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PurposeThe purpose of this paper is to propose a mobile and social-based question-and-answer (Q&A) system that analyzes users' social relationships and past answering behavior, considers users' interest similarity and answer quality to infer suitable respondents and forwards the questions to users that are willing to give high quality answers.Design/methodology/approachThis research applies first-order logic (FOL) inference calculation to generate question/interest ID that combines a users' social information, interests and social network intimacy to choose the nodes that can provide high-quality answers. After receiving a question, a friend can answer it, forward it to their friends according to the number of TTL (Time-to-Live) hops, or send the answer directly to the server. This research collected data from the TripAdvisor.com website and uses it for the experiment. The authors also collected previously answered questions from TripAdvisor.com; thus, subsequent answers could be forwarded to a centralized server to improve the overall performance.FindingsThe authors have first noticed that even though the proposed system is decentralized, it can still accurately identify the appropriate respondents to provide high-quality answers. In addition, since this system can easily identify the best answerers, there is no need to implement broadcasting, thus reducing the overall execution time and network bandwidth required. Moreover, this system allows users to accurately and quickly obtain high-quality answers after comparing and calculating interest IDs. The system also encourages frequent communication and interaction among users. Lastly, the experiments demonstrate that this system achieves high accuracy, high recall rate, low overhead, low forwarding cost and low response rate in all scenarios.Originality/valueThis paper proposes a mobile and social-based Q&A system that applies FOL inference calculation to analyze users' social relationships and past answering behavior, considers users' interest similarity and answer quality to infer suitable respondents and forwards the questions to users that are willing to give high quality answers. The experiments demonstrate that this system achieves high accuracy, high recall rate, low overhead, low forwarding cost and low response rate in all scenarios.

PurposeThis study applies D3.js and social network analysis (SNA) to examine the impact of collaboration patterns, research productivity patterns and publication patterns on the Ministry of Education (MOE) evaluation policies across all Management Information Systems (MIS) departments in Taiwan.Design/methodology/approachThis study first retrieved data from the Ministry of Science and Technology of Taiwan (MOST) website from 1982 to 2015, the Journal Citation Reports (JCR) website, the Web of Science (WOS) website and Google Scholar. Then it applied power-law degree distribution, cumulative distribution function, weighted contribution score, exponential weighted moving average and network centrality score to visualize the MIS collaborations and research patterns.FindingsThe analysis concluded that most MIS professors focused primarily on SCIE-/SSCI-/TSSCI-/core indexed journals after 2005. Professors from public universities were drawn to collaboration and publishing in high-quality-based journals, while professors from private universities focused more on quantity-based publications. Female professors, by contrast, have a slightly higher single-authorship publication rate in SCIE-/SSCI-/TSSCI-indexed journals than do male professors. Meanwhile, professors in northern Taiwan emphasized quantity-based journal publications, while a focus on quality was more typical in the south. Furthermore, National Cheng Kung University has the most single-authorship or intrauniversity publications in SCIE-/SSCI-/TSSCI-/core journals, and National Sun Yat-Sen University published more SSCI-indexed articles than SCIE-indexed articles. All of these findings show that there is an explicit relation between MOE evaluation policies and MIS faculty members' collaboration/publication strategies.Originality/valueThe above findings explain how MOE evaluation policies affected MIS faculty members' collaboration and publication strategies in Taiwan, and the authors hope that such findings can constitute a resource for understanding and characterizing networking with MIS departments in Taiwan.

