A Conceptual and Computational Framework for Aspect-Based Collaborative Filtering Recommender Systems

Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.

Multi-Topology Hierarchical Collaborative Hybrid Particle Swarm Optimization Algorithm for WSN

Wireless sensor networks(WSN)are widely used in many situations,but the disordered and random deployment mode will waste a lot of sensor resources.This paper proposes a multi-topology hierarchical collaborative particle swarm optimization(MHCHPSO)to optimize sensor deployment location and improve the coverage of WSN.MHCHPSO divides the population into three types topology:diversity topology for global exploration,fast convergence topology for local development,and collaboration topology for exploration and development.All topologies are optimized in parallel to overcome the precocious convergence of PSO.This paper compares with various heuristic algorithms at CEC 2013,CEC 2015,and CEC 2017.The experimental results show that MHCHPSO outperforms the comparison algorithms.In addition,MHCHPSO is applied to the WSN localization optimization,and the experimental results confirm the optimization ability of MHCHPSO in practical engineering problems.

Optimal edge-cloud collaboration based strategies for minimizing valid latency of railway environment monitoring system

Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.

Collaborative Clustering Parallel Reinforcement Learning for Edge-Cloud Digital Twins Manufacturing System

To realize high-accuracy physical-cyber digital twin(DT)mapping in a manufacturing system,a huge amount of data need to be collected and analyzed in real-time.Traditional DTs systems are deployed in cloud or edge servers independently,whilst it is hard to apply in real production systems due to the high interaction or execution delay.This results in a low consistency in the temporal dimension of the physical-cyber model.In this work,we propose a novel efficient edge-cloud DT manufacturing system,which is inspired by resource scheduling technology.Specifically,an edge-cloud collaborative DTs system deployment architecture is first constructed.Then,deterministic and uncertainty optimization adaptive strategies are presented to choose a more powerful server for running DT-based applications.We model the adaptive optimization problems as dynamic programming problems and propose a novel collaborative clustering parallel Q-learning(CCPQL)algorithm and prediction-based CCPQL to solve the problems.The proposed approach reduces the total delay with a higher convergence rate.Numerical simulation results are provided to validate the approach,which would have great potential in dynamic and complex industrial internet environments.

Edge-Cloud Collaborative Optimization Scheduling with Micro-Service Architecture

The architecture of edge-cloud cooperation is proposed as a compromising solution that combines the advantage of MEC and central cloud. In this paper we investigated the problem of how to reduce the average delay of MEC application by collaborative task scheduling. The collaborative task scheduling is modeled as a constrained shortest path problem over an acyclic graph. By characterizing the optimal solution, the constrained optimization problem is simplified according to one-climb theory and enumeration algorithm. Generally, the edge-cloud collaborative task scheduling scheme performance better than independent scheme in reducing average delay. In heavy workload scenario, high blocking probability and retransmission delay at MEC is the key factor for average delay. Hence, more task executed on central cloud with abundant resource is the optimal scheme. Otherwise, transmission delay is inevitable compared with execution delay. MEC configured with higher priority and deployed close to terminals obtain more performance gain.

Designing and Evaluating a Collaborative Knowledge Management Framework for Leaf Disease Detection

Knowledge Management(KM)has become a dynamic concept for inquiry in research.The management of knowledge from multiple sources requires a systematic approach that can facilitate capturing all important aspects related to a particular discipline,several KM frameworks have been designed to serve this purpose.This research aims to propose a Collaborative Knowledge Management(CKM)Framework that bridges gaps and overcomes weaknesses in existing frameworks.The paper also validates the framework by evaluating its effectiveness for the agriculture sector of Pakistan.A software LCWU aKMS was developed which serves as a practical implementation of the concepts behind the proposed CKMF framework.LCWU aKMS served as an effective system for rice leaf disease detection and identification.It aimed to enhance CKM through knowledge sharing,lessons learned,feedback on problem resolutions,help from co-workers,collaboration,and helping communities.Data were collected from 300 rice crop farmers by questionnaires based on hypotheses.Jennex Olfman model was used to estimate the effectiveness of CKMF.Various tests were performed including frequency measures of variables,Cronbach’s alpha reliability,and Pearson’s correlation.The research provided a KMS depicting KM and collaborative features.The disease detection module was evaluated using the precision and recall method and found to be 94.16%accurate.The system could replace the work of extension agents,making it a cost and time-effective initiative for farmer betterment.

An Investigation of a Collaborative Learning Framework in Classroom Teaching——an action research

This essay which is dedicated into doing action research explores the difficulties in conducting the collaborative learning in a student-centered classroom teaching,mostly based on teachers’reflection.This essay adapts a framework of conducting,monitoring and evaluating collaborative learning into specific teaching context and points out research areas in the next step of action research.

