Security integrated framework for semantic web based on social intelligence

An integrated security framework for a semantic web is proposed based on the social intelligence of an individual＇s avoiding harm and preserving transaction logic-integrity. The framework extends the semantic web model and controls the dynamic security of semantic web services, such as trust, logic and reasoning. It includes four layers, that is, a trust entrance layer, a social intelligence layer, a transaction layer, and a TCP/IP security protocols layer. The trust entrance layer deals with trustable features from users. Social intelligence layer is responsible for logical questions for a semantic web. The transaction layer carries out transaction reasoning. And the TCP/IP security protocols layer ensures security communication. These layers can cooperate to build closed-security-ring with different security grades. The integrated security framework provides an integrated security method for semantic web flow so that it is universal for various semantic web technologies.

Cross Intelligence Evaluation for Effective Emotional Intelligence Estimat

Afamous psychologist or researcher,Daniel Goleman,gave a theory on the importance of Emotional Intelligence for the success of an individual’s life.Daniel Goleman quoted in the research that“The contribution of an individual’s Intelligence Quotient(IQ)is only 20%for their success,the remaining 80%is due to Emotional Intelligence(EQ)”.However,in the absence of a reliable technique for EQ evaluation,this factor of overall intelligence is ignored in most of the intelligence evaluation mechanisms.This research presented an analysis based on basic statistical tools along with more sophisticated deep learning tools.The proposed cross intelligence evaluation uses two different aspects which are similar,i.e.,EQ and SQ to estimate EQ by using a trained model over SQ Dataset.This presented analysis ensures the resemblance between the Emotional and Social Intelligence of an Individual.The research authenticates the results over standard statistical tools and is practically inspected by deep learning tools.Trait Emotional Intelligence Questionnaire-Short Form(TEIQue-SF)and Social IQ dataset are deployed over aMulti-layered Long-Short TermMemory(M-LSTM)based deep learning model for accessing the resemblance between EQ and SQ.The M-LSTM based trained deep learning model registered,the high positive resemblance between Emotional and Social Intelligence and concluded that the resemblance factor between these two is more than 99.84%.This much resemblance allows future researchers to calculate human emotional intelligence with the help of social intelligence.This flexibility also allows the use of Big Data available on social networks,to calculate the emotional intelligence of an individual.

Social Intelligence in Education: A Mediator Analysis of Attitude

This article reports on a study of social intelligence in education.Authors surveyed approximately 1,188 students in the north of Vietnam to assess levels of social intelligence in education including awareness,attitude,and skills.Independent variables included region and the gender of students.Although there are no region and gender differences in social intelligence,correlations and mediating relationships are found.

Exploring Personalized Internet of Things(PIoT),social connectivity,and Artificial Social Intelligence(ASI):A survey

Pervasive Computing has become more personal with the widespread adoption of the Internet of Things(IoT)in our day-to-day lives.The emerging domain that encompasses devices,sensors,storage,and computing of personal use and surroundings leads to Personal IoT(PIoT).PIoT offers users high levels of personalization,automation,and convenience.This proliferation of PIoT technology has extended into society,social engagement,and the interconnectivity of PIoT objects,resulting in the emergence of the Social Internet of Things(SIoT).The combination of PIoT and SIoT has spurred the need for autonomous learning,comprehension,and understanding of both the physical and social worlds.Current research on PIoT is dedicated to enabling seamless communication among devices,striking a balance between observation,sensing,and perceiving the extended physical and social environment,and facilitating information exchange.Furthermore,the virtualization of independent learning from the social environment has given rise to Artificial Social Intelligence(ASI)in PIoT systems.However,autonomous data communication between different nodes within a social setup presents various resource management challenges that require careful consideration.This paper provides a comprehensive review of the evolving domains of PIoT,SIoT,and ASI.Moreover,the paper offers insightful modeling and a case study exploring the role of PIoT in post-COVID scenarios.This study contributes to a deeper understanding of the intricacies of PIoT and its various dimensions,paving the way for further advancements in this transformative field.

