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15 results for “SME”
CTU-SME-11: a labeled dataset with real benign and malicious network traffic mimicking a small medium-size enterprise environment
<p>As technology advances, the number and complexity of cyber-attacks increase, forcing defense techniques to be updated and improved. To help develop effective tools for detecting security threats it is essential to have reliable and representative security datasets. Many existing security datasets have limitations that make them unsuitable for research, including lack of labels, unbalanced traffic, and outdated threats.</p> <p>CTU-SME-11 is a labeled network dataset designed to address the limitations of previous datasets. The dataset was captured in a real network that mimics a small-medium enterprise setting. Raw network traffic (packets) was captured from 11 devices using tcpdump for a duration of 7 days, from 20th to 26th of February, 2023 in Prague, Czech Republic. The devices were chosen based on the enterprise setting and consists of IoT, desktop and mobile devices, both bare metal and virtualized. The devices were infected with malware or exposed to Internet attacks, and factory reset to restore benign behavior. </p> <p>The raw data was processed to generate network flows (Zeek logs) which were analyzed and labeled. The dataset contains two types of levels, a high level label and a descriptive label, which were put by experts. The former can take three values, benign, malicious or background. The latter contains detailed information about the specific behavior observed in the network flows. The dataset contains 99 million labeled network flows. The overall compressed size of the dataset is 80GB and the uncompressed size is 170GB.</p>
SME employee concerns regarding AI, their current knowledge regarding AI, their willingness to adopt it and learn about it.
<p><strong>Title</strong>: SME Employee Perspectives on AI Adoption</p> <p><strong>Abstract</strong>: This dataset comprises encoded interview responses, raw survey data, and cleaned survey data collected from employees of small and medium-sized enterprises (SMEs), as well as a codebook that acts as metadata storage. The focus of the data is on employee concerns, current knowledge, and willingness to adopt and learn about artificial intelligence (AI) technologies. This dataset is intended to support research into the factors influencing AI integration in SME environments and to assess the readiness of employees to engage with these technologies.</p> <p><strong>Data Collection Methods</strong>:</p> <ul> <li><strong>Interviews</strong>: Semi-structured interviews were conducted with a selection of employees from various departments within SMEs. Responses have been anonymized and encoded to protect participant privacy.</li> <li><strong>Surveys</strong>: Two sets of survey data are included: <ul> <li><strong>Raw Survey Data</strong>: Contains all original responses, including demographic information and unprocessed answers to questions regarding AI knowledge and perceptions.</li> <li><strong>Cleaned Survey Data</strong>: This dataset has been processed to remove incomplete responses and normalize the data for analysis.</li> </ul> </li> </ul> <p><strong>Key Variables</strong>:</p> <ul> <li><strong>Employee Concerns</strong>: Qualitative data on personal and professional concerns regarding AI, such as job security, privacy, and trust in technology.</li> <li><strong>Knowledge of AI</strong>: Employee self-assessments and objectively measured knowledge levels regarding AI technologies and their applications.</li> <li><strong>Willingness to Adopt AI</strong>: Measures of openness to integrating AI into their work processes and willingness to participate in AI-related training and development.</li> </ul>
REACH Incubator - open-call #2 Awarded SME dataset
<p>This file contain public dataset describing the awarded SME during the 2nd round of the REACH Incubator project.</p>
Fastprk2 video #1: introduction to the project (SME Instrument phase 1)
<p>Video showing Fastprk2 concept (Phase 1 of the project)</p>
Fastprk2 video #2: introduction to the project (SME Instrument phase 2)
<p>Video showing Fastprk2 (Phase 2 of the project)</p>
Plagiarm SME Performance Following the COVID-19 Pandemic and the Effect of Environmental Analysis on Green Business Strategy
<p>Plagiarm <strong>SME Performance Following the COVID-19 Pandemic and the Effect of Environmental Analysis on Green Business Strategy</strong></p>
Economic, institutional and environmental drivers of SME development for 21 EU states
