Abstract
Taking advantage of the flexibility of Distributed Energy Resources (DER) can help improve distribution network efficiency, reliability and resilience. EUniversal project aims to facilitate the use of flexibility services and interlink active system management of distribution system operators with electricity markets. congestion management and voltage control have been identified has the most relevant needs, within different operation timeframes, namely: from day, weeks and years ahead. This paper considers long-term flexibility services to support maintenance actions, increasing the periods where is technically possible to perform maintenance actions maintaining security of operation. The methodology developed to schedule planned maintenance actions based on forecasted network profiles, maintenance costs, network reconfiguration capability and flexibility contracted in long-term flexible markets will be presented. © The Institution of Engineering and Technology 2023.

Abstract
Cyberattacks are performed against all organizations including Higher Education Institutions (HEIs). When these attacks are successful, they can affect the regular operation of these institutions and may cause the leak of essential or sensitive data that can be misused or become inaccessible. Therefore, the adoption of current security services is important for devices and services exposed to the Internet that should run the latest and secure versions of web-related protocols and comply with the latest security-related guidelines and recommendations. This article surveys and analyzes the status of web-related security services, namely the Hyper Text Transfer Protocol Secure (HTTPS) and the Domain Name System Security Extensions (DNSSEC) services, in Brazilian HEIs. The results of this survey show that regarding HTTPS around 15% do not use any SSL / TLS certificate and of those supporting it, about 14% do not demand its usage. Regarding DNSSEC, the analysis shows that only around 2% of the HEIs are implementing this protocol. These results show that it is important to design an effective and continuous action plan for HEIs regarding the support or discontinuity of versions of these protocols, in order to improve their protection against cyberattacks.

Abstract
Background. Traditional burning is a practice with social and ecological value used worldwide. However, given the often improper and negligent use of fire, this practice is often associated with rural fire ignitions.Aims. Systematise experts' understanding of traditional burning and identify its challenges in the Portuguese context.Methods. Twenty-eight Portuguese experts from industry, academia, NGOs and public entities with in-depth involvement in fire and forest management were interviewed to create a mental model of traditional burning in Portugal.Key results. Eight dimensions were identified: motivations behind traditional burning, alternative solutions, risks before a traditional burn, risks during a traditional burn, underlying causes of risk, exogenous elements and factors, potential impacts, and activities leading to a successful traditional burn.Conclusions. This study provides a comprehensive understanding of traditional burn practice in the Portuguese context and offers a baseline to support stakeholders and policymakers in managing traditional burning's social and environmental impacts in the future.Implications. This research offers several implications across the eight dimensions identified, including the need to improve regulations on the use of fire and fuel reduction policies, promote fire use education and feasible and affordable alternatives to traditional burning, and increase communities' commitment to mitigation actions.

Abstract
The first edition of the Implicit Author Characterization from Texts for Search and Retrieval (IACT'23) aims at bringing to the forefront the challenges involved in identifying and extracting from texts implicit information about authors (e.g., human or AI) and using it in IR tasks. The IACT workshop provides a common forum to consolidate multi-disciplinary efforts and foster discussions to identify the wide-ranging issues related to the task of extracting implicit author-related information from the textual content, including novel tasks and datasets. We will also discuss the ethical implications of implicit information extraction. In addition, we announce a shared task focused on automatically determining the literary epochs of written books.

Abstract
Every year, the VISion Understanding and Machine intelligence (VISUM) summer school runs a competition where participants can learn and share knowledge about Computer Vision and Machine Learning in a vibrant environment. 2021 VISUM's focused on applying those methodologies in fashion. Recently, there has been an increase of interest within the scientific community in applying computer vision methodologies to the fashion domain. That is highly motivated by fashion being one of the world's largest industries presenting a rapid development in e-commerce mainly since the COVID-19 pandemic. Computer Vision for Fashion enables a wide range of innovations, from personalized recommendations to outfit matching. The competition enabled students to apply the knowledge acquired in the summer school to a real-world problem. The ambition was to foster research and development in fashion outfit complementary product retrieval by leveraging vast visual and textual data with domain knowledge. For this, a new fashion outfit dataset (acquired and curated by FARFETCH) for research and benchmark purposes is introduced. Additionally, a competitive baseline with an original negative sampling process for triplet mining was implemented and served as a starting point for participants. The top 3 performing methods are described in this paper since they constitute the reference state-of-the-art for this particular problem. To our knowledge, this is the first challenge in fashion outfit complementary product retrieval. Moreover, this joint project between academia and industry brings several relevant contributions to disseminating science and technology, promoting economic and social development, and helping to connect early-career researchers to real-world industry challenges.

Abstract
Dynamic Time Warping (DTW) is a robust method to measure the similarity between two sequences. This paper proposes a method based on DTW to analyse high-speed data streams. The central idea is to decompose the network traffic into sequences of histograms of packet sizes and then calculate the distance between pairs of such sequences using DTW with Kullback-Leibler (KL) distance. As a baseline, we also compute the Euclidean Distance between the sequences of histograms. Since our preliminary experiments indicate that the distance between two sequences falls within a different range of values for distinct types of streams, we then exploit this distance information for stream classification using a Random Forest. The approach was investigated using recent internet traffic data from a telecommunications company. To illustrate the application of our approach, we conducted a case study with encrypted Internet Protocol Television (IPTV) network traffic data. The goal was to use our DTW-based approach to detect the video codec used in the streams, as well as the IPTV channel. Results strongly suggest that the DTW distance value between the data streams is highly informative for such classification tasks.