This thesis explores the critical role of usability in software development and uses usability heuristics as a cost-effective and efficient method for evaluating various software functions and interfaces. With the proliferation of software development in the modern digital age, developing user-friendly interfaces that meet the needs and preferences of users has become a complex process. Usability heuristics, a set of guidelines based on principles of human-computer interaction, provide a starting point for designers to create intuitive, efficient, and easy-to-use interfaces that provide a seamless user experience. The study uses Jakob Nieson's ten usability heuristics to evaluate the usability of Stack Overflow posts, a popular Q\&A website for developers. Through the analysis of 894 posts related to usability, the study identifies common usability problems faced by users and developers, providing valuable insights into the effectiveness of usability guidelines in software development practice. The research findings emphasize the need for ongoing evaluation and improvement of software interfaces to ensure a seamless user experience. The thesis concludes by highlighting the potential of usability heuristics in guiding the design of user-friendly software interfaces and improving the overall user experience in software development.

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 …

Cloud-based collaborative editors enable real-time document processing via remote connections. Their common application is to allow Internet users to collaboratively work on their documents stored in the cloud, even if these users are physically a world apart. However, this convenience comes at a cost in terms of user privacy. Hence, the growth of popularity of cloud computing application stipulates the growth in importance of cloud security. A major concern with the cloud is who has access to user data. In order to address this issue, various third-party services offer encryption mechanisms for protection of the user data in the case of insider attacks or data leakage. However, these services often only encrypt data-at-rest, leaving the data which is being processed potentially vulnerable. The purpose of this study is to propose a prototype software system that encrypts collaboratively edited data in real-time, preserving the user experience similar to that of, e.g., Google Docs.

An event is something that happens at a time and location. Events include major life events such as graduating college or getting married, and also simple day-to-day activities such as commuting to work or eating lunch. Most work on event extraction detects events and the entities involved in events. For example, cooking events will usually involve a cook, some utensils and appliances, and a final product. In this work, we target the task of determining whether events result in their expected outcomes. Specifically, we target cooking and baking events, and characterize event outcomes into two categories. First, we distinguish whether something edible resulted from the event. Second, if something edible resulted, we distinguish between perfect, partial and alternative outcomes. The main contributions of this thesis are a corpus of 4,000 tweets annotated with event outcome information and experimental results showing that the task can be automated. The corpus includes tweets that have only text as well as tweets that have text and an image.

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 …

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.

There is a natural tendency for biological systems to change as their environments change. The fittest in the biological systems survive, adapt to their environment, and multiply while the weakest in the environment diminish. There have been attempts in computer science to model the processes of natural selection and survival which occur in biological systems in order to obtain more efficient and effective machine-learning algorithms. Genetic algorithms are the result of these attempts.

This research is the result of an effort to provide real-time machine recognition of hand-send Morse code through the use of the M6800 microcomputer. While the capability to recognize hand-send Morse code messages by machine has been demonstrated before on large scale special purpose computers, on minicomputers, and even on the M6800 microcomputer, the main contribution of this paper is to demonstrate it with relatively understandable hardware and software.

This interface was bought to perform floating-point arithmetic and for its function capabilities such as SIN, COS, and e^x. My application required an integer truncation function that is not performed by this calculator, so i wrote a small assembly language subroutine to do it. A potentially irritating problem is that the calculator chip does not automatically convert to scientific notation if the numbers become too big to display in floating point. The control program must keep track of the display mode.

Twitter has quickly become one of the most popular social media sites. It has 313 million monthly active users, and 500 million tweets are published daily. With the massive number of tweets, Twitter users share information about a location along with the temporal awareness. In this work, I focus on tweets where author of the tweets exclusively mentions a location in the tweet. Natural language processing systems can leverage wide range of information from the tweets to build applications like recommender systems that predict the location of the author. This kind of system can be used to increase the visibility of the targeted audience and can also provide recommendations interesting places to visit, hotels to stay, restaurants to eat, targeted on-line advertising, and co-traveler matching based on the temporal information extracted from a tweet. In this work I determine if the author of the tweet is present in the mentioned location of the tweet. I also determine if the author is present in the location before tweeting, while tweeting, or after tweeting. I introduce 5 temporal tags (before the tweet but > 24 hours; before the tweet but < 24 hours; during the tweet is posted; after the tweet is …

Component failures can result in catastrophic behaviors in safety critical embedded systems, sometimes resulting in loss of life. Component failures can be treated as off nominal behaviors (ONBs) with respect to the components and sub systems involved in an embedded system. A lot of research is being carried out to tackle the problem of ONBs. These approaches are mainly focused on the states (i.e., desired and undesired states of a system at a given point of time to detect ONBs). In this paper, an approach is discussed to detect component failures and critical components of an embedded system. The approach is based on fault tree analysis (FTA), applied to the requirements specification of embedded systems at design time to find out the relationship between individual component failures and overall system failure. FTA helps in determining both qualitative and quantitative relationship between component failures and system failure. Analyzing the system at design time helps in detecting component failures and critical components and helps in devising strategies to mitigate component failures at design time and improve overall safety and reliability of a system.

