This demo presents the next-generation version of KimBilet.com, an educational platform that leverages generative AI to create adaptive, mastery-driven learning experiences. Building on last year's personalized course generation, the new system introduces a fine-grained skill taxonomy and a feedback loop that evaluates and responds to learner performance in real time.

This lightning talk presents an initiative to integrate professional identity development activities into a large enrollment undergraduate CS First Year Seminar course. This CS majors only, required, one-credit hour course is an opportunity to excite students about computing as a profession. In fall 2024, inspired by Fink's Significant Learning Theory, where multiple dimensions contribute to changing how a student lives their personal and professional life, we added a reflection assignment and an external engagement requirement to foster professional identity. This experiential learning required three different types of campus activities outside the classroom: resume and interview preparation, networking with professionals, and student presentations. The first month students participate in a Career Services workshop for resumes, interviewing, or decoding job postings. The following month, students participate in one of several corporate partner activities or workshops, and the final month they attend a student project expo or research poster presentation. The last assignment has each student write a personal mission statement and write a letter to their current self from their sixty-year-old self! The letters and missions are emailed to the students the next semester, to remind them of their larger goals and values as a professional.

This demo presents KimBilet.com, an educational platform that utilizes generative AI to create personalized educational content on demand. Catering to high-school and college students, instructors, job seekers, and lifelong learners, the system generates customized courses based on user prompts, covering any topic of interest. Each course may include a sequence of AI-created lessons and quizzes, providing detailed feedback for every quiz option to enhance understanding. The platform supports intuitive navigation through keyboard shortcuts and allows users to jump between course items seamlessly. It also maintains a history of completed quizzes to help users track their learning progress. Future enhancements include topic suggestions based on past interests, support for coding exercises, and multilingual support. This demo will showcase how KimBilet.com leverages AI to offer adaptive learning experiences, engage attendees through interactive exploration, and discuss its potential applications in educational settings. Participants will gain insights into integrating AI-driven tools into teaching and learning processes to address diverse educational needs.

With billions of active social media accounts and millions of live video cameras, live new big data offer many opportunities for smart applications. However, the main consumers of the new big data have been humans. We envision the research on live knowledge , to automatically acquire real-time, validated, and actionable information. Live knowledge presents two significant and diverging technical challenges: big noise and concept drift. We describe the EBKA (evidence-based knowledge acquisition) approach, illustrated by the LITMUS landslide information system. LITMUS achieves both high accuracy and wide coverage, demonstrating the feasibility and promise of EBKA approach to achieve live knowledge.

Recently, research on detection of abnormal vehicle behavior is getting increasing attention through various approaches, including interval speed measurement using radars, binocular surveillance cameras, and infrared speed sensors. In most areas in the U.S., such detection is still performed manually by the highway patrol. This paper presents a novel approach for detecting abnormal vehicle behavior using the traditional traffic surveillance cameras. Once the vehicle is within the camera field of view (FOV), its estimated speed is automatically detected based on designated references with respect to dashed white lines on roads, such that the speed of the vehicle is computed using techniques. The proposed method, if validated and facilitated, can largely reduce the cost of upgrading surveillance infrastructure as well as the extra human administrative burden.

LITMUS is a real-time online and openly accessible service that collects high quality information on landslide events from social media. This service uses disaster related keywords, such as "landslide" and "mudslide", to analyze messages posted by English speaking users. However, comprehensive coverage of disasters must include multilingual support as there are events that are reported in languages other than English. We discuss and evaluate possible implementations of such support using "native" and "translated" approaches. "Native" approach involves a complete reimplementation of the existing infrastructure in another language whereas in the "translated" approach the existing infrastructure can be used without modification. As an illustration, we present a demo that extends LITMUS to implement a "native" approach for multilingual reporting of landslide events.

In this paper we propose and evaluate three approaches for automated classification of texts in over 60 languages without the need for a manually annotated dataset in those languages. All approaches are based on the randomized Explicit Semantic Analysis method using multilingual Wikipedia articles as their knowledge repository. We evaluate the proposed approaches by classifying a Twitter dataset in English and Portuguese into relevant and irrelevant items with respect to landslide as a natural disaster, where the highest achieved F1-score is 0.93. These approaches can be used in various applications where multilingual classification is needed, including multilingual disaster reporting using Social Media to improve coverage and increase confidence. As illustration, we present a demonstration that combines data from physical sensors and social networks to detect landslide events reported in English and Portuguese.

