Semantic representation is the task of conveying the meaning of a natural language utterance by converting it to a logical form that can be processed and understood by machines. It is utilised by many applications in natural language processing (NLP), particularly in tasks relevant to natural language understanding (NLU). Due to the widespread use of semantic parsing in NLP, many semantic representation schemes with different forms have been proposed; Universal Conceptual Cognitive Annotation (UCCA) is one of them. UCCA is a cross-lingual semantic annotation framework that allows easy annotation without requiring substantial linguistic knowledge. UCCA-annotated datasets have been released so far for English, French, German, Russian, and Hebrew. In this paper, we present a UCCA-annotated Turkish dataset of 400 sentences that are obtained from the METU-Sabanci Turkish Treebank. We provide the UCCA annotation specifications defined for the Turkish language so that it can be extended further. We followed a semi-automatic annotation approach, where an external semantic parser is utilised for the initial annotation of the dataset, which is manually revised by two annotators. We used the same semantic parser model to evaluate the dataset with zero-shot and few-shot learning, demonstrating that even a small sample set from the target language in the training data has a notable impact on the performance of the parser (15.6% and 2.5% gain over zero-shot for labelled and unlabelled results, respectively).

Following a three-year post-termination transition period to bring investor-state arbitration disputes, the investment protections afforded by Chapter 11 of the North American Free Trade Agreement (NAFTA) finally expired in June 2023. Chapter 11 was one of the most litigated, cited, commented, and copied investment treaties. An important, but largely ignored, part of its legacy is how the making of NAFTA Chapter 11 shaped its subsequent successful diffusion. Combining traditional legal assessment with computational text-as-data analysis, this article shows how the give and take during the negotiations generated buy-in on the part of Mexico and Canada and emulation by Latin American countries who helped to spread NAFTA Chapter 11 language globally. The link between the making and diffusion of NAFTA Chapter 11 highlights the power of negotiated compromise: sharing the pen with others may sometimes be the most effective way to write the rules that come to shape the world.

Researchers have long studied how the ideology of political leaders affects policymaking and social welfare. The limited coverage of cross-country ideology datasets, however, has meant that researchers have mainly focused on Organisation for Economic Co-operation and Development (OECD) countries. This letter therefore presents the Global Leader Ideology dataset, which vastly expands the scope of previous datasets by classifying chief executives as leftist, centrist, rightist or non-ideological in 182 countries annually from 1945 or independence to 2020. The letter describes the dataset's contents and coding, compares it to existing datasets, and illustrates its uses by exploring how the ideologies of political leaders differ around the world and over time. The letter thereby outlines a research agenda on the global causes of chief executives' ideologies and their effects on policies and socio-economic outcomes.

Knowledge about political representatives' behavior is crucial for a deeper understanding of politics and policy-making processes. Yet resources on legislative elites are scattered, often specialized, limited in scope or not always accessible. This article introduces the Comparative Legislators Database (CLD), which joins micro-data collection efforts on open-collaboration platforms and other sources, and integrates with renowned political science datasets. The CLD includes political, sociodemographic, career, online presence, public attention, and visual information for over 45,000 contemporary and historical politicians from ten countries. The authors provide a straightforward and open-source interface to the database through an R package, offering targeted, fast and analysis-ready access in formats familiar to social scientists and standardized across time and space. The data is verified against human-coded datasets, and its use for investigating legislator prominence and turnover is illustrated. The CLD contributes to a central hub for versatile information about legislators and their behavior, supporting individual-level comparative research over long periods.

This study presents the main motivation to investigate the COVID-19 pandemic, a major threat to the whole world from the day when it first emerged in China city of Wuhan. Predictions on the number of cases of COVID-19 are crucial in order to prevent and control the outbreak. In this research study, an artificial neural network with rectifying linear unit-based technique is implemented to predict the number of deaths, recovered and confirmed cases of COVID-19 in Pakistan by using previous data of 137 days of COVID-19 cases from the day 25 February 2020 when the first two cases were confirmed, until 10 July 2020. The collected data were divided into training and test data which were used to test the efficiency of the proposed technique. Furthermore, future predictions have been made by the proposed technique for the next 7 days while training the model on whole available data.

Recent trends in multimedia technologies indicate the need for richer imaging modalities to increase user engagement with the content. Among other alternatives, point clouds denote a viable solution that offers an immersive content representation, as witnessed by current activities in JPEG and MPEG standardization committees. As a result of such efforts, MPEG is at the final stages of drafting an emerging standard for point cloud compression, which we consider as the state-of-the-art. In this study, the entire set of encoders that have been developed in the MPEG committee are assessed through an extensive and rigorous analysis of quality. We initially focus on the assessment of encoding configurations that have been defined by experts in MPEG for their core experiments. Then, two additional experiments are designed and carried to address some of the identified limitations of current approach. As part of the study, state-of-the-art objective quality metrics are benchmarked to assess their capability to predict visual quality of point clouds under a wide range of radically different compression artifacts. To carry the subjective evaluation experiments, a web-based renderer is developed and described. The subjective and objective quality scores along with the rendering software are made publicly available, to facilitate and promote research on the field.

We present a new unified dataset of common-space ideal points, committee assignments, and financial interests for all state legislators in 1999. We describe the compilation of the dataset and offer a few possible applications.