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7 results for “sharing platforms”
Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)
<p>This platform is a multi-functional music data sharing platform for Computational Musicology research. It contains many music datas such as the sound information of Chinese traditional musical instruments and the labeling information of Chinese pop music, which is available for free use by computational musicology researchers.</p> <p>This platform is also a large-scale music data sharing platform specially used for Computational Musicology research in China, including 3 music databases: Chinese Traditional Instrument Sound Database (CTIS), Midi-wav Bi-directional Database of Pop Music and Multi-functional Music Database for MIR Research (CCMusic). All 3 databases are available for free use by computational musicology researchers. For the contents contained in the database, we will provide audio files recorded by the professional team of the conservatory of music, as well as corresponding labelled files, which have no commodity copyright problem and facilitate large-scale promotion. We hope that this music data sharing platform can meet the one-stop data needs of users and contribute to the research in the field of Computational Musicology.</p> <p> </p> <p>If you want to know more information or obtain complete files, please go to the official website of this platform:</p> <p><a href="https://ccmusic-database.github.io/en/">Music Data Sharing Platform for Academic Research</a></p> <p> </p> <ul> <li> <p><strong>Chinese Traditional Instrument Sound Database (CTIS)</strong></p> </li> </ul> <p>This database is developed by Prof. Han Baoqiang's team for many years, which collects sound information about Chinese traditional musical instruments. The database includes 287 Chinese national musical instruments, including traditional musical instruments, improved musical instruments and ethnic minority musical instruments.</p> <ul> <li> <p><strong>Multi-functional Music Database for MIR Research</strong></p> </li> </ul> <p>This database collects sound materials of pop music, folk music and hundreds of national musical instruments, and makes comprehensive annotation to form a multi-purpose music database for MIR researchers.</p> <ul> <li><strong>Midi-wav Bi-directional Database of Pop Music</strong></li> </ul> <p>This database contains hundreds of Chinese pop songs, and each song contains the corresponding midi-audio-lyric information. Among them, recording the vocal part and accompaniment part of audio independently is helpful to study the MIR task under the ideal situation. In addition, the information of singing techniques consistent with vocal part (such as breath sound, falsetto, breathing, vibrato, mute, slide, etc.) is marked in MuseScore, which constitutes a Midi-Wav bi-direction corresponding pop music database.</p>
Webinar Nuberu: Reliable RAN Virtualization in Shared Platforms @ IMDEA Networks
<p><strong>VIEW IN OUR YOUTUBE CHANNEL:</strong> <a href="https://youtu.be/iAoyuzHuZP0">https://youtu.be/iAoyuzHuZP0</a></p> <p>RAN virtualization will become a key technology for the last mile of next-generation mobile networks driven by initiatives such as the O-RAN alliance. However, due to the computing fluctuations inherent to wireless dynamics and resource contention in shared computing infrastructure, the price to migrate from dedicated to shared platforms may be too high. Indeed, we show in this paper that the baseline architecture of a base station¿s distributed unit (DU) collapses upon moments of deficit in computing capacity. Recent solutions to accelerate some signal processing tasks certainly help but do not tackle the core problem: a DU pipeline that requires predictable computing to provide carrier-grade reliability. We present Nuberu, a novel pipeline architecture for 4G/5G DUs specifically engineered for non-deterministic computing platforms. Our design has one key objective to attain reliability: to guarantee a minimum set of signals that preserve synchronization between the DU and its users during computing capacity shortages and, provided this, maximize network throughput. To this end, we use techniques such as tight deadline control, jitter-absorbing buffers, predictive HARQ, and congestion control. Using an experimental prototype, we show that Nuberu attains >95% of the theoretical spectrum efficiency in hostile environments, where state-of-art approaches lose connectivity, and at least 80% resource savings.</p>
