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Las Vegas, USA: IEEE Computer Society. <a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>","bibtex":"@inproceedings{Hasnain_Karl, title={Learning Flow Scheduling}, DOI={<a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>}, booktitle={2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)}, publisher={IEEE Computer Society}, author={Hasnain, Asif and Karl, Holger} }","mla":"Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>, IEEE Computer Society, doi:<a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>.","short":"A. Hasnain, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC), IEEE Computer Society, n.d."},"year":"2021","publication_status":"accepted","doi":"https://doi.org/10.1109/CCNC49032.2021.9369514","conference":{"start_date":"2021-01-09","name":"2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)","location":"Las Vegas, USA","end_date":"2021-01-12"},"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9369514"}],"title":"Learning Flow Scheduling","author":[{"first_name":"Asif","full_name":"Hasnain, Asif","id":"63288","last_name":"Hasnain"},{"first_name":"Holger","full_name":"Karl, Holger","id":"126","last_name":"Karl"}],"date_created":"2020-10-19T14:27:17Z","date_updated":"2022-01-06T06:54:20Z","publisher":"IEEE Computer Society","status":"public","abstract":[{"text":"Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load.","lang":"eng"}],"publication":"2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)","type":"conference","language":[{"iso":"eng"}],"keyword":["Flow scheduling","Deadlines","Reinforcement learning"],"ddc":["000"],"department":[{"_id":"75"}],"user_id":"63288","_id":"20125","project":[{"_id":"4","name":"SFB 901 - Project Area C"},{"_id":"16","name":"SFB 901 - Subproject C4"},{"name":"SFB 901","_id":"1"}]},{"citation":{"ieee":"S. Gottschalk, J. Kirchhoff, and G. Engels, “Extending Business Model Development Tools with Consolidated Expert Knowledge ,” in <i>Business Modeling and Software Design</i>, Sofia, 2021.","chicago":"Gottschalk, Sebastian, Jonas Kirchhoff, and Gregor Engels. “Extending Business Model Development Tools with Consolidated Expert Knowledge .” In <i>Business Modeling and Software Design</i>, edited by Boris Shishkov, 2021. <a href=\"https://doi.org/10.1007/978-3-030-79976-2_1\">https://doi.org/10.1007/978-3-030-79976-2_1</a>.","ama":"Gottschalk S, Kirchhoff J, Engels G. Extending Business Model Development Tools with Consolidated Expert Knowledge . In: Shishkov B, ed. <i>Business Modeling and Software Design</i>. ; 2021. doi:<a href=\"https://doi.org/10.1007/978-3-030-79976-2_1\">10.1007/978-3-030-79976-2_1</a>","short":"S. Gottschalk, J. Kirchhoff, G. Engels, in: B. Shishkov (Ed.), Business Modeling and Software Design, 2021.","bibtex":"@inproceedings{Gottschalk_Kirchhoff_Engels_2021, title={Extending Business Model Development Tools with Consolidated Expert Knowledge }, DOI={<a href=\"https://doi.org/10.1007/978-3-030-79976-2_1\">10.1007/978-3-030-79976-2_1</a>}, booktitle={Business Modeling and Software Design}, author={Gottschalk, Sebastian and Kirchhoff, Jonas and Engels, Gregor}, editor={Shishkov, BorisEditor}, year={2021} }","mla":"Gottschalk, Sebastian, et al. “Extending Business Model Development Tools with Consolidated Expert Knowledge .” <i>Business Modeling and Software Design</i>, edited by Boris Shishkov, 2021, doi:<a href=\"https://doi.org/10.1007/978-3-030-79976-2_1\">10.1007/978-3-030-79976-2_1</a>.","apa":"Gottschalk, S., Kirchhoff, J., &#38; Engels, G. (2021). Extending Business Model Development Tools with Consolidated Expert Knowledge . In B. Shishkov (Ed.), <i>Business Modeling and Software Design</i>. Sofia. <a href=\"https://doi.org/10.1007/978-3-030-79976-2_1\">https://doi.org/10.1007/978-3-030-79976-2_1</a>"},"year":"2021","has_accepted_license":"1","doi":"10.1007/978-3-030-79976-2_1","conference":{"location":"Sofia","end_date":"2021-07-07","start_date":"2021-07-05","name":"International Symposium on Business Modeling and Software Design"},"title":"Extending Business Model Development Tools with Consolidated Expert Knowledge ","author":[{"first_name":"Sebastian","full_name":"Gottschalk, Sebastian","id":"47208","last_name":"Gottschalk"},{"full_name":"Kirchhoff, Jonas","id":"39928","last_name":"Kirchhoff","first_name":"Jonas"},{"full_name":"Engels, Gregor","id":"107","last_name":"Engels","first_name":"Gregor"}],"date_created":"2020-11-02T13:27:29Z","date_updated":"2022-01-06T06:54:25Z","oa":"1","file":[{"file_name":"BMSD21.pdf","access_level":"open_access","file_id":"22768","file_size":1101782,"creator":"sego","date_created":"2021-07-20T16:17:55Z","date_updated":"2021-07-20T16:19:56Z","relation":"main_file","content_type":"application/pdf"}],"status":"public","editor":[{"first_name":"Boris","full_name":"Shishkov, Boris","last_name":"Shishkov"}],"type":"conference","publication":"Business Modeling and Software Design","file_date_updated":"2021-07-20T16:19:56Z","language":[{"iso":"eng"}],"ddc":["000"],"user_id":"47208","department":[{"_id":"66"},{"_id":"534"}],"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area C","_id":"4"},{"_id":"17","name":"SFB 901 - Subproject C5"}],"_id":"20244"},{"date_created":"2021-11-30T10:00:31Z","title":"The Strong Scaling Advantage of FPGAs in HPC for N-body Simulations","issue":"1","quality_controlled":"1","year":"2021","language":[{"iso":"eng"}],"publication":"ACM Transactions on Reconfigurable Technology and Systems","abstract":[{"lang":"eng","text":"N-body methods are one of the essential algorithmic building blocks of high-performance and parallel computing. Previous research has shown promising performance for implementing n-body simulations with pairwise force calculations on FPGAs. However, to avoid challenges with accumulation and memory access patterns, the presented designs calculate each pair of forces twice, along with both force sums of the involved particles. Also, they require large problem instances with hundreds of thousands of particles to reach their respective peak performance, limiting the applicability for strong scaling scenarios. This work addresses both issues by presenting a novel FPGA design that uses each calculated force twice and overlaps data transfers and computations in a way that allows to reach peak performance even for small problem instances, outperforming previous single precision results even in double precision, and scaling linearly over multiple interconnected FPGAs. For a comparison across architectures, we provide an equally optimized CPU reference, which for large problems actually achieves higher peak performance per device, however, given the strong scaling advantages of the FPGA design, in parallel setups with few thousand particles per device, the FPGA platform achieves highest performance and power efficiency."}],"author":[{"full_name":"Menzel, Johannes","last_name":"Menzel","first_name":"Johannes"},{"id":"16153","full_name":"Plessl, Christian","orcid":"0000-0001-5728-9982","last_name":"Plessl","first_name":"Christian"},{"first_name":"Tobias","full_name":"Kenter, Tobias","id":"3145","last_name":"Kenter"}],"volume":15,"oa":"1","date_updated":"2022-01-06T06:57:51Z","main_file_link":[{"open_access":"1","url":"https://dl.acm.org/doi/10.1145/3491235"}],"doi":"10.1145/3491235","publication_status":"published","publication_identifier":{"issn":["1936-7406","1936-7414"]},"citation":{"bibtex":"@article{Menzel_Plessl_Kenter_2021, title={The Strong Scaling Advantage of FPGAs in HPC for N-body Simulations}, volume={15}, DOI={<a href=\"https://doi.org/10.1145/3491235\">10.1145/3491235</a>}, number={1}, journal={ACM Transactions on Reconfigurable Technology and Systems}, author={Menzel, Johannes and Plessl, Christian and Kenter, Tobias}, year={2021}, pages={1–30} }","short":"J. Menzel, C. Plessl, T. Kenter, ACM Transactions on Reconfigurable Technology and Systems 15 (2021) 1–30.","mla":"Menzel, Johannes, et al. “The Strong Scaling Advantage of FPGAs in HPC for N-Body Simulations.” <i>ACM Transactions on Reconfigurable Technology and Systems</i>, vol. 15, no. 1, 2021, pp. 1–30, doi:<a href=\"https://doi.org/10.1145/3491235\">10.1145/3491235</a>.","apa":"Menzel, J., Plessl, C., &#38; Kenter, T. (2021). The Strong Scaling Advantage of FPGAs in HPC for N-body Simulations. <i>ACM Transactions on Reconfigurable Technology and Systems</i>, <i>15</i>(1), 1–30. <a href=\"https://doi.org/10.1145/3491235\">https://doi.org/10.1145/3491235</a>","ama":"Menzel J, Plessl C, Kenter T. The Strong Scaling Advantage of FPGAs in HPC for N-body Simulations. <i>ACM Transactions on Reconfigurable Technology and Systems</i>. 