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Item GAN-inspired Defense Against Backdoor Attack on Federated Learning Systems(IEEE, 2023-09) Sundar, Agnideven Palanisamy; Li, Feng; Zou, Xukai; Gao, Tianchong; Hosler, Ryan; Computer Science, Luddy School of Informatics, Computing, and EngineeringFederated Learning (FL) provides an opportunity for clients with limited data resources to combine and build better Machine Learning models without compromising their privacy. But aggregating contributions from various clients implies that the errors present in some clients’ resources will also get propagated to all the clients through the combined model. Malicious entities leverage this negative factor to disrupt the normal functioning of the FL system for their gain. A backdoor attack is one such attack where the malicious entities act as clients and implant a small trigger into the global model. Once implanted, the model performs the attacker desired task in the presence of the trigger but acts benignly otherwise. In this paper, we build a GAN-inspired defense mechanism that can detect and defend against the presence of such backdoor triggers. The unavailability of labeled benign and backdoored models has prevented researchers from building detection classifiers. We tackle this problem by utilizing the clients as Generators to construct the required dataset. We place the Discriminator on the server-side, which acts as a backdoored model detecting binary classifier. We experimentally prove the proficiency of our approach with the image-based non-IID datasets, CIFAR10 and CelebA. Our prediction probability-based defense mechanism successfully removes all the influence of backdoors from the global model.Item Local Differential Privacy Preservation via the Novel Encoding Method(IEEE, 2023-09) Zhang, Niu; Wang, Shunwei; Gao, Tianchong; Li, Feng; Sundar, Agnideven Palanisamy; Zou, Xukai; Computer Science, Luddy School of Informatics, Computing, and EngineeringIn recent years, the application of big data analysis has seen a significant increase across various fields with the aim of extracting useful information from massive data to enhance human life. These data contain a large amount of privacy, and directly using user data for analysis poses a significant risk of privacy leakage. To address this pressing issue, many scholars have incorporated the latest protection methodologies, such as local differential privacy (LDP), into big data analysis. Existing LDP methods typically convert data into binary strings and then add noise, but the weight of each bit unevenly distributes noise, eventually increasing utility loss. Inspired by this, the paper investigates the reduction of error through alternative numeral systems, leading to the proposition of a segmented-based LDP data preservation mechanism (LDPseg), where each bit flip has an equal impact on the outcome. Theoretical analysis reveals that under certain conditions, this mechanism maintains a lower error expectation and variance compared to binary systems. Real-world experimental results indicate that the proposed method exhibits positive performance in machine learning.Item Energy-Efficient and Robust QoS Control for Wireless Sensor Networks Using the Extended Gur Game(MDPI, 2025-01-25) Zhong, Xiaoyang; Liang, Yao; Li, Yimei; Computer Science, Luddy School of Informatics, Computing, and EngineeringOutdoor wireless sensor networks (WSNs) operate autonomously in dynamic and unattended real-world environments, where sensor nodes are typically powered by their batteries. In hash outdoor settings, such as mountainous regions or underwater locations, recharging or replacing sensor node batteries is particularly challenging. For these WSN deployments, ensuring quality of service (QoS) control while conserving energy is crucial. This paper presents a novel QoS control algorithm for WSNs, built on extensions to the Gur game framework. The proposed approach not only enhances QoS performance compared to existing Gur game-based WSN control algorithms but also addresses their fundamental energy consumption challenges, enabling sustainable communication and extended network lifetimes. We evaluate the approach through comprehensive TinyOS-based WSN simulations and comparisons with existing algorithms. The results demonstrate that our approach, referred to as the robust Gur game, significantly enhances QoS control and achieves a 27.33% improvement in energy efficiency over the original Gur game and shuffle algorithms, showcasing the significant benefits of the proposed method.Item HDFR: A Hydrologic Data and Modeling System with On-Demand Access to Environmental Sensing Data for Decision Making(IEEE, 2023-01) Luna, Daniel; Hernández, Felipe; Liang, Yao; Liang, Xu; Computer Science, Luddy School of Informatics, Computing, and EngineeringThis paper introduces the