Teachers often conduct surveys in order to collect data from a predefined group of students to gain insights into topics of interest. When analyzing surveys with open-ended textual responses, it is extremely time-consuming, labor-intensive, and difficult to manually process all the responses into an insightful and comprehensive report. In the analysis step, traditionally, the teacher has to read each of the responses and decide on how to group them in order to extract insightful information. Even though it is possible to group the responses only using certain keywords, such an approach would be limited since it not only fails to account for embedded contexts but also cannot detect polysemous words or phrases and semantics that are not expressible in single words. In this work, we present a novel end-to-end context-aware framework that extracts, aggregates, and abbreviates embedded semantic patterns in open-response survey data. Our framework relies on a pre-trained natural language model in order to encode the textual data into semantic vectors. The encoded vectors then get clustered either into an optimally tuned number of groups or into a set of groups with pre-specified titles. In the former case, the clusters are then further analyzed to extract a representative set of keywords or summary sentences that serve as the labels of the clusters. In our framework, for the designated clusters, we finally provide context-aware wordclouds that demonstrate the semantically prominent keywords within each group. Honoring user privacy, we have successfully built the on-device implementation of our framework suitable for real-time analysis on mobile devices and have tested it on a synthetic dataset. Our framework reduces the costs at-scale by automating the process of extracting the most insightful information pieces from survey data.
Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Technology Innovation Institute（技术创新研究院）；Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR)（新加坡科技研究局信息通信研究所）；Centre for Frontier AI Research, Agency for Science Technology and Research (A*STAR)（新加坡科技研究局前沿人工智能研究中心）；South China University of Technology（华南理工大学）
Diversity-aware clustering: Computational Complexity and Approximation Algorithms
Max Planck Institute for Software Systems（马克斯·普朗克软件研究所）；CISPA Helmholtz Center for Information Security（亥姆霍兹信息安全中心）；KTH Royal Institute of Technology（皇家理工学院）
Courant Institute of Mathematical Sciences（柯朗数学科学研究所）；New York University（纽约大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Center of Data Science（数据科学中心）
Context-Aware Hypergraph Construction for Robust Spectral Clustering
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；The University of Adelaide（阿德莱德大学）；State University of New York, Binghamton（纽约州立大学宾汉姆顿分校）
SPHENIC: Topology-Aware Multi-View Clustering for Spatial Transcriptomics
College of Medicine and Biological Information Engineering, Northeastern University（医学与生物信息工程学院，东北大学）；Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences（广州生物医学与健康研究院，中国科学院）；College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；Intelligent Game and Decision Lab, Academy of Military Sciences（智能游戏与决策实验室，军事科学院）；University of Chinese Academy of Sciences（中国科学院大学）
Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection
Université Côte d’Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Maasai Team（马赛团队）；CHU de Québec Research Centre（魁北克大学中心医院研究中心）；Laval University（拉瓦尔大学）
Hard Sample Aware Network for Contrastive Deep Graph Clustering
College of Computer, National University of Defense Technology（国防科技大学计算机学院）；College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）；Northwestern Polytechnical University（西北工业大学）；Beijing Information Science and Technology University（北京信息科技大学）
Autonomy-Aware Clustering: When Local Decisions Supersede Global Prescriptions
Department of Mechanical Engineering（机械工程系）；Indian Institute of Technology Delhi（印度理工学院德里）；Coordinated Science Laboratory（协调科学实验室）；University of Illinois Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校）
A review of clustering models in educational data science towards fairness-aware learning
L3S Research Center, Leibniz University Hannover（汉诺威莱布尼茨大学L3S研究中心）；Leibniz University Hannover（汉诺威莱布尼茨大学）；Institute for Didactics of Mathematics and Physics, Leibniz University Hannover（汉诺威莱布尼茨大学数学与物理教学研究所）；Research Institute CODE, Bundeswehr University Munich（慕尼黑联邦国防军大学CODE研究所）
CueGCL: Cluster-aware Personalized Self-Training for Unsupervised Graph Contrastive Learning
School of Computer Science and Engineering, Sun Yat-sen University（计算机科学与工程学院，中山大学）；School of Systems Science and Engineering, Sun Yat-sen University（系统科学与工程学院，中山大学）；School of Software Engineering, Sun Yat-Sen University（软件工程学院，中山大学）
SPORT: Structure-Aware Prototype Disentanglement for Incomplete Multi-View Clustering
College of Computer and Cyberspace Security, Hebei Normal University（河北师范大学计算机与网络空间安全学院）；Department of Statistics and Data Science, Southern University of Science and Technology（南方科技大学统计与数据科学系）；Key Laboratory of Tibetan Information Processing, Ministry of Education, Qinghai Normal University（青海师范大学藏文信息处理教育部重点实验室）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）
