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Amidst the numerous reported negative effects of COVID-19, the Japanese public was rattled by the headline ‘Doctoral Students Have Decreased by Half’. However, a closer examination of the information revealed that the reported drop may be a ‘false alarm’ based on a series of articles related to this critical decision-making moment for prospective students. Studies regarding doctoral programmes are limited due, in part, to the relative marginalization of degree holders and low recognition of degrees in Japan. Thus, this case study was conducted to clarify the particular situations faced by Japanese doctoral students and how news is reported through digital media. The research revealed that digital news portals may contribute to the deformation and diffusion of information, highlighting the importance of verifying digital information so that students can make informed career-path decisions based on accurate situations and potentials.

In light of the unprecedented growth of the aging population in Hong Kong, coupled with the massive diffusion of information and communication technology (ICT), the issue of the best approach to adopting ICT for solving aging problems has become a hot topic among stakeholders from industry, the academy, government, and elsewhere. Considering that older adults always exhibit a relatively low level of technology usage, product-service systems (PSS) could be an appropriate way of facilitating their acceptance of ICT. However, research on this topic is diverse and fragmented in conception and literature review. It is therefore necessary to conduct a systemic overview of the subject and propose a broader vision of PSS for ICT. This study reviews the recent progress of ICT for aging and its integration of pertinent product-service system as the basis for a roadmap which provides insights into future trends and challenges for the development of ICT-oriented PSS.

With the rapid diffusion of information technologies in recent years, learning and teaching approaches are facing profound changes. Video conferencing software is a visual tool for two or more users to communicate remotely. Its principle is to provide users with video calls, screen-sharing, meeting recording, and other helpful features through transmission lines and multimedia devices to achieve the purpose of long-distance meeting and learning. As online courses that anyone can register and participate for free, Massive Open Online Courses (MOOCs) are flexible, global and sustainable. MOOCs are based on the Internet and electronic devices. With studying the use of video conferencing software in distance education and the explosion of MOOCs, this paper also discusses distance education examinations. Result shows that during COVID-19, there has been a surg of interest in how to cheat in online examinations. This article summarizes the historical framework of distance education and explores the trends of distance education under the pandemic. It inspires educators to explore the advantages and challenges of distance education.

In recent years, Diffusion LMS algorithm has been thoroughly researched. This powerful approach enables distributed problem optimization over sensor networks to be solved. The best parameter vectors for all agents is likely not to be the same in such implementations. In addition, agents typically share information through noisy links. Here, we compared the average performance of some major distributed network-based combination policies: Static Combination Policy (metropolis policy and relative variance combination policy) and adaptive Combination Policy. The diffusion of information gathered from neighbors is an important problem in creating adaptive networks in as much as the network mean-square performance is dependent on the preference for combined weights. The aspect of the best selection of the combination weights is considered. We showed the adaptive combination policy outperformed the two analyzed static combination policy over noisy links. Also note that because we considered linearly complex algorithms in a single-task LMS diffusion, we analyzed the theoretical performance within a collaborative target tracking problem.

In this work, we perform a large-scale investigation of teacher connections in online social media. To this end, we first construct a large dataset of teachers on Pinterest, an image-based popular online social media. Our dataset includes 540 teachers across 5 states and 48 districts, as well as thousands of connections they have established. Then, considering some crucial teacher-related attributes (e.g., their states and grade levels), we characterize direct and indirect teacher connections. Through this characterization, we discover that teachers are predominately connected to their peers in the same district or at least within the same state, and seldom there exist links between teachers outside their districts and states. This hinders the proper diffusion of information and many other advantages that a teacher-teacher connection in an online social media can bring about, e.g., getting advice from their peers. To alleviate this problem, we utilize advances in machine learning and propose a link recommendation system suggesting teachers connect with their similar peers on Pinterest. Our system’s evaluation reveals that many new teacher-teacher connections are suggested, which leads to a more cohesive network among teachers rather than the existing localized ego networks.

Social network visualization has been extensively studied in research in diffusion of information. This paper focuses on extracting propagated Facebook posts from keywords created by users and visualizing important information diffusion path in social network graph. Furthermore, some interesting propagation patterns of user behavior in online social network were discovered. Our research contributions could be summarized as follow: 1) A novel method was proposed to determine the keyword occurrence in online social network, 2) This research constructed the map of keyword propagation and visualized the propagation patterns among different groups of people and 3) a case study of social phenomena was shown. In sum, our work eventually could be applied to improve the performance of tracking keyword spread in social media and would be beneficial for greater understanding about user behavior.

