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:::info Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. ::: Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References 5 Conclusion & Future work In summary, our new approach for generating floor plans from triangle mesh data collected by augmented reality (AR) headsets produces two styles: a detailed pen-and-ink style and a simplified drafting style. Our algorithms align the mesh data with primary coordinate axes to produce tidy floor plans with vertical and horizontal walls, while also allowing for the removal of ceilings and floors and the separation of multi-story buildings into individual stories. Our approach integrates with AR, supporting the addition of synthetic objects to physical geometry and providing a detailed 3D model and floor plan. Potential applications include navigation, interior design, furniture placement, facility management, building construction, and HVAC design. Moving forward, we plan to enable support for sloping ceilings, automate wall and door detection, and integrate with other tools such as energy simulators. Finally, we plan to compare our approach with existing state-of-the-art methods in terms of accuracy and computational time. We also plan to explore the applicability of block-based DBScan for 3D reconstruction from incomplete scans. Our approach has the potential to revolutionize the way we generate and visualize floor plans. References Adan, A., Huber, D.: 3d reconstruction of interior wall surfaces under occlusion and clutter. In: 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission. pp. 275–281 (2011). https://doi.org/10.1109/3DIMPVT.2011.42 Arikan, M., Schwärzler, M., Flöry, S., Wimmer, M., Maierhofer, S.: O-snap: Optimization-based snapping for modeling architecture. ACM Trans. Graph. 32(1) (feb 2013). https://doi.org/10.1145/2421636.2421642 Budroni, A., Boehm, J.: Automated 3d reconstruction of interiors from point clouds. International Journal of Architectural Computing 8(1), 55–73 (2010). https://doi.org/10.1260/1478-0771.8.1.55 Cabral, R.S., Furukawa, Y.: Piecewise planar and compact floorplan reconstruction from images. 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 628–635 (2014) Cai, R., Li, H., Xie, J., Jin, X.: Accurate floorplan reconstruction using geometric priors. Computers & Graphics 102, 360-369 (2022). https://doi.org/10.1016/j.cag.2021.10.011 Chen, J., Liu, C., Wu, J., Furukawa, Y.: Floor-sp: Inverse cad for floorplans by sequential room-wise shortest path. In: The IEEE International Conference on Computer Vision (ICCV) (2019) Chen, N., Lu, Z., Yu, X., Yang, L., Xu, P., Fan, Y.: Augmented reality-based home interaction layout and evaluation. In: Computer Graphics International Conference. pp. 395–406. Springer (2022) Dasgupta, S., Fang, K., Chen, K., Savarese, S.: Delay: Robust spatial layout estimation for cluttered indoor scenes. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 616–624 (2016). https://doi.org/10.1109/CVPR.2016.73 Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Reconstructing building interiors from images. In: 2009 IEEE 12th International Conference on Computer Vision. pp. 80–87 (2009). https://doi.org/10.1109/ICCV.2009.5459145 Gao, R., Zhao, M., Ye, T., Ye, F., Wang, Y., Bian, K., Wang, T., Li, X.: Jigsaw: Indoor floor plan reconstruction via mobile crowdsensing. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking. p. 249–260. MobiCom '14, Association for Computing Machinery, New York, NY, USA (2014). https://doi.org/10.1145/2639108.2639134 Hsiao, C.W., Sun, C., Sun, M., Chen, H.T.: Flat2layout: Flat representation for estimating layout of general room types. ArXiv abs/1905.12571 (2019) Ikehata, S., Yang, H., Furukawa, Y.: Structured indoor modeling. In: 2015 IEEE International Conference on Computer Vision (ICCV). pp. 1323–1331 (2015). https://doi.org/10.1109/ICCV.2015.156 Kruzhilov, I., Romanov, M., Babichev, D., Konushin, A.: Double refinement network for room layout estimation. