With advances in multimedia and computer graphics technology, 3D virtual avatars have demonstrated promising performance in education. However, existing research faces challenges in systematically studying student interaction. In this study, a Virtual Classroom Interaction and Modal Assessment (VCIMA) is proposed to integrate various factors to explore the impact on learners’ sense of presence, engagement, and preference. Specifically, we analyze key digital avatar types, learning perspectives, viewing devices, and their roles in shaping immersive learning experiences. Then, five elaborate scenarios around a physical demonstration are introduced to simulate diverse learning contexts, assess multimodal interactions, and evaluate how different instructional designs influence learner responses. Subsequently, a substantial corpus of experiments has been conducted under different conditions to verify the effectiveness of VCIMA and reveal that the adaptive multimodal feedback of 3D avatars in virtual reality enhances participation and presence. These findings provide actionable insights for designing future virtual learning environments, emphasizing the necessity of personalized interaction models.

This paper comparatively studies people’s opinions on AI privacy between the US and China. Based on data collected from Twitter and Weibo, we perform text clustering and content analysis to classify opinion types and analyze the symptoms of opinion polarization and drivers by regression analysis. Results show that US people express more concerns about AI privacy, focusing more on privacy disclosure by AI applications. In contrast, Chinese people are more optimistic about AI’s role in promoting privacy protection. Security, economics, and application are driving factors leading to the polarization of the US people, while technologies and algorithms influence the polarization of Chinese people. This study offers methodological guidance for examining the relationship between AI and user privacy. It also guides the government agencies and other practitioners in developing the policies on AI regulations for privacy protection.

The United States higher education is facing a unique challenge in information security management due to its distinctive characteristics, such as decentralised structure, academic freedom, and shared governance. These characteristics sharply distinguish higher education from traditional corporations. Gradually, this is beginning to pose a challenge for higher education institutions to adopt the appropriate information technology governance framework, which, for traditional corporations, mostly addresses security governance and leadership in a top-down manner. To address this issue, we examine the effect of perceived information security management approaches on perceived security practices. Our results show that the perceived flexible-oriented approach of information security management is more effective to use in implementing security controls in high education institutions. This seems to contradict most of the findings in the literature that suggest that a control-oriented approach is more effective in enforcing information security policies. Research contributions and implications are discussed, accordingly.

The paper describes a study that examines the factors affecting Chinese users’ online social media fatigue on WeChat from the perspective of perceived pressure. This study is based on the S-S-O (Stressor-Strain-Outcome) theoretical framework and uses the structural equation modeling to analyze the factors affecting Chinese users’ online social media fatigue on WeChat. The results show that information overload, privacy concerns and time cost produce a strong positive impact on social media fatigue intention while compulsive use have a negative impact on social media fatigue intention. Fear of missing out (FoMO) affects social media fatigue intention not significantly, but FoMO indirectly affects social media fatigue intention through compulsive use. Intrinsic motivation plays a negatively moderating role between social media fatigue intention and fatigue behavior while extrinsic motivation plays a positively moderating role between them.

The COVID-19 pandemic has heightened the necessity for pervasive data and system interoperability to manage healthcare information and knowledge. There is an urgent need to better understand the role of interoperability in improving the societal responses to the pandemic. This paper explores data and system interoperability, a very specific area that could contribute to fighting COVID-19. Specifically, the authors propose a unified health information system framework to connect data, systems, and devices to increase interoperability and manage healthcare information and knowledge. A blockchain-based solution is also provided as a recommendation for improving the data and system interoperability in healthcare.

With the increased popularity of mobile learning, there is a growing demand on the understanding of how the scalable technology, such as mobile devices, influences individual learning behaviours as well as their learning outcome. A theoretical model was built based on the adaptive structuration theory (AST) and the knowledge spiral theory. Using this model, we examined the relationship between structural sources, individuals’ adaptive structural behaviours, and their perceived learning. A Structural Equation Modeling method was employed in our empirical study. Findings indicate that users’ task adaptation had a positive influence on their perceived learning, In addition, their exploitive technology adaptation influenced the ultimate perceived learning, but the impact of users’ exploratory technology adaptation on learning was mediated by their task adaptation. Contrary to expectations, the effect of computer self-efficacy on exploitive and exploratory technology adaptation was negative, and exploratory technology adaptation negatively affected exploitive task adaptation. A detailed discussion of the findings and implications are provided in this paper.