This thesis describes an approach for object recognition using the chordiogram shape-based descriptor. Global shape representations are highly susceptible to clutter generated due to the background or other irrelevant objects in real-world images. To overcome the problem, we aim to extract precise object shape using superpixel segmentation, perceptual grouping, and connected components. The employed shape descriptor chordiogram is based on geometric relationships of chords generated from the pairs of boundary points of an object. The chordiogram descriptor applies holistic properties of the shape and also proven suitable for object detection and digit recognition mechanisms. Additionally, it is translation invariant and robust to shape deformations. In spite of such excellent properties, chordiogram is not scale-invariant. To this end, we propose scale invariant chordiogram descriptors and intend to achieve a similar performance before and after applying scale invariance. Our experiments show that we achieve similar performance with and without scale invariance for silhouettes and real world object images. We also show experiments at different scales to confirm that we obtain scale invariance for chordiogram.

In recent years, social media platforms such as Twitter and Facebook have emerged as effective tools for broadcasting messages worldwide during disaster events. With millions of messages posted through these services during such events, it has become imperative to identify valuable information that can help the emergency responders to develop effective relief efforts and aid victims. Many studies implied that the role of social media during disasters is invaluable and can be incorporated into emergency decision-making process. However, due to the "big data" nature of social media, it is very labor-intensive to employ human resources to sift through social media posts and categorize/classify them as useful information. Hence, there is a growing need for machine intelligence to automate the process of extracting useful information from the social media data during disaster events. This dissertation addresses the following questions: In a social media stream of messages, what is the useful information to be extracted that can help emergency response organizations to become more situationally aware during and following a disaster? What are the features (or patterns) that can contribute to automatically identifying messages that are useful during disasters? We explored a wide variety of features in conjunction with supervised learning algorithms …

In recent years, brain computer interfaces (BCIs) have gained popularity in non-medical domains such as the gaming, entertainment, personal health, and marketing industries. A growing number of companies offer various inexpensive consumer grade BCIs and some of these companies have recently introduced the concept of BCI "App stores" in order to facilitate the expansion of BCI applications and provide software development kits (SDKs) for other developers to create new applications for their devices. The BCI applications access to users' unique brainwave signals, which consequently allows them to make inferences about users' thoughts and mental processes. Since there are no specific standards that govern the development of BCI applications, its users are at the risk of privacy breaches. In this work, we perform first comprehensive analysis of BCI App stores including software development kits (SDKs), application programming interfaces (APIs), and BCI applications w.r.t privacy issues. The goal is to understand the way brainwave signals are handled by BCI applications and what threats to the privacy of users exist. Our findings show that most applications have unrestricted access to users' brainwave signals and can easily extract private information about their users without them even noticing. We discuss potential privacy threats posed by …

Mobile applications are growing in popularity and pose new problems in the area of software testing. In particular, mobile applications heavily depend upon user interactions and a dynamically changing environment of system events. In this thesis, we focus on user-driven events and use Q-learning, a reinforcement machine learning algorithm, to generate tests for Android applications under test (AUT). We implement a framework that automates the generation of GUI test cases by using our Q-learning approach and compare it to a uniform random (UR) implementation. A novel feature of our approach is that we generate user-driven event sequences through the GUI, without the source code or the model of the AUT. Hence, considerable amount of cost and time are saved by avoiding the need for model generation for generating the tests. Our results show that the systematic path exploration used by Q-learning results in higher average code coverage in comparison to the uniform random approach.

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.

Students often use the web as a source of help for problems that they encounter on programming assignments.In this work, we seek to understand how students use the web to search for help on their assignments.We used a mixed methods approach with 344 students who complete a survey and 41 students who participate in a focus group meetings and helped in recording data about their search habits.The survey reveals data about student reported search habits while the focus group uses a web browser plug-in to record actual search patterns.We examine the results collectively and as broken down by class year.Survey results show that at least 2/3 of the students from each class year rely on search engines to locate resources for help with their programming bugs in at least half of their assignments;search habits vary by class year;and the value of different types of resources such as tutorials and forums varies by class year.Focus group results exposes the high frequency web sites used by the students in solving their programming assignments.