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W.Q. (eds.) Pattern Recognition. pp. 557–568. Springer International Publishing, Cham (2020) Lee, C.Y., Badrinarayanan, V., Malisiewicz, T., Rabinovich, A.: Roomnet: Endto-end room layout estimation. 2017 IEEE International Conference on Computer Vision (ICCV) pp. 4875–4884 (2017) Liu, C., Wu, J., Furukawa, Y.: Floornet: A unified framework for floorplan reconstruction from 3d scans. In: ECCV (2018) Liu, H., Yang, Y.L., AlHalawani, S., Mitra, N.J.: Constraint-aware interior layout exploration for precast concrete-based buildings. Visual Computer (CGI Special Issue) (2013) McNeel, R., et al.: Rhinoceros 3d, version 6.0. Robert McNeel & Associates, Seattle, WA (2010) Microsoft: Spatial mapping. https://docs.microsoft.com/en-us/windows/mixed-reality/spatial-mapping (2022) Monszpart, A., Mellado, N., Brostow, G.J., Mitra, N.J.: Rapter: Rebuilding manmade scenes with regular arrangements of planes. ACM Trans. Graph. 34(4) (jul 2015). https://doi.org/10.1145/2766995 Mura, C., Mattausch, O., Pajarola, R.: Piecewise-planar reconstruction of multiroom interiors with arbitrary wall arrangements. Computer Graphics Forum 35(7), 179–188 (2016). https://doi.org/https://doi.org/10.1111/cgf.13015 Murali, S., Speciale, P., Oswald, M.R., Pollefeys, M.: Indoor scan2bim: Building information models of house interiors. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 6126–6133 (2017). https://doi. org/10.1109/IROS.2017.8206513 Okorn, B., Xiong, X., Akinci, B.: Toward automated modeling of floor plans. In: In Proceedings of the symposium on 3D data processing, visualization and transmission. vol. 2 (2010) Pintore, G., Gobbetti, E.: Effective mobile mapping of multi-room indoor structures. The visual computer 30(6-8), 707–716 (2014) Pintore, G., Mura, C., Ganovelli, F., Fuentes-Perez, L.J., Pajarola, R., Gobbetti, E.: State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments. Computer Graphics Forum (2020). https://doi.org/10.1111/cgf.14021 Ramakrishnan, S.K., Gokaslan, A., Wijmans, E., Maksymets, O., Clegg, A., Turner, J.M., Undersander, E., Galuba, W., Westbury, A., Chang, A.X., Savva, M., Zhao, Y., Batra, D.: Habitat-matterport 3d dataset (HM3d): 1000 large-scale 3d environments for embodied AI. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) (2021), https://openreview.net/forum?id=-v4OuqNs5P Turner, E., Zakhor, A.: Watertight as-built architectural floor plans generated from laser range data. In: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization Transmission. pp. 316–323 (2012). https: //doi.org/10.1109/3DIMPVT.2012.80 Weinmann, M., Wursthorn, S., Weinmann, M., Hübner, P.: Efficient 3d mapping and modelling of indoor scenes with the microsoft hololens: A survey. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 89(4), 319–333 (2021) Xiong, X., Adan, A., Akinci, B., Huber, D.: Automatic creation of semantically rich 3d building models from laser scanner data. Automation in Construction 31, 325–337 (2013). https://doi.org/10.1016/j.autcon.2012.10.006 Zhang, J., Kan, C., Schwing, A.G., Urtasun, R.: Estimating the 3d layout of indoor scenes and its clutter from depth sensors. In: 2013 IEEE International Conference on Computer Vision. pp. 1273–1280 (2013). https://doi.org/10.1109/ICCV.2013.161 Zou, C., Colburn, A., Shan, Q., Hoiem, D.: Layoutnet: Reconstructing the 3d room layout from a single rgb image. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2051–2059. IEEE Computer Society, Los Alamitos, CA, USA (jun 2018). https://doi.org/10.1109/CVPR.2018.00219 :::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. :::

