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\n\n \n \n \n \n \n \n Digital Personhood? The Status of Autonomous Software Agents in Private Law.\n \n \n \n \n\n\n \n Teubner, G.\n\n\n \n\n\n\n May 2018.\n
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@misc{teubner_digital_2018,\n\taddress = {Rochester, NY},\n\ttype = {{SSRN} {Scholarly} {Paper}},\n\ttitle = {Digital {Personhood}? {The} {Status} of {Autonomous} {Software} {Agents} in {Private} {Law}},\n\tshorttitle = {Digital {Personhood}?},\n\turl = {https://papers.ssrn.com/abstract=3177096},\n\tdoi = {10.2139/ssrn.3177096},\n\tabstract = {Already today in the economy and in society, autonomous software agents, i.e. mathematically formalized information flows, are attributed social identity and ability to act under certain conditions. Due to social action attribution, they have become non-human members of society. They pose three new liability risks: (1) the autonomy risk, which has its origin in stand-alone “decisions” taken by the software agents, (2) the association risk, which is due to the close cooperation between people and software agents, and (3) the network risk that occurs when computer systems operate in close integration with other computer systems. These risks pose a challenge for private law: to define a new legal status for autonomous digital information systems—however not simply as complete legal personhood of software agents, human-computer associations or multi-agent systems respectively. Rather, in response to each of the three risks, a legal status should be granted to each of the algorithmic types that is carefully calibrated to their specific role.},\n\tlanguage = {en},\n\turldate = {2024-11-18},\n\tpublisher = {Social Science Research Network},\n\tauthor = {Teubner, Gunther},\n\tmonth = may,\n\tyear = {2018},\n\tkeywords = {actant, digital personhood, hybrid, juridical person, legal person, liability, personification, software agents},\n}\n\n\n\n
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\n Already today in the economy and in society, autonomous software agents, i.e. mathematically formalized information flows, are attributed social identity and ability to act under certain conditions. Due to social action attribution, they have become non-human members of society. They pose three new liability risks: (1) the autonomy risk, which has its origin in stand-alone “decisions” taken by the software agents, (2) the association risk, which is due to the close cooperation between people and software agents, and (3) the network risk that occurs when computer systems operate in close integration with other computer systems. These risks pose a challenge for private law: to define a new legal status for autonomous digital information systems—however not simply as complete legal personhood of software agents, human-computer associations or multi-agent systems respectively. Rather, in response to each of the three risks, a legal status should be granted to each of the algorithmic types that is carefully calibrated to their specific role.\n
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\n\n \n \n \n \n \n \n Adaptive Learning of Local Semantic and Global Structure Representations for Text Classification.\n \n \n \n \n\n\n \n Zhao, J.; Zhan, Z.; Yang, Q.; Zhang, Y.; Hu, C.; Li, Z.; Zhang, L.; and He, Z.\n\n\n \n\n\n\n In Bender, E. M.; Derczynski, L.; and Isabelle, P., editor(s),
Proceedings of the 27th International Conference on Computational Linguistics, pages 2033–2043, Santa Fe, New Mexico, USA, August 2018. Association for Computational Linguistics\n
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@inproceedings{zhao_adaptive_2018,\n\taddress = {Santa Fe, New Mexico, USA},\n\ttitle = {Adaptive {Learning} of {Local} {Semantic} and {Global} {Structure} {Representations} for {Text} {Classification}},\n\turl = {https://aclanthology.org/C18-1173/},\n\tabstract = {Representation learning is a key issue for most Natural Language Processing (NLP) tasks. Most existing representation models either learn little structure information or just rely on pre-defined structures, leading to degradation of performance and generalization capability. This paper focuses on learning both local semantic and global structure representations for text classification. In detail, we propose a novel Sandwich Neural Network (SNN) to learn semantic and structure representations automatically without relying on parsers. More importantly, semantic and structure information contribute unequally to the text representation at corpus and instance level. To solve the fusion problem, we propose two strategies: Adaptive Learning Sandwich Neural Network (AL-SNN) and Self-Attention Sandwich Neural Network (SA-SNN). The former learns the weights at corpus level, and the latter further combines attention mechanism to assign the weights at instance level. Experimental results demonstrate that our approach achieves competitive performance on several text classification tasks, including sentiment analysis, question type classification and subjectivity classification. Specifically, the accuracies are MR (82.1\\%), SST-5 (50.4\\%), TREC (96\\%) and SUBJ (93.9\\%).},\n\turldate = {2025-02-10},\n\tbooktitle = {Proceedings of the 27th {International} {Conference} on {Computational} {Linguistics}},\n\tpublisher = {Association for Computational Linguistics},\n\tauthor = {Zhao, Jianyu and Zhan, Zhiqiang and Yang, Qichuan and Zhang, Yang and Hu, Changjian and Li, Zhensheng and Zhang, Liuxin and He, Zhiqiang},\n\teditor = {Bender, Emily M. and Derczynski, Leon and Isabelle, Pierre},\n\tmonth = aug,\n\tyear = {2018},\n\tpages = {2033--2043},\n}\n\n\n\n
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\n Representation learning is a key issue for most Natural Language Processing (NLP) tasks. Most existing representation models either learn little structure information or just rely on pre-defined structures, leading to degradation of performance and generalization capability. This paper focuses on learning both local semantic and global structure representations for text classification. In detail, we propose a novel Sandwich Neural Network (SNN) to learn semantic and structure representations automatically without relying on parsers. More importantly, semantic and structure information contribute unequally to the text representation at corpus and instance level. To solve the fusion problem, we propose two strategies: Adaptive Learning Sandwich Neural Network (AL-SNN) and Self-Attention Sandwich Neural Network (SA-SNN). The former learns the weights at corpus level, and the latter further combines attention mechanism to assign the weights at instance level. Experimental results demonstrate that our approach achieves competitive performance on several text classification tasks, including sentiment analysis, question type classification and subjectivity classification. Specifically, the accuracies are MR (82.1%), SST-5 (50.4%), TREC (96%) and SUBJ (93.9%).\n
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\n\n \n \n \n \n \n \n Context-Aware Saliency Map Generation Using Semantic Segmentation.\n \n \n \n \n\n\n \n Ahmadi, M.; Hajabdollahi, M.; Karimi, N.; and Samavi, S.\n\n\n \n\n\n\n In
Electrical Engineering (ICEE), Iranian Conference on, pages 616–620, Mashhad, May 2018. IEEE\n
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@inproceedings{ahmadi_context-aware_2018,\n\taddress = {Mashhad},\n\ttitle = {Context-{Aware} {Saliency} {Map} {Generation} {Using} {Semantic} {Segmentation}},\n\tisbn = {978-1-5386-4914-5 978-1-5386-4916-9},\n\turl = {https://ieeexplore.ieee.org/document/8472577/},\n\tdoi = {10.1109/ICEE.2018.8472577},\n\turldate = {2025-02-10},\n\tbooktitle = {Electrical {Engineering} ({ICEE}), {Iranian} {Conference} on},\n\tpublisher = {IEEE},\n\tauthor = {Ahmadi, Mahdi and Hajabdollahi, Mohsen and Karimi, Nader and Samavi, Shadrokh},\n\tmonth = may,\n\tyear = {2018},\n\tpages = {616--620},\n}\n\n\n\n
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