Did you stand at a door with a bunch of keys and tried to find the right one to unlock the door? Did you hold a flower and wonder the name of it? A need of object recognition could rise anytime and any where in our daily lives. With the development of mobile devices object recognition applications become possible to provide immediate assistance. However, performing complex tasks in even the most advanced mobile platforms still faces great challenges due to the limited computing resources and computing power. In this thesis, we present an object recognition system that resides and executes within a mobile device, which can efficiently extract image features and perform learning and classification. To account for the computing constraint, a novel feature extraction method that minimizes the data size and maintains data consistency is proposed. This system leverages principal component analysis method and is able to update the trained classifier when new examples become available . Our system relieves users from creating a lot of examples and makes it user friendly. The experimental results demonstrate that a learning method trained with a very small number of examples can achieve recognition accuracy above 90% in various acquisition conditions. In …

Segmentation, recognition and 3D reconstruction of objects have been cutting-edge research topics, which have many applications ranging from environmental and medical to geographical applications as well as intelligent transportation. In this dissertation, I focus on the study of segmentation, recognition and 3D reconstruction of objects using LiDAR data/MRI. Three main works are that (I). Feature extraction algorithm based on sparse LiDAR data. A novel method has been proposed for feature extraction from sparse LiDAR data. The algorithm and the related principles have been described. Also, I have tested and discussed the choices and roles of parameters. By using correlation of neighboring points directly, statistic distribution of normal vectors at each point has been effectively used to determine the category of the selected point. (II). Segmentation and 3D reconstruction of objects based on LiDAR/MRI. The proposed method includes that the 3D LiDAR data are layered, that different categories are segmented, and that 3D canopy surfaces of individual tree crowns and clusters of trees are reconstructed from LiDAR point data based on a region active contour model. The proposed method allows for delineations of 3D forest canopy naturally from the contours of raw LiDAR point clouds. The proposed model is suitable not …

We have a huge amount of video data from extensively available surveillance cameras and increasingly growing technology to record the motion of a moving object in the form of trajectory data. With proliferation of location-enabled devices and ongoing growth in smartphone penetration as well as advancements in exploiting image processing techniques, tracking moving objects is more flawlessly achievable. In this work, we explore some domain-independent qualitative and quantitative features in raw trajectory (spatio-temporal) data in videos captured by a fixed single wide-angle view camera sensor in outdoor areas. We study the efficacy of those features in classifying four basic high level actions by employing two supervised learning algorithms and show how each of the features affect the learning algorithms’ overall accuracy as a single factor or confounded with others.

Data is everywhere. The current Technological advancements in Digital, Social media and the ease at which the availability of different application services to interact with variety of systems are causing to generate tremendous volumes of data. Due to such varied services, Data format is now not restricted to only structure type like text but can generate unstructured content like social media data, videos and images etc. The generated Data is of no use unless been stored and analyzed to derive some Value. Traditional Database systems comes with limitations on the type of data format schema, access rates and storage sizes etc. Hadoop is an Apache open source distributed framework that support storing huge datasets of different formatted data reliably on its file system named Hadoop File System (HDFS) and to process the data stored on HDFS using MapReduce programming model. This thesis study is about building a Data Architecture using Hadoop and its related open source distributed frameworks to support a Data flow pipeline on a low commodity hardware. The Data flow components are, sourcing data, storage management on HDFS and data access layer. This study also discuss about a use case to utilize the architecture components. Sqoop, a framework …

The extensive use of global teams to develop software has prompted researchers to investigate various factors that can enhance a team’s performance. While a significant body of research exists on global software teams, previous research has not fully explored the interrelationships and collective impact of various factors on team performance. This study explored a model that added the characteristics of a team’s culture, ability, communication frequencies, response rates, and linguistic categories to a central framework of team performance. Data was collected from two student software development projects that occurred between teams located in the United States, Panama, and Turkey. The data was obtained through online surveys and recorded postings of team activities that occurred throughout the global software development projects. Partial least squares path modeling (PLS-PM) was chosen as the analytic technique to test the model and identify the most influential factors. Individual factors associated with response rates and linguistic characteristics proved to significantly affect a team’s activity related to grade on the project, group cohesion, and the number of messages received and sent. Moreover, an examination of possible latent homogeneous segments in the model supported the existence of differences among groups based on leadership style. Teams with assigned leaders …