PurposeThe purpose of this paper is to apply social network analysis (SNA) to study faculty research productivity, to identify key leaders, to study publication keywords and research areas and to visualize international collaboration patterns and analyze collaboration research fields from all Management Information System (MIS) departments in Taiwan from 1982 to 2015.Design/methodology/approachThe authors first retrieved results encompassing about 1,766 MIS professors and their publication records between 1982 and 2015 from the Ministry of Science and Technology of Taiwan (MOST) website. Next, the authors merged these publication records with the records obtained from the Web of Science, Google Scholar, IEEE Xplore, ScienceDirect, Airiti Library and Springer Link databases. The authors further applied six network centrality equations, leadership index, exponential weighted moving average (EWMA), contribution value and k-means clustering algorithms to analyze the collaboration patterns, research productivity and publication patterns. Finally, the authors applied D3.js to visualize the faculty members' international collaborations from all MIS departments in Taiwan.FindingsThe authors have first identified important scholars or leaders in the network. The authors also see that most MIS scholars in Taiwan tend to publish their papers in the journals such as Decision Support Systems and Information and Management. The authors have further figured out the significant scholars who have actively collaborated with academics in other countries. Furthermore, the authors have recognized the universities that have frequent collaboration with other international universities. The United States, China, Canada and the United Kingdom are the countries that have the highest numbers of collaborations with Taiwanese academics. Lastly, the keywords model, system and algorithm were the most common terms used in recent years.Originality/valueThis study applied SNA to visualize international research collaboration patterns and has revealed some salient characteristics of international cooperation trends and patterns, leadership networks and influences and research productivity for faculty in Information Management departments in Taiwan from 1982 to 2015. In addition, the authors have discovered the most common keywords used in recent years.

The challenge of a mobile ad-hoc network (MANET) or hybrid wireless network (HWN) is that every node must rely on others to forward its messages, but selfish nodes might refuse to forward those messages or move to other subnetworks at any time, thus degrading network speed and retrieval efficiency. This paper presents a system called “cRedit-based and Reputation Retrieval system” that employs a distributed message transmission protocol to avoid censorship problems, forwarding and reliable routing algorithms to ensure reliable message routing, reputation and credit algorithms to encourage greater cooperation incentives, location and refresh algorithms to deal with nodes’ limited view even in high-churn network, and a protection algorithm to address the problem of selfish nodes. A series of experiments confirmed the system’s effectiveness in achieving superior network scalability, high retrieval rates, low transmission costs, and mobility resilience in hybrid wireless networks.

As the number of people using the internet has surged over the past few years, more and more people are choosing to share and retrieve information online. There are several decentralized retrieval applications that provide file-sharing platforms for exactly this purpose. However, these applications cannot guarantee churn resilience, trustworthiness, or low cost of retrieval. Therefore, in this paper, we present a system called trustworthy and churn-resilient academic distribution and retrieval system in P2P networks, or TCR, which: (1) ensures that information will not be centralized by central network administrators; (2) utilizes LSH to classify nodes with similar research topics into a local subnetwork, and applies routing algorithms with trust score equations to determine the next trustworthy node to forward the message, thus ensuring each node can accurately and efficiently find its trustworthy nodes within only a few hops; (3) provides a trustworthy management system that itself deals in trustworthiness, ensuring that even when there is a large proportion of malicious nodes, the system can still detect and punish misbehaving nodes; (4) guarantees that nodes can still retrieve the desired files even when in high-churn networks. We finally demonstrate that our TCR entails low message costs, provides high match rates, detects malicious nodes, and ensures churn resilience and search efficiency when compared to other P2P retrieval systems.

Purpose The purpose of this paper is to present a system that analyzes trustworthiness and ranks applications to improve the search experience. Design/methodology/approach The system adopts pointwise mutual information to calculate comment semantics. It examines subjective (signed opinions, anonymous opinions and star ratings) and objective factors (download numbers, reputation ratings) before filtering, ranking and displaying). The authors invited three experts to check three categories and compared the results using Spearman and two statistics. Findings A high correlation between the proposed system and the expert ranking system suggests that the system can act as decision support. Research limitations/implications First, the authors have only tested the correlation between the proposed system and an expert ranking system; user satisfaction was not evaluated. The authors plan to conduct a later survey to gather user feedback. Second, the ranking system evaluates applications using fixed weights and disregards time. Therefore, in the future, the authors plan to enable their system to weight recent records over older ones. Practical implications User discussion forums, although helpful, have drawbacks. Not all reviews are trustworthy, and forums provide no filtering mechanisms to combat information overload. The solution to this is the authors’ system that crawls a forum, filters information, analyzes the trustworthiness of each comment and ranks the application for the user. Originality/value This paper develops a formula to analyze the trustworthiness of opinions, enabling the system to act as decision support when no professional advice is available.