Hydrologic Disaster Forecasting and Response (HDFR), an online data and modeling integration software system that facilitates the machine-to-machine access to and the management of environmental sensing data from space and ground products. Available data sources include in-situ measurements from weather and hydrographic stations; remote sensing products from Doppler precipitation radars in the United States, Earth-monitoring satellites that measure precipitation, soil moisture, and snow cover; and numerical weather prediction model outputs from the U.S. National Weather Service. Additionally, the HDFR system provides a suite of hydrologic modeling tools; including data fusion, storm severity assessment, and hydrologic model preprocessing for the Distributed Hydrology Soil Vegetation Model (DHSVM); that are seamlessly incorporated with the diverse suite of data products. Two example workflows demonstrate how this unified framework could help bridge the gap between the online and on-demand accessing of growing wealth of Earth-observing data and hydrologic prediction for scientific and engineering applications.Item Natural language processing to evaluate texting conversations between patients and healthcare providers during COVID-19 Home-Based Care in Rwanda at scale(Public Library of Science, 2025-01-15) Lester, Richard T.; Manson, Matthew; Semakula, Muhammed; Jang, Hyeju; Mugabo, Hassan; Magzari, Ali; Blackmer, Junhong Ma; Fattah, Fanan; Niyonsenga, Simon Pierre; Rwagasore, Edson; Ruranga, Charles; Remera, Eric; Ngabonziza, Jean Claude S.; Carenini, Giuseppe; Nsanzimana, Sabin; Computer Science, Luddy School of Informatics, Computing, and EngineeringCommunity isolation of patients with communicable infectious diseases limits spread of pathogens but our understanding of isolated patients' needs and challenges is incomplete. Rwanda deployed a digital health service nationally to assist public health clinicians to remotely monitor and support SARS-CoV-2 cases via their mobile phones using daily interactive short message service (SMS) check-ins. We aimed to assess the texting patterns and communicated topics to better understand patient experiences. We extracted data on all COVID-19 cases and exposed contacts who were enrolled in the WelTel text messaging program between March 18, 2020, and March 31, 2022, and linked demographic and clinical data from the national COVID-19 registry. A sample of the text conversation corpus was English-translated and labeled with topics of interest defined by medical experts. Multiple natural language processing (NLP) topic classification models were trained and compared using F1 scores. Best performing models were applied to classify unlabeled conversations. Total 33,081 isolated patients (mean age 33·9, range 0-100), 44% female, including 30,398 cases and 2,683 contacts) were registered in WelTel. Registered patients generated 12,119 interactive text conversations in Kinyarwanda (n = 8,183, 67%), English (n = 3,069, 25%) and other languages. Sufficiently trained large language models (LLMs) were unavailable for Kinyarwanda. Traditional machine learning (ML) models outperformed fine-tuned transformer architecture language models on the native untranslated language corpus, however, the reverse was observed of models trained on English-only data. The most frequently identified topics discussed included symptoms (69%), diagnostics (38%), social issues (19%), prevention (18%), healthcare logistics (16%), and treatment (8·5%). Education, advice, and triage on these topics were provided to patients. Interactive text messaging can be used to remotely support isolated patients in pandemics at scale. NLP can help evaluate the medical and social factors that affect isolated patients which could ultimately inform precision public health responses to future pandemics.Item Force-directed graph embedding with hops distance(IEEE, 2023-12) Lotfalizadeh, Hamidreza; Al Hasan, Mohammad; Computer Science, Luddy School of Informatics, Computing, and EngineeringGraph embedding has become an increasingly important technique for analyzing graph-structured data. By representing nodes in a graph as vectors in a low-dimensional space, graph embedding enables efficient graph processing and analysis tasks like node classification, link prediction, and visualization. In this paper, we propose a novel force-directed graph embedding method that utilizes the steady acceleration kinetic formula to embed nodes in a way that preserves graph topology and structural features. Our method simulates a set of customized attractive and repulsive forces between all node pairs with respect to their hop-distance. These forces are then used in Newton’s second law to obtain the acceleration of each node. The method is intuitive, parallelizable, and highly scalable. We evaluate our method on several graph analysis tasks