Since pressure is invisible in nature, it has been applied to enhance the authentication security. However, previous work focused on only two-level pressure detection, which makes it feasible for an attacker to guess a pressure level from a user's pressing behavior in the shoulder surfing attack. To mitigate the above risk, this article extends pressure-based authentication from two levels to three levels. It systematically evaluates its usability and security with 98 young adults in a mid-west university in two user studies. The first study with 67 participants showed that a three-level pressure-based password did not increase the difficulty of memorization, and it was more resistant to the shoulder surfing attack than a two-level pressure-based password (two-level = 79.39% successful attacks versus three-level = 44.71% successful attacks). However, three-level passwords have a higher false negative rate than two-level passwords, which implies that users lack a consistent pressing pattern on the medium level. To address the above issue, we designed an adaptive training process that assists users in forming a consistent pressing pattern. The training process features an automatic detection of consistent pressing patterns, which potentially reduces the training length. The second user study, with 31 participants, indicated that the false negative rate in three-level passwords is decreased significantly after the training from 59.90% to 2.15%, while maintaining a reasonable false acceptance rate of 20%.

Achieving optimal blood glucose control is a complex challenge for individuals with diabetes, necessitating a delicate balance among insulin dosage, food consumption, physical activity, and stress management. This paper introduces an innovative approach utilizing reinforcement learning (RL) to develop personalized and effective strategies for blood glucose regulation. Specifically, we employ the state-of-the-art soft actorcritic (SAC) RL algorithm, which concurrently maximizes anticipated rewards and policy entropy. We devise an entropydriven reward function to incentivize diverse action exploration while ensuring a secure and consistent blood glucose profile. This reward function considers both the policy's entropy and the deviation of the blood glucose level from the target range, thus optimizing blood glucose control and minimizing the risk of complications. Our methodology is applied, trained, and assessed using a sophisticated blood glucose dynamics simulator based on the UVA/Padova model. The results demonstrate that our proposed method, SAC with entropy-based reward shaping (SAC+RS), outperforms a comparative approach, SAC with Magni's risk-based reward function (SAC+MRS), in terms of risk scores, glucose levels, insulin levels, and reward values.

Alzheimer's Disease and Related Dementias (ADRD) pose substantial caregiving challenges, impacting caregivers' mental health and overall wellbeing. The increasing role of online platforms in providing social support to these caregivers marks an emerging research field. This study seeks to explore the nature of online discussions and social support dynamics within peer support groups for ADRD caregivers. A qualitative analysis was carried out on posts and comments from three distinct ADRD caregiver support communities on two popular online platforms – two sub-Reddits, and ALZ Connected. We found recurring themes across these platforms including the stressors associated with ADRD caregiving. Caregivers on these platforms not only engage in information exchange but share emotional expression, and mutual provision of empathy and courage. Emotional support seeking was found to be more prominent on Reddit than the Alzheimer Association's online support community, ALZConnected. We also found that COVID-19 significantly impacted the supportseeking strategies among caregivers on these platforms. Therefore, these insights underscore the pivotal role of online communities in providing providing relief and guidance to ADRD caregivers. Based on the prevalent support-seeking patterns and discussion themes identified, we propose recommendations for future research and design considerations for online support platforms tailored for ADRD caregivers.

Diabetes has a disproportionate impact on Native Americans (NAs) as a chronic health condition, yet there is a dearth of mobile apps specifically designed for this population. In this paper, we present the design and development of a culturally tailored mobile app for NAs, taking into account their cultural traditions. Our app incorporates NA's traditional foods, food availability, the importance of family and community, cultural practices and beliefs, local resources, and heritage heroes into the app interface and self-management design. The app includes personalized nutrition guidance, family and community-based support, seamless connection to tribal health providers, access to local resources, and integration of cultural elements. By considering the cultural context of NAs, the developed app has the potential to provide culturally sensitive and relevant features that address the unique needs and preferences of NA users, facilitating effective self-management of diabetes.

The increasing use of electronic health records (EHRs) and wearable devices has led to the creation of massive amounts of personal health data (PHD) that can be utilized for research and patient care. However, managing and integrating various types of PHD from different sources poses significant challenges, including data interoperability, data privacy, and data security. To address these challenges, this paper proposes a blockchain-based personal health knowledge graph for integrated health data management. The proposed approach utilizes knowledge graphs to structure and integrate various types of PHD, such as EHR, sensing, and insurance data, to provide a comprehensive view of an individual's health. The proposed approach utilizes blockchain to ensure data privacy and security. By storing PHD on a decentralized blockchain platform, patients have full control over their data and can grant access to specific entities as needed providing enhanced privacy and security.

