Social-path embedding-based transformer for graduation development prediction

As the education of students attracts more and more attention, the task of graduation development prediction has gradually become a hot topic in academia and industry. The task of graduation development prediction aims to predict the employment category of students in advance via academic achievement data, which can help administrators understand students’ learning status and set up a reasonable learning plan. However, existing research ignores the potential impact of social relationships on students’ graduation development choices. To fully explore social relationships among students, we propose a Social-path Embedding-based Transformer Neural Network (SPE-TNN) for the task of graduation development prediction in this paper. Specifically, SPE-TNN is divided into the Social-path selection layer, the Social-path embedding layer, the Transformer layer, and the Multi-layer projection layer. Firstly, the Social-path selection layer is designed to find social relationships that impact graduation development and embed them into the student’s performance features through the Social-path embedding layer. Secondly, the Transformer layer is adopted to balance the weights of the students’ features. Finally, the Multi-layer projection layer is used to achieve the student graduation development prediction. Experimental results on the real-world datasets show that SPE-TNN outperforms the existing popular approaches.

     

Distributed algorithm for mixed equilibrium problems with event-triggered strategy

Neural Computing and Applications - A new iterative method based on the event-triggered strategy for finding a solution to a mixed equilibrium problem (MEP) is introduced in this paper. The target...     

Multi-scale Interest Dynamic Hierarchical Transformer for sequential recommendation

Neural Computing and Applications - Existing sequential recommendation methods focus on modeling the temporal relationships of users’ historical behaviors and excel in exploiting users’...     

基于改进多嵌入空间的实时语义数据流推理

将语义数据流处理引擎与知识图谱嵌入表示学习相结合,可以有效提高实时数据流推理查询性能,但是现有的知识表示学习模型更多关注静态知识图谱嵌入,忽略了知识图谱的动态特性,导致难以应用于实时动态语义数据流推理任务。为了使知识表示学习模型适应知识图谱的在线更新并能够应用于语义数据流引擎,建立一种基于改进多嵌入空间的动态知识图谱嵌入模型PUKALE。针对传递闭包等复杂推理场景,提出3种嵌入空间生成算法。为了在进行增量更新时更合理地选择嵌入空间,设计2种嵌入空间选择算法。基于上述算法实现PUKALE模型,并将其嵌入数据流推理引擎CSPARQL-engine中,以实现实时语义数据流推理查询。实验结果表明,与传统的CSPARQL和KALE推理相比,PUKALE模型的推理查询时间分别约降低85%和93%,其在支持动态图谱嵌入的同时能够提升实时语义数据流推理准确率。     

基于超像素和光流引导的正射影像拼接方法

影像拼接是生成大规模数字正射影像的关键技术之一,但现有的影像拼接方法在进行多个影像拼接时存在拼接线穿过明显地物导致的鬼影现象。光流是观察者和场景间相对运动引起的影像边缘等的相对运动,其中,大光流对应影像间的变化区域,可用于检测正射影像间的明显地面区域。提出一种基于光流引导的新型影像拼接方法,通过超像素的密集光流提取影像中明显的地物信息,以避免接缝穿过明显的地面物体。采用由粗到细的接缝线优化策略,并在超像素级别上利用Dijkstra算法进行最佳拼接区域检测,从而提高接缝线检测的效率。在此基础上,结合归一化互相关成本函数在像素级别上进行拼接线的像素级优化,获得最优的接缝线。实验结果表明,该方法从主观视觉上能够生成高质量的接缝线,在保证拼接效率的情况下,SSIM质量评价指标较Dijkstra方法、图割方法以及商业软件OrthoVista得到明显提高。     

PPDM中面向k-匿名的MI Loss评估模型

隐私保护数据挖掘（PPDM）利用匿名化等方法使数据所有者在不泄露隐私信息的前提下,安全发布在数据挖掘中有效可用的数据集。k-匿名算法作为PPDM研究使用最广泛的算法之一,具有计算开销低、数据形变小、能抵御链接攻击等优点,但是在一些k-匿名算法研究中使用的数据可用性评估模型的权重设置不合理,导致算法选择的最优匿名数据集在后续的分类问题中分类准确率较低。提出一种使用互信息计算权重的互信息损失（MI Loss）评估模型。互信息反映变量间的关联关系,MI Loss评估模型根据准标识符和标签之间的互信息计算权重,并通过Loss公式得到各个准标识符的信息损失,将加权后的准标识符信息损失的和作为数据集的信息损失,以弥补评估模型的缺陷。实验结果证明,运用MI Loss评估模型指导k-匿名算法能够明显降低匿名数据集在后续分类中的可用性丢失,相较于Loss模型和Entropy Loss模型,该模型分类准确率提升了0.73%~3.00%。     