Online fake news can generate a negative impact on both users and society. Due to the concerns with spread of fake news and misinformation, assessing the network influence of online users has become an important issue. This study quantifies the influence of nodes by proposing an algorithm based on information entropy theory. Dynamic process of influence of nodes is characterized on mobile social networks (MSNs). Weibo (i.e., the Chinese version of microblogging) users are chosen to build the real network and quantified influence of them is analyzed according to the model proposed in this paper. MATLAB is employed to simulate and validate the model. Results show the comprehensive influence of nodes increases with the rise of two factors: the number of nodes connected to them and the frequency of their interaction. Indirect influence of nodes becomes stronger than direct influence when the network scope rises. This study can help relevant organizations effectively oversee the spread of online fake news on MSNs.

This Innovate Practice Full Paper describes our experience with teaching cybersecurity topics using guided inquiry collaborative learning. The goal is to not only develop the students' in-depth technical knowledge, but also “soft skills” such as communication, attitude, team work, networking, problem-solving and critical thinking. This paper reports our experience with developing and using the Guided Inquiry Collaborative Learning materials on the topics of firewall and IPsec. Pre- and post-surveys were conducted to access the effectiveness of the developed materials and teaching methods in terms of learning outcome, attitudes, learning experience and motivation. Analysis of the survey data shows that students had increased learning outcome, participation in class, and interest with Guided Inquiry Collaborative Learning.

Deal-seeking behavior is booming over the last decade. This article aims to identify online deal-seeking resources, understand the current status of online deal-seeking and identify key insights and trends for understanding online deal seeker's behaviors. The authors first conducted an in-depth review of the relevant literature including white papers, survey reports, online news articles, as well as, numerous postings on multiple deal-seeking websites. Then, a blog mining approach is used to mine relevant blog posts they found from the Internet. The article identifies different types of online deal-seeking resources and summarizes key insights and trends for understanding online deal seeker's behaviors. Online deal seeking behavior is a subject that is not well explored. The article summarizes key insights and trends for understanding online deal seeker's behaviors. Information-seeking theories are used to help explain shopper's online deal-seeking behaviors.

This poster presents our NSF collaborative project "Enhancing Cybersecurity Education Using POGIL". Although the POGIL (Process Oriented Guided Inquiry Learning) instructional approach has been used and evaluated in science and engineering disciplines, the use of POGIL in cybersecurity education is not in place due to the lack of POGIL materials in cybersecurity. To deliver key learning outcomes as well as "soft skills" in cybersecurity students, we are developing POGIL materials for teaching cybersecurity, implementing the POGIL teaching pedagogy in cybersecurity courses, and evaluating the teaching and learning effectiveness of the developed POGIL materials and teaching method. We are developing POGIL materials for fifteen topics in six areas: cryptography, access control, network security, risk management, web security, and secure coding. These POGIL materials will be implemented and evaluated in eight different courses at three universities. Through assessing the developed POGIL materials and teaching method, we seek to answer the following research question: Is using the POGIL method more effective than the traditional/lecture-based teaching method in terms of learning outcomes, learning experience, attitudes and motivation? The developed POGIL materials will contribute to the effective resources for cybersecurity education, and enhance cybersecurity education by developing student key skills as well as improving student attitudes, motivation and enjoyment in learning.

This article reports on a longitudinal analysis of query logs of a web‐based case library system during an 8‐year period (from 2005 to 2012). The analysis studies 3 different information‐seeking approaches: keyword searching, browsing, and case‐based reasoning (CBR) searching provided by the system by examining the query logs that stretch over 8 years. The longitudinal dimension of this study offers unique possibilities to see how users used the 3 different approaches over time. Various user information‐seeking patterns and trends are identified through the query usage pattern analysis and session analysis. The study identified different user groups and found that a majority of the users tend to stick to their favorite information‐seeking approach to meet their immediate information needs and do not seem to care whether alternative search options will offer greater benefits. The study also found that return users used CBR searching much more frequently than 1‐time users and tend to use more query terms to look for information than 1‐time users.