Caring for individuals with Alzheimer's Disease and Related Dementias (ADRD) is a complex and challenging task, especially for unprofessional caregivers who often lack the necessary training and resources. While online peer support groups have been shown to be useful in providing caregivers with information and emotional support, many caregivers are unable to benefit from them due to time constraints and limited knowledge of social media platforms. To address this issue, we propose the development of a voice assistant app that can collect relevant information and discussions from online peer support groups on social media. This app will use the collected information as a knowledge base and fine-tune a Generative Pre-trained Transformers (GPT) model to facilitate caregivers in accessing shared experiences and practical tips from peers. Initial evaluation of the app has shown promising results in terms of feasibility and potential impact on caregivers.

Picking the right food from a restaurant menu sometimes is not an easy thing for many people: visitors who are not familiar with local restaurants' meal names and their ingredients, people with religious diet constraints, patients with nutrition requirements, and people with special diet preferences. It is not easy for these diners to choose meals from restaurant menus as they do not provide enough information for the diners to make decisions in a brief period. In this paper, we propose an AI-empowered personalized restaurant menu decoder app that can help users make wise choices from any menu in any restaurant. With an easy-to-use interface, the app can quickly rank the restaurant's menu items based on the user’s preferences and concerns. Preliminary test results have demonstrated the good usability of the proposed system.

Electronic health record (EHR) systems have been widely adopted across healthcare organizations. While there are many benefits of using EHR such as improved accessibility and secure sharing of patient data, a shortcoming is that its manual data input is time-consuming and error prone. Physicians spend as much as 49.2% of their office time on EHR. In this paper, we present the design, development, and evaluation of a voice-based assistant, DocPal, to assist healthcare practitioners to access and update EHR through their voice. User survey and experimental evaluation illustrate that DocPal has good usability, time efficiency, and accuracy. When applied in the healthcare industry, we expect it to reduce data entry time and provide better patient care.

Alzheimer's disease (AD) poses serious challenges for both patients and their family caregivers. In this paper we present the design, development, and evaluation of an ontology model, ADCareOnto, to assist family caregivers providing personalized care for persons living with AD. ADCareOnto includes top-level categories, concepts, and relations about informal care for persons with AD. To enable personalization in care, ADCareOnto also includes a comprehensive user profile modeling that includes various characteristics of both AD patients and caregivers. AD care thus can be tailored based on the user's unique concerns, preferences, and needs. We verified and validated the design of ADCareOnto and evaluated it using a real use case. The results support the quality of its content and techniques.

Native Americans are disproportionately affected by diabetes and diabetes complications. To control this disease, self-management, especially diet management is very important. There have appeared many electronic tools to help diabetic patients to manage their diet and control their blood glucose level. However, due to their lack of consideration of the special requirement of this ethical group, these tools are not well-accepted by Native American communities. In this paper, we propose a culturally appropriate tool to help this population to manage their disease. Specifically, we propose a voice-based Artificial Intelligence-powered virtual assistant to help Native American diabetic patients to manage their daily diet, and to learn food and nutrition-related knowledge. Voice is the most natural communication modality and it is easy to use without any technical background. In addition, the communication and recommendation provided by the system are personalized based on each user's physical, social, and cultural profile. Therefore, it would be easy to be accepted by the target audience. The proposed virtual assistant has been implemented on the Amazon Alexa platform. Preliminary experiments have demonstrated the usefulness of the virtual assistant.

Following a healthy diet is essential for people with diabetes. For this purpose, there have been many digital tools and mobile apps developed for diabetes meal planning. Most of them focus on controlling the blood sugar level of users. However, they undervalued the social, cultural, and religious significance of food to people. There are numerous factors that affect a person's meal planning including taste, nutrition, budget, preference, habit, and health constraints. People can struggle to decide what to eat. They may easily be overwhelmed by different food options and various constraints. In this paper, we propose a personalized meal planning strategy to support diabetes management. We develop a novel hybrid Multi- Criteria Decision-Making scheme for meal planning. Our goal is to effectively plan affordable and culturally appropriate meals to get all the nutrition needed for diabetic patients while still being mindful of calories and carbs.

In this paper we present the design and development of an ontology model to assist personalized self-management for American Indian diabetes users. The most important part of this ontology is a biocultural user profile modeling that presents various characteristics of American Indian users. We describe the overall ontology development life cycle including six well defined development stages that were employed by ontology engineers and developers to plan for, design, implement, evaluate, and deliver ontology. The ontology development process is iterative, as each phase can be cyclically and incrementally repeated. The proposed ontology has been evaluated with different approaches, standards, and use case scenarios. The ontology is ready to be used as a knowledge base for semantically intelligent personalized diabetes self-management for American Indians.