区块链分片技术综述

区块链作为分布式账本的关键技术之一,其去中心化、可匿名、不可篡改的特性受到学术界和工业界的青睐,被广泛应用于金融、数字货币、公共服务等领域。分片技术作为区块链扩容的主流方式之一,能够在不降低区块链去中心化程度的同时实现高性能的链上扩容,从而解决区块链可拓展性不足以及吞吐量较低的问题。介绍近年来出现的分片技术以及相关协议,总结分片技术的关键理论与方法,从分片配置、重配置、片内共识协议、跨片共识协议、状态存储等方面对分片技术方案进行对比,归纳不同分片方案在网络分片、交易分片、状态分片等设计中存在的优势和不足。同时,阐述一些经典分片技术在性能和实现方式上的特点,对许可区块链和无许可区块链、片内共识协议、跨片共识协议、准入性方案、状态分片方式等进行分析和概述。在此基础上,从分片内、分片间以及系统层级的角度总结分片技术当前所面临的困境和挑战,并对该领域的发展前景及未来研究方向加以展望。     

卫生经济学应用于慢性病防控决策的战略研究

为提高当前慢性病防控体系的效率,更好地遏制慢性病的流行,保护公众健康,2015年中国工程院设立了"卫生经济学应用于慢性病防控决策的战略研究"重大咨询项目。课题组调研发现,作为一种重要的卫生决策工具,卫生经济学在慢性病防控决策过程中尚处于初级应用阶段。对卫生经济学的重要性认识不足,对卫生经济学的方法的掌握和应用能力有限,制约了其在慢性病防控决策中的应用,因此建议建立多元主体参与的卫生经济学研究力量,加强卫生经济学应用于慢性病防控决策的大数据积累及应用研究,提出将卫生经济学应用于我国慢性病防控决策的战略框架。     

The influence of different pre-formed holes on the dynamic response of square plates under air-blast loading

Studying the blast response of plates with pre-formed holes under blast loading serves as a significant method to decouple dynamic response of plates with pre-formed holes under combined blast and fragment loading. Based on material test system (MTS) and Split Hopkinson tension bar (SHTB), the mechanical properties of experimental steel under different strain rates were obtained. Then, the experiments on the responses of plates with square, diamond and circular holes under air-blast loading were carried out and the failure modes of plates with diamond holes were derived. Later, the deformation curves of target plate were obtained by laser reverse forming technique. Next, a constitutive model program considering strain rate, stress status and temperature effect was adopted to carry out numerical simulation calculation, which verified the accuracy of this numerical simulation. Finally, discussions were carried out on the damage level at places where cracks might form, the dynamic stress concentration coefficient and stress status change, and then the influence which pre-formed holes have on the failure mode was analyzed. The results of this study show that: (1) A fortran vectorized user-material subroutine (VUMAT) program considering strain rate, stress status and temperature effect can effectively predict the dynamic response of metallic materials; (2) the shape of pre-formed holes affects the damage level, dynamic stress concentration coefficient and stress status. Besides, failure occurs more easily in plates with diamond pre-formed holes; (3) pre-formed holes have an obvious influence on the failure modes of plates under different blast distance.     

Reducing Hysteresis and Enhancing Performance of Perovskite Solar Cells Using Low‐Temperature Processed Y‐Doped SnO2 Nanosheets as Electron Selective Layers

Despite the rapid increase of efficiency, perovskite solar cells (PSCs) still face some challenges, one of which is the current–voltage hysteresis. Herein, it is reported that yttrium‐doped tin dioxide (Y‐SnO₂) electron selective layer (ESL) synthesized by an in situ hydrothermal growth process at 95 °C can significantly reduce the hysteresis and improve the performance of PSCs. Comparison studies reveal two main effects of Y doping of SnO₂ ESLs: (1) it promotes the formation of well‐aligned and more homogeneous distribution of SnO₂ nanosheet arrays (NSAs), which allows better perovskite infiltration, better contacts of perovskite with SnO₂ nanosheets, and improves electron transfer from perovskite to ESL; (2) it enlarges the band gap and upshifts the band energy levels, resulting in better energy level alignment with perovskite and reduced charge recombination at NSA/perovskite interfaces. As a result, PSCs using Y‐SnO₂ NSA ESLs exhibit much less hysteresis and better performance compared with the cells using pristine SnO₂ NSA ESLs. The champion cell using Y‐SnO₂ NSA ESL achieves a photovoltaic conversion efficiency of 17.29% (16.97%) when measured under reverse (forward) voltage scanning and a steady‐state efficiency of 16.25%. The results suggest that low‐temperature hydrothermal‐synthesized Y‐SnO₂ NSA is a promising ESL for fabricating efficient and hysteresis‐less PSC.