Abstract
Agricultural cooperatives remain a significant component of the food and agriculture industry to help the stakeholders to provide services and have opportunities for themselves. One of the aims of an agricultural cooperative is to answer to the needs within the communities of the farmers. Agricultural cooperatives enable individual farmers to increase productivity and maximise their social welfare. Together the farmer members of an agricultural cooperative can buy input supplies cheaper and sell more of their products in larger markets at higher prices, which is not possible for an individual smallholder farmer otherwise. Some studies have shown that farmers who were members of cooperatives have gained higher revenue for their products and spent less on input. However, organising the hundreds of farmers into smaller groups to perform collective farming and marketing is crucial to strengthening their position in the food and agriculture industry. Thereby, in our work, we consider an agricultural cooperative of smallholder farmers as a multi-agent based coalitional model, where coalitions are formed based on the similarity among the smallholder farmers. In this paper, we propose a model and implement a heuristic-based algorithm to find the disjoint partition of the agents set. We evaluate the model and the algorithm based on the following criteria: (i) individual gain, (ii) runtime analysis, (iii) solution quality, and (iv) scalability. We theoretically prove that our coalitional model of an agricultural cooperative has conciseness, expressiveness and efficiency properties. Experimental results confirm that our algorithm is time efficient and scalable. We show, both empirically and theoretically, that our algorithm generates a solution within a bound of the optimal solution. We also show that our coalition model generates positive revenue for the smallholder farmers and the payoff division rule is individual rational. In addition, we generate a new dataset in the context of an agricultural cooperative to show the effectiveness and efficiency of the proposed coalitional model of the cooperative.

Abstract
Digital transformation creates a number of barriers that need to be surpassed by companies from the technological and organizational points of view. Concurrently, the complexity and nature of current market environments often demand new products, services, processes and business models, oftentimes supported by digital technologies. The objective of this paper is to contribute to a better understanding on the impact of a severe global crisis on the digital technologies' adoption process (and their associate drivers and barriers), with a special look on the strategies adopted by companies in terms of supply chain resilience. Specificities of the Portuguese industry are discussed through the analysis of five case studies.

Abstract
Sustainable mobility has become one of the most pressing issues in modern society. The need to raise awareness of climate change, combined with the overcrowding of metropolitan and urban areas, has produced a situation that requires an urgent solution. Some earlier approaches dealt primarily with transport-related issues, while some conceptual models attempted to increase the appeal of public transport by linking the services provided by public transport operators to a variety of city services. A practical and empirical answer, on the other hand, has not yet been given. This research addrebes these issues by taking a holistic approach and presenting a personalized recommendation system based on users' everyday activities as well as their mobility profiles. The crossing of both sources of information allows for a more user-centric experience, ensuring that the offers presented are adapted to the tastes of customers. The potential of such a system is proven using data from Porto, Portugal. Two types of data sources were used to obtain more accurate results: data from the automated fare collection system of the Porto Metropolitan Area, Portugal, and data from city services taken from Google Places. The fundamental idea behind tackling this problem is to encourage people to use public transport by providing them with incentives such as discounts, promotions and service offers to encourage them to use cleaner and more efficient modes of transport. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Abstract
Virtual Reality (VR) telepresence platforms are being challenged to support live performances, sporting events, and conferences with thousands of users across seamless virtual worlds. Current systems have struggled to meet these demands which has led to high-profile performance events with groups of users isolated in parallel sessions. The core difference in scaling VR environments compared to classic 2D video content delivery comes from the dynamic peer-to-peer spatial dependence on communication. Users have many pair-wise interactions that grow and shrink as they explore spaces. In this paper, we discuss the challenges of VR scaling and present an architecture that supports hundreds of users with spatial audio and video in a single virtual environment. We leverage the property of spatial locality with two key optimizations: (1) a Quality of Service (QoS) scheme to prioritize audio and video traffic based on users' locality, and (2) a resource manager that allocates client connections across multiple servers based on user proximity within the virtual world. Through real-world deployments and extensive evaluations under real and simulated environments, we demonstrate the scalability of our platform while showing improved QoS compared with existing approaches.

Abstract
Deep learning has recently gained popularity in the field of video anomaly detection, with the development of various methods for identifying abnormal events in visual data. The growing need for automated systems to monitor video streams for anomalies, such as security breaches and violent behaviours in public areas, requires the development of robust and reliable methods. As a result, there is a need to provide tools to objectively evaluate and compare the real-world performance of different deep learning methods to identify the most effective approach for video anomaly detection. Current state-of-the-art metrics favour weakly-supervised strategies stating these as the best-performing approaches for the task. However, the area under the ROC curve, used to justify this statement, has been shown to be an unreliable metric for highly unbalanced data distributions, as is the case with anomaly detection datasets. This paper provides a new perspective and insights on the performance of video anomaly detection methods. It reports the results of a benchmark study with state-of-the-art methods using a novel proposed framework for evaluating and comparing the different models. The results of this benchmark demonstrate that using the currently employed set of reference metrics led to the misconception that weakly-supervised methods consistently outperform semi-supervised ones.

Abstract
Visual representations have the potential to improve information understanding. We explore this idea in the development of NewsLine, an open-source web-based prototype that focuses on narrative visualizations of news content. Having structured data as input, the prototype produces a storyline which showcases the narrative's events and participants, allowing the user to interact with the visualization in a number of ways. We built an information hub around the storyline to allow for multiple levels of exploration, specifically the main visualization, the event information module, and the sidebar. The visualization depicts the sequence of events that make up a news story, as well as the interactions between the involved parties in each event. The event information module presents additional information on a particular event. The sidebar is the “control center” of the visualization, unlocking a number of interactions and configurations. The prototype was evaluated with a user study with journalists and also with an online survey which gathered feedback from 178 potential end users. From these, 106 participants (60.6%) provided a rating of four or above (one to five scale) when asked to quantify their interest in using the application. Moreover, participants were asked to rank the importance of the visualization elements used. The results highlight that two elements stand out as the most important, the events and the entities. Overall, the participants generally found the application to be useful, but in need of some work in order for it to be made available to a broader public.