With the rapid development of mobile device technology, smartphones are now not only the tool for young people but also for elderly people. However, the complicated steps of interacting with smartphones are stopping them from having a good user experience. One of the reasons is that application designers do not take consideration of the user group of elderly people. Our pilot survey shows that most elderly people lack the skills required to use a smartphone without obstacles, like typing. We also conducted an experiment with 8 participants that targeting on the usability of a daily used application, Contact List (CL), and based on a Chinese language system. We developed an android application that proposed a new method of showing the contact list according to the language usage of Chinese for this study. By asking participants to finish the same tasks on the traditional CL applications on their phones or on our application and observing their operations, we obtained useful feedback in terms of usability issues. Our experiment also tried to find out whether the method we proposed in the new application can lead to a better user experience for elderly people.

On-boarding refers to bringing in an employee to a company and granting access to new hires. However, a person may go through different stages of employment, hold different jobs by the same employer and have different levels of information access during the employment duration. A shared services organization may have either limited or wide-spread access within certain groups. Off-boarding implies the removal of access of information or physical devices such as keys, computers or mobile devices when the employee leaves. Off-boarding is the management of the separation an employee from an institution. Many organizations use different steps that constitute the off-boarding process. Incomplete tracking of an employee's access is a security risk and can lead to unintended exposure of company information and assets. Blockchain technology combines blocks of information together using a cryptographic algorithm based on the existing previous block and is verified by the peers in the blockchain network. This process creates an immutable record of employee system access providing an audit trail of access for any point in time to ensure that all access permissions can be removed once employment ends. This project proposes using blockchain technology to consolidate information across disparate groups, and to automate access removal …

Epilepsy affects up to 1% of the world's population and approximately 2.5 million people in the United States. A considerable portion (30%) of epilepsy patients are refractory to antiepileptic drugs (AEDs), and surgery can not be an effective candidate if the focus of the seizure is on the eloquent cortex. To overcome the problems with existing solutions, a notable portion of biomedical research is focused on developing an implantable or wearable system for automated seizure detection and control. Seizure detection algorithms based on signal rejection algorithms (SRA), deep neural networks (DNN), and neighborhood component analysis (NCA) have been proposed in the IoMT framework. The algorithms proposed in this work have been validated with both scalp and intracranial electroencephalography (EEG, icEEG), and demonstrate high classification accuracy, sensitivity, and specificity. The occurrence of seizure can be controlled by direct drug injection into the epileptogenic zone, which enhances the efficacy of the AEDs. Piezoelectric and electromagnetic micropumps have been explored for the use of a drug delivery unit, as they provide accurate drug flow and reduce power consumption. The reduction in power consumption as a result of minimal circuitry employed by the drug delivery system is making it suitable for practical biomedical applications. …

Edge computing and CAV (Connected Autonomous Vehicle) fields can work as a team. With the short latency and high responsiveness of edge computing, it is a better fit than cloud computing in the CAV field. Moreover, containerized applications are getting rid of the annoying procedures for setting the required environment. So that deployment of applications on new machines is much more user-friendly than before. Therefore, this paper proposes a framework developed for the CAV edge computing scenario. This framework consists of various programs written in different languages. The framework uses Docker technology to containerize these applications so that the deployment could be simple and easy. This framework consists of two parts. One is for the vehicle on-board unit, which exposes data to the closest edge device and receives the output generated by the edge device. Another is for the edge device, which is responsible for collecting and processing big load of data and broadcasting output to vehicles. So the vehicle does not need to perform the heavyweight tasks that could drain up the limited power.

Article presents work on annotating reflections, an essential counselor behavioral code in motivational interviewing for psychotherapy on conversations that are a combination of casual and therapeutic dialogue.

Is there a significant difference in the design of deep networks for the tasks of classifying object-centric images and scenery images? How to design networks that extract the most representative features for scene recognition? To answer these questions, we design studies to examine the scales and richness of image features for scenery image recognition. Three methods are proposed that integrate the scale factor to the deep networks and reveal the fundamental network design strategies. In our first attempt to integrate scale factors into the deep network, we proposed a method that aggregates both the context and multi-scale object information of scene images by constructing a multi-scale pyramid. In our design, integration of object-centric multi-scale networks achieved a performance boost of 9.8%; integration of object- and scene-centric models obtained an accuracy improvement of 5.9% compared with single scene-centric models. We also exploit bringing the attention scheme to the deep network and proposed a Scale Attentive Network (SANet). The SANet streamlines the multi-scale scene recognition pipeline, learns comprehensive scene features at various scales and locations, addresses the inter-dependency among scales, and further assists feature re-calibration as well as the aggregation process. The proposed network achieved a Top-1 accuracy increase by 1.83% on …

This work develops a simple and low-cost microphone-based spirometer with a scalable infrastructure that can be used to monitor COPD and Asthma symptoms. The data acquired from the system is archived in the cloud for further procuring and reporting. To develop this system, we utilize an off-the-shelf ESP32 development board, MEMS microphone, oxygen mask, and 3D printable mounting tube to keep the costs low. The system utilizes the MEMS microphone to measure the audio signal of a user's exhalation, calculates diagnostic estimations and uploads the estimations to the cloud to be remotely monitored. Our results show a practical system that can identify COPD and Asthma symptoms and report the data to both the patient and the physician. The system developed can provide a means of gathering respiratory data to better assist doctors and assess patients to provide remote care.

