Research on the Collaborative Governance of Social Responsibility in Online Audiovisual Enterprises

This paper aims to analyze the present conditions of the social responsibility ecosystem in online audiovisual enterprises in the digital age. It focuses on the governance of social responsibility in these enterprises and conducts an in-depth analysis of the problems and influencing factors related to the social responsibility aberrations of online audiovisual enterprises. Drawing upon social responsibility theory and collaborative governance theory, this research constructs a social responsibility guidance and governance system guided by the public, supported by the voluntary fulfillment of responsibilities by online audiovisual enterprises, and based on the collaborative participation of diverse stakeholders. It explores and optimizes the implementation pathways of this system, providing theoretical support and practical guidance for promoting the sustainable development of online audiovisual enterprises. Furthermore, it aims to contribute to the creation of a harmonious Internet ecosystem.

Contextualized Analysis of Social Networks:Collaboration in Scientific Communities

Currently, the collaboration in scientific communities has been studied in order to explain, among other things, the knowledge diffusion. The quality of Graduate Programmes is often associated with the scientific collaboration. This paper discusses how scientific collaboration processes can be identified and characterized through social and complex networks. For this purpose, collaboration networks of bibliographic production, research projects, and committees of PhD theses and Masters’ dissertations by researchers from a graduate program in computational modeling were studied. The data were obtained from CAPES’ reports of the period from 2001 to 2009. Among the studied indices, centrality indices indicate the presence of prominent researchers who influence others and promptly interact with other researchers in the network. The indices of complex networks reveal the presence of the small-world (i.e. these networks are favorable to increase coordination between researchers) phenomenon and indicate a behavior of scale-free degree distribution (i.e. some researchers promote clustering more than others) for one of the studied networks.

Global Collaboration in Artificial Intelligence:Bibliometrics and Network Analysis from 1985 to 2019

Purpose:This study aims to explore the trend and status of international collaboration in the field of artificial intelligence(AI)and to understand the hot topics,core groups,and major collaboration patterns in global AI research.Design/methodology/approach:We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science(WoS)and studied international collaboration from the perspectives of authors,institutions,and countries through bibliometric analysis and social network analysis.Findings:The bibliometric results show that in the field of AI,the number of published papers is increasing every year,and 84.8%of them are cooperative papers.Collaboration with more than three authors,collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns.Through social network analysis,this study found that the US,the UK,France,and Spain led global collaboration research in the field of AI at the country level,while Vietnam,Saudi Arabia,and United Arab Emirates had a high degree of international participation.Collaboration at the institution level reflects obvious regional and economic characteristics.There are the Developing Countries Institution Collaboration Group led by Iran,China,and Vietnam,as well as the Developed Countries Institution Collaboration Group led by the US,Canada,the UK.Also,the Chinese Academy of Sciences(China)plays an important,pivotal role in connecting the these institutional collaboration groups.Research limitations:First,participant contributions in international collaboration may have varied,but in our research they are viewed equally when building collaboration networks.Second,although the edge weight in the collaboration network is considered,it is only used to help reduce the network and does not reflect the strength of collaboration.Practical implications:The findings fill the current shortage of research on international collaboration in AI.They will help inform scientists and policy makers about the future of AI research.Originality/value:This work is the longest to date regarding international collaboration in the field of AI.This research explores the evolution,future trends,and major collaboration patterns of international collaboration in the field of AI over the past 35 years.It also reveals the leading countries,core groups,and characteristics of collaboration in the field of AI.

A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric vehicles.However,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust issues.In this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch center.Based on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social welfare.We use blockchain to record power trading data for trusted pricing and use smart contracts for transaction matching.The simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

Opportunities for Remote Collaboration in a Social Web Search Model That Integrates Parents and Children

Traditionally, search engines are designed to support a single user working alone. However, the construction of knowledge is enriched when one adds collaboration to search tasks. We identified opportunities for remote collaboration in a Social Web search model that integrates parents and children guided by 5W + 1H (who, what, where, when, why, how) dimensions. Our social search model aims at improving the search process for children. We found 7 opportunities for remote collaboration on the search process, based on implicit-explicit interactions.