What's in a Cyber Threat Intelligence sharing platform? - Appendix
<p>This upload contains the Artificats i.e. surveys and answer datasets for our ACSAC 2021 paper.<br> <br> Full paper reference:</p> <p>Borce Stojkovski, Gabriele Lenzini, Vincent Koenig, and Salvador Rivas. 2021. What’s in a Cyber Threat Intelligence sharing platform?: A mixed-methods user experience investigation of MISP. In Annual Computer Security Applications Conference (ACSAC ’21), December 6–10, 2021, Virtual Event, USA. ACM, New York, NY, USA. https://doi.org/10.1145/3485832.3488030</p> <p>The research is supported by the Luxembourg National Research Fund through grant PRIDE15/10621687/SPsquared<br> </p> <p>+++++++++++++++++++++++++++++++++++++++<br> I) CONTENT:<br> +++++++++++++++++++++++++++++++++++++++</p> <p>A - Surveys<br> |_ "1 - UEQ+demographics.pdf" <br> |_ "2 - Sentence Completion.pdf"</p> <p>B - Raw participant data<br> |_ "1 - UEQ.csv"<br> |_ "2 - MISP - SC-Q1.csv"<br> |_ "2 - MISP - SC-Q2.csv"<br> |_ "2 - MISP - SC-Q3.csv"<br> |_ "2 - MISP - SC-Q4.csv"<br> |_ "2 - MISP - SC-Q5.csv"<br> |_ "2 - MISP - SC-Q6.csv"<br> |_ "2 - MISP - SC-Q7.csv"<br> |_ "2 - MISP - SC-Q8.csv"</p> <p><br> +++++++++++++++++++++++++++++++++++++++<br> II) DETAILS:<br> +++++++++++++++++++++++++++++++++++++++</p> <p>---------------------------------------<br> A - Surveys / 1 - UEQ+demographics.pdf<br> ---------------------------------------<br> - The file consists of two sections, namely the UEQ (page 1) and the Demographics part (pages 2-3). <br> - The User Experience Questionnaire (UEQ) is a validated instrument for measuring the user experience of interactive products that consists of 6 scales with 26 items in total [2]. For more information refer to the UEQ handbook [2] or to Section 4.1 of our paper.<br> - The Demographics part consists of 15 questions investigating the types of users and their respective needs on the MISP platform.</p> <p> <br> ---------------------------------------<br> A - Surveys / 2 - Sentence Completion.pdf<br> ---------------------------------------<br> - The file consists of 8 questions i.e. sentence completion stems presented to our study participants as explained in Section 4.1 of our paper.</p> <p><br> ---------------------------------------<br> B - Raw participant data / "1 - UEQ.csv"<br> ---------------------------------------<br> The file contains the (annonymized) raw participant data corresponding to the UEQ and demographics sections of the survey (file: "A - Surveys / 1 - UEQ+demographics.pdf"). It consists of the following fields:<br> - Participant ID<br> - 26 values (Q-ID 1.1 .. 1.26) corresponding to the 26 items of the UEQ scale<br> - 8 values (Q-ID 2.1.1 .. 2.1.8) corresponding to the Demographics question No. 1 ("Which of the following roles best describes how you (intend to) use MISP?")<br> - 14 values (Q-ID 2.2.1 .. 2.2.14) correspondign to the Demographics question No. 2. ("Which of the following categories best describes the organization you work in?")<br> - 1 value (Q-ID 2.3) corresponding to the Demographics question No. 3 ("How long have you been using MISP?")<br> - 1 value (Q-ID 2.4) corresponding to the Demographics question No. 4 ("If applicable, how often do you use MISP?")<br> - 1 value (Q-ID 2.5) corresponding to the Demographics question No. 5 ("Have you attended a training session on MISP before?")<br> - 1 value (Q-ID 2.6) corresponding to the Demographics question No. 6 ("Have you used the MISP training materials before?")