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Estonia Interview Transcripts
No full text<p>Interviews in Russian and English taken by team members of the Gulag Echoes Project between Winter of 2021 and Summer 2022.</p>
Dataset for software vulnerability detection
No full text<ol><li>raw_C.csv<ul><li>Raw (it may includes comments) source code in C.</li><li>Attributes:<ul><li>cwe_id</li><li>name: method name</li><li>repo_name: repository name</li><li>repo_url: repository url</li><li>old_path</li><li>file_change_id: used as a key in SQLite</li><li>method_change_id: used as a key in SQLite</li><li>code: source code</li><li>before_change: indicates whether the code is vulnerable (1) or not (0)</li></ul></li></ul></li></ol>
Russian Interview Transcripts_Vepsi
No full text<p>Transcripts of interviews conducted between October 2019 and September 2020 by Gulagechoes team members. Transcripts in Russian and some in English.</p>
A single cell atlas of the human liver tumor microenvironment
No full text<p>Matlab scripts used to analyze data associated with the manuscript entitled "A single cell atlas of the human liver tumor microenvironment".</p>
<p>*please used Matlab 2019b to run the following m files.</p>
<p> </p>
<p>Files:</p>
<p>inputData.mat: mat contains all raw and preprocessed data used in the study</p>
<p>Create_Interactions_Network.m: Matlab script used to calculate Ligand-Receptor interaction score between different cell types. The script creates panels of Figure 3 and Table S5.</p>
<p>Hepatocytes_Reconstruction.m: Matlab script used to reconstruct human hepatocytes zonation along the lobule axis. The script creates panel 'c' of Figure 4, Figure S4, and Table S7.</p>
<p>Cancer_Cells_Spatial_Analysis.m: Matlab script used to calculate differential gene expression between malignant cells found at different zones (malignant border, malignant core, and fibrotic zone) captured by laser microdissection. The script creates panel 'd' of Figure 4</p>
<p>helperFunctions.zip: This folder contains required functions used by the m files.</p>
Investigating the Relationship between ASEAN's Carbon Emissions and Foreign Direct Investment: A Road to Sustainable Growth
No full text<p><strong>Abstract: </strong>In order to promote sustainable development and lessen environmental deterioration, understanding the underlying factors that contribute to environmental degradation and lowering carbon footprints are now more important than ever. This study therefore examines how environmental policies have affected FDI inflow and carbon emission reduction in the ASEAN from 2000 to 2022. Applying the panel smooth transition regression model, the results demonstrated that there is no linear link between the two variables. Additionally, it illustrates that while foreign direct investment increases carbon emissions when the economy is in a low regime, the connection between the two variables changes as the economy moves to a high regime and turns negative and significant. The study also demonstrates that once Carbon emission exceeds a specific threshold, FDI can diminish it. On the basis of these findings, fresh policymakers' insights are offered, and many proposals for policies to enhance ASEAN's environmental quality are made. Additionally, it is recommended that Carbon emission and Foreign direct investment be taken into account while creating sustainable environmental policy.</p><p>Keywords: Sustainable Development, Institutional Quality, Foreign Direct Investment, Carbon Emission</p>
Do Firms Buy Back their Securities to Signal Financial Prowess? An Empirical Study
No full text<p><strong>Abstract: </strong>In this research study, the hypothesis that the share buyback does not lead to abnormal returns is rejected (Lakonishok and Vermaelen, 1990). From the analysis of the 15 firms, the evidence is found that the open market operations and repurchase leads to a sudden surge in the share price and abnormal returns in the stock markets. The buyback of the securities is an extremely renowned phenomenon and the impact of this phenomenon on the share price is well recognized. In the financial literature, the impact of the share purchase on the market price of the securities and Earning Per Share is far well recognized. Thus, buyback is the process through which companies purchase their securities from existing shareholders. This study aims to discuss the impact of the announcement and the record date of buyback of approximately 15 share buybacks during the period 2022-2023 on the returns and the market share price. We take the shares and scrips that are listed on the National Stock Exchange (NSE). The returns are calculated by taking the difference between the current day's price and the previous day's price. CAR (Cumulative Abnormal Returns) and BHAR (Buy and Hold Abnormal returns) are calculated for the companies that have undertaken share repurchases in 2022-2023. The abnormal returns are further tested for significance. The study highlights that the buyback of the securities results in abnormal returns (positive or negative) in approximately 50% of the cases. And in most of the cases, the Abnormal returns are also found to be significant. There is a lack of information about the impact of the buyback of securities on the price of the securities in the Indian markets. Thus, the analysis of the data reveals that there is ample opportunity for arbitrage in the stock markets and the firm's buyback securities to signal financial prowess in wake of undervaluation. </p><p><strong>Keywords:</strong> Buyback of securities, NSE, Stock Markets, Abnormal returns</p><p><strong>JEL Classification Number:</strong> O16</p>
Uzbekistan Transnational Sample: Audio Files
No full text<p>Audio files created by the Gulagechoes team, interviews taken in Fergana Valley, Uzbekistan in 2020. </p>
Enhancing Agricultural Decision Support with AIoT: A Research Travelogue
No full text<p><strong><span>Abstract: </span></strong><span>Research in Artificial Intelligence has been enormously efficacious. It has touched almost all fields varying from machine learning to expert systems. Agriculture Technology on the other hand has also seen tremendous growth over the decades. Over the past decade, the digital revolution has penetrated agriculture to boost decision-making and productivity. Various trailblazing technologies in agriculture such as Sensors, Global Positioning Systems, Big-Data, Artificial-Intelligence, Machine-Learning, Robotics, and the Internet of Things are leading the way to improved yields, lesser costs and reduced impact on the environment. It intersects with agricultural management by addressing key challenges and opportunities related to resource allocation, operational efficiency, risk management, supply chain coordination, and decision support in modern agricultural practices. The main aim of this paper is to provide a comprehensive review of the recent developments in the field of Agriculture with the penetration of Artificial Intelligence of Things. </span></p>
<p><strong><span>Keywords: </span></strong><span>Artificial Intelligence, Agriculture Technology, Internet-of-Things, Machine-Learning, Artificial-Intelligence-of-Things</span></p>