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Proceedings of the 41st International Conference on Machine Learning

作者： Cong Hua Qianqian Xu Shilong Bao Zhiyong Yang Qingming Huang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost.

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Zero-knowledge Proof-based Practical Federated Learning on Blockchain

arXiv

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arXiv 2023年

作者： Xing, Zhibo Zhang, Zijian Li, Meng Liu, Jiamou Zhu, Liehuang Russello, Giovanni Asghar, Muhammad Rizwan The School of Computer Science The University of Auckland Auckland New Zealand The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Southeast Institute of Information Technology Beijing Institute of Technology Fujian351100 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China The Department of Computer Science University of Surrey The School of Computer Science The University of Auckland Auckland New Zealand

Since the concern of privacy leakage extremely discourages user participation in sharing data, federated learning has gradually become a promising technique for both academia and industry for achieving collaborative learning without leaking information about the local data. Unfortunately, most federated learning solutions cannot efficiently verify the execution of each participant's local machine learning model and protect the privacy of user data, simultaneously. In this article, we first propose a Zero-knowledge Proof-based Federated Learning (ZKP-FL) scheme on blockchain. It leverages zero-knowledge proof for both the computation of local data and the aggregation of local model parameters, aiming to verify the computation process without requiring the plaintext of the local data. We further propose a Practical ZKP-FL (PZKP-FL) scheme to support fraction and non-linear operations. Specifically, we explore a Fraction-Integer mapping function, and use Taylor expansion to efficiently handle non-linear operations while maintaining the accuracy of the federated learning model. We also analyze the security of PZKP-FL. Performance analysis demonstrates that the whole running time of the PZKP-FL scheme is approximately less than one minute in parallel execution. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Characterization of intratumor microbiome in cancer immunotherapy

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The Innovation 2023年第5期4卷 27-29页

作者： Zhao Zhang Qian Gao Xiangmei Ren Mei Luo Yuan Liu Peilin Liu Yun Liu Youqiong Ye Xiang Chen Hong Liu Leng Han MOE Key Laboratory of Metabolism and Molecular Medicine Department of Biochemistry and Molecular BiologySchool of Basic Medical SciencesFudan UniversityShanghai200032China Department of Dermatology Hunan Engineering Research Center of Skin Health and DiseaseHunan Key Laboratoryof Skin Cancer and PsoriasisNational Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaHunan 410008China Center for Epigenetics and Disease Prevention Institute of Biosciences and TechnologyTexas A&M UniversityHoustonTX 77030USA Department of Clinical Laboratory Xiangya HospitalCentral South UniversityChangsha 410008China Shanghai Institute of Immunology Department of Immunology and MicrobiologyShanghai Jiao Tong University School of MedicineShanghai 200025China Department of Translational Medical Sciences Collge of MedicineTexas A&M UniversityHoustonTX 77030USA Brown Center for Immunotherapy Indiana University School of MedicineIndianapolisIN 46202USA Department of Biostatistics and Health Data Science Indiana University School of MedicineIndianapolisIN 46202USA

Dear Editor,As an illuminating cancer hallmark,polymorphic microbiomes profoundly impact cancer phenotypes by promoting or repressing cancer initiation and progression.1 Diversity and composition in the gut microbiome are significantly associated with the response rate of anti-PD1 immunotherapy in melanoma.2 In addition to the gut microbiome,a large number of microbiomes colonizing in human tumors have been shown to play significant roles in cancer development.3 However,a comprehensive understanding of intratumor microbiomes in cancer immunotherapy is lacking,largely due to the challenge of investigating intratumor microbiomes in anti-cancer immunotherapy.