Oak Ridge

The 2.0 Conferences series featuring five global events were successfully hosted at the InterContinental, Dubai Festival City, Dubai, UAE, from February 20–22, 2024. HackerNoon, serving as the event's media partner, played a crucial role in amplifying the reach and engagement of the conference. HackerNoon is known for its commitment to providing a space for technologists to share knowledge and ideas. Learn more here.

Conferences Spark Dubai Festival Esteemed Media Festival City Media Partner

It’s been an amazing journey and we are so thankful to the team at the RSA Conference for working with us for over a decade. I remember before we went […] The post Celebrating our 12th Anniversary at RSA conference 2024 appeared first on Cyber Defense Magazine.

amazing journey

GRAND CAYMAN, Cayman Islands, May 3rd, 2024/Chainwire/--Protocol launches, growth trajectory, and industry-leading technology point toward more success to come. On the first anniversary of Sui's Mainnet launch, the Sui community is celebrating a landmark year that saw it rise from a nascent ecosystem to the top tier of Layer 1 blockchains, amassing household name partners and shipping multiple technology breakthroughs in the process. In the build-up to its launch in 2023, the chatter around Sui reached a level of excitement that has not been matched by any chain that has launched since. The first anniversary of Sui represents a culmination of the remarkable milestones achieved by the network in its first year. While centralized institutions face growing public distrust due to decades of anti-competitive and anti-consumer behavior and legacy blockchains lack the speed and technology to solve the problem, in a single year, Sui has emerged as the decentralized solution most capable of disrupting the status quo at scale. The innovations of Sui begin with Sui's novel programming language, Move, which was created by Mysten Labs Co-founder and CTO Sam Blackshear. Move introduced a new architecture centered around objects, enabling performance and functionality that was simply not available on existing blockchains. The result is a blockchain that is singular in the industry — the universal coordination layer for intelligent assets. “Like many transformative innovations before us, we knew the problem we had to solve and built the technology to solve it without applying labels, but a year after launch, it is clear that Sui is a vibrant developer ecosystem,” said Evan Cheng, Co-founder and CEO of Mysten Labs. “Developers are taking advantage of Sui's performance, and its scalable, composable, on-chain storage, and native accessibility features to build sustainable business models with a consumer ownership-first approach. While the Network's achievements to date are remarkable, the road to onboarding the next billion users to web3 has just started.” Technology Innovations The results Sui has achieved have validated the team's approach. In addition to not experiencing a single minute of downtime or instance of degraded performance since Mainnet launch, in the last 12 months, Sui has achieved: Lightning-fast transaction speeds — time to finality clocked at just 400 milliseconds Massive scale — 297,000 peak TPS in a controlled environment Record-setting Mainnet performance — 65.8M transactions executed in a single day, the most of any blockchain ever Extremely low fees — predictable, stable transaction fees even during high usage Yet technology improvements are a constant at Sui. In recent weeks, including at its first global in-person conference, Sui Basecamp, Sui unveiled Mysticeti, which significantly shortens Sui's end-to-end time to finality, and Pilotfish, which enables nearly unlimited horizontal scaling by enabling individual validators to use multiple machines to extend their capacity. Sui has also introduced unique implementations of technologies that make adoption easier and make the blockchain accessible to mainstream users, both at the enterprise and retail levels. zkLogin is on-chain authorization with traditional OAuth providers like Google and Twitter, allowing all users to directly operate on-chain with the single sign-on process they have become accustomed to, removing the hurdle of managing wallet addresses and seed phrases. zkSend is an application exclusive to Sui that utilizes zkLogin to enable users to send and claim tokens simply by sharing or clicking a link. Sponsored transactions, enabled by Sui's extremely low fees, empower builders to remove a final hurdle for engagement. Finally, Enoki, which was announced at Sui Basecamp, removes requirements for enterprises looking to incorporate blockchain technology with a turnkey solution that gives them access to build seamlessly on Sui. “The rate at which the ecosystem's deep and talented developer community has shipped powerful protocols and industry-first technology breakthroughs has been breathtaking, and it's only a year in,” said Adeniyi Abiodun, Co-founder and CPO at Mysten Labs. “Sui's purpose is to redefine how individuals and businesses collaborate to create, grow, and share value in a digital-first economy and since its mainnet launch, Sui has demonstrated important and unique capabilities in that regard.”   Network Momentum – Sui is a destination for DeFi, Gaming and Commerce Also owing to the strength of the network and especially notable for an ecosystem so early in its development, Sui has quickly become one of the preeminent destinations for DeFi activity. Within 9 months of its Mainnet launch, Sui ranked in the Top 10 of all blockchains in TVL. A month later, Sui's DEX volume also achieved top 10 status. Throughout, Wormhole stats show Sui as a top destination for bridging from Ethereum, including over a 30-day period where Sui saw more inflows than all other blockchains combined, and more than twice as much as the next closest blockchain. In each of these cases, Sui eclipsed numerous well-known networks that have existed for far longer. Builders and enterprises integrating with Sui are also adding to the growing momentum. Bluefin, a derivatives exchange that had already executed billions in transaction volume in its first iteration on another chain, shut that integration down to build on Sui. Solend, a leading lending protocol on Solana, chose Sui for its first integration beyond its initial network, launching the Suilend protocol which has already amassed over M TVL. Additionally, First Digital Labs, creator of FDUSD, the fastest-growing stablecoin in crypto with over B in market cap, chose Sui for its first expansion since its launch on Ethereum and BNB. “As we mark the first anniversary of Sui's launch, the growth and innovation within the Sui ecosystem have been sensational and Sui has quickly ascended to the forefront of Layer 1 blockchains,” said Greg Siourounis, Managing Director of the Sui Foundation. “Sui's global footprint has continued to expand at a remarkable rate as more and more builders see Sui as the best platform for enabling real world solutions aimed at addressing the world's most pressing challenges.” Gaming is another focus for Sui that will continue in the coming months and years. With low, predictable fees that allow game developers to build with confidence and dynamic objects that make games built on Sui more expressive, Sui is the optimal blockchain for gaming studios. Dozens of development teams are currently building games on Sui, including established studios such as NHN (Pebble City), ONBUFF x SNK (Samurai Shodown), NDUS Interactive (Xociety), Orange Comet (The Walking Dead: Lands), and Ambrus Studio (E4C: Final Salvation). Leading professional esports team, Team Liquid, will leverage Sui for the relaunch of its fan loyalty program, and at Sui Basecamp in April, Sui joined gaming infrastructure developer Playtron in announcing SuiPlay0X1, the first handheld gaming device with native blockchain capabilities to wide acclaim. Other notable ecosystem partnerships achieved in Sui's first year include those with Alibaba Cloud and Google Cloud, which focused on enhancing security, scalability, developer tools and user experiences across a range of Web 3.0 and AI-powered applications. BytePlus, the web3 arm of ByteDance, partnered with Sui ecosystem partner Mysten Labs to explore collaboration on data warehousing, AI recommendation algorithms, and AI visual algorithms in web3 game platforms and socialFi projects on Sui.  The crowds that gathered for Sui Basecamp 2024 represented the culmination of Sui's arrival, as over 1,000 projects, partners, investors and enthusiasts from 65 countries around the world came to Paris in April to celebrate Sui at the vaunted Layer 1's inaugural global conference. Contact Sui Foundation media@sui.io :::tip This story was distributed as a release by Chainwire under HackerNoon's Business Blogging Program. Learn more about the program here. ::: n