Fear of crime refers to the subjective assessment of the risk of being a victim of a crime and the magnitude of its consequences. Fear of crime is highly dependent on variable and complex psychological, cultural and social factors. Recently, a mathematical model for the diffusion of information related to the fear of crime among subjects was proposed. This model captures the effect of communication among people by considering random interactions between persons. Nevertheless, recent evidence suggests that fear to crime can be strongly influenced by the underlying communication network that mediates the interactions among subjects. In particular, the topology of this network may change the opinions of the community members. In this work, we studied how the fear of crime changes when the interaction between persons is supported on a complex network that exhibits a community structure. We hypothesized that even if these supporting structures allow efficient communication, in the case of fear propagation this particular structure may promote an isolation effect of the fear of crime for the different communities, when compared with a model of random interactions. Our results support this assumption, and also suggests that this separation effect is higher for communities with low susceptibility to crime, i.e., groups with low susceptibility to crime have higher feelings of security than groups with high susceptibility to crime when considering interactions in communities. This result is important because in many cases security planners belong to the group with low susceptibility to crime, resulting in an undervaluation of the most vulnerable populations

The spread of information on graphs has been extensively studied in engineering, biology, and economics. Re- cently, however, several authors have started to address the more challenging inverse problem, of localizing the origin of an epidemic, given observed traces of infection. In this paper, we introduce a novel technique to estimate the location of a source of multiple epidemics on a general graph, assuming knowledge of the start times of rumours, and using observations from a small number of monitors.

We examine the ability of networks formed by mobile sensor nodes to diffuse information in the case when communication is only possible during opportunistic encounters. Our setting assumes that mobile nodes are continuously sensing the world and acquiring new information. We form an abstract model of this situation and show by theoretical analysis, simulation, and real mobility data that the diffusion of information in this setting cannot be as efficient as when we allow arbitrary contact patterns between the nodes with the same overall contact statistics. This establishes a fundamental asymptotic limitation on the information diffusion capacity of such opportunistic mobile sensor networks --- the encounter patterns arising out of physical motions in a geometric space are not ideal for information diffusion.

Analysis of online communities has pioneered extensive research initiatives towards impact of diffusion of information in online social network communities. There are various social analytical and mining techniques that assists in information propagation over online social networks. The main purpose behind diffusion is to spread the information in a social network in a less computational way which has been described in the proposed solution. For this purpose, our research involves community detection with centrality measures executed over nodes, i.e. social entities that assists in targeting advertising and anonymization over the social web. We detect communities from a social network using community detection algorithms, which is followed by identifying significant entities in those communities using centrality measures. This eventually helps to better track the spreading of information to maximum people in the connected network to promote better advertising of products or services, according to needs, preferences and search criteria.

Internet and Web-based communication tools, such as the Skype platform offered by Microsoft, enable us to communicate with other people worldwide at low cost. This provides a good opportunity for nonnative English educators to cultivate children's English skills. While most Japanese schools provide access to the Internet, Internet- and Web-based communication tools are underexploited; e.g., only a few teachers have registered Skype accounts. To introduce a new study program in a school, teachers should prove its benefits in advance. However, scales to evaluate skills obtained from these programs have not been developed. Thus, we attempt to develop a scale to perform this work. First, we developed a scale based on international postures. Second, we performed an empirical test using Skype to measure the scale in junior high school and high school environments. This study shows the results of the empirical test and discusses its contribution to the diffusion of information and communication technology in the educational industry.

The paper addresses opportunities that the fast diffusion of Information and Communication Technology is opening for people with different levels of physical restrictions, or disabilities. For these people mobile technology not only allows ubiquity for communications but also anytime access to some services that are vital for their security and autonomy thus preventing social exclusion. More specifically, the paper describes evaluation study and four developed ICT based services aimed to prevent social exclusion and ease everyday life of persons with disabilities. Findings of the study show that there is enormous need for the services aimed for disabled to promote their equal status in society.