As the tremendous momentum cloud computing has grown, the modern data center networks are facing challenge to handle the increasing traffic demand among virtual machines (VMs). Simply adding more switches and links may increase network capacity but at the same time increase the complexity and infrastructure cost. Thus, intelligent VM placement has been proposed to reduce the intra-DC traffic. Prior solutions model the traffic-aware VM placement problem as a Balanced Minimum K-cut Problem (BMKP). However, the assumptions of “once-for-all” VM placement on physical servers with equal VM slots are often not realistic in practical data centers, and thus the naive BMKP model may lead to suboptimal placement solutions. In this work, we revisit the problem by considering the server heterogeneity and propose an incremental traffic-aware VM placement algorithm. Given that the BMKP model cannot be directly applied, we make a number of transformations to re-establish the model. First, by introducing pseudo VM slots on physical servers with less VM slots, we allow the number of available VM slots of each server to be different. Second, pseudo edges with infinite costs are added between existing VMs, and thus previously deployed VMs on the same physical server will still be packed together. Third, a change on the number of pseudo VM slots is applied, so that existing VMs placed on different physical servers will still be separated. In this way, we reduce the problem to a new BMKP problem, which results in a much better solution. The evaluation results show that DVMP can reduce up to 28%, 39% and 55% traffic compared with naive BMKP model, greedy VM placement and random VM placement, respectively.

A large amount of user-generated content is now freely available on social media sites. To increase their competitive advantage, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the content on their competitors’ social media sites. In this article, we describe a framework to integrate several techniques including quantitative analysis, text mining, and sentiment analysis for analyzing and comparing social media content from business competitors. Specifically, we conducted an in-depth case study which applies our developed framework to the analysis and comparison of social media content on the Facebook sites of the three largest drugstore chains in the United States: Walgreens, CVS, and Rite Aid. We found similarities and differences in the social media use among the three drugstore chains. We discuss the implications of our findings and provide recommendations to help companies develop their social media competitive analysis strategies.

Teaching information security courses is technically challenging. In an information security course, students and instructors often end up struggling in low-level and complicated software installation, system setup, service configuration, command operations, and data manipulation while losing concentration in learning the important information security principles. To help students in information security courses learn information security principles more effectively and efficiently, we used the workflow technology to create scenario-based workflows in order to improve the effectiveness of teaching and learning of several key information security principles and techniques. Two case studies simulating real-life scenarios, including one for an online banking system and one for an online grading system, are recreated within a laboratory setting using workflow technology and are then presented in information security classes. Our educational practice shows that the benefits of using workflow technology in information security education have been well received by students.

Social media have been used by some companies to support customer co-creation in recent years. However, few academic studies have been done to investigate the use of social media in customer co-creation. To understand the current state-of-the-art and future trends about the use of social media in customer co-creation, we conducted two studies to analyze relevant posts on blogs and social media-based online forums. This study reveals some interesting patterns, themes and future trends in this specific area. Recommendations are given to help managers engage in co-creation activities with customers.

As a new IT paradigm for users, cloud computing has the potential to transform the way that IT resources are utilized and consumed. Many multinational enterprises (MNEs) are interested in cloud computing but do not know how to adopt and implement cloud computing in their enterprise settings. In an effort to help MNEs understand cloud computing and develop successful enterprise adoption strategies for cloud computing, the authors propose a hybrid cloud model for MNEs and illustrate the utility of this model by using two case studies. Insights for adopting and implementing this model in international settings are provided as well.

The explosive expansion of data center sizes aggravates the power consumption and carbon footprint, which has restricted the sustainable growth of cloud services and seriously troubled data center operators. In recent years, plenty of advanced data center network architectures have been proposed. They usually employ richly-connected topologies and multi-path routing to provide high network capacity. Unfortunately, they also undergo inefficient network energy usage during the traffic valley time. To address the problem, many energy-aware flow scheduling algorithms are proposed recently, primarily considering how to aggregate traffic by flexibly choosing the routing paths, with flows fairly sharing the link bandwidths. In this paper, we leverage software defined network (SDN) technique and explore a new solution to energy-aware flow scheduling, i.e., scheduling flows in the time dimension and using exclusive routing (EXR) for each flow, i.e., a flow always exclusively utilizes the links of its routing path. The key insight is that exclusive occupation of link resources usually results in higher link utilization in high-radix data center networks, since each flow does not need to compete for the link bandwidths with others. When scheduling the flows, EXR leaves flexibility to operators to define the priorities of flows, e.g., based on flow size, flow deadline, etc. Extensive simulations and testbed experiments both show that EXR can effectively save network energy compared with the regular fair-sharing routing (FSR), and significantly reduce the average flow completion time if assigning higher scheduling priorities to smaller flows.