Among the many types of ambiguous and diverse human behaviors, preference ranking and preferred temporal relationships are two human subjective perceptions, and both can be expressed as sequences. For example, information management researchers prefer journal A over B, and this preference can be represented as the ranking sequence: (A > B). When the order of submission is first A and then B, it can be expressed as the temporal sequence: (A → B). In practice, these two preference sequences may be applicable to people with regard to the same items at the same time, which is called “two-dimensional preference” in this study. Based on these concepts, this study defines a novel model and an associated algorithm for mining two-dimensional patterns by combining preference ranking and temporal sequences. The discovered two-dimensional patterns can be categorized into eight types, including consensus, ranking-compromise, temporal-compromise, and conflict patterns. Two experiments in two application areas, namely journal submission and stock purchase, were designed to collect authentic datasets, and demonstrate their managerial meaning. The experimental results show that in most cases, the ordering of the temporal sequences follows the preference ranking sequence, except for several two-dimensional patterns involving high-risk items.

As ubiquitous networked devices continue to play an increased role in the daily lives of most people, there is a growing desire to share ever more information and perspectives from across the world. In addition, with the recent technical advancements, the networks have turned to a large-scale wireless networks such as mobile ad hoc networks. However, the problem behind such wireless networks is the difficulty of data distribution and information retrieval in such decentralized and large mobile wireless networks, while still maintaining low overhead. Therefore, we present a system we call decentralized search for large and mobile wireless networks, or DSLM, which: (1) divides the entire network into smaller regions; (2) allows nodes to join, leave, distribute metadata, or make requests; (3) applies the LSH method to map the metadata or request to a geographical region; (4) uses a region-based geographic routing method to route messages between nodes; (5) allows nodes to maintain only a partial view of the network; and (6) applies a relocation method to address mobility issues. We finally demonstrate that our DSLM requires low overhead and could still achieve high retrieval rates and mobility resilience.

Education is one of the most important elements in our lives, as it provides a direct gain in knowledge. Thus, in order to provide better academic achievement, more and more instructors are adapting collaborative learning to their classes. However, two major problems lurk behind collaborative learning. First, it is difficult to apply collaborative learning because students tend to engage passively with traditional lectures. As a result, interactive technologies such as clickers have been applied to increase interactive learning and raise the rate of interaction. Nevertheless, these interactive technologies still have some limitations, such as limited mobility, high costs, setup issues, technical difficulties, and little support of higher-order thinking skills. Second, recently there has been an increasing number of EMI (English as Medium of Instruction) courses added to university course offerings in countries where English is not the first language. The goal of these courses is to support university internationalization and address the global status of English. However, it is even harder to achieve in-class interaction in large EMI lecture courses. In addition, some studies argue that EMI courses might affect the overall learning of course content because of students’ poor lecture comprehension and passive engagement in class. In order to address the limitations of interactive technologies, encourage more collaborative learning, facilitate greater in-class interaction in large lecture courses, and resolve students’ poor lecture comprehension in EMI courses, the author introduces MEMIS (Mobile-Supported English-Medium Instruction System), which includes both a T&S (Teachers & Students) mobile app and an EMI pedagogical method. The author first provides detailed implementation and all the features of the T&S mobile app, which improves students’ perceptions of classroom participation and in-class interaction. Then the author explains how she develops her EMI pedagogy such that it utilizes the features of the T&S mobile app to: 1) increase students’ overall learning in her EMI course, 2) develop students’ construction of knowledge and higher-order thinking, and 3) achieve all six cognitive processes from Bloom’s revised taxonomy. The author adapts a quasi-experimental design, statistically analyzes the effectiveness of both exam scores and final grades between a control group and an experimental group, and then asks students to evaluate their experiences and provide feedback about MEMIS. Based on both statistical results and students’ overall feedback, the author confirms that MEMIS is an effective approach that improves lecture comprehension, encourages more class engagement, promotes collaborative learning, and achieves better learning outcomes.