Social media plays a fundamental role in the diffusion of information. There are two different ways of information diffusion in social media platforms such as Twitter and Weibo. Users can either re-share messages posted by their friends or re-create messages based on the information acquired from other non-local information sources such as the mass media. By analyzing around 60 million messages from a large micro-blog site, we find that about 69 % of the diffusion volume can be attributed to users' re-sharing behaviors, and the remaining 31 % are caused by user re-creating behaviors. The information diffusions caused by the two kinds of behaviors have different characteristics and variation trends, but most existing models of information diffusion do not distinguish them. The recent availability of massive online social streams allows us to study the process of information diffusion in much finer detail. In this paper, we introduce a novel model to capture and simulate the process of information diffusion in the micro-blog platforms, which distinguishes users' re-sharing behaviors from re-creating behaviors by introducing two different components. Thus, our model not only considers the effect of the underlying network structure, but also the influence of other non-local information sources. The empirical results show the superiority of our proposed model in the fitting and prediction tasks of information diffusion.

Retweeting behavior is a key mechanism for information diffusion in Microbloging services such as Weibo. It is the mechanism of retweeting that leads to the fast and wide diffusion of information in Weibo. Game theory has been applied to various fields as an important tool for studying user's behavior. In this paper, a game model has been applied on studying Weibo user's retweeting behavior. We propose a user's retweeting game model. By Markov equilibrium strategy (MPE) we estimate the retweeting probability of the message, of which the character and existence have been proved. Experiment results show that in the retweeting game model the trust factor δ and the number of information k have a great influence on the retweeting rate. Compared the retweeting game model with the Weibo data, we find that the retweeting game model can be used to describe the user's retweeting behaviors.

The internal feedback structure exists in nonhierarchical complex networks in the form of cycles. Diffusion of information from a node to reach out all the rest occurs through penetration layers (depths). The information credibility or accuracy may be challenged in each layer due to internal feedback loops. In this work, we study feedback diffusion capacity and analyze existing patterns of relationships and ties among complex networks entities to find how the feedback of information is diffused and propagated through the network. As the information propagates to subsequent layers, on average, the feedback density heterogeneity in complex networks reaches saturation after the third penetration in random and scale-free networks. The maximum penetration defines the feedback capacity of the network. The experiment results show that the third penetration depth covers almost 90% of the nodes. Whereas in the small-world networks, the feedback capacity is dynamic and continue increasing as the network size increases. Small world network has the best feedback capacity among networks configuration.

Internal feedback structure exists in non-hierarchical random network in form of cycles. Diffusion of information from a node to reach out all the rest occurs throughout penetration layers (depths). In each layer, the information credibility or accuracy may be challenged due to internal feedback loops. As the information propagates to subsequent layers, on average, the feedback density heterogeneity in random networks reaches saturation after the third penetration in random networks. The maximum penetration defines the feedback capacity of the network.

Diffusion of information and viral content, social contagion and influence are still topics of broad evaluation. As theory explaining the role of influentials moves slightly to reduce their importance in the propagation of viral content, authors of the following paper have studied the information epidemic in a social networking platform in order to confirm recent theoretical findings in this area. While most of related experiments focus on the level of individuals, the elementary entities of the following analysis are dyads. The authors study behavioral motifs that are possible to observe at the dyadic level. The study shows significant differences between dyads that are more vs less engaged in the diffusion process. Dyads that fuel the diffusion proccess are characterized by stronger relationships (higher activity, more common friends), more active and networked receiving party (higher centrality measures), and higher authority centrality of person sending a viral message., Comment: ASONAM 2012, The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 1291-1297

Development and diffusion of information technology (IT) have opened a new possibility in education. Digital technology will also provide a variety of teaching methods in education. It can easily merge each media into a multi-media and this will enable to provide new directions for laboratory work with virtual experiments. We will present the contents of the multi-media directions of the fundamental experiments of physics and also will discuss the versatilities of the multi-media directions.

Adaptive networks have the capability to pursue solutions of global stochastic optimization problems by relying only on local interactions within neighborhoods. The diffusion of information through repeated interactions allows for globally optimal behavior, without the need for central coordination. Most existing strategies are developed for cooperative learning settings, where the objective of the network is common to all agents. We consider in this work a team setting, where a subset of the agents form a team with a common goal while competing with the remainder of the network. We develop an algorithm for decentralized competition among teams of adaptive agents, analyze its dynamics and present an application in the decentralized training of generative adversarial neural networks.