数字经济背景下高技术企业协同创新组态分析

高技术企业在推动经济结构优化、解决中国科技“卡脖子难题”上起到了中流砥柱的作用。在数字经济背景下,本文基于资源保存理论和TOE框架,以中国31个省(区、市)为研究对象,运用模糊集定性比较分析(fsQCA)方法探索技术、组织和环境对高技术企业协同创新的多重联动影响。研究发现:企业协同创新是多种要素共同作用的结果,导致高企业协同创新的组态包括“要素聚合型”、“自我发展型”和“内外协同型”3条;同时目前中国高技术企业协同创新在东部和中部地区发展较为均衡,而在西部地区存在一定的失调。因此,本文根据研究结果提出差异化的发展策略:强化要素协同,推进高技术企业协同创新要素互动与流通;创新技术平台,赋能低协同创新企业共享资源和技术成果;利用区位优势,助推企业协同创新和区域合作互促互进。

新发展格局下吸引境外医疗消费回流协同治理框架构建研究

“双循环”新发展格局下吸引境外医疗消费回流是应对中国式现代化、“健康中国”战略和人口老龄化国家战略的重要举措,是当前我国推动供给侧结构性改革和医疗健康产业高质量发展的动力引擎。医疗消费具有产业和事业的双重属性,涉及政府部门、行业协会、医疗机构、旅游企业、中介机构等多方主体。因此,吸引境外医疗消费回流需要统筹政府政策顶层设计、行业市场环境优化、企业市场战略调整等方面构建协同治理框架,通过政府激励、行业聚力、企业齐心,多方协同共同推动我国医疗消费服务行业高质量发展,培育优良的医疗消费服务环境,从而吸引更多的境外医疗消费回流。

基于大数据和协同过滤技术的个性化教学平台的研究应用

协同过滤技术属于推荐算法,能够实现对不同用户资源的个性化推荐。我国在推进教学改革过程中,强调教学资源共享和个性化推荐,实现教学平台打造。本文将大数据技术、协同过滤技术,应用到个性化教学平台建设中,阐述系统基本功能、架构,使用个性推荐算法,实现个性化资源的推广和应用。该平台既可实现资源的共享,又能展示个性化资源,以满足实践教学的基本需求,改善教学资源不平衡的情况。研究结果表明,该平台功能强大、性能良好,可基于用户推荐不同资源,辅助开展个性化教学活动。

数字乡村协同建设探赜——基于整合性协同治理框架

数字乡村建设是实现农业农村现代化的系统性工程,多元主体数字化协同参与是数字乡村建设的动力源。但政府、企业、村民等主体之间存在较多差异,为协同过程增加了阻碍。引入整合性协同治理框架为分析数字乡村协同建设的过程和实现路径提供理论指导,从该框架的结构要素出发,构建数字乡村协同建设分析模型来梳理数字乡村协同建设过程。分析得出:数字乡村协同建设存在协同基础有待完善,多主体利益冲突,多重关系交织,理论与行动脱节,评价反馈措施阙如的问题。应夯实协同建设的基础,探索均衡利益的协调激励措施,构建强化信任的主体关系网络,打造共建共治共享的行动规范,规范评价反馈体系来弥补协同建设的不足之处。

与AI“共舞”:系统化视角下的AI-员工协作

AI-员工协作是一个以高效完成任务为目标,由“AI-人-组织”构成的交互系统。促进AI-员工协作对于推动AI与实体经济的深度融合,以及员工在数字化时代的心理健康与职业发展至关重要。AI与员工的交互关系错综复杂,现有研究呈现碎片化特点,缺乏对AI-员工协作的整体认识。因此,有必要在厘清相关概念的基础上,对AI-员工协作的相关研究进行系统地梳理。通过对相关研究的系统性回顾,本文厘清了AI和AI-员工协作的内涵,梳理了AI-员工协作系统的构成要素,分析了构成要素的作用和影响,并进一步基于系统化的视角构建了一个研究框架。最后,基于AI-员工协作的研究框架提出未来研究展望。

城市综合能源系统协同调控与互动交易研究综述

城市是工业、服务业的聚集地,是经济增长的主引擎,未来城市综合能源系统的发展目标将不仅仅局限于满足社会能源需求,还需要将视角提升到支撑城市全局发展层面。因此,城市综合能源系统协同调控与互动交易相关研究不仅是近年来研究的热点问题,也是促进人类和城市可持续发展的必要解决问题。该文首先分析了相关研究的发展趋势和发文热度。然后在模型层面,分别讨论了城市综合能源系统的基本协同调控模型以及满足零碳需求、韧性提升需求、互动交易场景下的协同调控模型;在决策框架和算法层面,就目前常用的双层优化、鲁棒优化、人工智能优化方法分别进行概括分析;对城市综合能源系统仿真软件的特点进行了归纳总结。最后,对城市综合能源系统中多能源协同运行灵活性潜力挖掘、全设备模型建模方法与信息交换技术、多元用户互动特性分析、信息-物理-社会深度融合的需求和挑战进行了展望。

多元协同治理如何助力公共服务合作生产?——基于R县“村超”赛事的制度分析

公共服务合作生产对构建以人民为中心的“共建共治共享”社会治理格局具有重要意义。“村超”赛事反映了社会、政府和市场主体共同参与的多元协同合作生产模式,该模式起源于社会公众的自组织行为,即公众主动参与公共服务供给;多元协同治理通过社会酝酿机制、市场助推机制、议程吸纳机制和行政赋能机制提升合作生产治理效能;多元协同治理下的合作生产是多层行动舞台联动的产物,具有明显的阶段性特征。针对不同层次行动舞台和合作生产所处不同阶段,上述四种机制的运用需要考虑其权变性和阶段适配性。将合作生产理论与协同治理理论相结合拓展了合作生产的理论体系,此外,基于典型案例阐释中国情境下的合作生产机制和制度化过程也具有创新性。

