Search results for international ACM

:::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

:::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

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Ryen W. White, Microsoft Research, Redmond, WA, USA. ::: Table of Links Abstract and Taking Search to task AI Copilots Challenges Opportunities The Undiscovered Country and References 5 THE UNDISCOVERED COUNTRY AI copilots will transform how we search. Tasks are central to people's lives and more support is needed for complex tasks in search settings. Some limited support for these tasks already exists in search engines, but copilots will expand the task frontier to make more tasks actionable and address the “last mile” in search interaction: task completion [58]. Moving forward, search providers should invest in “better together” experiences that utilize copilots plus traditional search, make these joint experiences more seamless for searchers, and add more support for their use in practice, e.g., help people to quickly understand copilot capabilities and potential and/or recommend the best modality for the current task or task stage. This includes experiences where both modalities are offered separately and can be selected by searchers and those where there is unification and the selection happens automatically based on the query and the conversation context. The foundation models that power copilots have other search-related applications, e.g., for generating and applying intent taxonomies [43] or for evaluation [19]. We must retain a continued focus on human-AI cooperation, where searchers stay in control while the degree of system support increases as needed [44], and on AI safety. Searchers need to be able to trust copilots in general but also be able to verify their answers with minimal effort. Overall, the future is bright for IR, and AI research in general, with the advent of generative AI and the copilots that build upon it. Copilots will help augment and empower searchers in their information seeking journeys. Computer science researchers and practitioners should embrace this new era of assistive agents and engage across the full spectrum of exciting practical and scientific opportunities, both within information seeking as we focused on here, and onwards into other important domains such as personal productivity [5] and scientific discovery [22]. REFERENCES [1] Eugene Agichtein, Ryen W White, Susan T Dumais, and Paul N Bennet. 2012. Search, interrupted: understanding and predicting search task continuation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 315-324. [2] Marcia J Bates. 1990. Where should the person stop and the information search interface start? Information Processing & Management 26, 5 (1990), 575–591. [3] Nicholas J Belkin. 1980. Anomalous states of knowledge as a basis for information retrieval. Canadian journal of information science 5, 1 (1980), 133–143. [4] Paul N Bennett, Ryen W White, Wei Chu, Susan T Dumais, Peter Bailey, Fedor Borisyuk, and Xiaoyuan Cui. 2012. Modeling the impact of short-and long-term behavior on search personalization. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 185–194. [5] Christian Bird, Denae Ford, Thomas Zimmermann, Nicole Forsgren, Eirini Kalliamvakou, Travis Lowdermilk, and Idan Gazit. 2022. Taking Flight with Copilot: Early insights and opportunities of AI-powered pair-programming tools. Queue 20, 6 (2022), 35–57. [6] Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021). [7] Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2021. Machine unlearning. In 2021 IEEE Symposium on Security and Privacy (SP). IEEE, 141–159. [8] Andrei Broder. 2002. A taxonomy of web search. In ACM Sigir forum, Vol. 36. ACM New York, NY, USA, 3–10. [9] Andrei Z Broder and Preston McAfee. 2023. Delphic Costs and Benefits in Web Search: A utilitarian and historical analysis. arXiv preprint arXiv:2308.07525 (2023). [10] Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712 (2023). [11] Katriina Byström and Kalervo Järvelin. 1995. Task complexity affects information seeking and use. Information processing & management 31, 2 (1995), 191–213. [12] Robert Capra and Jaime Arguello. 2023. How does AI chat change search behaviors? arXiv preprint arXiv:2307.03826 (2023). [13] Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See https://vicuna. lmsys. org (accessed 14 April 2023) (2023). [14] Antonia Creswell and Murray Shanahan. 2022. Faithful reasoning using large language models. arXiv preprint arXiv:2208.14271 (2022). [15] Brenda Dervin. 1998. Sense-making theory and practice: An overview of user interests in knowledge seeking and use. Journal of knowledge management 2, 2 (1998), 36–46. [16] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). [17] Karl Duncker and Lynne S Lees. 1945. On problem-solving. Psychological monographs 58, 5 (1945), i. [18] Brad Everman, Trevor Villwock, Dayuan Chen, Noe Soto, Oliver Zhang, and Ziliang Zong. 2023. Evaluating the Carbon Impact of Large Language Models at the Inference Stage. In 2023 IEEE International Performance, Computing, and Communications Conference (IPCCC). IEEE, 150–157. [19] Guglielmo Faggioli, Laura Dietz, Charles LA Clarke, Gianluca Demartini, Matthias Hagen, Claudia Hauff, Noriko Kando, Evangelos Kanoulas, Martin Potthast, Benno Stein, et al. 2023. Perspectives on Large Language Models for Relevance Judgment. In Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval. 39–50. [20] Jianfeng Gao, Chenyan Xiong, Paul Bennett, and Nick Craswell. 