Liu, Tianyun
Zhang, Xinghua
Zhang, Zhenyu
Wang, Yubin
Li, Quangang
Zhang, Shuai
Liu, Tingwen
Chapter History
First Online: 18 September 2023
Ethics Statement
: I understand that using technology can have ethical implications, especially in collection, processing, and privacy of form retrieval data. I acknowledge and recognize the importance of complying with ethical standards and the hazards of potential risks.In the data collection and processing, my training data comes from two publicly available tabular search datasets. Although we do not collect or store any sensitive information, we should strictly restrict the retrieval text of users and ensure that it does not contain any dangerous information.In addition, when the model used in police or military related applications, we should pay special attention to its use in these areas, which must conducted in a more responsible manner. To prevent models from providing inaccurate search results for police or military personnel, users are responsible for ensuring that they comply with ethical principles and laws and regulations when using model outputs, and for screening search results.In summary, I strive to ensure that the model outputs search results in an ethical and responsible manner, and I urge my users to do the same. I will continue to adhere to ethical standards and stay abreast of emerging ethical issues in the fields of machine learning and data mining.
Conference Information
Conference Acronym: ECML PKDD
Conference Name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Conference City: Turin
Conference Country: Italy
Conference Year: 2023
Conference Start Date: 18 September 2023
Conference End Date: 22 September 2023
Conference Number: 23
Conference ID: ecml2023
Conference URL: https://2023.ecmlpkdd.org/
Peer Review Information (provided by the conference organizers)
Type: Double-blind
Conference Management System: CMT
Number of Submissions Sent for Review: 829
Number of Full Papers Accepted: 196
Number of Short Papers Accepted: 0
Acceptance Rate of Full Papers: 24% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.
Average Number of Reviews per Paper: 3.63
Average Number of Papers per Reviewer: 4.5
External Reviewers Involved: Yes
Additional Info on Review Process: Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.