Poemage Prototype

During 2013-14, our group developed the algorithm for a computational framework for interpreting sound in poetry, which allows us to detect sonic patterns and relationships in poetry. We can define these patterns algebraically and so describe them computationally through rules supported by a data abstraction. Using an NEH start-up grant to pay a postdoctoral fellow in English and a graduate student in Computer Science, we will use an innovative interactive design process to develop a prototype visualization tool, Poemage. Because our framework allows us to identify and visualize complex configurations and dynamics of sound, including but not limited to rhyme, in real time, Poemage will allow users to detect these dynamics in poems of their choosing, while inviting them to identify (and adjust for) what they deem interesting

Using phishing to test social engineering awareness of financial employees

Social engineering is the biggest security threat to financial institutions because it exploits the weakest link in any security system: the human element. It is proposed here that combining specialized training on social engineering followed by repeated audit tests will be more effective at lowering employee vulnerability than standard security training alone. This research developed a training module specializing in social engineering with an extra emphasis on phishing, then used phishing trials on financial employees to audit their awareness and knowledge of social engineering to determine if it lowers the vulnerability level to phishing attacks --Document

Using phishing to test social engineering awareness of financial employees

Social engineering is the biggest security threat to financial institutions because it exploits the weakest link in any security system: the human element. It is proposed here that combining specialized training on social engineering followed by repeated audit tests will be more effective at lowering employee vulnerability than standard security training alone. This research developed a training module specializing in social engineering with an extra emphasis on phishing, then used phishing trials on financial employees to audit their awareness and knowledge of social engineering to determine if it lowers the vulnerability level to phishing attacks --Document

Stereoscopic vision in vehicle navigation.

Traffic sign (TS) detection and tracking is one of the main tasks of an autonomous vehicle which is addressed in the field of computer vision. An autonomous vehicle must have vision based recognition of the road to follow the rules like every other vehicle on the road. Besides, TS detection and tracking can be used to give feedbacks to the driver. This can significantly increase safety in making driving decisions. For a successful TS detection and tracking changes in weather and lighting conditions should be considered. Also, the camera is in motion, which results in image distortion and motion blur. In this work a fast and robust method is proposed for tracking the stop signs in videos taken with stereoscopic cameras that are mounted on the car. Using camera parameters and the detected sign, the distance between the stop sign and the vehicle is calculated. This calculated distance can be widely used in building visual driver-assistance systems

Swarm intelligence for clustering dynamic data sets for web usage mining and personalization.

Swarm Intelligence (SI) techniques were inspired by bee swarms, ant colonies, and most recently, bird flocks. Flock-based Swarm Intelligence (FSI) has several unique features, namely decentralized control, collaborative learning, high exploration ability, and inspiration from dynamic social behavior. Thus FSI offers a natural choice for modeling dynamic social data and solving problems in such domains. One particular case of dynamic social data is online/web usage data which is rich in information about user activities, interests and choices. This natural analogy between SI and social behavior is the main motivation for the topic of investigation in this dissertation, with a focus on Flock based systems which have not been well investigated for this purpose. More specifically, we investigate the use of flock-based SI to solve two related and challenging problems by developing algorithms that form critical building blocks of intelligent personalized websites, namely, (i) providing a better understanding of the online users and their activities or interests, for example using clustering techniques that can discover the groups that are hidden within the data; and (ii) reducing information overload by providing guidance to the users on websites and services, typically by using web personalization techniques, such as recommender systems. Recommender systems aim to recommend items that will be potentially liked by a user. To support a better understanding of the online user activities, we developed clustering algorithms that address two challenges of mining online usage data: the need for scalability to large data and the need to adapt cluster sing to dynamic data sets. To address the scalability challenge, we developed new clustering algorithms using a hybridization of traditional Flock-based clustering with faster K-Means based partitional clustering algorithms. We tested our algorithms on synthetic data, real VCI Machine Learning repository benchmark data, and a data set consisting of real Web user sessions. Having linear complexity with respect to the number of data records, the resulting algorithms are considerably faster than traditional Flock-based clustering (which has quadratic complexity). Moreover, our experiments demonstrate that scalability was gained without sacrificing quality. To address the challenge of adapting to dynamic data, we developed a dynamic clustering algorithm that can handle the following dynamic properties of online usage data: (1) New data records can be added at any time (example: a new user is added on the site); (2) Existing data records can be removed at any time. For example, an existing user of the site, who no longer subscribes to a service, or who is terminated because of violating policies; (3) New parts of existing records can arrive at any time or old parts of the existing data record can change. The user\u27s record can change as a result of additional activity such as purchasing new products, returning a product, rating new products, or modifying the existing rating of a product. We tested our dynamic clustering algorithm on synthetic dynamic data, and on a data set consisting of real online user ratings for movies. Our algorithm was shown to handle the dynamic nature of data without sacrificing quality compared to a traditional Flock-based clustering algorithm that is re-run from scratch with each change in the data. To support reducing online information overload, we developed a Flock-based recommender system to predict the interests of users, in particular focusing on collaborative filtering or social recommender systems. Our Flock-based recommender algorithm (FlockRecom) iteratively adjusts the position and speed of dynamic flocks of agents, such that each agent represents a user, on a visualization panel. Then it generates the top-n recommendations for a user based on the ratings of the users that are represented by its neighboring agents. Our recommendation system was tested on a real data set consisting of online user ratings for a set of jokes, and compared to traditional user-based Collaborative Filtering (CF). Our results demonstrated that our recommender system starts performing at the same level of quality as traditional CF, and then, with more iterations for exploration, surpasses CF\u27s recommendation quality, in terms of precision and recall. Another unique advantage of our recommendation system compared to traditional CF is its ability to generate more variety or diversity in the set of recommended items. Our contributions advance the state of the art in Flock-based 81 for clustering and making predictions in dynamic Web usage data, and therefore have an impact on improving the quality of online services

