Social Robot Detection Method with Improved Graph Neural Networks

Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.

A Visual-Based Gesture Prediction Framework Applied in Social Robots

In daily life,people use their hands in various ways for most daily activities.There are many applications based on the position,direction,and joints of the hand,including gesture recognition,gesture prediction,robotics and so on.This paper proposes a gesture prediction system that uses hand joint coordinate features collected by the Leap Motion to predict dynamic hand gestures.The model is applied to the NAO robot to verify the effectiveness of the proposed method.First of all,in order to reduce jitter or jump generated in the process of data acquisition by the Leap Motion,the Kalman filter is applied to the original data.Then some new feature descriptors are introduced.The length feature,angle feature and angular velocity feature are extracted from the filtered data.These features are fed into the long-short time memory recurrent neural network(LSTM-RNN)with different combinations.Experimental results show that the combination of coordinate,length and angle features achieves the highest accuracy of 99.31%,and it can also run in real time.Finally,the trained model is applied to the NAO robot to play the finger-guessing game.Based on the predicted gesture,the NAO robot can respond in advance.

Semi-GSGCN: Social Robot Detection Research with Graph Neural Network

Malicious social robots are the disseminators of malicious information on social networks,which seriously affect information security and network environments.Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks.Supervised classification based on manual feature extraction has been widely used in social robot detection.However,these methods not only involve the privacy of users but also ignore hidden feature information,especially the graph feature,and the label utilization rate of semi-supervised algorithms is low.Aiming at the problems of shallow feature extraction and low label utilization rate in existing social network robot detection methods,in this paper a robot detection scheme based on weighted network topology is proposed,which introduces an improved network representation learning algorithm to extract the local structure features of the network,and combined with the graph convolution network(GCN)algorithm based on the graph filter,to obtain the global structure features of the network.An end-to-end semi-supervised combination model(Semi-GSGCN)is established to detect malicious social robots.Experiments on a social network dataset(cresci-rtbust-2019)show that the proposed method has high versatility and effectiveness in detecting social robots.In addition,this method has a stronger insight into robots in social networks than other methods.

Unmasking Social Robots’Camouflage:A GNN-Random Forest Framework for Enhanced Detection

The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content integrity.Social robot detection has emerged as a pivotal yet intricate task,aimed at mitigating the dissemination of misleading information.While graphbased approaches have attained remarkable performance in this realm,they grapple with a fundamental limitation:the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human profiles.To unravel this challenge and thwart the camouflage tactics,this work proposed an innovative social robot detection framework based on enhanced HOmogeneity and Random Forest(HORFBot).At the core of HORFBot lies a homogeneous graph enhancement strategy,intricately woven with edge-removal techniques,tometiculously dissect the graph intomultiple revealing subgraphs.Subsequently,leveraging the power of contrastive learning,the proposed methodology meticulously trains multiple graph convolutional networks,each honed to discern nuances within these tailored subgraphs.The culminating stage involves the fusion of these feature-rich base classifiers,harmoniously aggregating their insights to produce a comprehensive detection outcome.Extensive experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.

Emotion Modelling for Social Robotics Applications： A Review

Robots of today are eager to leave constrained industrial environments and embrace unexplored and unstructured areas, for extensive applications in the real world as service and social robots. Hence, in addition to these new physical frontiers, they must face human ones, too. This implies the need to consider a human-robot interaction from the beginning oft_he design; the possibility for a robot to recognize users＇ emotions and, in a certain way, to properly react and ＂behave＂. This could play a fundamental role in their integration in society. However, this capability is still far from being achieved. Over the past decade, several attempts to implement automata for different applications, outside of the industry, have been pursued. But very few applications have tried to consider the emotional state of users in the behavioural model of the robot, since it raises questions such as： how should human emotions be modelled for a correct representation of their state of mind？ Which sensing modalities and which classification methods could be the most feasible to obtain this desired knowl- edge？ Furthermore, which applications are the most suitable for the robot to have such sensitivity？ In this context, this paper aims to provide a general overview of recent attempts to enable robots to recognize human emotions and interact properly.