We study the problem of using Social Media to detect natural disasters, of which we are interested in a special kind, namely landslides. Employing information from Social Media presents unique research challenges, as there exists a considerable amount of noise due to multiple meanings of the search keywords, such as "landslide" and "mudslide". To tackle these challenges, we propose REX, a rapid ensemble classification system which can filter out noisy information by implementing two key ideas: (I) a new method for constructing independent classifiers that can be used for rapid ensemble classification of Social Media texts, where each classifier is built using randomized Explicit Semantic Analysis; and (II) a self-correction approach which takes advantage of the observation that the majority label assigned to Social Media texts belonging to a large event is highly accurate. We perform experiments using real data from Twitter over 1.5 years to show that REX classification achieves 0.98 in F-measure, which outperforms the standard Bag-of-Words algorithm by an average of 0.14 and the state-of-the-art Word2Vec algorithm by 0.04. We also release the annotated datasets used in the experiments as a contribution to the research community containing 282k labeled items.

Modern world data come from an increasing number of sources, including data from physical sensors like weather satellites and seismographs as well as social networks and web logs. While progress has been made in the filtering of individual social networks, there are significant advantages in the integration of big data from multiple sources. For physical events, the integration of physical sensors and social network data can improve filtering efficiency and quality of results beyond what is feasible in each individual data stream. Disasters are representative physical events with real world impact. As illustration and demonstration, we have built the LITMUS landslide information service that combines data from both physical sensors and social networks in real-time. LITMUS filters and combines reliable but indirect physical data with direct report social media data on landslides to achieve high quality and wide coverage of landslide information.

The use of Social Media for event detection, such as detection of natural disasters, has gained a booming interest from research community as Social Media has become an immensely important source of real-time information. However, it poses a number of challenges with respect to high volume, noisy information and lack of geo-tagged data. Extraction of high quality information (e.g., Accurate locations of events) while maintaining good performance (e.g., Low latency) are the major problems. In this paper, we propose two approaches for tackling these issues: an augmented Explicit Semantic Analysis approach for rapid classification and a composition of clustering algorithms for location estimation. Our experiments demonstrate over 98% in precision, recall and F-measure when classifying Social Media data while producing a 20% improvement in location estimation due to clustering composition approach. We implement these approaches as part of the landslide detection service LITMUS, which is live and openly accessible for continued evaluation and use.

Landslides are an illustrative example of multi-hazards, which can be caused by earthquakes, rainfalls and human activity among other reasons. Detection of landslides presents a significant challenge, since there are no physical sensors that would detect landslides directly. A more recent approach in detection of natural hazards, such as earthquakes, involves the use of social media. We propose a multi-service composition approach and describe LITMUS, which is a landslide detection service that combines data from both physical and social information services by filtering and then joining the information flow from those services based on their spatiotemporal features. Our results show that with such approach LITMUS detects 25 out of 27 landslides reported by USGS in December 2013 and 40 more landslide locations unreported by USGS during this period. LITMUS is a prototype tool that is used to investigate and implement research ideas in the area of disaster detection. We list some of the current work being done on refining the system that allows us to identify 137 landslide locations unreported by USGS during a more recent period of September 2014. Finally, we describe a live demonstration that displays landslide detection results on a web map in real-time.

Social media have been used in the detection and management of natural hazards such as earthquakes. However, disasters often lead to other kinds of disasters, forming multi-hazards. Landslide is an illustrative example of a multi-hazard, which may be caused by earthquakes, rainfalls, water erosion, among other reasons. Detecting such multi-hazards is a significant challenge, since physical sensors designed for specific disasters are insufficient for multi-hazards. We describe LITMUS -- a landslide detection service based on a multi-service composition approach that combines data from both physical and social information services by filtering and then joining the information flow from those services based on their spatiotemporal features. Our results show that with such approach LITMUS detects 25 out of 27 landslides reported by USGS in December and 40 more landslides unreported by USGS. Also, LITMUS provides a live demonstration that displays results on a web map.