Potential friendship discovery in social networks based on hybrid ensemble multiple collaborative filtering models in a 5G network environment

At present, 5G network technology is being applied to various social network modes, and it can provide technical and traffic support for social networks. Potential friendship discovery technology in 5G-enabled social networks is beneficial for users to make potential friends and expand their range of activities and social hierarchy, which is highly sought after in today's social networks and has great economic and application value. However, the sparsity of the dominant user association dataset in 5G-enabled social networks and the limitations of traditional collaborative filtering algorithms are two major challenges for the friend recommendation problem. Therefore, in order to overcome these problems regarding previous models, we propose a Hybrid Ensemble Multiple Collaborative Filtering Model (HEMCF) for discovering potential buddy relationships. The HEMCF model draws on a special autoencoder method that can effectively exploit the association matrix between friends and additional information to extract a hidden representation of users containing global structural information. Then, it uses the random walk-based graph embedding algorithm DeepWalk to extract another hidden representation of users in the buddy network containing local structural information. Finally, in the output module, the HEMCF model stacks and multiplies the two types of hidden representations of users to ensure that the information mentioned above is concentrated in the final output to generate the final prediction value. The magnitude of the prediction value represents the probability of the users being friends, with larger values representing a high probability of the two users being friends, and vice versa. Experimental results show that the proposed method boosts the accuracy of the relationship prediction over baselines on 3 real-world public datasets dramatically.

Studying Group Dynamics through Social Networks Analysis in a Medical Community

In 2008, the Brazilian Department of Science and Technology created the INCTs (Brazilian Science and Technology Institutes). One of them was the Cancer Control INCT. Due to its importance and considering that there are different groups working together in the same area, it is important that they collaborate intensely. Envisioning an empowerment of scientific collaboration, the BRINCA project was created to support a set of analyses of the social networks from this particular INCT. These analyses were created by mining curricular and publications bases, and identifying different types of scientific relationships and areas. We were able to observe, for instance, how the interaction is amongst researchers from related areas, which researchers were more collaborative and which ones were isolated from the network. These analyzes were used by the INCT coordination to understand and act to improve scientific collaboration.

A Heuristic Reputation Based System to Detect Spam Activities in a Social Networking Platform, HRSSSNP

The introduction of the social networking platform has drastically affected the way individuals interact. Even though most of the effects have been positive, there exist some serious threats associated with the interactions on a social networking website. A considerable proportion of the crimes that occur are initiated through a social networking platform [1]. Almost 33% of the crimes on the internet are initiated through a social networking website [1]. Moreover activities like spam messages create unnecessary traffic and might affect the user base of a social networking platform. As a result preventing interactions with malicious intent and spam activities becomes crucial. This work attempts to detect the same in a social networking platform by considering a social network as a weighted graph wherein each node, which represents an individual in the social network, stores activities of other nodes with respect to itself in an optimized format which is referred to as localized data set. The weights associated with the edges in the graph represent the trust relationship between profiles. The weights of the edges along with the localized data set are used to infer whether nodes in the social network are compromised and are performing spam or malicious activities.

Establishing a Social Governance Model Based on Collaboration,Participation,and Common Interests:Value,Structure and Roadmap

The 19th National Congress of the Communist Party of China(CPC)put forward the proposal of establishing a social governance model based on collaboration,participation,and common interests,with the aim of developing new approaches to social governance and further modernizing China’s social system and capacity for governance.As a structuralized social relation addressing diversified governance bodies,the model has consolidated the social governance innovations achieved since the 18th CPC National Congress.Focusing on three dimensions—"value,""structure"and"roadmap,"this paper tries to interpret the value connotations and defining features behind the model and explores its development roadmap.Such a social governance model is a response to political,social and public value appeals,and an adjustment to the order of governance bodies,resource allocations and the benefit distribution structure.Its construction is a systematic and long-term project,which should aim continuously at satisfying people’s ever-higher needs,perfecting laws and regulations,cultivating a pool of specialized organizations and talents,exerting the technology advantages of the Internet in governance,and promoting socialization,legalization,specialization and intellectualization in social development.