2023. Neural Approaches to Conversational Information Retrieval. Vol. 44. Springer Nature. [21] Ahmed Hassan Awadallah, Ryen W White, Patrick Pantel, Susan T Dumais, and Yi-Min Wang. 2014. Supporting complex search tasks. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management. 829–838. [22] Tom Hope, Doug Downey, Daniel S Weld, Oren Etzioni, and Eric Horvitz. 2023. A computational inflection for scientific discovery. Commun. ACM 66, 8 (2023), 62–73. [23] Peter Ingwersen and Kalervo Järvelin. 2005. The turn: Integration of information seeking and retrieval in context. Vol. 18. Springer Science & Business Media. [24] Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. Comput. Surveys 55, 12 (2023), 1–38. [25] Thorsten Joachims. 2002. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. 133–142. [26] Jeonghyun Kim. 2006. Task difficulty as a predictor and indicator of web searching interaction. In CHI'06 extended abstracts on human factors in computing systems. 959–964. [27] David R Krathwohl. 2002. A revision of Bloom's taxonomy: An overview. Theory into practice 41, 4 (2002), 212–218. [28] Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33 (2020), 9459–9474. [29] Yuelin Li and Nicholas J Belkin. 2008. A faceted approach to conceptualizing tasks in information seeking. Information processing & management 44, 6 (2008), 1822–1837. [30] Yuanchun Li and Oriana Riva. 2021. Glider: A reinforcement learning approach to extract UI scripts from websites. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1420–1430. [31] Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov. 2021. Towards understanding and mitigating social biases in language models. In International Conference on Machine Learning. PMLR, 6565–6576. [32] Gary Marchionini. 2006. Exploratory search: from finding to understanding. Commun. ACM 49, 4 (2006), 41–46. [33] James Mayfield, Eugene Yang, Dawn Lawrie, Samuel Barham, Orion Weller, Marc Mason, Suraj Nair, and Scott Miller. 2023. Synthetic Cross-language Information Retrieval Training Data. arXiv preprint arXiv:2305.00331 (2023). [34] Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Awadallah. 2023. Orca: Progressive learning from complex explanation traces of gpt-4. arXiv preprint arXiv:2306.02707 (2023). [35] Marc Najork. 2023. Generative Information Retrieval. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1–1. [36] Alexandra Olteanu, Jean Garcia-Gathright, Maarten de Rijke, Michael D Ekstrand, Adam Roegiest, Aldo Lipani, Alex Beutel, Alexandra Olteanu, Ana Lucic, AnaAndreea Stoica, et al. 2021. FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. In ACM SIGIR Forum, Vol. 53. ACM New York, NY, USA, 20–43. [37] Soo Young Rieh, Kevyn Collins-Thompson, Preben Hansen, and Hye-Jung Lee. 2016. Towards searching as a learning process: A review of current perspectives and future directions. Journal of Information Science 42, 1 (2016), 19–34. [38] Shawon Sarkar and Chirag Shah. 2021. An integrated model of task, information needs, sources and uncertainty to design task-aware search systems. In Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval. 83–92. [39] Reijo Savolainen. 2012. Expectancy-value beliefs and information needs as motivators for task-based information seeking. Journal of Documentation 68, 4 (2012), 492–511. [40] Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761 (2023). [41] Chirag Shah. 2023. Generative AI and the Future of Information Access. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (Birmingham, United Kingdom) (CIKM '23). Association for Computing Machinery, New York, NY, USA, 3. https://doi.org/10.1145/3583780.3615317 [42] Chirag Shah, Ryen White, Paul Thomas, Bhaskar Mitra, Shawon Sarkar, and Nicholas Belkin. 2023. Taking search to task. In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval. 1–13. [43] Chirag Shah, Ryen W White, Reid Andersen, Georg Buscher, Scott Counts, Sarkar Snigdha Sarathi Das, Ali Montazer, Sathish Manivannan, Jennifer Neville, Xiaochuan Ni, et al. 2023. Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies. arXiv preprint arXiv:2309.13063 (2023). [44] Ben Shneiderman. 2022. Human-centered AI. Oxford University Press. [45] Adish Singla, Ryen White, and Jeff Huang. 2010. Studying trailfinding algorithms for enhanced web search. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. 443–450. [46] Jaime Teevan, Kevyn Collins-Thompson, Ryen W White, and Susan Dumais. 2014. Slow search. Commun. ACM 57, 8 (2014), 36–38. [47] Jaime Teevan, Susan T Dumais, and Eric Horvitz. 2005. Personalizing search via automated analysis of interests and activities. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. 449–456. [48] Jaime Teevan, Meredith Ringel Morris, and Steve Bush. 2009. Discovering and using groups to improve personalized search. In Proceedings of the second acm international conference on web search and data mining. 15–24. [49] Maartje ter Hoeve, Robert Sim, Elnaz Nouri, Adam Fourney, Maarten de Rijke, and Ryen W White. 