2021;15(1):1-30. doi:<a href=\"https://doi.org/10.1145/3491235\">10.1145/3491235</a>","chicago":"Menzel, Johannes, Christian Plessl, and Tobias Kenter. “The Strong Scaling Advantage of FPGAs in HPC for N-Body Simulations.” <i>ACM Transactions on Reconfigurable Technology and Systems</i> 15, no. 1 (2021): 1–30. <a href=\"https://doi.org/10.1145/3491235\">https://doi.org/10.1145/3491235</a>.","ieee":"J. Menzel, C. Plessl, and T. Kenter, “The Strong Scaling Advantage of FPGAs in HPC for N-body Simulations,” <i>ACM Transactions on Reconfigurable Technology and Systems</i>, vol. 15, no. 1, pp. 1–30, 2021, doi: <a href=\"https://doi.org/10.1145/3491235\">10.1145/3491235</a>."},"page":"1-30","intvolume":"        15","user_id":"3145","department":[{"_id":"27"},{"_id":"518"}],"_id":"28099","article_type":"original","type":"journal_article","status":"public"},{"citation":{"ama":"Sharma A, Demir C, Ngonga Ngomo A-C, Wehrheim H. MLCHECK–Property-Driven Testing of Machine Learning Classifiers. In: <i>Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)</i>. IEEE.","chicago":"Sharma, Arnab, Caglar Demir, Axel-Cyrille Ngonga Ngomo, and Heike Wehrheim. “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” In <i>Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)</i>. IEEE, n.d.","ieee":"A. Sharma, C. Demir, A.-C. Ngonga Ngomo, and H. Wehrheim, “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.”","bibtex":"@inproceedings{Sharma_Demir_Ngonga Ngomo_Wehrheim, title={MLCHECK–Property-Driven Testing of Machine Learning Classifiers}, booktitle={Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)}, publisher={IEEE}, author={Sharma, Arnab and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille and Wehrheim, Heike} }","mla":"Sharma, Arnab, et al. “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” <i>Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)</i>, IEEE.","short":"A. Sharma, C. Demir, A.-C. Ngonga Ngomo, H. Wehrheim, in: Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, n.d.","apa":"Sharma, A., Demir, C., Ngonga Ngomo, A.-C., &#38; Wehrheim, H. (n.d.). MLCHECK–Property-Driven Testing of Machine Learning Classifiers. <i>Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)</i>."},"year":"2021","publication_status":"accepted","title":"MLCHECK–Property-Driven Testing of Machine Learning Classifiers","author":[{"first_name":"Arnab","full_name":"Sharma, Arnab","id":"67200","last_name":"Sharma"},{"last_name":"Demir","full_name":"Demir, Caglar","id":"43817","first_name":"Caglar"},{"first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo"},{"id":"573","full_name":"Wehrheim, Heike","last_name":"Wehrheim","first_name":"Heike"}],"date_created":"2021-12-07T11:11:36Z","publisher":"IEEE","date_updated":"2022-01-06T06:58:02Z","status":"public","abstract":[{"text":"In recent years, we observe an increasing amount of software with machine learning components being deployed. This poses the question of quality assurance for such components: how can we validate whether specified requirements are fulfilled by a machine learned software? Current testing and verification approaches either focus on a single requirement (e.g., fairness) or specialize on a single type of machine learning model (e.g., neural networks).