A review of over 160,000 customer cases indicates that about 90% of time is spent by the product support for solving around 10% of subset of tickets where a trivial solution may not exist. Many of these challenging cases require the support of several engineers working together within a "swarm", and some also need to go to development support as bugs. These challenging customer issues represent a major opportunity for machine learning and knowledge graph that identifies the ideal engineer / group of engineers(swarm) that can best address the solution, reducing the wait times for the customer. The concrete ML task we consider here is a learning-to-rank(LTR) task that given an incident and a set of engineers currently assigned to the incident (which might be the empty set in the non-swarming context), produce a ranked list of engineers best fit to help resolve that incident. To calculate the rankings, we may consider a wide variety of input features including the incident description provided by the customer, the affected component(s), engineer ratings of their expertise, knowledge base article text written by engineers, response to customer text written by engineers, and historic swarming data. The central hypothesis test is that by including a holistic set of contextual data around which cases an engineer has solved, we can significantly improve the LTR algorithm over benchmark models. The article proposes a novel approach of modelling Knowledge Graph embeddings from multiple data sources, including the swarm information. The results obtained proves that by incorporating this additional context, we can improve the recommendations significantly over traditional machine learning methods like TF-IDF.
Addressing Design Issues in Medical Expert System for Low Back Pain Management: Knowledge Representation, Inference Mechanism, and Conflict Resolution Using Bayesian Network
University of Kalyani（卡里亚尼大学）；Banaras Hindu University（贝拿勒斯印度大学）；ESI Institute of Pain Management（ESI疼痛管理研究所）
REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics
University of Birmingham（伯明翰大学）；Texas Tech University（得克萨斯理工大学）；SUNY at Binghamton（纽约州立大学宾汉姆顿分校）
本文介绍了一种机器人的架构，结合了互补优势的概率图形模型和声明式编程表示和理由与逻辑为基础的概率描述的不确定性和领域知识。一个动作语言扩展到支持非布尔流畅性和非确定性的因果关系的法律。这种动作语言用于在两个粒度级别上描述紧密耦合的转换图，其中精细分辨率转换图被定义为域的粗分辨率转换图的细化。粗分辨率系统描述和包括（优先级）默认值的历史记录被转换为Answer Set Prolog（ASP）程序。对于任何给定的目标，ASP程序中的推理都提供了一个抽象的行动计划。为了实现每个这样的抽象动作，机器人自动缩放到与该动作相关的精细分辨率转换图的部分。在传感和驱动的不确定性的概率表示，然后包括在这个缩放的精细分辨率系统描述，并用于构建一个部分可观察的马尔可夫决策过程（POMDP）。通过求解POMDP获得的策略被反复调用，以将抽象动作实现为一系列具体动作，相应的观察结果被记录在粗分辨率历史中并用于后续推理。该体系结构进行评估，在模拟和移动机器人在室内域中移动对象，以表明它支持推理违反默认值，噪声观测和不可靠的行动，在复杂的领域。
Low-Resolution Face Recognition via Adaptable Instance-Relation Distillation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Baidu Inc.（百度公司）；School of Cyber Security at University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Department of Communication Engineering, Shanghai University（上海大学通信工程系）
Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Beijing Electronic Science and Technology Institute（北京电子科技学院）；CloudWalk Technology Co., Ltd（云从科技股份有限公司）
Data Upcycling Knowledge Distillation for Image Super-Resolution
Hong Kong University of Science and Technology (GZ)（香港科技大学（广州））；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Southern University of Science and Technology（南方科技大学）；East China Normal University（华东师范大学）
Efficient Low-Resolution Face Recognition via Bridge Distillation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；SenseTime Group Limited（商汤集团）；Beihang University（北京航空航天大学）；Peng Cheng Laboratory（鹏城实验室）
Technische Universität München（慕尼黑工业大学）；IIE, CAS（中国科学院信息工程研究所）；Harbin Institute of Technology（哈尔滨工业大学）；NJUST（南京理工大学）；Tencent AI Lab（腾讯AI实验室）；Peng Cheng Laboratory（鹏城实验室）
CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments
School of Mathematics and Sciences, Fudan University（复旦大学数学科学学院）；Shanghai Center for Mathematical Sciences, Fudan University（复旦大学上海数学中心）；Alibaba Group（阿里巴巴集团）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；Center for Applied Mathematics & Shanghai Key Laboratory of Contemporary Applied Mathematics, Fudan University（复旦大学应用数学中心暨上海市现代应用数学重点实验室）