Cayman Islands GRAND CAYMAN Sui

CSA was the only nonprofit to be named in the reportSAN FRANCISCO (RSA Conference) – May 7, 2024 – The Cloud Security Alliance (CSA), the world's leading organization dedicated to defining standards, certifications, and best practices to help ensure a secure cloud computing environment, is pleased to announce that it was selected as a leading security exhibitor at RSA by Enterprise Management Associates (EMA) as part of its annual Vendor Vision report. As a vendor-neutral nonprofit with a div...

Security Exhibitor Vendor Vision

This week in cybersecurity from the editors at Cybercrime Magazine – Listen to the “History of the RSA Conference” Sausalito, Calif. – May 3, 2024 Cybersecurity Ventures, DarkReading, Fortune, and The Wall Street Journal are a few of the media partners for this year’s RSA Conference in San The post Cybersecurity Ventures Heads To San Francisco For RSA Conference 2024 appeared first on Cybercrime Magazine.

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:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Jinrui Yang, School of Computing & Information Systems, The University of Melbourne (Email: jinruiy@student.unimelb.edu.au); (2) Timothy Baldwin, School of Computing & Information Systems, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence, UAE (Email: (tbaldwin,trevor.cohn)@unimelb.edu.au); (3) Trevor Cohn, School of Computing & Information Systems, The University of Melbourne. ::: Table of Links Abstract and Intro Background and Related Work Multi-EuP Experiments and Findings Language Bias Discussion Conclusion, Limitations, Ethics Statement, Acknowledgements, References, and Appendix 6 Conclusion In this paper, we introduce Multi-EuP, a novel dataset for multilingual information retrieval across 24 languages, collected from European Parliament debates. The demographic information provided by the Multi-EuP dataset serves a dual purpose: not only does it contribute to multilingual retrieval tasks, but it also holds significant potential for advancing research in the realm of fairness and bias. This dataset can play a pivotal role in investigating issues of equitable representations and mitigation of biases within document ranking settings. Multi-EuP facilitates diverse information retrieval (IR) scenarios, encompassing one-vs-one, one-vs-many, and many-vs-many settings. We demonstrated the utility of Multi-EuP as a benchmark for evaluating both monolingual and multilingual IR. Our study reveals the presence of language bias in multilingual IR when employing BM25. We further validate the effectiveness of mitigating this bias through the strategic implementation of whitespace as a language tokenizer. We propose to conduct future work in three main areas. First, we intend to expand our investigation of language bias to encompass a broader range of ranking methods, including neural methods such as mDPR (Zhang et al., 2021), mColBERT (Lawrie et al., 2023) and PLAID-X(Santhanam et al., 2022). Second, we will expand the dataset by developing an automated API to retrieve data published by the European Parliament (EP), thereby ensuring realtime synchronization of our dataset. Lastly, our current experiments have explored language bias only, but we plan to further investigate gender bias, age bias, and nationality bias. Limitations The limitations of the Multi-EuP dataset are notable but navigable. Primarily, the temporal coverage of the dataset is confined to the past three years. This temporal constraint arises due to the fact that, preceding 2020, documents released by the EU were predominantly available in mono-lingual versions only. However, a potential remedy lies in the amalgamation of the Europarl (Koehn, 2005) collection, enabling a more comprehensive and holistic MultiEuP dataset. Furthermore, it is worth noting the domain skew of the dataset, in that Multi-EuP inevitably centers on political matters. While this presents challenges, particularly in terms of the intricate nuances of political language, it inherently serves as an excellent foundational stepping stone for delving into the intricacies of multilingual retrieval. We believe, however, that this dataset can serve as a launching pad for broader explorations encompassing crossdomain and open-domain transfer learning scenarios, thus contributing