\r\nIn this paper, we propose property-driven testing of machine learning models. Our approach MLCheck encompasses (1) a language for property specification, and (2) a technique for systematic test case generation. The specification language is comparable to property-based testing languages. Test case generation employs advanced verification technology for a systematic, property dependent construction of test suites, without additional user supplied generator functions. We evaluate MLCheck using requirements and data sets from three different application areas (software\r\ndiscrimination, learning on knowledge graphs and security). Our evaluation shows that despite its generality MLCheck can even outperform specialised testing approaches while having a comparable runtime","lang":"eng"}],"publication":"Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"7"},{"_id":"77"},{"_id":"574"}],"user_id":"477","_id":"28350","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"11","name":"SFB 901 - Subproject B3"},{"_id":"10","name":"SFB 901 - Subproject B2"}]},{"type":"journal_article","publication":"14th ACM Workshop on Artificial Intelligence and Security","status":"public","user_id":"83504","department":[{"_id":"632"}],"_id":"24143","language":[{"iso":"eng"}],"citation":{"ieee":"J. P. Drees <i>et al.</i>, “Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!,” <i>14th ACM Workshop on Artificial Intelligence and Security</i>, 2021.","chicago":"Drees, Jan Peter, Pritha Gupta, Eyke Hüllermeier, Tibor Jager, Alexander Konze, Claudia Priesterjahn, Arunselvan Ramaswamy, and Juraj Somorovsky. “Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!” <i>14th ACM Workshop on Artificial Intelligence and Security</i>, 2021.","ama":"Drees JP, Gupta P, Hüllermeier E, et al. Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! <i>14th ACM Workshop on Artificial Intelligence and Security</i>. Published online 2021.","mla":"Drees, Jan Peter, et al. “Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!” <i>14th ACM Workshop on Artificial Intelligence and Security</i>, 2021.","short":"J.P. Drees, P. Gupta, E. Hüllermeier, T. Jager, A. Konze, C. Priesterjahn, A. Ramaswamy, J. Somorovsky, 14th ACM Workshop on Artificial Intelligence and Security (2021).","bibtex":"@article{Drees_Gupta_Hüllermeier_Jager_Konze_Priesterjahn_Ramaswamy_Somorovsky_2021, title={Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!}, journal={14th ACM Workshop on Artificial Intelligence and Security}, author={Drees, Jan Peter and Gupta, Pritha and Hüllermeier, Eyke and Jager, Tibor and Konze, Alexander and Priesterjahn, Claudia and Ramaswamy, Arunselvan and Somorovsky, Juraj}, year={2021} }","apa":"Drees, J. P., Gupta, P., Hüllermeier, E., Jager, T., Konze, A., Priesterjahn, C., Ramaswamy, A., &#38; Somorovsky, J. (2021). Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! <i>14th ACM Workshop on Artificial Intelligence and Security</i>."},"year":"2021","author":[{"full_name":"Drees, Jan Peter","last_name":"Drees","first_name":"Jan Peter"},{"first_name":"Pritha","last_name":"Gupta","id":"54803","full_name":"Gupta, Pritha"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129","first_name":"Eyke"},{"first_name":"Tibor","full_name":"Jager, Tibor","last_name":"Jager"},{"first_name":"Alexander","full_name":"Konze, Alexander","last_name":"Konze"},{"first_name":"Claudia","last_name":"Priesterjahn","full_name":"Priesterjahn, Claudia"},{"last_name":"Ramaswamy","orcid":"https://orcid.org/ 0000-0001-7547-8111","full_name":"Ramaswamy, Arunselvan","id":"66937","first_name":"Arunselvan"},{"orcid":"0000-0002-3593-7720","last_name":"Somorovsky","id":"83504","full_name":"Somorovsky, Juraj","first_name":"Juraj"}],"date_created":"2021-09-10T09:56:27Z","date_updated":"2022-01-06T06:56:08Z","title":"Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!"},{"type":"conference","status":"public","_id":"24382","department":[{"_id":"367"},{"_id":"355"},{"_id":"321"}],"user_id":"83151","language":[{"iso":"eng"}],"year":"2021","citation":{"ama":"Gevers K, Schöppner V, Hüllermeier E. Heated tool butt welding of two different materials –  Established methods versus artificial intelligence. In: ; 2021.","ieee":"K. Gevers, V. Schöppner, and E. Hüllermeier, “Heated tool butt welding of two different materials –  Established methods versus artificial intelligence,” presented at the International Institute of Welding, online, 2021.","chicago":"Gevers, Karina, Volker Schöppner, and Eyke Hüllermeier. “Heated Tool Butt Welding of Two Different Materials –  Established Methods versus Artificial Intelligence,” 2021.","short":"K. Gevers, V. Schöppner, E. Hüllermeier, in: 2021.","mla":"Gevers, Karina, et al. <i>Heated Tool Butt Welding of Two Different Materials –  Established Methods versus Artificial Intelligence</i>. 