<br> - 1 value (Q-ID 2.7) corresponding to the Demographics question No. 7 ("Have you used the MISP virtual machine before? ")<br> - 1 value (Q-ID XTR2.1) corresponding to the Demographics question No. 8 ("Have you used PyMISP - the Python library to access MISP via the API before? ")<br> - 1 value (Q-ID XTR2.2) corresponding to the Demographics question No. 9 ("Have you cloned a MISP repository before? ")<br> - 1 value (Q-ID XTR2.3) corresponding to the Demographics question No. 10 ("Have you contributed to any of the MISP repositories before? ")<br> - 1 value (Q-ID 2.8) corresponding to the Demographics question No. 11 ("Do you have an engineering or computer science background? ")<br> - 1 value (Q-ID 2.9) corresponding to the Demographics question No. 12 ("What is the highest level of school you have completed / degree you have received? ")<br> - 1 value (Q-ID 2.10) corresponding to the Demographics question No. 13 ("What is your age group?")<br> - 1 value (Q-ID 2.11) corresponding to the Demographics question No. 14 ("To which gender identity do you most identify?")</p> <p><br> ---------------------------------------<br> B - Raw participant data / "2 - SC-Q[1..8].csv"<br> ---------------------------------------<br> The 8 files contain the raw participant responses to the different sentence completion questions (see file: "A - Surveys / 2 - Sentence Completion.pdf"). Each file consists of two fields:<br> - Participant ID <br> - 1 value for the sentence completion input.</p> <p>For instance, the file "2 - SC-Q1.csv" corresponds to the Sentence completion stem "When I use MISP, I feel …". Similarly, "2 - SC-Q2.csv" corresponds to the Sentence completion stem "MISP is best for …". The same logic applies to all 8 files ("2 - SC-Q[1..8].csv").</p> <p>REFERENCES:</p> <p>[1] MISP. 2021. MISP - Open Source Threat Intelligence Platform & Open Standards For Threat Information Sharing. Retrieved August 15, 2021 from https://www.misp-project.org</p> <p>[2] UEQ. 2021. User Experience Questionnaire. Retrieved August 15, 2021 from https://www.ueq-online.org/</p> <p>[3] UEQ. 2021. User Experience Questionniare Handbook. Version 8 (31.12.2019). Retrieved August 15, 2021 from: https://www.ueq-online.org/Material/Handbook.pdf</p>
Platforms & registries for sharing participant-level COVID-19 data
<p>This dataset presents an overview of platforms and registries that store, harmonize, and, in some cases, share COVID-19-related participant-level data, including clinical-epidemiological, human and pathogen OMICs, and high dimensional imaging data. The dataset provides an in-depth review of adherence to the <a href="https://www.go-fair.org/fair-principles/">FAIR principles</a>, governance, benefit sharing and other ethical concerns related to resources that share harmonized, participant-level data for the research response to COVID-19. Systematic searches were conducted between April 2020 and June 2021 to identify relevant platforms and registries. We applied natural language processing to the CORD-19 dataset in March of 2021 and consulted with COVID-19 focused researchers in Asia, Africa, and Latin America to identify non-English language COVID-19-related data sharing resources.</p>
Simulation Data for Project on Incentivizing Participation in Peer-to-Peer Ride-Sharing Platform
<p>Unlike commercial ridesharing, non-commercial peer-to-peer (P2P) ridesharing has been subject to limited research---although it can promote viable solutions in non-urban communities. This paper focuses on the core problem in P2P ridesharing: the matching of riders and drivers. We elevate users' preferences as a first-order concern and introduce novel notions of fairness and stability in P2P ridesharing. We propose algorithms for efficient matching while considering user-centric factors including users' preferred departure time, fairness, and stability.</p> <p>The dataset includes our simulation settings and results. Results suggest that individually rational, fair and stable solutions can be obtained in reasonable computational times, and can improve baseline outcomes based on system-wide efficiency exclusively. </p>
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Building Customer Loyalty on Online Food Platforms: Sharing Economy Practices in Indonesia
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