Link Mining in Online Social Networks with Directed Negative Relationships

One of the most important work to analyse online social networks is link mining. A new type of social networks with positive and negative relationships are burgeoning. We present a link mining method based on random walk theory to mine the unknown relationships in directed social networks which have negative relationships. Firstly, we define an extended Laplacian matrix based on this type of social networks.Then, we prove the matrix can be used to compute the similarities of the node pairs. Finally, we propose a link mining method based on collaboration recommendation method. We apply our method in two real social networks. Experimental results show that our method do better in terms of sign accuracy and AUC for mining unknown links in the two real datasets.

A Personalized Recommendation Algorithm with User Trust in Social Network

In the era of big data, personalized recommendation has become an important research issue in social networks as it can find and match user’s preference. In this paper, the user trust is integrated into the recommendation algorithm, by dividing the user trust into 2 parts: user score trust and user preference trust. In view of the common items in user item score matrix, the algorithm combines the number of items with the score similarity between users, and establishes an asymmetric trust relationship matrix so as to calculate the user’s score trust. For the non common score items, we use the attribute information of items and the scoring weight to calculate the user’s preference trust. Based on the user trust in social network, a new collaborative filtering recommendation algorithm is proposed. Besides, a new matrix factorization recommendation algorithm is proposed by combining the user trust with matrix factorization. We did the experiments comparing with the related algorithms on the real data sets of social network. The results show that the proposed algorithms can effectively improve the accuracy of recommendation.

“一带一路”共建国家农业科技创新合作网络结构演化及影响因素探究

基于WOS数据库中跨国农业科技论文合著数据,借助社会网络分析法和QAP回归模型,剖析2006—2020年“一带一路”共建国家农业科技创新合作网络结构演化特征及影响因素。研究发现:(1)“一带一路”共建国家农业科技创新合作网络整体结构指标均得到了不同程度的优化,且各指标优化速度快于全球农业科技创新合作网络。(2)“一带一路”共建国家和全球国家农业科技创新合作网络去中心化趋势均较明显,多核共存特征更加突出。(3)中国和意大利在合作网络中同时扮演着核心者和中间人角色。(4)合作网络呈现出双向溢出、主受益、“经纪人”3种类型的块集聚特征,亚洲国家是推动农业科技创新合作的主引擎。(5)制度、经济、科研和认知邻近对3个阶段的合作网络都具有显著的正向影响,而空间邻近对合作的阻碍作用逐渐减弱。“一带一路”共建国家应进一步重视跨国农业科技创新合作,明确自身在合作网络中的地位与角色,充分借助网络核心国家的重要作用,加速融入农业科技创新国际合作核心网络.

英国大型阅读推广项目多元协作模式解析

“多元协作”组织实施模式是英国大型阅读推广项目得以成功开展的主要因素之一。政府、社会组织、公共图书馆、企业、公众等多元主体以不同角色和作用结成协作关系及协作网络,形成了特色而有效的协作机制。该模式具有跨界协作、伙伴关系、双核心、专业性、广泛性和适应性等特点及优势,但也存在松散性和不稳定性的潜在问题。我国可从创新和改进政府引导扶持方式、树立公共图书馆多元协作“核心”地位、主动吸纳社会资金并充分利用社会资源、推动跨领域协作、确立规范稳定的协作机制等方面对其经验加以借鉴。

社会协作标注探究法:一种促进研究生文献阅读核心能力的教学模式探索

文献阅读是研究生的核心科学研究能力,然而阅读无深度、问题难提出、创新鲜生成以及教学模式难支撑等问题,制约着研究生的科学研究能力培养。针对这些问题,文章基于社会建构主义、知识创造理论和翻转教学模式,利用社会协作标注技术,构建了社会协作标注探究法教学模式。同时,文章采用基于设计的研究方法开展教学实验,使研究生利用钉钉的文档协作标注功能进行文献协作标注学习。实验表明,该模式能显著提升研究生的文献阅读深度学习、批判性思维能力、创造性思维能力和学习体验。为确保协作标注学习效果,教师需精心设计学习资源、学习支架、学习型组织和翻转学习活动。文章通过研究,旨在为提升研究生的文献阅读能力提供教学模式参考。

