Machine learning (ML) solutions are prevalent in many applications. However, many challenges exist in making these solutions business-grade. For instance, maintaining the error rate of the underlying ML models at an acceptably low level. Typically, the true relationship between feature inputs and the target feature to be predicted is uncertain, and hence statistical in nature. The approach we propose is to separate the observations that are the most likely to be predicted incorrectly into 'attention sets'. These can directly aid model diagnosis and improvement, and be used to decide on alternative courses of action for these problematic observations. We present several algorithms (`strategies') for determining optimal rules to separate these observations. In particular, we prefer strategies that use feature-based slicing because they are human-interpretable, model-agnostic, and require minimal supplementary inputs or knowledge. In addition, we show that these strategies outperform several common baselines, such as selecting observations with prediction confidence below a threshold. To evaluate strategies, we introduce metrics to measure various desired qualities, such as their performance, stability, and generalizability to unseen data; the strategies are evaluated on several publicly-available datasets. We use TOPSIS, a Multiple Criteria Decision Making method, to aggregate these metrics into a single quality score for each strategy, to allow comparison.
Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning
Bowie State University（鲍伊州立大学）；Télécom SudParis（南巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）；Brown University（布朗大学）；Northern Virginia Community College（北弗吉尼亚社区学院）
Smartajweed Automatic Recognition of Arabic Quranic Recitation Rules
University of Venice（威尼斯大学）；Cairo University（开罗大学）
《古兰经》是一部经典，它是一部经典。这意味着你必须给古兰经中的每一个字母它应有的特点，并将其应用到这个特定的字母在这个特定的情况下，而阅读，这可能在其他时间不同。这些特征包括旋律规则，比如在哪里停下来，停多久，什么时候合并发音中的两个字母，什么时候拉伸一些字母，甚至什么时候在一些字母上施加更大的力量。大部分的论文主要集中在主要的背诵规则和发音，但不是（Ahkam AL Tajweed），赋予不同的节奏和不同的旋律发音（Tajweed）的不同规则。这也被认为是非常重要和必不可少的阅读古兰经，因为它可以给不同的含义的话。本文详细讨论了利用支持向量机和阈值评分系统对《古兰经》背诵规则（Tajweed）进行自动识别的完整系统
Automatic Extraction of Rules for Generating Synthetic Patient Data From Real-World Population Data Using Glioblastoma as an Example
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB（弗劳恩霍夫光学与系统技术及图像利用研究所）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所）；Bremen Cancer Registry, Leibniz Institute for Prevention Research and Epidemiology - BIPS（不莱梅癌症登记处，预防研究与流行病学研究所 - BIPS）；Institute for Pathology, Hannover Medical School（汉诺威医学院病理研究所）；Institute of Neuropathology, Medical Center - University of Freiburg（神经病理学研究所，弗赖堡大学医疗中心）；Research Data Infrastructures and Data Science, Leibniz Institute for Prevention Research and Epidemiology - BIPS（研究数据基础设施与数据科学，预防研究与流行病学研究所 - BIPS）；Department of Neuropathology, Center for Pathology, Klinikum Bremen-Mitte（神经病理学系，病理中心，不莱梅-中部诊所）；Institute of Neuropathology, RWTH Aachen University Hospital（神经病理学研究所，亚琛工业大学医院）；Robert Koch Institute, Center for Cancer Registry Data（罗伯特·科赫研究所，癌症登记数据中心）
Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?
The Hong Kong University of Science and Technology（香港科学与技术大学）；The Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）；City University of Hong Kong（香港城市大学）