With recent advances in networking technology, emerging networks continue to play an increasing role in the lives of most users. The Internet search and retrieval system is so powerful that it helps us to share information and perspectives from across the world. However, the threat of censorship exists on some centralized search engines, since all of their information is currently controlled by these sites administrators. The restriction and control of information are pervasive enough within governments and organizations to censor or intrude on even the most free and uncontrolled communication media. For this reason, the Peer-to-Peer (P2P) search and retrieval system is designed to resist censorship over the network. Nevertheless, its decentralized nature makes it very difficult to infer information that cannot be measured directly, such as the proportion of subverted and selfish nodes. Moreover, the situation is even more challenging when the network becomes extremely large. Hence, I propose a dynamic adaptive algorithm that can: 1) tackle the censorship and security issues; 2) determine the proportion of subverted and selfish nodes; 3) defend against malicious and selective forwarding attacks by appropriately adjusting the number of requests to ensure high match probability; 4) guarantee robustness and scalability even with different random networks and varied network sizes. In several experiments, I demonstrate that my algorithm can effectively and accurately estimate these metrics and manage the system, even when the network has a large proportion of malicious nodes, a large proportion of selfish nodes, or a mere partial view of network membership.

This study proposes a model to detect the change in students' learning performance.This model helps educators to understand learning behaviors in dynamic environments.Experiments are conducted with real-life datasets to evaluate the effectiveness of the model. In recent years, as information technology has become more prevalent, learning management systems have arisen around e-learning and web-based platforms. As a result, huge quantities of data about students' learning process have been recorded and stored. Teachers can apply data-mining techniques to mine students' learning performance. One such technique is association rule mining, which can find correlations between student characteristics and performance. For instance, a rule (Attendance=Middle)?(Gender=Male)?(Semester=Low) indicates that the semester grade of students is at the Low level if their gender is Male and attendance rate is Middle, where Low and Middle are predetermined linguistic terms given by teachers. Teachers can rely on such rules to formulate their teaching strategies. However, these rules may be varied in different semesters because of the change of student characteristics or teaching method of teachers. The above rule is used to describe student behavior during the last semester, yet, within this semester, the rule changes to (Attendance=Low)?(Gender=Female)?(Semester=Low). Without updating this knowledge, teachers might adopt inappropriate teaching strategies for students who are learning in different ways across different semesters. In this study, we propose a new change mining model to discover the change in student learning performance and characteristics on the basis of association rules. We conducted experiments with real-life datasets to evaluate the effectiveness of the proposed model.

Education is one of the most important elements in our lives, as is provides a direct gain in knowledge. Thus, in order to provide better academic achievement, more and more instructors are adapting collaborative learning to their classes. Collaborative learning, by definition, means that students have to become socially active and achieve learning goals in a group-based environment. The benefits of collaborative learning include learning concepts more effectively, encouraging more participation, improving learning satisfaction, developing teamwork skills, and promoting higher-order thinking. However, it is difficult to apply collaborative learning because students tend to engage passively with traditional lectures. As a result, interactive technologies such as clickers have been applied to increase interactive learning and raise the rate of interaction. Nevertheless, these interactive technologies still have some limitations, such as limited mobility, high costs, setup issues, technical difficulties, and little support of higher-order thinking skills. Technology is accelerating at an exponential rate, so almost every student now lives with a smartphone. Therefore, we have designed the Smartphone-Supported Collaborative Learning System (SSCLS), which includes the MyResponse mobile app and the Delphi method. SSCLS not only addresses the limitations of mobile technologies, but also achieves collaborative learning. In this study, we first provide detailed implementation of MyResponse mobile app and explain how we apply it to increase the rates of in-class participation and interaction. Next, we describe how we apply the Delphi method to develop students’ knowledge and promote higher-order thinking. Then, we explain how we utilize SSCLS in a course to achieve five cognitive processes from Bloom’s revised taxonomy and facilitate the construction of knowledge. Furthermore, we ask students to evaluate their experiences and provide feedback about SSCLS. Based on students’ overall feedback, we finally conclude that SSCLS can support collaborative learning.

