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| 1 | 基于属性异构网络表示学习的专利交易推荐显示文摘融合异构信息进行专利交易推荐可以促进交易,但存在因忽略专利属性而影响推荐结果的问题。本研究提出基于属性异构网络(attribute heterogeneous network,AHN)表示学习的专利交易推荐模型(patent transaction recommendation based on AHN representation learning,AHNRL-PTR)。首先筛选专利和组织中影响专利交易的属性;其次构建专利交易AHN,然后在AHN中引入网络表示学习,并基于多维高斯分布解决节点表示的不确定性,基于KL散度(Kullback-Leibler divergence)解决节点间距离非对称性。最后,以粤港澳大湾区有效发明授权专利数据进行实证研究,得出结论:第一,相比于metapath2vec、TADW(text-associated DeepWalk)和AHNRL-PTR模型的两个变体方法,AHNRL-PTR模型的推荐精度最高,超过86%,说明融合组织及专利属性,并聚焦节点表示的不确定性和非对称性问题的解决,能大幅提高推荐精度;第二,在非准确指标IntraSim和Popularity上,AHNRL-PTR的表现优于metapath2vec和两个变体方法,反映该方法的推荐结果具有一定的多样性,且可以挖掘推荐冷门专利;第三,基于两个非准确指标将组织聚类为六类,分别为中介型、领域骨干型、研究型、族群型、成长型、专业型,体现了推荐结果的可解释性和个性化水平。本研究可为专利交易智能化推荐服务提供决策支持。 | 何喜军 吴爽爽 武玉英 才久然 庞婷 Chee Seng Chan | 2022 | 情报学报2022，41，11： | 3 |
| 2 | Federated Learning with Privacy-preserving and Model IP-right-protection显示文摘In the past decades,artificial intelligence(AI)has achieved unprecedented success,where statistical models become the central entity in AI.However,the centralized training and inference paradigm for building and using these models is facing more and more privacy and legal challenges.To bridge the gap between data privacy and the need for data fusion,an emerging AI paradigm feder-ated learning(FL)has emerged as an approach for solving data silos and data privacy problems.Based on secure distributed AI,feder-ated learning emphasizes data security throughout the lifecycle,which includes the following steps:data preprocessing,training,evalu-ation,and deployments.FL keeps data security by using methods,such as secure multi-party computation(MPC),differential privacy,and hardware solutions,to build and use distributed multiple-party machine-learning systems and statistical models over different data sources.Besides data privacy concerns,we argue that the concept of“model”matters,when developing and deploying federated models,they are easy to expose to various kinds of risks including plagiarism,illegal copy,and misuse.To address these issues,we introduce FedIPR,a novel ownership verification scheme,by embedding watermarks into FL models to verify the ownership of FL models and protect model intellectual property rights(IPR or IP-right for short).While security is at the core of FL,there are still many articles re-ferred to distributed machine learning with no security guarantee as“federated learning”,which are not satisfied with the FL definition supposed to be.To this end,in this paper,we reiterate the concept of federated learning and propose secure federated learning(SFL),where the ultimate goal is to build trustworthy and safe AI with strong privacy-preserving and IP-right-preserving.We provide a com-prehensive overview of existing works,including threats,attacks,and defenses in each phase of SFL from the lifecycle perspective. | Qiang Yang Anbu Huang Lixin Fan Chee Seng Chan Jian Han Lim Kam Woh Ng Ding Sheng Ong Bowen Li | 2023 | Machine Intelligence Research2023，20，1： | 1 |
| 3 | 粤港澳大湾区与旧金山湾区技术转让网络演化模式比较研究显示文摘采集2003-2019年粤港澳大湾区(以下简称GBA)和旧金山湾区(以下简称SFBA)发明专利转让数据,从宏观、中观、微观3个维度,分别构建GBA与SFBA的城市间、组织间以及主体间技术转让网络,以探索湾区网络结构特征及演化规律。研究发现:(1)GBA的城市间技术转让网络由以“广州”为核心的单核结构向以“深圳-东莞-广州”为核心的多核结构演化,但香港地区、澳门地区与内地核心城市间联系松散,在网络中处于边缘位置,SFBA的城市间网络由以“Santa Clara-San Mateo”为核心的双核结构向以“Santa Clara-San Francisco-San Mateo”为核心的多核结构演化;(2)企业间转让是GBA组织间网络中最重要的转让模式,产学研间转让呈现衰退趋势,金融机构在网络演化中的作用未得到有效发挥,企业与金融机构间的转让是SFBA网络中最重要的模式,且呈现快速增长趋势;(3)SFBA和GBA的主体间技术转让网络均具有高频率小群体和低频率大群体两种结构模式,网络演化由少部分关键主体主导,且形成了基于核心主体的星型转让模式;(4)与SFBA主体间网络相比,GBA网络结构稳定性较低,持续参与技术转让的主体规模较小,主体间技术创新能力差距较大,未能形成优势互补、均衡发展的技术转让互惠模式。 | 何喜军 吴爽爽 张佑 Chan Chee Seng 庞婷 | 2023 | 科技进步与对策2023，40，9： | 0 |
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