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.

Abstract
Water scarcity and security drive attention to water reuse in policy and business. However, water reuse may generate new water loops and challenge water governance with new and different types of water, risks, involved actors, and responsibilities. These challenges demand robust institutional arrangements related to water governance. This article assesses the institutional arrangements associated with four case studies in Spain, Italy, Croatia, and Israel. The findings reveal that the more diverse the water uses and users, the more challenges and risks, particularly those associated with institutional arrangements such as quality standards, sanctions, and conflict prevention, are likely to emerge. The weaknesses of governance models and regulations to deal with changes, uncertainties, and public resistance call for special attention to the design of the institutional arrangements before the adoption. Independent of the type of technology adopted, governance may be improved by ensuring internal and external water monitoring; integrating water management with spatial concerns; improving training, expert engagement, and civil society awareness; and reducing water reuse costs. In addition, alternative models that guarantee the efficiency of governance in attaining objectives and assuring the participation of new water users in the management of water reuse loops may also improve governance.

Abstract
We present a new large-scale emotion-labeled symbolic music dataset consisting of 12 k MIDI songs. To create this dataset, we first trained emotion classification models on the GoEmotions dataset, achieving state-of-the-art results with a model half the size of the baseline. We then applied these models to lyrics from two large-scale MIDI datasets. Our dataset covers a wide range of fine-grained emotions, providing a valuable resource to explore the connection between music and emotions and, especially, to develop models that can generate music based on specific emotions. Our code for inference, trained models, and datasets are available online.

Abstract
The increasing integration of renewable energy sources (RES) at different voltage levels of the distribution grid has led to technical challenges, namely voltage and congestion problems. Conversely, the integration of new Distributed Energy Resources (DER) provides the necessary flexibility to accommodate higher RES integration levels. This work describes the development of innovative functional modules, based on optimal power flow calculations and grid forecasting, dedicated to the predictive management of the distribution grid considering DER flexibility, which are integrated into a commercial SCADA/DMS solution. © The Institution of Engineering and Technology 2023.