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Hits 41 – 60 of 2.304

41
Space-Efficient Huffman Codes Revisited ...
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42
A Note on the Performance of Algorithms for Solving Linear Diophantine Equations in the Naturals ...
Motroi, Valeriu; Ciobaca, Stefan. - : arXiv, 2021
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43
Absent Subsequences in Words ...
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44
Lexicographically Fair Learning: Algorithms and Generalization ...
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45
Near-Optimal Quantum Algorithms for String Problems ...
Akmal, Shyan; Jin, Ce. - : arXiv, 2021
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46
Dynamic Suffix Array with Sub-linear update time and Poly-logarithmic Lookup Time ...
Amir, Amihood; Boneh, Itai. - : arXiv, 2021
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47
The Labeled Direct Product Optimally Solves String Problems on Graphs ...
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48
Weighted Ancestors in Suffix Trees Revisited ...
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49
Load-Balancing Succinct B Trees ...
I, Tomohiro; Köppl, Dominik. - : arXiv, 2021
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50
Breaking the $O(n)$-Barrier in the Construction of Compressed Suffix Arrays ...
Kempa, Dominik; Kociumaka, Tomasz. - : arXiv, 2021
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51
Grammar Index By Induced Suffix Sorting ...
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52
Constructing Strings Avoiding Forbidden Substrings ...
Bernardini, Giulia; Marchetti-Spaccamela, Alberto; Pissis, Solon P.. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021
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53
FM-Indexing Grammars Induced by Suffix Sorting for Long Patterns ...
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54
Lyndon Words Accelerate Suffix Sorting ...
Bertram, Nico; Ellert, Jonas; Fischer, Johannes. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021
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55
Construction of Sparse Suffix Trees and LCE Indexes in Optimal Time and Space ...
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56
Faster Algorithms for Longest Common Substring ...
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57
Detecting Signal Corruptions in Voice Recordings for Speech Therapy ; Igenkänning av Signalproblem i Röstinspelningar för Logopedi
Nylén, Helmer. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021
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58
Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey
In: Applied Sciences ; Volume 11 ; Issue 20 (2021)
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59
Authorship Attribution of Social Media and Literary Russian-Language Texts Using Machine Learning Methods and Feature Selection
In: Future Internet; Volume 14; Issue 1; Pages: 4 (2021)
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60
New AI lexicon : labour ; Why AI needs ethics from below
Posada, Julian. - 2021
Abstract: There cannot be “fairness” in Artificial Intelligence (AI) without ensuring that fair work principles are respected. “Transparency” cannot be achieved when hundreds of workers are excluded and silenced. AI cannot be “responsible” without caring for the livelihoods of the people in developing countries who make this technology possible. Protecting workers and their communities, their rights, and their perspectives on the technologies that they help create remain essential steps to justice and co-liberation in artificial intelligence development. In one of the introduction posts for the platform workers for a major tech company, one of the frequently asked questions included “I got a task asking for something illegal. What do I do?”
Keyword: ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIAS; BUILT-IN BIAS; DIGITIZATION; ETHICS; EXPLOITATION; GLOBAL; HUMAN RIGHTS VIOLATIONS; LABOUR MARKET; LEGAL FRAMEWORK; MACHINE LEARNING; POVERTY; PRECARIOUS EMPLOYMENT; PRIVACY PROTECTION; RECORDS KEEPING; SURVEILLANCE; VENEZUELA
URL: http://hdl.handle.net/10625/61006
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