2020. Conversations with documents: An exploration of document-centered assistance. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval. 43–52. [50] Paul Thomas, Seth Spielman, Nick Craswell, and Bhaskar Mitra. 2023. Large language models can accurately predict searcher preferences. arXiv preprint arXiv:2309.10621 (2023). [51] Randall H Trigg. 1988. Guided tours and tabletops: Tools for communicating in a hypertext environment. ACM Transactions on Information Systems (TOIS) 6, 4 (1988), 398–414. [52] Sarah K Tyler and Jaime Teevan. 2010. Large scale query log analysis of re-finding. In Proceedings of the third ACM international conference on Web search and data mining. 191–200. [53] Pertti Vakkari. 2001. A theory of the task-based information retrieval process: A summary and generalisation of a longitudinal study. Journal of documentation 57, 1 (2001), 44–60. [54] Pertti Vakkari. 2016. Searching as learning: A systematization based on literature. Journal of Information Science 42, 1 (2016), 7–18. [55] Nicholas Vincent. 2022. The Paradox of Reuse, Language Models Edition.https://nmvg.mataroa.blog/blog/the-paradox-of-reuse-language-modelsedition/. Accessed: 2023-09-12. [56] Yu Wang, Xiao Huang, and Ryen W White. 2013. Characterizing and supporting cross-device search tasks. In Proceedings of the sixth ACM international conference on Web search and data mining. 707–716. [57] Ryen W White. 2016. Interactions with search systems. Cambridge University Press. [58] Ryen W White. 2018. Opportunities and challenges in search interaction. Commun. ACM 61,12 (2018), 36–38. [59] Ryen W White. 2018. Skill discovery in virtual assistants. Commun. ACM 61, 11 (2018), 106–113. [60] Ryen W White. 2022. Intelligent futures in task assistance. Commun. ACM 65, 11 (2022), 35–39. [61] Ryen W. White. 2023. Tasks, Copilots, and the Future of Search. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR '23). Association for Computing Machinery, New York, NY, USA, 5–6. https://doi.org/10.1145/3539618.3593069 [62] Ryen W White, Mikhail Bilenko, and Silviu Cucerzan. 2007. Studying the use of popular destinations to enhance web search interaction. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. 159–166. [63] Ryen W White, Wei Chu, Ahmed Hassan, Xiaodong He, Yang Song, and Hongning Wang. 2013. Enhancing personalized search by mining and modeling task behavior. In Proceedings of the 22nd international conference on World Wide Web. 1411–1420. [64] Ryen W White, Adam Fourney, Allen Herring, Paul N Bennett, Nirupama Chandrasekaran, Robert Sim, Elnaz Nouri, and Mark J Encarnación. 2019. Multi-device digital assistance. Commun. ACM 62, 10 (2019), 28–31. [65] Ryen W White, Ian Ruthven, and Joemon M Jose. 2005. A study of factors affecting the utility of implicit relevance feedback. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. 35–42. [66] Ryen W White, Ian Ruthven, Joemon M Jose, and CJ Van Rijsbergen. 2005. Evaluating implicit feedback models using searcher simulations. ACM Transactions on Information Systems (TOIS) 23, 3 (2005), 325–361. [67] Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023. AutoGen: Enabling nextgen LLM applications via multi-agent conversation framework. arXiv preprint arXiv:2308.08155 (2023). [68] Iris Xie. 2008. Interactive information retrieval in digital environments. IGI global. [69] Jinyun Yan, Wei Chu, and Ryen W White. 2014. Cohort modeling for enhanced personalized search. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. 505–514. [70] Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al. 2021. Differentially private fine-tuning of language models. arXiv preprint arXiv:2110.06500 (2021). [71] Hamed Zamani, Susan Dumais, Nick Craswell, Paul Bennett, and Gord Lueck. 2020. Generating clarifying questions for information retrieval. In Proceedings of the web conference 2020. 418–428. [72] Jieyu Zhang, Ranjay Krishna, Ahmed H Awadallah, and Chi Wang. 2023. EcoAssistant: Using LLM Assistant More Affordably and Accurately. arXiv preprint arXiv:2310.03046 (2023). [73] Yi Zhang, Sujay Kumar Jauhar, Julia Kiseleva, Ryen White, and Dan Roth. 2021. Learning to decompose and organize complex tasks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2726–2735. [74] Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Lingpeng Kong, Jiajun Chen, Lei Li, and Shujian Huang. 2023. Multilingual machine translation with large language models: Empirical results and analysis. arXiv preprint arXiv:2304.04675 (2023). [75] Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593 (2019).

ACM ACM SIGIR international ACM International Conference SIGIR conference