The iTrust search and retrieval network is designed to impede attempts to censor or filter information accessed over the Internet, providing trustworthy access to information on the Web. In iTrust, a fully distributed membership algorithm and a detection/defensive adaptation algorithm act in concert to protect against malicious nodes in membership. By applying statistical inference for both detection and defensive algorithms, I was able to measure information that cannot be observed directly, such as the current size of the membership and the current proportion of malicious nodes in the network. Experimentation demonstrates that both algorithms are able to estimate these metrics quickly and accurately, to the point that the nodes can use them to manage the iTrust system, despite a high rate of membership churn, a large number of malicious nodes, and a mere partial view of network membership. Copyright © 2015 John Wiley & Sons, Ltd.

This paper presents the architecture of the iTrust system together with algorithms for maintaining censorship resistance. In iTrust, metadata describing documents, and requests containing keywords, are distributed to randomly chosen nodes in the iTrust network. If a node receives a request containing keywords that match metadata it holds, it sends the URL of the matching document to the requesting node, which then retrieves the document from the source node. A novel detection algorithm estimates the proportion of operational nodes in the iTrust network, by comparing the empirical probabilities of the number of responses received for a node’s request with the analytical probabilities for a match, for various proportions of operational nodes. A novel defensive adaptation algorithm increases the number of nodes to which the requests are distributed, in order to maintain the same high probability of a match when some of the nodes are non-operational or malicious as when all of the nodes are operational. Extensive experimental evaluations demonstrate the effectiveness of the architecture and the algorithms for maintaining censorship resistance in the iTrust network.

In the last decade, information and communication technologies have been highly developed. For convenience, many applications have been installed in smartphones instead of desktop computers. As a popular platform, Google Play presents thousands of mobile applications. Because there are so many dazzling applications, it is difficult for users to determine which are suitable for their needs. Many factors are likely to influence the purchase of an application, such as advertisements, word of mouth, and other media. In deciding whether to purchase an application, users probably refer to customer reviews. Indeed, users may take a significant amount of time to evaluate the legitimacy of the reviews. In this paper, we introduce a concept for recommending applications. Based on pointwise mutual information, we calculate the positive or negative score of semantic orientation in each review. We also consider subjective factors (i.e., public opinion, anonymous opinion, and star rating) and objective factors (i.e., download number and reputation).

Today, everyone relies heavily on the Internet to access information, and it changes the way we live, including our use of social networking services and community-based classified systems. However, two major problems lurk behind both social networking and community-based classified systems. privacy and security. First, the current systems could be modified someday to filter, conceal or censor information. Second, the anonymity inherent in the community-based classifieds does not provide any safeguard to the users. In order to address the privacy concerns and give better protection to the users, we present a trustworthy and communal social classifieds, named TCSC, in this paper. Our TCSC is a decentralized communal social classifieds infrastructure, and it contains: 1) distributed search and retrieval system, 2) reputation system, 3) protection system, and 4) randomized message routing system. We further demonstrate that our TCSC can accurately and retain significant utility even in the presence of malicious nodes.

In this paper, we present the iTrust over Wi-Fi Direct system, which is a peer-to-peer publication, search and retrieval system for mobile ad hoc networks. We describe the iTrust over Wi-Fi Direct architecture and components, as implemented on the Android platform for mobile devices, and show how user applications can easily interface with iTrust over Wi-Fi Direct. We also describe the iTrust over Wi-Fi Direct networking model, and the interactions with the Android and Linux stacks. In addition, we describe the peer management protocol for iTrust over Wi-Fi Direct on the Android platform, which enables peers to construct a mobile ad hoc network by automatically discovering and connecting peers. We discuss deficiencies of the Android platform for Wi-Fi Direct, and present our solution to address those limitations. Finally, we present a performance evaluation of iTrust over Wi-Fi Direct in terms of the match probabilities without and with message forwarding, the peer management overhead, and the resource transfer latency.

Education is one of the most important elements in our lives because it is a direct way to broaden our knowledge. In order to enhance academic learning experience, an increasing number of instructors are changing their pedagogies to make their students socially active in class participation. In this article, the author describes how the development and implementation of the technology-supported learning environment will be beneficial in student and instructor engagement and the overall learning motivation. The author first provides detailed implementation of the technology-supported learning environment. Next, she explains how to utilize such environment to increase in-class participation and to support collaborative learning. She then examines students’ overall learning and feedback, and analyzes the costs and benefits of this pedagogy. Finally, she concludes that this environment can promote greater classroom engagement and support collaborative learning.