The massive amount of medical data accumulated from patients and healthcare providers has become a vast reservoir of knowledge source that may enable promising applications such as risk predictive modeling, clinical decision support, disease or safety surveillance. However, discovering knowledge from the big medical data can be very complex because of the nature of this type of data: they normally contain large amount of unstructured data; they may have lots of missing values; they can be highly complex and heterogeneous. To address these challenges, in this paper we propose a Collaborative Filtering-Enhanced Deep Learning approach. In particular, we estimate missing values based on patients' similarity, i.e., we predict one patient's missing features based on the values of similar patients. This is implemented with the Collaborative Topic Regression method, which tightly couples topic model and probability matrix factorization and is able to utilize the rich information hidden in the data. Then a deep neural network-based method is applied for the prediction of health risks. This method can help us handle complex and multi-modality data. Extensive experiments on a real-world dataset have been performed and the results show improvements of our proposed algorithm over the state-of-the-art methods.

Mobile medical applications are increasingly being used by patients and consumers. However, due to their complexity, these applications are normally only accessible to smartphone users. People using low-end cellphones cannot benefit from this new technology. The goal of this paper is to expand health service platform to lower-end cellphones, so that people in underdeveloped regions can benefit from it. In particular, we propose a scalable platform for lightweight health applications with novel and proactive client communication. Through the effective support of a multi-layered cloud platform, we assure the scalability, elasticity and reliability of the server side. With simple Short Messaging Service (SMS) channels, health workers and patients can access complex healthcare services with low-end cellphones. The multi-layered architecture provides separation of concerns and decoupling of communication and business logic. Furthermore, our proposed plug-in model can expand and customize functionalities. Extensive experimental results have demonstrated the effectiveness of the proposed platform.

One of the greatest challenges of an enterprise's service center is to ensure that their engineers and customers are provided with the right information in a timely fashion. For this purpose, modern organizations operate a wide range of information support systems to assist customers with critical service requests and to provide proactive monitoring, where possible, to prevent service requests from occurring in the first place. It is often the case that relevant information is scattered over the Internet and/or maintained on disparate systems, buried in large amount of noisy data, and in heterogeneous formats, thereby complicating the access to reusable knowledge and extending the response time to reach a resolution. To address these challenges, in this paper we propose an effective knowledge mining solution to improve the quality of service request resolution. We model the service resolution problem as an online search and classification problem, and use domain knowledge in the form of ontology to guide effective machine learning. Our proposed solution has been extensively evaluated with experiments and has been used in a real enterprise customer center.

The epidemic of diabetes in American Indian (AI) communities is a serious public health challenge. The incidence and prevalence of diabetes have increased dramatically with accompanying increases in body weight and diminished physical activity. Daily diabetes care is primarily handled by the patients and their families, and the effectiveness of diabetes control is largely impacted by self-care strategies and behaviors. Thanks to the quasi-ubiquitous use of cell phones in most AI tribes, in this paper we propose a cell phone- based proactive diabetes self-care system, MobiDiaBTs. It is customized for AI patients using a personalized approach that considers the unique social, cultural, political, and demographic characteristic of AIs. The platform effectively and automatically collects users' physical and social behavior data and offers real-time diabetes health recommendations. It also can help a patient to interact with fellow patients in a trust-worthy and privacy-preserving environment.

Online social networks have enabled communication, collaboration and information sharing in the healthcare domain. Despite the benefits offered by new healthcare social networking applications, there are many challenges. The unique trust and privacy requirements of healthcare make trust management an utmost important issue. In this paper, we propose a personalized self-managed trust model, MedTrust, to establish trust relationships among participating healthcare social network users. The model identifies key trust factors in a healthcare social networking environment, and uses fuzzy logic to represent and evaluate trust. Experiments were conducted and demonstrated that our approach can generate accurate and realistic outcomes in assessing trust and predicting the scope and impact of different trust factors.

Social media is rapidly changing the nature and speed of healthcare interaction. As more and more people go online to search for their health-related issues, providing them with appropriate information would save them from being overwhelmed by mountains of information. For this purpose, in this paper we propose a personalized healthcare recommending system to recommend highly relevant and trustworthy healthcare-related information to users. The system identifies key factors impacting the recommendation in a healthcare social networking environment, and uses semantic web technology and fuzzy logic to represent and evaluate the recommendation. Experiments were conducted and demonstrated that our approach can generate good outcomes in making recommendation and predicting the scope and impact of different factors.