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Hatem A. Alharbi, CSchool of Electronic and Electrical Engineering, University of Leeds, LS2 9JT, United Kingdom; (2) Taisir E.H. Elgorashi, School of Electronic and Electrical Engineering, University of Leeds, LS2 9JT, United Kingdom; (3) Jaafar M.H. Elmirghani, School of Electronic and Electrical Engineering, University of Leeds, LS2 9JT, United Kingdom. ::: Table of Links Abstract & Introduction Related Works Repealing Net Neutrality Profit-Driven Model Results Conclusions & References Biographies V. CONCLUSIONS In this paper, we developed a MILP model to optimize the pricing scheme used by ISPs to charge CPs for delivering their video content under the repeal of net neutrality where ISPs can treat data intensive traffic less favorably. A techno-economic Mixed Integer Linear Programming (MILP) model is developed to maximize the ISP profit by optimizing the ISP pricing scheme to charge different classes of service differently subject to PED. We considered three classes of service that represent different data rate requirements of video content. The analysis addressed three CP delivery scenarios; cloud-based delivery, cloud-fog based delivery and fog-based delivery. The results show that the discriminatory pricing scheme can increase the ISPs profit by a factor of 8. The results also show that by influencing the way end-users consume data-intensive content, the core network traffic and consequently power consumption are reduced by up to 49% and 55%, respectively, compared to the net neutrality scenario. Acknowledgements The authors would like to acknowledge funding from the Engineering and Physical Sciences Research Council (EPSRC), INTERNET (EP/H040536/1), STAR (EP/K016873/1) and TOWS (EP/S016570/1) projects. The first author would like to acknowledge the Government of Saudi Arabia and Taibah University for funding his PhD scholarship. All data are provided in full in the results section of this paper. REFERENCES Cisco, “The Zettabyte Era: Trends and Analysis,” 2017. Ycharts.com, “AT&T Profit Margin (Quarterly),” 2018. [Online]. Available: https://ycharts.com/companies/T/profit_margin. [Accessed: 01-Aug-2018]. Ycharts, “Netflix Profit Margin (Quarterly),” 2018. [Online]. Available: https://ycharts.com/companies/NFLX/profit_margin. [Accessed: 01-Aug-2018]. T. Garrett, L. E. Setenareski, L. M. Peres, L. C. E. Bona, E. P. D. Jr, and A. Mislove, “Monitoring Network Neutrality : A Survey on Traffic Differentiation Detection,” IEEE Commun. Surv. Tutorials, no. c, pp. 1– 32, 2018. A. M. Kakhki, D. Choffnes, A. Mislove, and E. Katzbassett, “BingeOn Under the Microscope : Understanding T-Mobile ' s Zero-Rating Implementation,” Proc. 2016 Work. QoE-based Anal. Manag. Data Commun. Networks (Internet-QoE '16) ACM, pp. 43–48, 2016. J. S. Huang and B. Zhu, “Video Traffic Detection Method for Deep Packet Inspection,” Adv. CSIE, vol. 2, pp. 135– 140, 2012. Cisco Systems, “Traffic Profiling,” pp. 1–12. D. Tsilimantos, T. Karagkioules, A. Nogales-g, and S. Valentin, “Traffic profiling for mobile video streaming,” arXiv, pp. 1–7, 2017. W. Dai, J. W. Baek, and S. Jordan, “Neutrality between a vertically integrated cable provider and an over-the-top video provider,” J. Commun. Networks, vol. 18, no. 6, pp. 962–974, 2016. 10. Amazon, “Netflix Case Study - Amazon Web Services (AWS),” 2018. [Online]. Available: https://aws.amazon.com/solutions/case-studies/netflix/. [Accessed: 15-Aug-2018]. 11. AT&T, “AT&T Content Delivery Network (CDN) Services,” 2018. [Online]. Available: https://www.business.att.com/solutions/Family/cloud/cont ent-delivery-network/. [Accessed: 15-Aug-2018]. 12. [Comcast, “CDN - Comcast Technology Solutions,” 2018. [Online]. Available: https://www.comcasttechnologysolutions.com/resources/c omcast-cdn-one-sheet. [Accessed: 15-Aug-2018]. 13. “AT & T Switched Ethernet Classic ( ASE Classic ) and On-Demand ( ASEoD ).” 14. D. Grunwald, “The Internet Ecosystem : The Potential for Discrimination,” Fed. Commun. Law J., vol. 63, no. 2, pp. 411–443, 2011. 15. A. Odlyzko, “Network Neutrality, Search Neutrality, and the Never-ending Conflict between Efficiency and Fairness in Markets,” Rev. Netw. Econ., vol. 8, no. 1, pp. 40–60, 2009. 16. P. Nooren, A. Leurdijk, and N. van Eijk, “Net neutrality and the value chain for video,” Info, vol. 14, no. 6, pp. 45– 58, 2012. 17. R. T. B. Ma, J. Wang, and D. M. Chiu, “Paid prioritization and its impact on net neutrality,” IEEE J. Sel. Areas Commun., vol. 35, no. 2, pp. 367–379, 2017. 18. H.M.M., Ali, A.Q. Lawey, T.E.H. El-Gorashi, and J.M.H. Elmirghani, “Future Energy Efficient Data Centers With Disaggregated Servers,” IEEE/OSA Journal of Lightwave Technology, vol. 35, No. 24, pp. 5361 – 5380, 2017. 19. X. Dong, T. El-Gorashi, and J. Elmirghani, "Green IP Over WDM Networks With Data Centers," Lightwave Technology, Journal of, vol. 29, no. 12, pp. 1861-1880, June 2011. 20. N. I. Osman, T. El-Gorashi, L. Krug, and J. M. H. Elmirghani, “Energy Efficient Future High-Definition TV,” Journal of Lightwave Technology, vol. 32, no. 13, pp. 2364-2381, July 2014. 21. Lawey, T.E.H. El-Gorashi, and J.M.H. Elmirghani, “BitTorrent Content Distribution in Optical Networks,” IEEE/OSA Journal of Lightwave Technology, vol. 32, No. 21, pp. 3607 – 3623, 2014. 22. A.M. Al-Salim, A. Lawey, T.E.H. El-Gorashi, and J.M.H. Elmirghani, “Energy Efficient Big Data Networks: Impact of Volume and Variety,” IEEE Transactions on Network and Service Management, vol. 15, No. 1, pp. 458 - 474, 2018. 23. A.M. Al-Salim, A. Lawey, T.E.H. El-Gorashi, and J.M.H. Elmirghani, “Greening big data networks: velocity impact,” IET Optoelectronics, vol. 12, No. 3, pp. 126-135, 2018. 24. M.S. Hadi, A. Lawey, T.E.H. El-Gorashi, and J.M.H. Elmirghani, “Patient-Centric Cellular Networks Optimization using Big Data Analytics,” IEEE Access, pp. 49279 - 49296, vol. 7, 2019. 