With the technological improvements, the number of Internet connected devices is increasing tremendously. We also observe an increase in cyberattacks since the attackers want to use all these interconnected devices for malicious intention. Even though there exist many proactive security solutions, it is not practical to run all the security solutions on them as they have limited computational resources and even battery operated. As an alternative, Zero Trust Architecture (ZTA) has become popular is because it defines boundaries and requires to monitor all events, configurations, and connections and evaluate them to enforce rejecting by default and accepting only if they are known and accepted as well as applies a continuous trust evaluation. In addition, we need to be able to respond as quickly as possible, which cannot be managed by human interaction but through autonomous computing paradigm. Therefore, in this work, we propose a framework that would implement ZTA using autonomous computing paradigm. The proposed solution, Autonomic ZTA Management Engine (AZME) framework, focusing on enforcing ZTA on network, uses a set of sensors to monitor a network, a set of user-defined policies to define which actions to be taken (through controller). We have implemented a Python prototype as a proof-of-concept …

In this work, we leverage the recent developments in computer science to address several of the challenges in current literature-based discovery (LBD) solutions. First, LBD solutions cannot use semantics or are too computational complex. To solve the problems we propose a generative model OverlapLDA based on topic modeling, which has been shown both effective and efficient in extracting semantics from a corpus. We also introduce an inference method of OverlapLDA. We conduct extensive experiments to show the effectiveness and efficiency of OverlapLDA in LBD. Second, we expand LBD to a more complex and realistic setting. The settings are that there can be more than one concept connecting the input concepts, and the connectivity pattern between concepts can also be more complex than a chain. Current LBD solutions can hardly complete the LBD task in the new setting. We simplify the hypotheses as concept sets and propose LBDSetNet based on graph neural networks to solve this problem. We also introduce different training schemes based on self-supervised learning to train LBDSetNet without relying on comprehensive labeled hypotheses that are extremely costly to get. Our comprehensive experiments show that LBDSetNet outperforms strong baselines on simple hypotheses and addresses complex hypotheses.

This work presents an investigation of a piezoelectric sensor based energy harvesting system, which collects energy from the surrounding environment. Increasing costs and scarcity of fossil fuels is a great concern today for supplying power to electronic devices. Furthermore, generating electricity by ordinary methods is a complicated process. Disposal of chemical batteries and cables is polluting the nature every day. Due to these reasons, research on energy harvesting from renewable resources has become mandatory in order to achieve improved methods and strategies of generating and storing electricity. Many low power devices being used in everyday life can be powered by harvesting energy from natural energy resources. Power overhead and power energy efficiency is of prime concern in electronic circuits. In this work, an energy harvester is modeled and simulated in Simscape™ for the functional analysis and comparison of achieved outcomes with previous work. Results demonstrate that the harvester produces power in the 0 μW to 100 μW range, which is an adequate amount to provide supply to low power devices. Power efficiency calculations also demonstrate that the implemented harvester is capable of generating and storing power for low power pervasive applications.

This work describes an approach to determine whether people participate in the events they tweet about. Specifically, we determine whether people are participants in events with respect to the tweet timestamp. We target all events expressed by verbs in tweets, including past, present and events that may occur in future. We define event participant as people directly involved in an event regardless of whether they are the agent, recipient or play another role. We present an annotation effort, guidelines and quality analysis with 1,096 event mentions. We discuss the label distributions and event behavior in the annotated corpus. We also explain several features used and a standard supervised machine learning approach to automatically determine if and when the author is a participant of the event in the tweet. We discuss trends in the results obtained and devise important conclusions.

With the increasing demand on wireless and portable devices, the radio frequency front end blocks are required to feature properties such as wideband, high frequency, multiple operating frequencies, low cost and compact size. However, the current radio frequency system blocks are designed by combining several individual frequency band blocks into one functional block, which increase the cost and size of devices. To address these issues, it is important to develop novel approaches to further advance the current design methodologies in both space and spectrum domains. In recent years, the concept of artificial materials has been proposed and studied intensively in RF/Microwave, Terahertz, and optical frequency range. It is a combination of conventional materials such as air, wood, metal and plastic. It can achieve the material properties that have not been found in nature. Therefore, the artificial material (i.e. meta-materials) provides design freedoms to control both the spectrum performance and geometrical structures of radio frequency front end blocks and other high frequency systems. In this dissertation, several artificial materials are proposed and designed by different methods, and their applications to different high frequency components and circuits are studied. First, quasi-conformal mapping (QCM) method is applied to design plasmonic wave-adapters and couplers …

Possession is an asymmetric semantic relation between two entities, where one entity (the possessee) belongs to the other entity (the possessor). Automatically extracting possessions are useful in identifying skills, recommender systems and in natural language understanding. Possessions can be found in different communication modalities including text, images, videos, and audios. In this dissertation, I elaborate on the techniques I used to extract possessions. I begin with extracting possessions at the sentence level including the type and temporal anchors. Then, I extract the duration of possession and co-possessions (if multiple possessors possess the same entity). Next, I extract possessions from an entire Wikipedia article capturing the change of possessors over time. I extract possessions from social media including both text and images. Finally, I also present dense annotations generating possession timelines. I present separate datasets, detailed corpus analysis, and machine learning models for each task described above.