Nowadays more and more people are going online to seek for information, service and products to address their health concerns. Given the mountains of online health-related data, it is time and energy consuming for people to locate the right information directly related to their health concerns. Therefore, effective recommendation is very important to save people's time and energy by providing them with the appropriate information. In this paper, we propose a recommendation system which utilizes semantic web technology and healthcare social networking to provide personalized recommendation to speed patient recovery and improve healthcare outcomes. Extensive experiments have been performed to evaluate the performance of the system. The results demonstrated the effectiveness of the proposed strategy.

The availability of large volumes of Semantic Web data has created the potential of discovering vast amounts of knowledge. Semantic relation discovery is a fundamental technology in analytical domains, such as business intelligence and homeland security. Because of the decentralized and distributed nature of Semantic Web development, semantic data tend to be created and stored independently in different organizations. Under such circumstances, discovering semantic relations faces numerous challenges, such as isolation, scalability, and heterogeneity. This paper proposes an effective strategy to discover semantic relationships over large-scale distributed networks based on a novel hierarchical knowledge abstraction and an efficient discovery protocol. The approach will effectively facilitate the realization of the full potential of harnessing the collective power and utilization of the knowledge scattered over the Internet.

In a Delay Tolerant Network (DTN), the nodes have intermittent connectivity and complete path(s) between the source and destination may not exist. The communication takes place opportunistically when any two nodes enter the effective range. One of the major challenges in DTNs is message forwarding when a sender must select a best neighbor that has the highest probability of forwarding the message to the actual destination. However, finding an appropriate route remains an NP-hard problem. This paper presents a concept of Checkpoint (CP) based message forwarding in DTNs. The CPs are autonomous high-end wireless devices with large buffer storage and are responsible for temporarily storing the messages to be forwarded. The CPs are deployed at various places within the city parameter that are covered by bus routes and where human meeting frequencies are higher. For the simulative analysis a synthetic human mobility model in ONE simulator is constructed for the city of Fargo, ND, USA. The model is tested over various DTN routing protocols and the results indicate that using CP overlay over the existing DTN architecture significantly decreases message delivery time as well as buffer usage.

Privacy protection is a vital issue for safe social interactions within social networking sites (SNS). Although SNSs such as My Space and Face book allow users to configure their privacy settings, it is not a simple task for normal users with hundreds of online friends. In this paper, we propose an intelligent semantics-based privacy configuration system, named SPAC, to automatically recommend privacy settings for SNS users. SPAC learns users' privacy configuration patterns and make predictions by utilizing machine learning techniques on users' profiles and privacy setting history. To increase the accuracy of the predicted privacy settings, especially in the context of heterogeneous user profiles, we enhance privacy configuration predictor by integrating it with structured semantic knowledge in the SNS. This, in turn, allows SPAC to make inferences based on additional source of knowledge, resulting in improved accuracy of privacy recommendation. Our experimental results have proven the effectiveness of our approach.

Interference has been proven to have an effect on the performance in wireless mesh networks (WMN). Using multichannels can improve the performance of WMNs by reducing interference influence. In this paper, we study how to design a robust WMN for a set of mesh nodes, each with Q Networking Interface Cards (NICs) and pre-defined connection requests. Our scheme aims to construct an interference-aware network topology for the nodes, then set up a pair of link-disjoint paths for each request with fault- tolerant capability. We propose two novel schemes to improve the network design. First, we embrace the network interference for providing resource- efficient protections. Second, protection links are shared and reused by multiple connections for protection, which further improves the efficiency of network resource usage. Our simulation results show that our scheme outperformed previous schemes.

Wireless Mesh Networks represent a promising technology to provide wireless Internet connectivity over a large community. This new technology not only allows a fast, easy and inexpensive network deployment, but also enables many new applications including community-scale peer-based communication or sharing of network resources and services. In this work, we propose an effective publish/subscribe communication paradigm to facilitate efficient data dissemination over wireless mesh networks. The proposed publish/subscribe scheme dynamically routes and delivers events and services from sources to interested users with minimum communication overhead and memory storage. The effectiveness of the system is demonstrated through comprehensive simulation studies.

Mobile ad hoc social networks are self-configuring social networks that connect users using mobile devices, such as laptops, PDAs, and cellular phones. These social networks facilitate users to form virtual communities of similar interests or commonalities. This paper proposes a concrete, generalized, and novel framework to develop a fully functional mobile ad hoc social network. The proposed framework provides effective and efficient solutions to social network construction, semantics-based user profile matching, and multi-hop semantics-based routing. Moreover, the proposed framework because of its generality is applicable in applications of critical importance, such as disaster-recovery, homeland security, and personnel control. Furthermore, the proposed framework is rigorously benchmarked using an elaborate simulation setup and released as a prototype system that can be run on cellular phones.