25. M.S. Hadi, A. Lawey, T.E.H. El-Gorashi, and J.M.H. Elmirghani, “Big Data Analytics for Wireless and Wired Network Design: A Survey, Elsevier Computer Networks, vol. 132, No. 2, pp. 180-199, 2018. 26. J. M. H. Elmirghani, T. Klein, K. Hinton, L. Nonde, A. Q. Lawey, T. E. H. El-Gorashi, M. O. I. Musa, and X. Dong, “GreenTouch GreenMeter core network energy-efficiency improvement measures and optimization,” IEEE/OSA Journal of Optical Communications and Networking, vol. 10, no. 2, pp. A250-A269, Feb 2018. 27. M. Musa, T.E.H. El-Gorashi and J.M.H. Elmirghani, “Bounds on GreenTouch GreenMeter Network Energy Efficiency,” IEEE/OSA Journal of Lightwave Technology, vol. 36, No. 23, pp. 5395-5405, 2018. 28. X. Dong, T.E.H. El-Gorashi and J.M.H. Elmirghani, “On the Energy Efficiency of Physical Topology Design for IP over WDM Networks,” IEEE/OSA Journal of Lightwave Technology, vol. 30, pp.1931-1942, 2012. 29. B. Bathula, M. Alresheedi, and J.M.H. Elmirghani, “Energy efficient architectures for optical networks,” Proc IEEE London Communications Symposium, London, Sept. 2009. 30. B. Bathula, and J.M.H. Elmirghani, “Energy Efficient Optical Burst Switched (OBS) Networks,” IEEE GLOBECOM'09, Honolulu, Hawaii, USA, November 30- December 04, 2009. 31. X. Dong, T.E.H. El-Gorashi and J.M.H. Elmirghani, “Green optical orthogonal frequency-division multiplexing networks,” IET Optoelectronics, vol. 8, No. 3, pp. 137 – 148, 2014. 32. X. Dong, T. El-Gorashi, and J. Elmirghani, “IP Over WDM Networks Employing Renewable Energy Sources,” Lightwave Technology, Journal of, vol. 29, no. 1, pp. 3-14, Jan 2011. 33. X. Dong, A.Q. Lawey, T.E.H. El-Gorashi, and J.M.H. Elmirghani, “Energy Efficient Core Networks,” Proc 16th IEEE Conference on Optical Network Design and Modelling (ONDM'12), 17-20 April, 2012, UK. 34. P. Mailé, G. Simon, and B. Tuffin, “Toward a net neutrality debate that conforms to the 2010s,” IEEE Commun. Mag., vol. 54, no. 3, pp. 94–99, 2016. 35. M. Musa, T.E.H. El-Gorashi and J.M.H. Elmirghani, “Bounds for Energy-Efficient Survivable IP Over WDM Networks with Network Coding,” IEEE/OSA Journal of Optical Communications and Networking, vol. 10, no. 5, pp. 471-481, 2018. 36. M. Musa, T.E.H. El-Gorashi and J.M.H. Elmirghani, “Energy Efficient Survivable IP-Over-WDM Networks With Network Coding,” IEEE/OSA Journal of Optical Communications and Networking, vol. 9, No. 3, pp. 207- 217, 2017. 37. L. Nonde, T.E.H. El-Gorashi, and J.M.H. Elmirghani, “Energy Efficient Virtual Network Embedding for Cloud Networks,” IEEE/OSA Journal of Lightwave Technology, vol. 33, No. 9, pp. 1828-1849, 2015. 38. A.N. Al-Quzweeni, A. Lawey, T.E.H. El-Gorashi, and J.M.H. Elmirghani, “Optimized Energy Aware 5G Network Function Virtualization,” IEEE Access, vol. 7, pp. 44939 - 44958, 2019. 39. Al-Azez, Z., Lawey, A., El-Gorashi, T.E.H., and Elmirghani, J.M.H., “Energy Efficient IoT Virtualization Framework with Peer to Peer Networking and Processing” IEEE Access, vol. 7, pp. 50697 - 50709, 2019. 40. R. S. Pindyck and D. L. Rubinfeld, Microeconomics. 2005. 41. R. Cadman, C. Dineen, and R. Cadman, “Price and Income Elasticity of Demand for Broadband Subscriptions : A Cross-Sectional Model of OECD Countries,” SPC Network, 19, pp. 3–8, 2009. 42. H. Galperin and C. A. Ruzzier, “Price elasticity of demand for broadband : Evidence from Latin America and the Caribbean,” Telecomm. Policy, vol. 37, pp. 429–438, 2013. 43. USAToday, “Interent bill too high,” 2018. 44. G. S. G. Shen and R. S. Tucker, “Energy-Minimized Design for IP Over WDM Networks,” IEEE/OSA J. Opt. Commun. Netw., vol. 1, no. 1, pp. 176–186, 2009. 45. Cisco, “United States - 2021 Forecast Highlights,” pp. 1– 6, 2016. 46. B. Insider, “Netflix subscribers over the years,” 2019. [Online]. Available: https://www.businessinsider.com/netflix-subscriberschart-2017-1?r=US&IR=T. [Accessed: 01-Jul-2018]. 47. J. Networks, “PTX1000, PTX10001, PTX10002, AND PTX10003 FIXEDCONFIGURATION PACKET TRANSPORT ROUTERS,” 2019. 48. Ycharts.com, “Comcast Corp Historical Profit Margin (Quarterly) Data,” 2018. [Online]. Available: https://ycharts.com/companies/CMCSA/profit_margin. [Accessed: 23-Aug-2018]. 49. J. Baliga, K. Hinton, and R. S. Tucker, “Energy Consumption of the Internet,” in COIN-ACOFT 2007 - Joint International Conference on the Optical Internet and the 32nd Australian Conference on Optical Fibre Technology, 2007, pp. 1–3. 50. AT&T, “AT&T's 38 Global Internet Data Centers.” [Online]. Available: https://www.business.att.com/content/productbrochures/e b_idcmap.pdf. [Accessed: 15-Aug-2018]. 51. Cisco System, “Cisco CRS-1 4-Slot Single-Shelf System,” 2014. 52. Cisco System, “Cisco ONS 15454 40 Gbps CP-DQPSK Full C-Band Tuneable Transponder Card.” 53. Oclaro, “OTS-4400 Regenerator,” 2014. 54. MRV, “EDFA Optical Amplifiers,” 2012. 55. “Intelligent Optical System 600,” Glimmerglass, 2013. [Online]. Available: http://www.glimmerglass.com/ products/intelligent-optical-systems/. [Accessed: 16-Mar2017]. 56. “GreenTouch Green Meter Research Study : Reducing the Net Energy Consumption in Communications Networks by up to 90 % by 2020,” White Pap., pp. 1–25, 2015. 57. A. Q. Lawey, T. E. H. El-Gorashi, and J. M. H. Elmirghani, “Distributed energy efficient clouds over core networks,” J. Light. Technol., vol. 32, no. 7, pp. 1261–1281, 2014.