2021.","bibtex":"@inproceedings{Gevers_Schöppner_Hüllermeier_2021, title={Heated tool butt welding of two different materials –  Established methods versus artificial intelligence}, author={Gevers, Karina and Schöppner, Volker and Hüllermeier, Eyke}, year={2021} }","apa":"Gevers, K., Schöppner, V., &#38; Hüllermeier, E. (2021). <i>Heated tool butt welding of two different materials –  Established methods versus artificial intelligence</i>. International Institute of Welding, online."},"date_updated":"2022-01-06T06:56:19Z","date_created":"2021-09-14T11:34:31Z","author":[{"first_name":"Karina","last_name":"Gevers","full_name":"Gevers, Karina","id":"83151"},{"last_name":"Schöppner","full_name":"Schöppner, Volker","id":"20530","first_name":"Volker"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"title":"Heated tool butt welding of two different materials –  Established methods versus artificial intelligence","conference":{"name":"International Institute of Welding","start_date":"2021-07-12","end_date":"2021-07-14","location":"online"}},{"title":"I-AID: Identifying Actionable Information From Disaster-Related Tweets","doi":"10.1109/ACCESS.2021.3107812","date_updated":"2022-01-06T06:56:32Z","author":[{"last_name":"M. Zahera","full_name":"M. Zahera, Hamada","first_name":"Hamada"},{"first_name":"Rricha","full_name":"Jalota, Rricha","last_name":"Jalota"},{"first_name":"Mohamed","full_name":"Ahmed Sherif, Mohamed","last_name":"Ahmed Sherif"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille"}],"date_created":"2021-09-20T13:01:18Z","volume":9,"year":"2021","citation":{"apa":"M. Zahera, H., Jalota, R., Ahmed Sherif, M., &#38; Ngonga Ngomo, A.-C. (2021). I-AID: Identifying Actionable Information From Disaster-Related Tweets. <i>{IEEE} Access</i>, <i>9</i>, 118861–118870. <a href=\"https://doi.org/10.1109/ACCESS.2021.3107812\">https://doi.org/10.1109/ACCESS.2021.3107812</a>","short":"H. M. Zahera, R. Jalota, M. Ahmed Sherif, A.-C. Ngonga Ngomo, {IEEE} Access 9 (2021) 118861–118870.","bibtex":"@article{M. Zahera_Jalota_Ahmed Sherif_Ngonga Ngomo_2021, title={I-AID: Identifying Actionable Information From Disaster-Related Tweets}, volume={9}, DOI={<a href=\"https://doi.org/10.1109/ACCESS.2021.3107812\">10.1109/ACCESS.2021.3107812</a>}, journal={{IEEE} Access}, author={M. Zahera, Hamada and Jalota, Rricha and Ahmed Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2021}, pages={118861–118870} }","mla":"M. Zahera, Hamada, et al. “I-AID: Identifying Actionable Information From Disaster-Related Tweets.” <i>{IEEE} Access</i>, vol. 9, 2021, pp. 118861–70, doi:<a href=\"https://doi.org/10.1109/ACCESS.2021.3107812\">10.1109/ACCESS.2021.3107812</a>.","ieee":"H. M. Zahera, R. Jalota, M. Ahmed Sherif, and A.-C. Ngonga Ngomo, “I-AID: Identifying Actionable Information From Disaster-Related Tweets,” <i>{IEEE} Access</i>, vol. 9, pp. 118861–118870, 2021, doi: <a href=\"https://doi.org/10.1109/ACCESS.2021.3107812\">10.1109/ACCESS.2021.3107812</a>.","chicago":"M. Zahera, Hamada, Rricha Jalota, Mohamed Ahmed Sherif, and Axel-Cyrille Ngonga Ngomo. “I-AID: Identifying Actionable Information From Disaster-Related Tweets.” <i>{IEEE} Access</i> 9 (2021): 118861–70. <a href=\"https://doi.org/10.1109/ACCESS.2021.3107812\">https://doi.org/10.1109/ACCESS.2021.3107812</a>.","ama":"M. Zahera H, Jalota R, Ahmed Sherif M, Ngonga Ngomo A-C. I-AID: Identifying Actionable Information From Disaster-Related Tweets. <i>{IEEE} Access</i>. 2021;9:118861-118870. doi:<a href=\"https://doi.org/10.1109/ACCESS.2021.3107812\">10.1109/ACCESS.2021.3107812</a>"},"intvolume":"         9","page":"118861-118870","language":[{"iso":"eng"}],"_id":"24719","user_id":"65716","department":[{"_id":"574"}],"status":"public","type":"journal_article","publication":"{IEEE} Access"},{"language":[{"iso":"eng"}],"department":[{"_id":"574"}],"user_id":"65716","_id":"24720","status":"public","publication":"{ACM} Comput. 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