基于协同优化的耐压柱壳多学科多目标优化

为提高耐压柱壳的设计效率和结构性能,本文基于多学科设计优化思想,进行了耐压柱壳多学科设计优化的学科分解,确定了耐压柱壳多学科优化设计的设计参数,明确了设计变量间的耦合关系,设计了耐压柱壳参数化分析流程,进行了设计变量灵敏度分析。在利用响应面模型建立了具有较高拟合精度耐压柱壳近似模型的基础上,以质量排水量比和极限载荷为目标函数,建立了基于协同优化方法的耐压柱壳多学科多目标优化模型,在确定了系统层和学科层的优化求解方法基础上,设计了基于协同优化的耐压柱壳多学科多目标优化框架,进行了优化的求解工作,得到了优化方案。与初始方案的对比表明:所得到的Pareto解集中,部分方案在2个目标函数上均得到了优化,验证了优化框架的有效性。

学分转移促进“三教”协同创新发展

学分是“三教”体系中共有的组成部分,可成为促进“三教”协同创新发展的有力支撑。学分转移之于“三教”协同创新发展具有重要价值意蕴,集中体现在顺应我国人口宏观发展趋势、回应民间教育质量立场、提高“三教”整体吸引力、应对劳动力市场快速更迭,以及满足当代学习者多样化学习需求上。以学分转移为核心促进“三教”协同创新发展的理论图景包含促进教育机会公平、保障教育教学质量、助力学习者流动、满足人才出口端需要以及提高“三教”整体社会认可度等方面。促进“三教”协同创新发展可通过推动中高等教育机构学分衔接、促进中学后教育机构学分互认、将学分转移标准纳入国家资历框架、基于省域构建学分转移系统等路径实现。

高质量发展背景下粤港澳大湾区职业教育协同发展研究综述

“高质量发展”是二十大报告中频繁出现的关键词之一,实现高质量的经济发展必然需要建立高质量的教育体系,恰逢新修订的《职业教育法》的颁布,在此背景下,推进粤港澳大湾区职业教育协同已然成为中国职业教育发展的关键。当前粤港澳大湾区职业教育协同发展主要从资源互补、创新机制、产教融合三方面出发,但仍然存在资源分配不均、协同治理机制不完善、产业匹配度不高等困境,为此,提出了以“协同学”为视角,三地资源优势互补,加强顶层设计,构建“三地联动”的发展模式;以“产业集群理论”为视角,提出优化三地经济结构,建立开放共享的服务体系;以搭建资历框架为视角,实现学历认证,推动大湾区教育资源的区域流动,深入推进大湾区教育内涵和方向的国际化。

创新管理与企业合规之间的平衡:困境与突破

合规已成为企业经营的底线要求,创新则是引领市场的制胜法宝。然而,二者在实践中却常常陷入合规束缚创新与创新突破合规的困境。学界和业界都在不断探索行之有效的优化思路。本文立足企业视角剖析当前合规创新管理实践中存在的共性困境,分析症结成因;从顶层设计优化、协同机制构建、全流程防控、文化引领融合等维度提出优化策略,以期为更多企业提供实践指引与智力支持。

数智社会背景下青少年体质健康促进个性化的逻辑理路

文章系统地探讨和构建一条符合青少年体质健康促进的个性化需求的逻辑理路。一是深入解读数智社会的基本特征及其对青少年体质健康的影响,揭示数智技术在提高健康干预精准度和个性化服务方面的价值。二是详细梳理从数据采集与个人画像构建、个性化需求识别与目标设定、定制化干预方案设计与实施到效果监测与反馈调整的逻辑链条,阐述大数据、人工智能、物联网等技术在数据驱动、精准施策、动态优化过程中的关键作用。面对数智技术使用过程中可能出现的数据安全、技术依赖、教育资源不均等问题,进一步提出强化数据隐私保护、缩小数字鸿沟、提升教师数智素养、完善相关法规和行业标准、倡导多主体协同参与等一系列针对性策略,旨在为促进青少年体质健康的个性化实践提供保障。三是展望未来,指出深化数智技术面临的挑战,提出相应对策,以期全面、个性化地促进青少年体质健康。

锌基MOFs材料在肿瘤治疗领域的应用进展

金属-有机框架(MOFs)是由含氧或氮的有机配体与过渡金属通过自组装连接而形成的具有周期性网状结构的晶体材料。该类材料因具有尺寸结构可调、高孔隙率、比表面积大、低晶体密度等一系列优点,而受到多个学科研究人员的重视。其中,锌(Zn)基MOFs材料因其优越的生物相容性和易功能化修饰等特点,受到了广泛的关注。文章主要从药物递送和协同抗癌等方面总结了Zn基MOFs材料的生物应用.