Energy Efficient Lightwave Technology OSA Journal vol.

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Kota Chin, University of Tsukuba, National Institute of Information and Communications Technology Japan; (2) Keita Emura, Kanazawa University, Japan National Institute of Information and Communications Technology Japan; (3) Kazumasa Omote, University of Tsukuba National Institute of Information and Communications Technology Japan. ::: Table of Links Abstract & Introduction Preliminaries Proposed Anonymous Yet Accountable Contract Wallet System Implementation Conclusion & References V. CONCLUSION In this paper, we proposed an anonymous yet accountable contract wallet system based on account abstraction and accountable ring signatures. The proposed system is implemented using Solidity for zkSync. Moreover, we discussed potential of the proposed system, e.g., medical information sharing and asset management. Since the current implementation results using Solidity show the required costs are expensive, our result here might be regarded as somewhat conceptual. However, to the best of our knowledge, no previous implementation result is known that confirms the cost to run an accountable ring signature scheme in Solidity to date, and we believe that our result can be seen as an important stepping stone to provide anonymity and accountability simultaneously in blockchain systems. Investigating other applications of the proposed system will be left to future work. The underlying account ring signature scheme does not provide post-quantum security due to the discrete logarithm-based construction. Thus, it is difficult to accept the current construction as a platform to manage large amounts of assets due to the progress of quantum computing. Because a post-quantum accountable ring signature scheme has been proposed in [7], it would be interesting to employ the scheme, precisely, how to implement it using Solidity is left to future work. Acknowledgment: The authors would like to thank Dr. Miyako Ohkubo (NICT) for her invaluable comments and suggestions. This work was supported by JSPS KAKENHI Grant Numbers JP21K11897 and JP22H03588. REFERENCES [1] Arrest of suspected developer of Tornado Cash. https://www.fiod.nl/arrest-of-suspected-developer-of-tornado-cash/. August 12, 2022. [2] StarkNet. https://starkware.co/starknet/. [3] zkSync. https://zksync.io/. [4] Jean-Philippe Aumasson, Daniel J. Bernstein, Ward Beullens, Christoph Dobraunig, Maria Eichlseder, Scott Fluhrer, Stefan-Lukas Gazdag, Andreas H ¨ulsing, Panos Kampanakis, Stefan K ¨olbl, Tanja Lange, Martin M. Lauridsen, Florian Mendel, Ruben Niederhagen, Christian Rechberger, Joost Rijneveld, Peter Schwabe, and Bas Westerbaan. SPHINCS+: Submission to the NIST post-quantum project, v.3.1. https://sphincs.org/data/sphincs+-r3.1-specification.pdf. [5] Asaph Azaria, Ariel Ekblaw, Thiago Vieira, and Andrew Lippman. MedRec: Using blockchain for medical data access and permission management. In 2016 2nd International Conference on Open and Big Data (OBD), pages 25–30, 2016. [6] Eli Ben-Sasson, Iddo Bentov, Yinon Horesh, and Michael Riabzev. Scalable, transparent, and post-quantum secure computational integrity. IACR Cryptol. ePrint Arch., page 46, 2018. [7] Ward Beullens, Samuel Dobson, Shuichi Katsumata, Yi-Fu Lai, and Federico Pintore. Group signatures and more from isogenies and lattices: Generic, simple, and efficient. In EUROCRYPT, pages 95–126, 2022. [8] Ward Beullens, Shuichi Katsumata, and Federico Pintore. Calamari and Falafl: Logarithmic (linkable) ring signatures from isogenies and lattices. In ASIACRYPT, pages 464–492, 2020. [9] Dmytro Bogatov, Angelo De Caro, Kaoutar Elkhiyaoui, and Bj ¨orn Tackmann. Anonymous transactions with revocation and auditing in hyperledger fabric. In CANS, pages 435–459, 2021. [10] Jonathan Bootle, Andrea Cerulli, Pyrros Chaidos, Essam Ghadafi, Jens Groth, and Christophe Petit. Short accountable ring signatures based on DDH. In ESORICS, pages 243–265, 2015. [11] Vitalik Buterin, Ansgar Dietrichs, Matt Garnett, Will Villanueva, and Sam Wilson. EIP-2938: Account Abstraction. https://eips.ethereum.org/EIPS/eip-2938, 2020. [12] Jan Camenisch, Manu Drijvers, and Maria Dubovitskaya. Practical ucsecure delegatable credentials with attributes and their application to blockchain. In ACM CCS, pages 683–699, 2017. [13] David Chaum and Eug`ene van Heyst. Group signatures. In EUROCRYPT, pages 257–265, 1991. [14] Aisling Connolly, J´er ˆome Deschamps, Pascal Lafourcade, and Octavio Perez-Kempner. Protego: Efficient, revocable and auditable anonymous credentials with applications to hyperledger fabric. In INDOCRYPT, pages 249–271, 2022. [15] L´eo Ducas, Eike Kiltz, Tancr`ede Lepoint, Vadim Lyubashevsky, Peter Schwabe, Gregor Seiler, and Damien Stehl´e. CRYSTALS-Dilithium: A lattice-based digital signature scheme. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2018(1):238–268, 2018. [16] Kai Fan, Shangyang Wang, Yanhui Ren, Hui Li, and Yintang Yang. MedBlock: Efficient and secure medical data sharing via blockchain. Journal of Medical Systems, 42(8):136:1–136:11, 2018. [17] Tomilayo Fatokun, Avishek Nag, and Sachin Sharma. Towards a blockchain assisted patient owned system for electronic health records. Electronics, 10(5), 2021. [18] Pierre-Alain Fouque, Jeffrey Hoffstein, Paul Kirchner, Vadim Lyubashevsky, Thomas Pornin, Thomas Prest, Thomas Ricosset, Gregor Seiler, William Whyte, and Zhenfei Zhang. FALCON: Fast-fourier lattice-based compact signatures over NTRU. https://falcon-sign.info/falcon.pdf. [19] Tomoki Fujitani, Keita Emura, and Kazumasa Omote. A privacypreserving enforced bill collection system using smart contracts. In AsiaJCIS, pages 51–60. IEEE, 2021. [20] Merlin George and Anu Mary Chacko. MediTrans - patient-centric interoperability through blockchain. International Journal of Network Management, 32(3), 2022. [21] Jens Groth. Short non-interactive zero-knowledge proofs. In ASIACRYPT, pages 341–358, 2010. [22] Yiheng Liang. Identity verification and management of electronic health records with blockchain technology. In ICHI, pages 1–3. IEEE, 2019. [23] Joseph K. Liu, Victor K. Wei, and Duncan S. Wong. Linkable spontaneous anonymous group signature for ad hoc groups (extended abstract). In ACISP, pages 325–335, 2004. [24] Xingye Lu, Man Ho Au, and Zhenfei Zhang. Raptor: A practical latticebased (linkable) ring signature. In ACNS, pages 110–130, 2019. [25] Mohammad Moussa Madine, Ammar Ayman Battah, Ibrar Yaqoob, Khaled Salah, Raja Jayaraman, Yousof Al-Hammadi, Sasa Pesic, and Samer Ellahham. Blockchain for giving patients control over their medical records. IEEE Access, 8:193102–193115, 2020. [26] Abdullah Al Omar, Md. Zakirul Alam Bhuiyan, Anirban Basu, Shinsaku Kiyomoto, and Mohammad Shahriar Rahman. Privacy-friendly platform for healthcare data in cloud based on blockchain environment. Future Gener. Comput. Syst., 95:511–521, 2019. [27] Bryan Parno, Jon Howell, Craig Gentry, and Mariana Raykova. Pinocchio: nearly practical verifiable computation. Communications of the ACM, 59(2):103–112, 2016. [28] Sen Qiao, Varun Madathil, and Kemafor Anyanwu. Integrating group signatures in complex decentralized marketplace transactions for improved buyer privacy. In Blockchain, pages 139–148. IEEE, 2022. [29] Ronald L. Rivest, Adi Shamir, and Yael Tauman. How to leak a secret. In ASIACRYPT, pages 552–565, 2001. [30] Alex Roehrs, Cristiano Andre da Costa, and Rodrigo da Rosa Righi. OmniPHR: A distributed architecture model to integrate personal health records. Journal of Biomedical Informatics, 71:70–81, 2017. [31] Teppei Sato, Keita Emura, Tomoki Fujitani, and Kazumasa Omote. An anonymous trust-marking scheme on blockchain systems. IEEE Access, 9:108772–108781, 2021. [32] Kazuo Takaragi, Takashi Kubota, Sven Wohlgemuth, Katsuyuki Umezawa, and Hiroki Koyanagi. Secure revocation features in eKYC - privacy protection in central bank digital currency. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 106-A(3), 2023. [33] Qi Xia, Emmanuel Boateng Sifah, Kwame Omono Asamoah, Jianbin Gao, Xiaojiang Du, and Mohsen Guizani. MeDShare: Trust-less medical data sharing among cloud service providers via blockchain. IEEE Access, 5:14757–14767, 2017. [34] Shouhuai Xu and Moti Yung. Accountable ring signatures: A smart card approach. In CARDIS, pages 271–286, 2004.

Accountable Contract Contract Wallet Japan National Technology Japan

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Linda Pigureddu, University of Turin, Italy, 332079@edu.unito.it; (2) Cristina Gena, Dept. of Computer Science, University of Turin, Italy, cristina.gena@unito.it. ::: Table of Links Introduction The Project Memes Conclusion and References CONCLUSION The analysis of the interactions and dialogues that took place during the therapeutic laboratory for autonomies endorsed with the inclusion of the Pepper robot provided an interesting perspective on the communication dynamics of children in the lab. The results obtained showed the effectiveness of the robot in promoting autonomy and functional acquisition, demonstrating that the therapeutic method based on assistive robotics is a valuable resource to support the rehabilitation needs of communication and social skills of autistic children. The analysis highlights new possibilities for their engagement and active participation as a co-designer, as we already experienced in the past with neuro-typical children [19], representing an innovative opportunity to promote the development and wellness of autistic people and opening to new perspectives for therapeutic intervention more aware of the needs of autistic minds. In conclusion, this paper highlights the need to provide the robot with the ability of adapt to the children's peculiarity and features and sharing her/his vocabulary, especially by recognizing and using memes during interactions, to inspire greater trust in children and allowing the use of common slang, already in use with classmates, allowing them to consider Pepper a peer and effectively insert it in the role of mediator. In addition, it would allow children to use a simplified type of communication during meetings to compensate for the typical communication deficits associated with autism spectrum disorders. In the future we will work in this directions (co-design, user's adaptations, using and recognizing memes during children-robot