四川省区域协同创新网络体系研究——基于“一干多支,五区共兴”发展战略的思考

在创新驱动经济高质量发展的战略背景下,结合四川“一干多支,五区共兴”的区域协调发展布局,利用熵权法评估四川省各市(州)的创新能力,通过社会网络分析法对四川省创新协同网络进行分析,探究了区域协同创新体系建设的可行路径。研究结果表明:成都市是四川省的创新高地,成都平原整体的创新能力显著强于其他四区,存在明显的断层现象;四川省的创新能力呈现出“中心—边缘”的空间格局,成都市的极点效应辐射范围仅在成都平原内,未能同整个四川的创新系统融合;遂宁、南充、自贡等地的中介作用效果不佳,创新辐射的承接效果较差,未能有效带动四川地区创新能力的发展。基于此,提出要建立成都平原与各区间完备的创新互动机制;强化成都辐射,重视中心节点的承接作用;立足区域特点,促进特色发展;促进创新资源有效配置,实现区域创新一体化发展等政策建议。

中国式现代化导向的市域社会治理及多主体协同的路径优化研究

市域社会治理包含于中国式现代化的体系框架之下,后者之于前者具有战略和实践层面的引领意义。从理论逻辑看,市域社会治理在国家治理体系与治理能力现代化实践中,具有承上启下的战略地位,对实现全过程人民民主、建立合作共赢的开放治理格局具有推动作用。从实践逻辑看,实现市域社会治理离不开多元主体协同,要求在推进治理要素、治理方式、治理机制等方面取得更加务实的成果。在理顺两者关系的基础上,本研究梳理出市域社会治理的客观诉求,认为实现具有中国式现代化特色的市域社会治理需要以多主体协同为切入点,并针对性提出了市域社会治理多主体协同的路径优化举措。

人工智能推动的高技能人才需求与能力结构变迁研究——以人机智能协作的模式差异为视角

人工智能对许多职业产生了明显影响,人机智能协作将成为常态。基于对不同任务类型下人类和当前人工智能技术的优势特征分析,人机智能协作可归纳为智能工具型、协同合作型和重塑补充型三种典型模式。三种模式下的人机分工和协同工作方式差别显著,因此也造成了社会人才需求尤其是高技能应用型人才的需求状况和适应性能力结构要求的巨大差异。智能工具型协作模式下高技能人才的职业能力核心是新型智能化业务实践能力,协同合作型协作模式下的高技能人才的职业能力核心则是以统筹人机智能系统实现职业功能为根本的智能协作能力,重塑补充型协作模式下高技能人才需求则会被人工智能大量替代。

自主与协同:环境人文社会科学知识体系的跨学科建构

环境问题是当代人文社会科学知识生产系统必须予以回答、予以回应的重大问题、前沿问题。环境与环境问题的话语生成和社会建构,既来源于人类生产与生活活动中所遭受的来自自然环境的现实困扰和感性认知,更是通过从自然科学到人文社会科学的学科化及跨学科交叉的知识生产、知识传播得以建构。环境问题的社会建构以及国家环境保护实践的主体性,决定了基于国家和民族的环境人文社会科学知识自主性之必然。环境人文社会科学相关学科在初始发展阶段,并未能够真正地深植到中国的实践土壤。建构中国环境人文社会科学自主知识体系,需要立足中国场域、面向中国实践,推进自主理论建构和知识创新。环境人文社会科学内部学科之间存在着学科知识封闭、学科交叉不足的问题,学科之间尚未真正形成学科知识的有机联系和学科交叉的有效机制。推动环境人文社会科学自主协同知识体系的建构,是我国环境人文社会科学体系化发展及其跨学科知识生产机制创新的内在要求。

家校社协同育人视角下社会教育的价值意蕴、现实困境与行动路径

以构建家校社协同育人体系为旨归发展社会教育,不仅有利于落实立德树人的根本任务,彰显协同育人实践中的“社会教育力”,而且还有利于促进协同教育资源的均衡、流动与共享,发挥终身教育理念引领人的可持续全面发展的作用。当前,我国社会教育的发展亟须解决以下问题:社会教育的概念与定位不准确,社会教育的参与主体不够多元,社会教育管理的行政性与专业性失调,社会教育资源分配不均、整合不足。为发挥家校社协同育人体系中社会教育功能的最大化效益,需要廓清社会教育的基本概念和定位,激发多主体开展社会教育的动机与活力,从社会互构论出发实现社会教育的正向谐变,并以数字化技术驱动社会教育资源的流通与共享。