Cyber-Physical-Social System Between a Humanoid Robot and a Virtual Human Through a Shared Platform for Adaptive Agent Ecology

Two artificial agents(a humanoid robot and a virtual human) are enriched with various similar intelligence,autonomy, functionalities and interaction modalities. The agents are integrated in the form of a cyber-physical-social system(CPSS) through a shared communication platform to create a social ecology. In the ecology, the agents collaborate(assist each other) to perform a real-world task(search for a hidden object)for the benefits of humans. A robot-virtual human bilateral trust model is derived and a real-time trust measurement method is developed. The role of taking initiative in the collaboration is switched between the agents following a finite state machine model triggered by bilateral trust, which results in a mixedinitiative collaboration. A scheme is developed to evaluate the performance of the agents in the ecology through the CPSS.The results show that the robot and the virtual human perform satisfactorily in the collaboration through the CPSS. The results thus prove the effectiveness of the real-world ecology between artificial agents of heterogeneous realities through a shared platform based on trust-triggered mixed-initiatives. The results can help develop adaptive social ecology comprising intelligent agents of heterogeneous realities to assist humans in various tasks through collaboration between the agents in the form of a CPSS.

利用异配及同配关系的社交机器人检测方法

现有基于图的方法未区分用户间的异配、同配关系,造成用户表示中存在异配边带来的噪声,弱化了社交机器人与人类用户之间的差异性,检测准确率降低;社交机器人持续演化模拟人类用户的关注数、推文发布数等元数据,容易导致较难识别的仿真社交机器人比例增加,检测召回率降低.提出一种利用异配及同配关系的社交机器人检测方法,通过记忆网络构建异配、同配关系原型来识别用户关系类型,减少异配边对用户表示的干扰,增加不同类型用户的特征区分性;在损失函数中引入调节因子,提高较难分类用户在模型参数更新过程中的损失贡献,增强了模型对仿真社交机器人的识别效果.实验结果表明,提出的方法优于当前先进方法,此方法通过区分用户间的异配和同配关系,降低异配边的权重,增强了用户表示的类别区分度,即使在低同配性分数的情况下也有效提升了检测准确率.

Human Interaction Dynamics for Its Use in Mobile Robotics:Impedance Control for Leader-follower Formation

A complete characterization of the behavior in human-robot interactions(HRI) includes both: the behavioral dynamics and the control laws that characterize how the behavior is regulated with the perception data. In this way, this work proposes a leader-follower coordinate control based on an impedance control that allows to establish a dynamic relation between social forces and motion error. For this, a scheme is presented to identify the impedance based on fictitious social forces, which are described by distance-based potential fields.As part of the validation procedure, we present an experimental comparison to select the better of two different fictitious force structures. The criteria are determined by two qualities: least impedance errors during the validation procedure and least parameter variance during the recursive estimation procedure.Finally, with the best fictitious force and its identified impedance,an impedance control is designed for a mobile robot Pioneer 3AT,which is programmed to follow a human in a structured scenario.According to results, and under the hypothesis that moving like humans will be acceptable by humans, it is believed that the proposed control improves the social acceptance of the robot for this kind of interaction.

机器人干预孤独症谱系障碍者社交技能的元分析

机器人技术为孤独症谱系障碍者教育与康复带来新的可能。为系统评估机器人对孤独症谱系障碍者社交技能的干预效果及其影响因素,对2013年至2023年间17篇利用机器人干预孤独症谱系者社交技能的单一被试研究进行元分析,结果显示,机器人干预一定程度上可以促进孤独症谱系障碍者社交技能的发展,且在基础社交技能上效果显著;干预对象、过程以及部分机器人特征是可能的调节因素。未来可进一步提高机器人干预技术,完善机器人干预策略。