communication) having the robot able to communicate and express adapting to user tastes and preferences and and try again to field a real-world evaluation that takes into account the effectiveness of different levels of user adaptation [23]. REFERENCE [1] M. Biondi e M. Maj, DSM-5: diagnostic and statistical manual of mental disorders: text revision, 5. ed. Milano: Raffaello Cortina. [2] E. Hollander, R. Hagerman, e C. Ferretti, Textbook of Autism Spectrum Disorders, Second Edition. American Psychiatric Association Publishing, 2022. [3] ISS - Istituto superiore di sanità, «Osservatorio Nazionale Autismo», OssNA. https://osservatorionazionaleautismo.iss.it [4] P. Pennisi et al., «Autism and social robotics: A systematic review», Autism Research, vol. 9, fasc. 2, pp. 165–183, 2016, doi: 10.1002/aur.1527. [5] S. Shamsuddin, H. Yussof, S. Mohamed, e F. A. Hanapiah, «Design and Ethical Concerns in Robotic Adjunct Therapy Protocols for Children with Autism», Procedia Computer Science, vol. 42, pp. 9–16, gen. 2014, doi: 10.1016/j.procs.2014.11.027. [6] K. Dautenhahn e I. Werry, «Towards Interactive Robots in Autism Therapy: Background, Motivation and Challenges», Pragmatics & Cognition, vol. 12, pp. 1–35, giu. 2004, doi: 10.1075/pc.12.1.03dau. [7] C. Li, Q. Jia, e Y. Feng, «Human-Robot Interactoin Design for Robot-Assisted Intervention for Children with Autism Based on E-S Theory», in 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China: IEEE, ago. 2016, pp. 320–324. doi: 10.1109/IHMSC.2016.103. [8] A. Peca et al., «Robot Enhanced Therapy for Children with Autism Disorders: Measuring Ethical Acceptability», IEEE Technol. Soc. Mag., vol. 35, fasc. 2, pp. 54–66, giu. 2016, doi: 10.1109/MTS.2016.2554701. [9] B. Robins, K. Dautenhahn, e J. Dubowski, «Does appearance matter in the interaction of children with autism with a humanoid robot?», IS, vol. 7, fasc. 3, pp. 479–512, nov. 2006, doi:10.1075/is.7.3.16rob. [10] E. S. Kim, «Robots for social skills therapy in autism: evidence and designs toward clinical utility», 2013, doi: 10.13140/2.1.3038.2083. [11] «Autismo», Neuraxpharm Italia. https://www.neuraxpharm.com/it/disordini/autismo [12] L. Hull, K. V. Petrides, e W. Mandy, «The Female Autism Phenotype and Camouflaging: a Narrative Review», Rev J Autism Dev Disord, vol. 7, fasc. 4, pp. 306–317, dic. 2020, doi: 10.1007/s40489-020-00197-9. [13] D. Cohen et al., «NAO, a humanoid robot as a therapeutic mediator for young people with autism». [14] Cristina Gena, Claudio Mattutino, Andrea Maieli, Elisabetta Miraglio, Giulia Ricciardiello, Rossana Damiano, Alessandro Mazzei: Autistic Children's Mental Model of an Humanoid Robot. UMAP (Adjunct Publication) 2021: 128-129 [15] Rossana Damiano, Cristina Gena, Andrea Maieli, Claudio Mattutino, Alessandro Mazzei, Elisabetta Miraglio, Giulia Ricciardiello: UX Personas for Defining Robot's Character and Personality 213-216. IUI Workshops 2022: 213-216. [16] Cristina Gena, Claudio Mattutino, Stefania Brighenti, Andrea Meirone, Francesco Petriglia, Loredana Mazzotta, Federica Liscio, Matteo Nazzario, Valeria Ricci, Camilla Quarato, Cesare Pecone, Giuseppe Piccinni: et al., Sugar, Salt & Pepper - Humanoid robotics for autism, in Joint Proceedings of the ACM IUI 2021 Workshops co-located with 26th ACM Conference on Intelligent User Interfaces (ACM IUI 2021), College Station, United States, April 13-17, 2021, D. Glowacka e V. R. Krishnamurthy, A c. di, in CEUR Workshop Proceedings, vol. 2903. CEUR-WS.org, 2021. [17] Cristina Gena, Rossana Damiano, Claudio Mattutino, Alessandro Mazzei, Andrea Meirone, Loredana Mazzotta, Matteo Nazzario, Valeria Ricci, Stefania Brighenti, Federica Liscio, Francesco Petriglia: Preliminary results of a therapeutic lab for promoting autonomies in autistic children. arXiv, doi: 10.48550/arXiv.2305.02982. [18] B. C. Carbo, «The Use of Social Stories with Individuals with Autism Spectrum Disorders», University of Delaware ProQuest Dissertations Publishing, Newark, DE, 2005. [Online]. Disponibile su: https://udspace.udel.edu/server/api/core/bitstreams/cd5e8850-e57c-41d2-a7b4ac5e25a4b659/content [19] V. Cietto, C. Gena, I. Lombardi, C. Mattutino and C. Vaudano, "Co-designing with kids an educational robot," 2018 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), Genova, Italy, 2018, pp. 139-140, doi: 10.1109/ARSO.2018.8625810. [20] A. Mousa, «Sherry Turkle, Reclaiming Conversation. The Power of Talk in a Digital Age», Questions de communication, fasc. 34, Art. fasc. 34, dic. 2018, doi:10.4000/questionsdecommunication.16891. [21] H.-M. Chiang e M. Carter, «Spontaneity of Communication in Individuals with Autism», J Autism Dev Disord, vol. 38, fasc. 4, pp. 693–705, apr. 2008, doi: 10.1007/s10803-007-0436-7. [22] Gena, C., Cena, F., Vernero, F., Grillo, P. The evaluation of a social adaptive website for cultural events. User Model User-Adap Inter 23, 89–137 (2013) https://doi.org/10.1007/s11257-012-9129-9 [23] Gena, Cristina, and Ilaria Torre. "The importance of adaptivity to provide onboard services: A preliminary evaluation of an adaptive tourist information service onboard vehicles." Applied Artificial Intelligence 18.6 (2004): 549-580.

autism Autistic Children Cristina Gena doi