to the broader landscape of language understanding and retrieval. Ethics Statement The dataset contains publicly-available EP data that does not include personal or sensitive information, with the exception of information relating to public officeholders, e.g., the names of the active members of the European Parliament, European Council, or other official administration bodies. The collected data is licensed under the Creative Commons Attribution 4.0 International licence. [8] Acknowledgements This research was funded by Melbourne Research Scholarship and undertaken using the LIEF HPCGPGPU Facility hosted at the University of Melbourne. This facility was established with the assistance of LIEF Grant LE170100200. We would like to thank George Buchanan for providing valuable feedback. References Luiz Henrique Bonifacio, Israel Campiotti, Roberto de Alencar Lotufo, and Rodrigo Frassetto Nogueira. 2021. mMARCO: A multilingual version of MS MARCO passage ranking dataset. CoRR, abs/2108.13897. Ilias Chalkidis, Manos Fergadiotis, and Ion Androutsopoulos. 2021. MultiEURLEX - a multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6974–6996, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. 8 https://eur-lex.europa.eu/cont Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. 2020. TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages. Transactions of the Association for Computational Linguistics, 8:454–470. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for opendomain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769–6781, Online. Association for Computational Linguistics. Omar Khattab and Matei Zaharia. 2020. Colbert: Efficient and effective passage search via contextualized late interaction over BERT. CoRR, abs/2004.12832. Philipp Koehn. 2005. Europarl: A parallel corpus for statistical machine translation. In Proceedings of Machine Translation Summit X: Papers, pages 79–86, Phuket, Thailand. Dawn Lawrie, Eugene Yang, Douglas W. Oard, and James Mayfield. 2023. Neural approaches to multilingual information retrieval. arXiv cs.IR 2209.01335. Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, JhengHong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021. Pyserini: A Python toolkit for reproducible information retrieval research with sparse and dense representations. https://github.com/ castorini/pyserini. Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. CoRR, abs/1611.09268. Ella Rabinovich, Raj Nath Patel, Shachar Mirkin, Lucia Specia, and Shuly Wintner. 2017. Personalized machine translation: Preserving original author traits. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1074–1084, Valencia, Spain. Association for Computational Linguistics. Razieh Rahimi, Azadeh Shakery, and Irwin King. 2015. Multilingual information retrieval in the language modeling framework. Information Retrieval Journal, 18:246–281. Keshav Santhanam, Omar Khattab, Christopher Potts, and Matei Zaharia. 2022. PLAID: An efficient engine for late interaction retrieval. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM '22, page 1747–1756, New York, NY, USA. Association for Computing Machinery. Jörg Tiedemann and Santhosh Thottingal. 2020. OPUSMT – building open translation services for the world. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 479–480, Lisboa, Portugal. European Association for Machine Translation. Eva Vanmassenhove and Christian Hardmeier. 2018. Europarl datasets with demographic speaker information. In Proceedings of the 21st Annual Conference of the European Association for Machine Translation, page 391, Alicante, Spain. Denny Vrandeciˇ c and Markus Krötzsch. 2014. ´ Wikidata: A free collaborative knowledge base. Communications of the ACM, 57:78–85. Konrad Wojtasik, Vadim Shishkin, Kacper Wołowiec, Arkadiusz Janz, and Maciej Piasecki. 2023. BEIRPL: Zero shot information retrieval benchmark for the Polish language. arXiv cs.IR 2305.19840. Peilin Yang, Hui Fang, and Jimmy Lin. 2017. Anserini: Enabling the use of lucene for information retrieval research. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1253–1256. Xinyu Zhang, Xueguang Ma, Peng Shi, and Jimmy Lin. 2021. Mr. TyDi: A multi-lingual benchmark for dense retrieval. arXiv cs.CL 2108.08787. A. Appendix [8] https://eur-lex.europa.eu/content/ legal-notice/legal-notice.html