Iterative Design and Testing of a Mobile Application to Support Food Consumption Monitoring and Decision Making

Food overconsumption is a major contributor to weight gain leading to obesity. Constant exposure to larger amounts of food and beverage has caused many individuals to experience “portion distortion,†the perception that bigger portion sizes are appropriate for consumption at a single sitting. Independently and accurately changing this perception can be very difficult even if one has a desire to do so. In response to these observations, we developed and tested Picture-Perfect Portions, a mobile application designed to combat overconsumption, at the individual level, by leveraging the power of simple visualizations to help adults understand and adjust their food consumption practices. Data were collected from 141 participants eating a meal of macaroni and cheese in a laboratory setting. In a 2 x 2 x 2 between-subjects experimental design, participants were assigned to one of eight conditions: 1) Small (17 cm diameter) Plate, Without Picture-Perfect Portions (App), Without 400-Calorie food consumption target (Goal), 2) Small Plate, With App, Without Goal, 3) Small Plate, Without App, With Goal, 4) Small Plate, With App, With Goal, 5) Large (26.4 cm diameter) Plate, Without App, Without Goal, 6) Large Plate, With App, Without Goal, 7) Large Plate, Without App, With Goal, or 8) Large Plate, With App, With Goal. Both grams of food consumed of first serving (grams consumed, first serving) and total grams of food consumed during the meal (grams consumed, all servings) were measured as the main dependent variables. These variables were log-transformed for analysis. In total, fifty participants used and evaluated the app. The mean System Usability Scale (SUS) score for Picture-Perfect Portions is 75.2 ± 12.4 (median 78.8). This suggests that Picture-Perfect Portions is an application with high overall system usability. An ANOVA of ln(grams consumed, first serving) for all participants revealed a main effect of PLATE SIZE such that, on average, participants given a large plate consumed more of the first serving than participants given a small plate. A main effect of PLATE SIZE was also observed for the dependent variable ln(grams consumed, all servings) such that, on average, the total amount of food consumed by participants given a large plate was more than the total amount of food consumed by participants given a small plate. A main effect of DEVICE was observed for all participants under the “With Goal†treatment such that, on average, participants with a 400-Calorie consumption goal and the assistance of Picture-Perfect Portions ate less of the first serving than participants with a 400-Calorie consumption goal without the assistance of the app. In addition, a significant effect of DEVICE on ln(grams consumed, first serving) was observed for the “Small Plate†treatment such that, on average, participants using the app ate less than participants not using the app. These results demonstrate the powerful effect of plate size on individuals’ food consumption. They also, however, demonstrate that there are scenarios in which “just-in-time†feedback from an application such as Picture-Perfect Portions can impact food consumption, especially for those individuals with a specific food consumption goal

ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION

This dissertation studies the factors that negatively impact the accuracy of the collaborative filtering recommendation systems based on nonnegative matrix factorization (NMF). The keystone in the recommendation system is the rating that expresses the user\u27s opinion about an item. One of the most significant issues in the recommendation systems is the lack of ratings. This issue is called cold-start issue, which appears clearly with New-Users who did not rate any item and New-Items, which did not receive any rating. The traditional recommendation systems assume that users are independent and identically distributed and ignore the connections among users whereas the recommendation actually is a social activity. This dissertation aims to enhance NMF-based recommendation systems by utilizing the imputation method and limiting the errors that are introduced in the system. External information such as trust network and item categories are incorporated into NMF-based recommendation systems through the imputation. The proposed approaches impute various subsets of the missing ratings. The subsets are defined based on the total number of the ratings of the user or item before the imputation, such as impute the missing ratings of New-Users, New-Items, or cold-start users or items that suffer from the lack of the ratings. In addition, several factors are analyzed that affect the prediction accuracy when the imputation method is utilized with NMF-based recommendation systems. These factors include the total number of the ratings of the user or item before the imputation, the total number of imputed ratings for each user and item, the average of imputed rating values, and the value of imputed rating values. In addition, several strategies are applied to select the subset of missing ratings for the imputation that lead to increasing the prediction accuracy and limiting the imputation error. Moreover, a comparison is conducted with some popular methods that are in common with the proposed method in utilizing the imputation to handle the lack of ratings, but they differ in the source of the imputed ratings. Experiments on different large-size datasets are conducted to examine the proposed approaches and analyze the effects of the imputation on accuracy. Users and items are divided into three groups based on the total number of the ratings before the imputation is applied and their recommendation accuracy is calculated. The results show that the imputation enhances the recommendation system by capacitating the system to recommend items to New-Users, introduce New-Items to users, and increase the accuracy of the cold-start users and items. However, the analyzed factors play important roles in the recommendation accuracy and limit the error that is introduced from the imputation

Microcelebrity Practices: A Cross-Platform Study Through a Richness Framework

Social media have introduced a contemporary shift from broadcast to participatory media, which has brought about major changes to the celebrity management model. It is now common for celebrities to bypass traditional mass media and take control over their promotional discourse through the practice of microcelebrity. The theory of microcelebrity explains how people turn their public persona into media content with the goal of gaining and maintaining audiences who are regarded as an aggregated fan base. To accomplish this, the theory suggests that people employ a set of online self-presentation techniques that typically consist of three core practices: identity constructions, fan interactions and promoting visibility beyond the existing fan base. Studies on single platforms (e.g., Twitter), however, show that not all celebrities necessarily engage in all core practices to the same degree. Importantly, celebrities are increasingly using multiple social media platforms simultaneously to expand their audience, while overcoming the limitations of a particular platform. This points to a gap in the literature and calls for a cross-platform study. This dissertation employed a mixed-methods research design to reveal how social media platforms i.e., Twitter and Instagram, helped celebrities grow and maintain their audience. The first phase of the study relied on a richness scoring framework that quantified social media activities using affordance richness, a measure of the ability of a post to deliver the information necessary in affording a celebrity to perform an action by using social media artifacts. The analyses addressed several research questions regarding social media uses by different groups of celebrities and how the audience responded to different microcelebrity strategies. The findings informed the design of the follow-up interviews with audience members. Understanding expectations and behaviors of fans is relevant not only as a means to enhance the practice’s outcome and sustain promotional activity, but also as a contribution to our understandings about contemporary celebrity-fans relationships mediated by social media. Three findings are highlighted. First, I found that celebrities used the two platforms differently, and that different groups of celebrities emphasized different core practices. This finding was well explained by the interviews suggesting that the audiences had different expectations from different groups of celebrities. Second, microcelebrity strategies played an important role in an audience’s engagement decisions. The finding was supported by the interviews indicating that audience preferences were based on some core practices. Lastly, while their strategies had no effect on follow and unfollow decisions, the consistency of the practices had significant effects on the decisions. This study makes contributions to the theory of Microcelebrity and offers practical contributions by providing broad insights from both practitioners’ and audiences’ perspectives. This is essential given that microcelebrity is a learned practice rather than an inborn trait

Computer Science 2019 APR Self-Study & Documents

UNM Computer Science APR self-study report and review team report for Spring 2019, fulfilling requirements of the Higher Learning Commission