可靠主义与机器人的证言

当前,人类越来越依赖机器人和其他形式的人工智能来实现自身的信仰,知识的见证来源或工具性知识来源的问题逐渐受到关注,可靠主义视角下的机器人证言或知识来源的分析不仅有助于对基本知识来源分类的认识论问题进行澄清,而且也拓展了可靠主义理论对于机器人知识的理解,进一步推动了当代知识论研究的实践转向。通过对可靠主义的概念界定及其划界问题的探讨,从机器人“这么说”中获得的知识分类、机器人证言的确证性、机器人知识的工具性三个层面对相关文献进行梳理与分析,可以得出将机器人证言视为知识来源往往会产生重要的社会和法律后果。有鉴于此,拟证明至少有一些机器人能够做证,并对可靠主义划界问题的三种解决路径进行探究,包括认知主义的解决路径、负责任的解决路径、自然信任的解决路径,但这些路径最终都没有成功解决可靠主义的划界问题。同时,基于机器人欺骗的实证性案例研究,提出可靠主义划界问题的社会信任解决路径,认为尽管某些被设计成具有欺骗性机器人,但人类对话者应该将它们及其证言视为知识的确证来源之一,发展可靠的人机关系要在认识论上立足于能够反映当代技术与人类本真关系的信念视域,在方法论上对机器人知识采用价值多元的方法,这对于现在和未来动态且可持续的人机交互具有重要意义。

服务机器人人机交互社会力模型研究

服务机器人在人机交互中应遵守人类社会规则,应用于人与人之间行为动态关系的传统社会力模型不能完全适用于机器人交互设计,机器人和人之间的动态交互塑造了全新的交互行为范式。通过实验测量室内交互场景下人与服务机器人的交互距离,提出人机交互过程中情感“亲密力”的存在,实验结果发现人与机器人静态交互受亲密力和交互力影响、动态交互受亲密力和社会文化力影响、从动到静交互过程受亲密力和速度力影响,最终构建服务机器人人机交互社会力模型,为提升服务机器人人机交互体验提供理论依据。

用户需求驱动下的养老服务机器人设计研究

目的养老服务机器人作为未来老年人日常生活必备的家庭产品,受到用户的广泛关注。以用户需求为导向有助于设计出符合目标用户群体的产品。基于我国人口老龄化背景,提出一种用户需求驱动下的产品设计方法,设计一款能够从功能、外观、交互方面有效提升老年人满意度的养老服务机器人。方法首先,通过调研得出用户需求并运用KANO模型对用户需求进行属性归类;然后,运用层次分析法构建用户需求层次模型,对用户需求的重要度进行排序;再次,引入QFD将用户需求转化为具体设计要素,并据此指导养老服务机器人创新设计;最后,对设计方案的有效性进行评估。结果建立了客观、科学的养老服务机器人用户需求框架、构建了养老服务机器人QDF质量屋,完成了养老服务机器人的创新设计。结论通过对KANO、AHP、QFD模型的集成运用,能够更科学、客观地挖掘用户需求并有效指导产品研发,同时能为同类产品研发提供新思路。

社交机器人的适老化设计:动因、实践与未来展望

[目的/意义]对社交机器人适老化设计的研究文献进行系统梳理与详尽述评,以促进社交机器人适老化设计概念体系的成熟和完善,进而为设计符合老年人个性化需求的社交机器人、助力老年人跨越数字鸿沟、促进老年人身心健康发展并提升生活品质提供启示。[方法/过程]采用理论生成元综合的方法,对72篇中英文文献进行梳理与归纳,呈现社交机器人的适老化设计动因、设计范式、设计层次模型和设计组件4个核心研究范畴。[结果/结论]从设计科学和情报学双重学科视角出发,着重分析了社交机器人适老化设计的实践进展及逻辑关联,梳理了当前国际主流研究观点,构建了一个社交机器人适老化设计的基本研究框架,并提出若干个未来值得关注的方向。