Computational Linguistics dataset Language Bias machine translation

Kaisen Linux is a distribution dedicated for IT professional based on Debian GNU/Linux. Large tools are integrated for diagnostics, rescue system and networks, lab creation and many more!

Traditional Zero Trust approaches must adapt to the nuances of Generative AI (GenAI) technology to strengthen cybersecurityRSA Conference (San Francisco) – May 7, 2024 – The Cloud Security Alliance (CSA), the world's leading organization dedicated to defining standards, certifications, and best practices to help ensure a secure cloud computing environment, has issued a new report, Confronting Shadow Access Risks: Considerations for Zero Trust and Artificial Intelligence (AI) Deployments. Auth...

Confronting Shadow Shadow Access

Report series charts course for responsible and secure development and deployment of AIRSA Conference (San Francisco) – May 6, 2024 – The Cloud Security Alliance (CSA), the world's leading organization dedicated to defining standards, certifications, and best practices to help ensure a secure cloud computing environment, today issued AI Organizational Responsibilities - Core Security Responsibilities, AI Resilience: A Revolutionary Benchmarking Model for AI, and Principles to Practice: Respon...

AIRSA Conference Organizational Responsibilities

Mapping identifies misalignment and gaps between updated CCM and CFTRSA Conference (San Francisco) – May 8, 2024 – The Cloud Security Alliance (CSA), the world's leading organization dedicated to defining standards, certifications, and best practices to help ensure a secure cloud computing environment, today announced an additional mapping and gap analysis between its flagship Cloud Controls Matrix (CCM) and the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CS...

CCZT helps security professionals build knowledge to drive the definition, implementation, and management of Zero TrustSAN FRANCISCO (RSA Conference) – May 6, 2024 – The Cloud Security Alliance (CSA), the world's leading organization dedicated to defining standards, certifications, and best practices to help ensure a secure cloud computing environment, is pleased to announce that Cyber Defense Magazine has named its Certificate of Competence in Zero Trust (CCZT) as a Global InfoSec Award winn...