Abstract
Time expressions embedded in text are important for many downstream tasks in NLP and IR. They have been, for example, utilized for timeline summarization, named entity recognition, temporal information retrieval, question answering and others. In this paper, we introduce a novel analytical approach to analyzing characteristics of time expressions in diachronic text collections. Based on a collection of news articles published over a 33-years' long time span, we investigate several aspects of time expressions with a focus on their interplay with publication dates of containing documents. We utilize a graph-based representation of temporal expressions to represent them through their co-occurring named entities. The proposed approach results in several observations that could be utilized in automatic systems that rely on processing temporal signals embedded in text. It could be also of importance for professionals (e.g., historians) who wish to understand fluctuations in collective memories and collective expectations based on large-scale, diachronic document collections. © 2022 ACM.

Abstract
The paper presents the Scratch4All project, co-financed by the Portuguese Social Innovation Mission Structure, namely the presentation of the methodology applied in the impact assessment of the first year of the project. A project that uses Scratch software to teach programming languages to fourth grade students (elementary school). Based on the defined indicators, on the characteristics of the beneficiaries and on the activities implemented, adequate measurement instruments were developed to verify the impacts on the target audience. Using secondary and primary sources, indicators were developed to measure the level of change that occurred in each project beneficiary, using quantitative tools. Regarding the software used, Scratch, it was possible to perceive that it enables greater group work skills, working on current social issues and/or technological profiles, fosters greater responsibility in the use of technologies, within the scope of digital citizenship, generates an increase in creativity and innovation, greater willingness to investigate and research different topics, greater communication and collaborative work. © 2022 IEEE Computer Society. All rights reserved.

Abstract
Hashtags are commonly used in social media communication not only to categorize conversations but particularly to raise attention and generate debate of certain topics. Hashtag activism is one of the areas that is gaining particular attention from academics and the overall society. The focus of this chapter is hashtag attributes. Particularly, it analyses and compares four hashtags related to violence against women that circulated on social networks during the COVID-19 pandemic: #16Days, #IsolatedNotAlone, #womensupportingwomen, and #NiUnaMenos. The chapter highlights important aspects to increase the effectiveness of communication with the use of hashtags.

Abstract
Ever since the arrival of streaming platforms, society has become more focused on understanding how to take the next step forward in terms of innovation. Streaming is now a new reality that is growing by the second, and businesses have begun to spend more money on streaming services in order to adapt and improve their products for this new era. This is also the case of Disney+, released at the end of 2019, which had an immediate unexpected growth upon its launch. In comparison to other streaming platforms, Disney+ had an easier debut because of its wide range of unique material available to watch. This study explores Disney+ as a brand, a streaming platform, its features and how they compare to others, as well as the company's business model and revenue, all of which contributed to Disney+ 's current position among other streaming platforms. Regarding Disney+, a survey with 84 responses was conducted, and the results were analysed using descriptive and inferential (chi-square independence test) statistics. As the value of the calculated test statistic (11.24) (chi-square test) is higher than the value in the chi-square table, we conclude that there is a statistically significant association between age and being influenced by advertising.

Abstract
This paper proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

Abstract
In the published article, there was an error in Table 2 as published. The units of the Total body center of mass sway in x-axis (TBCMx) and y-axis (TBCMy) were shown in mm when they should be in cm. The corrected Table 2 and its caption appear below. In the published article, there was an error in Table 3 as published. The units of the Total body center of mass sway in x-axis (TBCMx) and y-axis (TBCMy) were shown in mm. The correct unit is cm. The corrected Table 3 and its caption appear below. In the published article, there was an error in Figure 3 as published. The units of the Total body center of mass sway in x-axis were shown in mm in the vertical axis of the plot. The correct unit is cm. The corrected Figure 3 and its caption appear below. In the published article, there was an error in Supplementary Table S.I. The units of the Total body center of mass sway in x-axis (TBCMx) and y-axis (TBCMy) were shown in mm. The correct unit is cm. The correct material statement appears below. In the published article, there was a mistake on the computation description of one of the assessed parameters (total body center of mass). A correction has been made to “Data Processing,” Paragraph 3: “For each gait cycle, we computed the 24 spatiotemporal and kinematic gait parameters listed in Table 2 and defined in (15). The total body center of mass (TBCM) sway was computed as the standard deviation of the distance (in the x/y-axis, i.e., medial-lateral and vertical directions) of the total body center of mass (TBCM), in relation to the RGBD sensor’s coordinate system, for all gait cycle frames. For each frame, TBCM’s position is the mean position of all body segments’ CM, which was obtained according to (21).” The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated. © 2022 Vilas-Boas, Rocha, Cardoso, Fernandes, Coelho and Cunha.