新型工业化时期老龄产业的地位和机理

老龄化是人类文明进入发达形态的标志性现象,当观察和研究老龄化现象和老龄社会时,经济学的范式承诺就必须从“经济人”假设转变为结构性人口假设,每个国家都必须以新的思维和行为方式来安排全社会的生存模式。随着社会生产力的显著进步,人口受教育程度的提高,老龄人口发挥“活力”的需要也日趋增强;工业机器人技术可以为老龄智能机器人发展提供多方面的技术供应和创新借鉴,最大限度减少失能老人的无助和增强老人老龄产业的经济学效应,不仅体现在赋能助能老龄人口,使其活力化,而且体现在可以激发产业发展的供给侧创新和需求侧拉动,形成老龄产品市场供求结构的新格局。健康的自益性、公益性和公论性特质在老龄化国家和老龄社会受到社会各方的高度重视,大量经济资源和技术创新能力必然会流向老龄产业,在新型工业化时代,企业界更普遍地具有社会责任意识和行为特征,社会企业的发展空间也更为宽广,并具有很大的社会期望迫切性;老龄产业的发展将获得更大机会的空间拓展和形态完善,承担特殊的经济发展使命,成为新型工业化时代极具人本关怀特质的标志性产业之一。

社交机器人对社会舆论的影响因素研究

近年来,社交机器人已经成为人工智能领域的一个最新的研究方向。由于新冠疫情、俄乌冲突、英国脱欧等一系列重大事件中,社交机器人对公众舆论产生了重要影响,学界对社交机器人的关注度日渐升高。本文提出了基于社交机器人的社会舆论形成过程模型,分为舆论出现、争夺影响力、舆论形成3个阶段,社交机器人可以在3个阶段介入和影响。研究了社交机器人的级联效应、工作原理、影响因素,指出媒体类型、事件模糊性和争议性、受众群体等均是影响社会舆论的主要因素。调查问卷实验与案例分析表明:社交机器人对信息传播速度、网民意见/倾向的形成等具有显著影响,实验数据对今后研究具有重要参考意义。

社交机器人在孤独症谱系障碍儿童中的应用

孤独症谱系障碍(Autistic Spectrum Disorders,ASD)儿童的核心症状之一是社交互动障碍。早期干预对ASD儿童的社交能力发展至关重要,而传统康复干预方法存在耗时长、花费高昂、专业康复治疗师短缺等诸多局限性。随着人工智能技术的发展,社交机器人被广泛应用于ASD儿童社交能力的干预研究。通过梳理社交机器人在ASD儿童干预中的可行性,分析社交机器人在ASD儿童社交互动中的研究现状,探讨社交机器人在ASD儿童干预中实验环境、方法等方面存在的优势与挑战。未来社交机器人在ASD儿童中的研究可考虑从探索人机双方特点开发新的社交场景,结合多模态和脑科学技术揭示人机互动的心理过程以及关注人工智能技术发展构建社交机器人闭环系统等方面展开。

生成式人工智能对社交机器人的影响与治理对策

[研究目的]生成式人工智能使作为社交媒体舆情传播核心的社交机器人具有更加强大的舆论操纵能力,且随着生成式人工智能的快速发展,社交机器人与人类在社交媒体中的活动更加难以区分,隐匿性更强,危害性更大,需要提早研究社交机器人的治理策略,以维护网络舆情安全和国家安全。[研究方法]结合生成式人工智能的特点,剖析了其对社交机器人操纵舆情能力的影响,采取“技术管控技术”的理念,提出基于生成式人工智能的社交机器人情报分析建模思路,在模型中增加向量数据库和本体推理,增强了模型的相似性检索、语义理解、多模态数据分析、动态数据更新以及少样本学习能力。[研究结论]通过将“大语言模型”和“知识图谱”进行深度融合,利用知识图谱中的知识减少大语言模型的“幻觉问题”。同时,利用大语言模型完善和补全知识图谱,增强了对社交媒体情报信息的检索和应用能力。在社交迷雾演化全生命周期提高了对社交媒体舆情,特别是社交机器人的监测识别、感知预警、追踪溯源和研判管控能力。

乌班图机器人:迈向跨文化机器人学的关系型概念框架

近来,人们越来越关注社交机器人的文化维度。一种维特根斯坦式的哲学框架将“机器人在什么意义上与文化相关以及如何与文化相关”这一议题概念化。此外,一种更具文化多样性和关系性的社交机器人学路径,能够强调技术在应对现代性挑战和协助文化变革方面发挥作用。因此,机器人可以帮助我们参与文化对话,反思我们自己的文化,并改变我们做事的方式。