关键词： immunotherapy PD1 cancer

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Web Log Analysis in Genealogy System

Web Log Analysis in Genealogy System

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IEEE International Conference on Big knowledge (ICBK)

作者： Xiaojian Liu Yi Zhu Shengwei Ji Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

A large amount of log data is generated every moment in many real-world applications. System logs, mobile application logs, web logs etc., all of which contain rich information and can be analyzed for valuable information. At the same time, genealogy attracts more and more attention in recent decades, especially for Chinese. People utilize the genealogy system to seek roots, find source of family, build a family tree and so on. Due to the complexity of genealogy data and specific features of genealogy system, how to extract and analyze the valued genealogy information effectively is a huge challenge. Motivated by this, we propose a novel analysis method using web logs in genealogy system for drawing user profile and providing personalized recommendations. More specifically, firstly, all web logs are extracted from genealogy system based on the enhanced logs which record the behaviors and data of user. Secondly, the extracted logs are formatted for analysis, and we analyze the logs to extract various user-related attributes for drawing user profile. Finally, personalized recommendations like interested people and family tree are provided for each user in system. The high availability and effectiveness of our proposed method is verified on genealogy system-Huapu system.

关键词： data mining databases data visualization Tools Big data Feature extraction knowledge engineering

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An Abnormal data Analysis and Processing Method for Genealogy Graph databases

An Abnormal Data Analysis and Processing Method for Genealog...

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Jianxuan Shao Guliu Liu Shengwei Ji Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Large amounts of data are generated continually in the real world. The objects in the data and the relationships between them have become increasingly complex. Graph is a powerful tool for representing these data and the complex relationships between them. To effectively describe the entities and connections in these data, the concept of a knowledge graph was proposed, and knowledge graph has become one of the bases for storing graph data, and the importance of it is self-evident. Genealogy data is a kind of graph data where nodes are used to represent the person in the genealogy data, and edges are used to represent the relationship between the person. Moreover, in a genealogy graph database, the "critical" relationship that is unique for an entity is defined. In contrast, the abnormal data derive from the existence of multiple "critical" relationships between entities. In a genealogy graph, abnormal data will cause the wrong relationships and redundant entities. To avoid such abnormal data in genealogy graph database, the abnormal data are categorized into four different types, and the corresponding processing methods are proposed for each type of abnormal data, respectively. The experiment results demonstrate that the processing method can effectively solve these abnormal data.

关键词： databases Biology Task analysis Anomaly detection data models knowledge engineering Big data

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Rule-enhanced Noisy knowledge Graph Embedding via Low-quality Error Detection

Rule-enhanced Noisy Knowledge Graph Embedding via Low-qualit...

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Yan Hong Chenyang Bu Tingting Jiang Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

knowledge graphs (KGs) have been widely applied in many fields such as recommendation systems and knowledge reasoning. Embedding KGs into a continuous vector space has quickly gained significant attention. However, most traditional KG embedding models assume that all the facts in the existing KGs are completely correct, ignoring that KG construction usually involves automatic mechanisms. These automatic construction processes inevitably generate a lot of noises and conflicts, including low-quality errors (e.g., entity type errors). Moreover, these low-quality noises could greatly influence the quality of rule extraction, which may reduce the efficiency of Rule-Guided Embedding model (RUGE). To address this problem, an efficient method to eliminate those entity type errors in triples is proposed and applied to RUGE. Experimental results demonstrate that the filtering of low-quality noises can greatly improve the accuracy of knowledge representation learning as well as the quality of rules, further illustrating the effectiveness of our method.

关键词： Noise measurement Semantics knowledge representation data mining Predictive models data models

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An integrated classification model for massive short texts with few words 19

An integrated classification model for massive short texts w...

引用

2019 International Conference on Robotics Systems and Vehicle Technology, RSVT 2019

作者： Tang, Xuetao Zhu, Yi Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China

ISBN: (纸本)9781450362429

The excellent performance of short texts classification has emerged in the past few years. However, massive short texts with few words like invoice data are different with traditional short texts like tweets in its no contextual and less semantic information, which hinders the application of conventional classification algorithms. To address these problems, we propose an integrated classification model for massive short texts with few words. More specifically, the word embedding model is introduced to train the word vectors of massive short texts with few words to form the feature space, and then the vector representation of each instance in texts is trained based on sentence embedding. With this integrated model, higher level representations are learned from massive short texts with few words. It can boost the performance of the base subsequent classifiers such as K-Nearest Neighbor. Extensive experiments conducted on dataset including 16 million real data demonstrate the superior classification performance of our proposed model compared with all competing state-of-the-art models. © 2019 Association for Computing Machinery.