<p><span>SMEs are seen as important actors of national and regional development in many countries. This is a panel<span> </span>data set on economic, institutional and environmental drivers of SME development for 21 EU states (Austria, Republic of Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia and Spain) during 2011-2020 . </span></p>
CB-PASTAX (tilt) SME Climate Database
<p>This data publication contains SME climate data for more than 200,000 firms across Germany, Austria, Netherlands, France, and Spain. The methodology and software behind the data were developed under the project CB-PASTAX within the programme tilt. The data are created using webscraped company-data and links them on product-level to climate databases such as Life-Cycle-Assessment (LCA) data and climate scenario data to derive three climate indicators (relative emission indicator, sector decarbonisation indicator, transition risk indicator) on product- and company-level. The data can be used to analyse the climate profile of SMEs across various regions and sectors. This version published on Zenodo contains an anonymised dataset, which means that the company names as well as the original product names are faked. The sector classification as well as the data we match from climate databases still gives an indication of the products. Real company and product names can be requested. </p> <p>For more information on the methodology behind the data and on how to use them, please refer to the project website with a link to the online portal where you will find extensive documentation: <a href="https://www.tiltsmes.org/tilt-under-the-cb-pastax-grant">https://www.tiltsmes.org/tilt-under-the-cb-pastax-grant.</a> The code developed to create the data is published open source on GitHub. You can find an overview here: <a href="https://2degreesinvesting.github.io/tilt/">https://2degreesinvesting.github.io/tilt/</a>.</p> <p><em>This project is co-funded by the European Union under Grant No. LIFE20 GIC/DE/001765, recognised under the CB-PASTAX programme. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor the granting authority can be held responsible for them.</em></p>
Examining the Factors Influencing the Website Continuous Actual Usage of SME Owners in Indonesia
<p><strong><span>The technologies encourage SMEs to expand their market. Websites have developed into important tools for ensuring the continued existence of SMEs through customer loyalty. The research problem is that SME owners have not continued to improve their websites due to factors that influence continuing intention to use the website, such as cost, facilities, etc. This study aims to examine the factors that influence SME owners' decisions to continue using websites for their businesses. A purposive sampling strategy is used to determine the size of this study sample. It also used quantitative techniques and data collecting via a Google Form survey, which included 225 of 250 SME owner respondents from the JABODETABEK area of Indonesia. The data, collected between March and June 2024, is processed using SmartPLS-SEM. The research models used are Technology Acceptance Model and Technology Organization Environment by analyzing eight variables: Relative Advantage, Complexity, Perceived Cost, Facilitating Conditions, Security Concern, Perceived Trust, Continuous Intention to Use, and Continuous Actual Usage.<span> </span>There are eight hypothesis that are significant, Relative Advantage on Continuous Intention to Use, Complexity on Continuous Intention to Use, Perceived Cost on Continuous Intention to Use, Facilitating Conditions on Continuous Intention to Use, Facilitating Conditions on Perceived Trust, Security Concern on Perceived Trust, Perceived Trust on Continuous Actual Usage, and Continuous Intention to Use on Continuous Actual Usage. More research is needed to determine whether this is an effective strategy to improve the technological digitalization of SMEs in Indonesia</span></strong></p>
SME(Sharing, Mind & Enjoyment) App for Adolescents
ClinicalTrials.gov study NCT03361475. IPD Sharing: Not stated. Countries: 1. Publications: 1.
MERLIN H2020 Project: Video of the MERLIN Webinar SME Growth (I): grant opportunities
<p>Video of the MERLIN Webinar SME Growth (I): grant opportunities</p>
Developing a transformational digital strategy in an SME: The role of responsible management
<p>Ths is the dataset for our open source Emerald publication.</p>
Effect and Process Evaluation of the SME Tool
ClinicalTrials.gov study NCT06330415. IPD Sharing: NO. Countries: 0. Publications: 1.
SME Ambassadors Pilot Project Research Study
ClinicalTrials.gov study NCT03332823. IPD Sharing: NO. Countries: 1. Publications: 0.
The under-loading cascading failure model: a tool for assessing the vulnerability of SME cooperation networks
<p>There are two datasets and a code for the numerical simulation.</p>
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