Today's giant data centers are power hungry. Data center energy saving not only helps control the operational cost, but also benefits the sustainable growth of cloud services. Due to the adoption of much more switches in modern data centers as well as the mature server-side power management techniques, energy saving for the data center network is becoming increasingly important. Most previous works on saving data center network energy focus on aggregating flows to as few switches as possible. However, in this paper we argue that this method may not work for network-limited flows, the throughputs of which are elastic based on the competing flows. To save the network energy consumed by this kind of elastic flows, we propose a flow scheduling approach called Willow, which takes both the number of switches involved and their active working durations into consideration. We formulate this problem by programming and design a greedy approximate algorithm to schedule flows in an online manner. Simulations based on MapReduce traces show that Willow can save up to 60 percent network energy compared with ECMP scheduling in typical settings, and outperforms other classical heuristic algorithms such as simulated annealing and particle swarm optimization. Testbed Experiments demonstrate that this kind of dynamic energy-efficient flow scheduling causes negligible impact on upper-layer applications.

Many industrial enterprises acquire disparate systems and applications over the years. The need to integrate these different systems and applications is often prominent for satisfying business requirements and needs. In an effort to help researchers in industrial informatics understand the state-of-the-art of the enterprise application integration, we examined the architectures and technologies for integrating distributed enterprise applications, illustrated their strengths and weaknesses, and identified research trends and opportunities in this increasingly important area.

The advances in cloud computing and internet of things (IoT) have provided a promising opportunity to resolve the challenges caused by the increasing transportation issues. We present a novel multilayered vehicular data cloud platform by using cloud computing and IoT technologies. Two innovative vehicular data cloud services, an intelligent parking cloud service and a vehicular data mining cloud service, for vehicle warranty analysis in the IoT environment are also presented. Two modified data mining models for the vehicular data mining cloud service, a Naïve Bayes model and a Logistic Regression model, are presented in detail. Challenges and directions for future work are also provided.

Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.

Purpose – As mobile malware and virus are rapidly increasing in frequency and sophistication, mobile social media has recently become a very popular attack vector. The purpose of this paper is to survey the state-of-the-art of security aspect of mobile social media, identify recent trends, and provide recommendations for researchers and practitioners in this fast moving field. Design/methodology/approach – This paper reviews disparate discussions in literature on security aspect of mobile social media though blog mining and an extensive literature search. Based on the detailed review, the author summarizes some key insights to help enterprises understand security risks associated with mobile social media. Findings – Risks related to mobile social media are identified based on the results of the review. Best practices and useful tips are offered to help enterprises mitigate risks of mobile social media. This paper also provides insights and guidance for enterprises to mitigate the security risks of mobile social media. Originality/value – The paper consolidates the fragmented discussion in literature and provides an in-depth review to help researchers understand the latest development of security risks associated with mobile social media.

Abstract A conceptually relaxed utilization of the variable “search experience” makes it difficult for researchers to perform meaningful cross‐study comparisons. The purpose of this study was to examine how search experience is defined and measured when used as a research variable. We implemented a qualitative analysis of 32 library and information science (LIS) research articles. We found that there was inconsistent terminology usage and measurements. Specifically, there were 21 unique labels to describe the search experience and 18 different measurements. The majority of the studies used a generic label “search experience” and relied on the reader to grasp specific context of the electronic information retrieval environment to which the variable applies from the description of the overall research design. In addition, there was a strong preference for measures that represented subjective self‐reporting about the level of exposure to some information retrieval system. It is evident that there is a need for articles to contain detailed definitions of search experience variables for readers to truly understand the findings.