关键词： Embeddings

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Disentangled Representation Learning with Transmitted Information Bottleneck

arXiv

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arXiv 2023年

作者： Dang, Zhuohang Luo, Minnan Jia, Chengyou Dai, Guang Wang, Jihong Chang, Xiaojun Wang, Jingdong The School of Computer Science and Technology The Ministry of Education Key Laboratory of Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi’an710049 China The SGIT AI Laboratory Xi’an710048 China The State Grid Shaanxi Electric Power Company Ltd. State Grid Corporation of China Xi’an710048 China The School of Information Science and Technology University of Science and Technology of China Hefei230026 China Abu Dhabi United Arab Emirates Baidu Inc Beijing100085 China

Encoding only the task-related information from the raw data, i.e., disentangled representation learning, can greatly contribute to the robustness and generalizability of models. Although significant advances have been made by regularizing the information in representations with information theory, two major challenges remain: 1) the representation compression inevitably leads to performance drop;2) the disentanglement constraints on representations are in complicated optimization. To these issues, we introduce Bayesian networks with transmitted information to formulate the interaction among input and representations during disentanglement. Building upon this framework, we propose DisTIB (Transmitted Information Bottleneck for Disentangled representation learning), a novel objective that navigates the balance between information compression and preservation. We employ variational inference to derive a tractable estimation for DisTIB. This estimation can be simply optimized via standard gradient descent with a reparameterization trick. Moreover, we theoretically prove that DisTIB can achieve optimal disentanglement, underscoring its superior efficacy. To solidify our claims, we conduct extensive experiments on various downstream tasks to demonstrate the appealing efficacy of DisTIB and validate our theoretical analyses. Copyright © 2023, The Authors. All rights reserved.

关键词： Information theory

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Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

arXiv

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arXiv 2025年

作者： Ma, Chenxiang Chen, Xinyi Li, Yanchen Yang, Qu Wu, Yujie Li, Guoqi Pan, Gang Tang, Huajin Tan, Kay Chen Wu, Jibin Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Institute of Automation Chinese Academy of Sciences Beijing100045 China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Privileged graph distillation for cold start recommendation

arXiv

引用

arXiv 2021年

作者： Wang, Shuai Zhang, Kun Wu, Le Ma, Haiping Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Anhui University China

The cold start problem in recommender systems is a long-standing challenge, which requires recommending to new users (items) based on attributes without any historical interaction records. In these recommendation systems, warm users (items) have privileged collaborative signals of interaction records compared to cold start users (items), and these Collaborative Filtering (CF) signals are shown to have competing performance for recommendation. Many researchers proposed to learn the correlation between collaborative signal embedding space and the attribute embedding space to improve the cold start recommendation, in which user and item categorical attributes are available in many online platforms. However, the cold start recommendation is still limited by two embedding spaces modeling and simple assumptions of space transformation. As user-item interaction behaviors and user (item) attributes naturally form a heterogeneous graph structure, in this paper, we propose a privileged graph distillation model (PGD). The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links. The student model is composed of an entity-attribute graph without CF links. Specifically, the teacher model can learn better embeddings of each entity by injecting complex higher-order relationships from the constructed heterogeneous graph. The student model can learn the distilled output with privileged CF embeddings from the teacher embeddings. Our proposed model is generally applicable to different cold start scenarios with new user, new item, or new user-new item. Finally, extensive experimental results on the real-world datasets clearly show the effectiveness of our proposed model on different types of cold start problems, with average 6.6%, 5.6%, and 17.1% improvement over state-of-the-art baselines on three datasets, respectively. © 2021, CC BY.

关键词： Embeddings

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