Events over the past year have demonstrated the utility of mobile devices for coordinating mass gatherings and organizing protests in support of social change. However, governments have countered by censoring or disabling centralized search services and social networking sites. This paper describes a decentralized search and retrieval system, named iTrust, that provides resistance against the vulnerabilities of centralized services. It describes the iTrust with SMS interface and the iTrust SMS-HTTP bridge, which enable any SMS-capable mobile phone to communicate with and obtain information from HTTP nodes in the iTrust network. It also describes the iTrust over SMS protocol, which enables mobile phones to communicate directly with each other and share information in a peer-to-peer fashion bypassing the Internet. The paper also describes the Android user interface, which builds on the basic SMS capabilities of mobile phones and offers a user-friendly way of accessing the iTrust with SMS or iTrust over SMS implementation. Finally, the paper presents a performance evaluation of the iTrust search and retrieval system.

The iTrust system is a decentralized and distributed system for information publication, search and retrieval over the Internet, which is designed to make it difficult to censor or filter information. In this paper, we present four membership protocols for the iTrust network, namely, the non-adaptive, retry, adaptive and combined adaptive membership protocols. We compare the performance of these membership protocols, with respect to four performance metrics, namely, membership accuracy, match probability, response time and message cost, for various parameter values when the membership churn is high and when the membership is stable.

Research productivity has been always an important part of every academic’s job, since it has a profound effect on faculty promotion and tenure decisions. In addition, some scholars believe that co-authorship between faculty members has a great impact on their academic life and faculty advancement. Since 2005, the Ministry of Education of Taiwan (MOE) has developed two university programs and evaluation policies for improving the competitiveness and internationalization of Taiwan universities, and has clearly stated that there is a strong relationship between faculty promotion and research performance. However, none of them has used social network analysis (SNA) to examine research productivity and co-authorship under two university programs and evaluation policies from MOE in Taiwan. Therefore, in this study, we first uses SNA to analyze the research productivity, collaboration patterns, and publication strategies of faculty members in a Management Information Systems (MIS) department at a national university in Taiwan. Then, we used D3, a well-known drawing tool to create data visualization using JavaScript libraries, to visualize and discuss how these two university programs and evaluation policies from the MOE affected these patterns and strategies. We hope that our study not only provides beneficial information to the MIS department, but can be treated as an important source for MOE committees in their future adjustment of university programs and policies.

The iTrust system is a decentralized and distributed system for publication, search and retrieval of information over the Internet and the Web, that is designed to make it difficult to censor or filter information. In the distributed ranking algorithm for iTrust presented in this paper, a source node that publishes a document indexes the words in the document and produces a term-frequency table for the document. A requesting node that issues a query and receives a response uses the URL in the response to retrieve the term-frequency table from the source node. The requesting node then uses the term-frequency tables from multiple source nodes and a ranking formula to score the documents with respect to its query. Our evaluations of the distributed ranking algorithm for iTrust demonstrate that the algorithm exhibits stability in ranking documents and that it counters scamming by malicious nodes.

This paper describes the iTrust over SMS decentralized search and retrieval system for mobile networks. Any mobile device in the iTrust network can communicate with any other mobile device in the iTrust network to distribute, search for, and retrieve information. Third-party developers can use the iTrust over SMS API on the Android platform to add this search and retrieval functionality to existing applications quickly and easily. Developers of applications for other mobile device platforms can use the iTrust over SMS protocol to create compatible applications that can communicate using iTrust over SMS. In addition to the iTrust over SMS components and protocol, this paper presents a performance evaluation of the iTrust over SMS system, which shows that the probability of information retrieval is high, even if some of the mobile devices are not available. It also shows that the average search latency is consistently less if all of the participating nodes use the same mobile service provider, and is consistently more if the nodes use different mobile service providers.