UnIC: Towards Unmanned Intelligent Cluster and Its Integration into Society

Collaborative unmanned systems have emerged to meet our society’s wide-ranging grand challenges,with their advantages including high performance,efficiency,flexibility,and inherent resilience.Increasing levels of group/team autonomy have also been achieved due to the embodiment of artificial intelligence(AI).However,the current networked unmanned systems are primarily designed for and applicable to a narrow range of domain-specific missions,and do not have sufficient human-level intel-ligence and human needs fulfillment for the challenging missions in our lives.We propose in this paper a vision of human-centric networked unmanned systems:Unmanned Intelligent Cluster(UnIC).Within this vision,distributed unmanned systems and humans are connected via knowledge sharing and social awareness to achieve collaborative cognition.This paper details UnIC’s concept,sources of intelligence,and layered architecture,and reviews enabling technologies for achieving this vision.In addition to the technological aspects,the social acceptance issues are highlighted.

Human-AI Cooperation in Education: Human in Loop and Teaching as leadership

Using the differences and complementarities between human intelligence and artificial intelligence(AI),a hybrid-augmented intelligence,that is both stronger than human intelligence and AI,is created through Human-AI Cooperation(HAC)for teaching and learning.Human-AI Cooperation is infiltrating into all links of education,and recent research has focused a lot on the impact of teaching,learning,management,and evaluation with Human-AI Cooperation.However,AI still has its limits of intelligence,and cannot cooperate as humans.Thus,it is critical to study the obstacles of Human-AI Cooperation in education,as AI plays a role as a partner,not a tool.This study discussed for the first time how teachers and AI cooperate based on Multiple Intelligences of AI proposed by Andrzej Cichocki and puts forward a new Human-AI Cooperation teaching mode:human in the loop and teaching as leadership.It is proposed that humans in the loop and teaching as leadership can solve the problem that AI cannot cope with complex and dynamic teaching tasks in open situations,as well as the limits of intelligence for AI.

Artificial Social Intelligence:A Comparative and Holistic View

In addition to a physical comprehension of the world,humans possess a high social intelligence-the intelligence that senses social events,infers the goals and intents of others,and facilitates social interaction.Notably,humans are distinguished from their closest primate cousins by their social cognitive skills as opposed to their physical counterparts.We believe that artificial social intelligence(ASI)will play a crucial role in shaping the future of artificial intelligence(AI).This article begins with a review of ASI from a cognitive science standpoint,including social perception,theory of mind(ToM),and social interaction.Next,we examine the recently-emerged computational counterpart in the AI community.Finally,we provide an in-depth discussion on topics related to ASI.

Call for Papers Special Issue on Language,Knowledge,and Social Intelligence

Tsinghua Science and Technology started publication in 1996. It is an international bimonthly academic journal sponsored by Tsinghua University, and aims at presenting state-of-the-art scientific achievements in computer science and other IT fields. It enters into IEEE Xplore Digital Library from 2012, and adopts the open access mode in 2013. In 2015, Tsinghua Science and Technology is indexed in the Science Citation Index Expanded.

Call for Papers Special Issue on Language, Knowledge, and Social Intelligence

Tsinghua Science and Technology started publication in 1996.It is an international bimonthly academic journal sponsored by Tsinghua University,and aims at presenting state-of-the-art scientific achievements in computer science and other IT fields.It enters into IEEE Xplore Digital Library from 2012,and adopts the open access mode in 2013.In 2015,Tsinghua Science and Technology is indexed in the Science Citation Index Expanded.

From Eliza to XiaoIce：challenges and opportunities with social chatbots

Conversational systems have come a long way since their inception in the 1960 s.After decades of research and development,we have seen progress from Eliza and Parry in the 1960 s and 1970 s,to task-completion systems as in the Defense Advanced Research Projects Agency(DARPA) communicator program in the 2000 s,to intelligent personal assistants such as Siri,in the 2010 s,to today's social chatbots like Xiao Ice.Social chatbots' appeal lies not only in their ability to respond to users' diverse requests,but also in being able to establish an emotional connection with users.The latter is done by satisfying users' need for communication,affection,as well as social belonging.To further the advancement and adoption of social chatbots,their design must focus on user engagement and take both intellectual quotient(IQ) and emotional quotient(EQ) into account.Users should want to engage with a social chatbot;as such,we define the success metric for social chatbots as conversation-turns per session(CPS).Using Xiao Ice as an illustrative example,we discuss key technologies in building social chatbots from core chat to visual awareness to skills.We also show how Xiao Ice can dynamically recognize emotion and engage the user throughout long conversations with appropriate interpersonal responses.As we become the first generation of humans ever living with artificial intelligenc(AI),we have a responsibility to design social chatbots to be both useful and empathetic,so they will become ubiquitous and help society as a whole.