massive data collection from different devices, spanning from mobile devices to all sorts of IoT devices, protecting the privacy of users is a fundamental concern. In order to prevent unwanted disclosures, several Privacy-Preserving Mechanisms (PPMs) have been proposed. Nevertheless, due to the lack of a standardized and universal privacy definition, configuring and evaluating PPMs is quite challenging, requiring knowledge that the average user does not have. In this paper, we propose a privacy toolkit - Privkit - to systematize this process and facilitate automated configuration of PPMs. Privkit enables the assessment of privacy-preserving mechanisms with different configurations, while allowing the quantification of the achieved privacy and utility level of various types of data. Privkit is open source and can be extended with new data types, corresponding PPMs, as well as privacy and utility assessment metrics and privacy attacks over such data. This toolkit is available through a Python Package with several state-of-the-art PPMs already implemented, and also accessible through a Web application. Privkit constitutes a unified toolkit that makes the dissemination of new privacy-preserving methods easier and also facilitates reproducibility of research results, through a repository of Jupyter Notebooks that enable reproduction of research results.

Abstract
Organizations assume a key role in the goal of achieving sustainable development and are influential elements on the path to sustainability. Allied with competitiveness, today, there is also a strategy based on sustainability, anchored in the concept of responsibility, minimizing the potential negative effects of our actions through innovative products, services, processes, and models. Measuring and monitoring these efforts is currently a challenge for organizations. This study adopts a mixed methods approach to address this challenge and identifies 13 tools and 16 dimensions that are central elements in the process of measuring and monitoring sustainable innovation. The findings indicate that the dimensions related to social and governance components are the most relevant in sustainable innovation, while inclusion and entrepreneurship are dimensions that are not highly valued by these tools. © 2024 The Authors

Abstract
Data-to-text systems offer a transformative approach to generating textual content in data-rich environments. This paper describes the architecture and deployment of Prosebot, a community-driven data-to-text platform tailored for generating textual summaries of football matches derived from match statistics. The system enhances the visibility of lower-tier matches, traditionally accessible only through data tables. Prosebot uses a template-based Natural Language Generation (NLG) module to generate initial drafts, which are subsequently refined by the reading community. Comprehensive evaluations, encompassing both human-mediated and automated assessments, were conducted to assess the system's efficacy. Analysis of the community-edited texts reveals that significant segments of the initial automated drafts are retained, suggesting their high quality and acceptance by the collaborators. Preliminary surveys conducted among platform users highlight a predominantly positive reception within the community.

Abstract
The textile industry, a vital sector in global production, relies heavily on dyeing processes to meet stringent quality and consistency standards. This study addresses the challenge of identifying and mitigating non-conformities in dyeing patterns, such as stains, fading and coloration issues, through advanced data analysis and machine learning techniques. The authors applied Random Forest and Gradient Boosted Trees algorithms to a dataset provided by a Portuguese textile company, identifying key factors influencing dyeing non-conformities. Our models highlight critical features impacting non-conformities, offering predictive capabilities that allow for preemptive adjustments to the dyeing process. The results demonstrate significant potential for reducing non-conformities, improving efficiency and enhancing overall product quality.

Abstract
In today's world, the idea of sustainable fashion is gaining traction. Finding a link between pricing and the purchase of sustainable clothes is the aim of this study. Regression models and t-tests of two independent samples (two-tailed tests) were applied by means of the application of a questionnaire. The study found that consumers' willingness to pay for price increases is related with non-linear (quadratic or exponential) product pricing. The results of this study suggest that consumers are willing to pay higher prices for sustainable clothing. Through an understanding of the relationship between price and consumer behavior, businesses can more effectively align their pricing strategies with the demands of environmentally conscious consumers. © 2024, IGI Global. All rights reserved.

Abstract
In this letter, we introduce the computation-limited (comp-limited) signals, a communication capacity regime where the computational complexity of signal processing is the primary constraint for communication performance, overriding factors such as power or bandwidth. We present the Spectro-Computational (SC) analysis, a novel mathematical framework designed to enhance classic concepts of information theory -such as data rate, spectral efficiency, and capacity - to accommodate the computational complexity overhead of signal processing. We explore a specific Shannon regime where capacity is expected to increase indefinitely with channel resources. However, we identify conditions under which the time complexity overhead can cause capacity to decrease rather than increase, leading to the definition of the comp-limited signal regime. Furthermore, we provide examples of SC analysis and demonstrate that the Orthogonal Frequency Division Multiplexing (OFDM) waveform falls under the comp-limited regime unless the lower-bound computational complexity of the N-point Discrete Fourier Transform (DFT) problem verifies as ohm (N)$ , which remains an open challenge in the theory of computation.