This paper presents novel statistical algorithms for detecting and defending against malicious attacks in the iTrust information retrieval network. The novel detection algorithm determines empirically the probabilities of the exact numbers of matches based on the number of responses that the requesting node receives. It calculates analytically the probabilities of the exact numbers of matches and the probabilities of one or more matches when some proportion of the nodes have been subverted or are non-operational. It compares the empirical and analytical probabilities to estimate the proportion of subverted or non-operational nodes. The novel defensive adaptation algorithm then increases the number of nodes to which the metadata and the requests are distributed to maintain the same probability of a match when some of the nodes are subverted or non-operational as when all of the nodes are operational. Experimental results substantiate the effectiveness of the statistical algorithms for detecting and defending against malicious attacks.

The iTrust system is a decentralized and distributed information publication, search and retrieval system, whose objective is to prevent censorship and filtering of information accessed over the Internet. In iTrust, metadata describing information are randomly distributed to multiple participating nodes. Similarly, requests containing keywords are randomly distributed to multiple participating nodes. If a participating node receives a request and the keywords in the request match the metadata it holds, the participating node sends the URL for the information to the requesting node. The requesting node then can retrieve the information from the source node. In this paper, we present the iTrust messaging and membership protocols. We establish lower bounds for the probabilities of a match if all of the participating nodes are operational and if a proportion of the participating nodes are non-operational or subverted. We provide probabilistic results for n participating nodes, where the metadata and the requests are distributed to a multiple of the square root of n nodes. These results show that distribution of the metadata and the requests to relatively few nodes suffices to achieve a high probability of a match, even if some of the nodes are non-operational or subverted.

Recent events have demonstrated the utility of mobile devices to coordinate mass gatherings and organize protests in support of social change and the cause of democracy. However, a common attack against the social networking abilities of mobile phone wielding protesters has been government action to censor centralized search and social networking sites. This paper describes a decentralized search and retrieval system, named iTrust, that provides greater resistance against the vulnerabilities inherent in centralized services. In particular, it describes the iTrust with SMS interface and the iTrust SMS-HTTP bridge, which enable any SMS-capable mobile phone to communicate with other nodes in the iTrust network. It also describes an Android mobile phone interface that builds on the basic SMS capabilities of a mobile phone and that offers a user-friendly way of accessing the iTrust with SMS implementation. Finally, the paper presents an evaluation of the iTrust search and retrieval system.

We demonstrate a patient-centered health monitoring system that promotes patient-centered healthcare and self-management. Many patients with chronic diseases require daily measurements and medical attention over a long period of time. Patients often forget to take readings and medications on time. We have developed a patient-centered health monitoring system that interfaces two health monitoring devices, a blood pressure monitor and a digital weight scale, to a laptop computer via Bluetooth wireless communication. Each device takes the patient's measurements, and automatically transmits the measured data to the laptop. The patient and/or a healthcare professional can see the historical data from the blood pressure monitor and the digital weight scale as a list or a graph on the laptop. The system not only performs a statistical analysis of the measured data but also displays the results, to enable the patient to understand his/her health conditions and to become engaged in monitoring his/her health.

In recent decades, increasing numbers of EMI (English as Medium of Instructions) courses have been added to university course offerings in countries where English is not the first language, as a way of supporting university internalization and addressing the global status of English. However, some studies argue that EMI courses might affect the overall learning of course content because of students' poor lecture comprehension and passive engagement in class. In order to facilitate student engagement and improve learning experiences in EMI courses, the author introduces a pedagogical method that would facilitate students' overall learning in her EMI course. Based on students' overall feedback, the author confirms that her pedagogy is an effective method that improves lecture comprehension, encourages more class engagement, and promotes collaborative learning. Finally, the author recommends that other instructors apply this pedagogy to their EMI classes for better learning outcomes.

Treatment of obesity by embedding catgut in acupuncture points has a satisfactory therapeutic effect in many patients. Even though results of its effectiveness are mixed, serious complications are rarely reported with this Chinese traditional therapy. Here an unusual complication of the treatment is reported: multiple tender subcutaneous nodules developed where the catgut was embedded over the lower abdomen and both medial thighs 1 month after treatment. Clinicians should be alert to this possible cause of a rather strange presenting physical sign.