Nam, Youngeun http://orcid.org/0009-0008-8333-6488
Trirat, Patara http://orcid.org/0000-0002-0889-813X
Kim, Taeyoon
Lee, Youngseop
Lee, Jae-Gil http://orcid.org/0000-0002-8711-7732
Chapter History
First Online: 17 September 2023
Ethical Statement
: This work adheres to ethical standards and guidelines for scientific research. We use publicly available datasets and obtain all necessary permissions and approvals before conducting the experiments and data collection. Therefore, we ensure the privacy and anonymity of all human participants involved in the data collection process. In particular, the RCS and KPI datasets are the communication service datasets significantly associated with real users. Both RCS and KPI datasets were completely anonymized with their types and features before we received them. Our research aims to advance the field of anomaly detection having critical applications in various domains, such as finance, healthcare, and cyber security. However, there might be potential malicious impacts when inappropriately using our work. For example, the advancement and findings from <i>Time-CAD</i> might be adversely exploited for devising more subtle and sophisticated attacks or deceptions.
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.