and show that it achieves competitive performance compared to state-of-the-art unsupervised embedding techniques.Item Tracking Public Attitudes Toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-Based Sentiment Analysis(JMIR, 2022-03-29) Jang, Hyeju; Rempel, Emily; Roe, Ian; Adu, Prince; Carenini, Giuseppe; Janjua, Naveed Zafar; Computer Science, Luddy School of Informatics, Computing, and EngineeringBackground: The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation, poses a major barrier to achieving herd immunity. Objective: We aim to investigate Twitter users' attitudes toward COVID-19 vaccination in Canada after vaccine rollout. Methods: We applied a weakly supervised aspect-based sentiment analysis (ABSA) technique, which involves the human-in-the-loop system, on COVID-19 vaccination-related tweets in Canada. Automatically generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiments toward "vaccination" changed over time. In addition, we analyzed the most retweeted or liked tweets by observing most frequent nouns and sentiments toward key aspects. Results: After applying the ABSA system, we obtained 170 aspect terms (eg, "immunity" and "pfizer") and 6775 opinion terms (eg, "trustworthy" for the positive sentiment and "jeopardize" for the negative sentiment). While manually verifying or editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for analysis. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to "vaccine distribution," "side effects," "allergy," "reactions," and "anti-vaxxer," and positive sentiments related to "vaccine campaign," "vaccine candidates," and "immune response." These results indicate that the Twitter users express concerns about the safety of vaccines but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of the remaining tweets, the most retweeted or liked tweets showed more positive sentiment overall toward key aspects (P<.001), especially vaccines (P<.001) and vaccination (P=.009). Further investigation of the most retweeted or liked tweets revealed two opposing trends in Twitter users who showed negative sentiments toward vaccines: the "anti-vaxxer" population that used negative sentiments as a means to discourage vaccination and the "Covid Zero" population that used negative sentiments to encourage vaccinations while critiquing the public health response. Conclusions: Our study examined public sentiments toward COVID-19 vaccination on tweets over an extended period in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination.Item Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis(JMIR, 2021-02-10) Jang, Hyeju; Rempel, Emily; Roth, David; Carenini, Giuseppe; Janjua, Naveed Zafar; Computer Science, Luddy School of Informatics, Computing, and EngineeringBackground: Social media is a rich source where we can learn about people's reactions to social issues. As COVID-19 has impacted people's lives, it is essential to capture how people react to public health interventions and understand their concerns. Objective: We aim to investigate people's reactions and concerns about COVID-19 in North America, especially in Canada. Methods: We analyzed COVID-19-related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people's sentiment about COVID-19-related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians. Results: Topic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, "vaccines," "economy," and "masks") and 60 opinion terms such as "infectious" (negative) and "professional" (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing. Conclusions: Analyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19-related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions.Item Case Study: Mapping an E-Voting Based Curriculum to CSEC2017(ACM, 2023-03) Zheng, Muwei; Swearingen, Nathan; Mills, Steven; Gyurek, Croix; Bishop, Matt; Zou, Xukai; Computer Science, Luddy School of Informatics, Computing, and EngineeringAn electronic voting (E-voting) oriented cybersecurity curriculum, proposed by Hostler et al. [4] in 2021, leverages the rich security features of E-voting systems and E-voting process to teach essential concepts of cybersecurity. Existing curricular guidelines describe topics in computer security, but do not instantiate them with examples. This is because their goals are different. In this case study, we map the e-voting curriculum into the CSEC2017 curriculum guidelines, to demonstrate how such a mapping is done. Further, this enables teachers to select the parts of the e-voting curriculum most relevant to their classes, by basing the selection on the relevant CSEC2017 learning objectives. We conclude with a brief discussion on generalizing this mapping to other curricular guidelines.Item ASPER: Attention-based approach to extract syntactic patterns denoting semantic relations in sentential context(Elsevier, 2023-06) Kabir, Md. Ahsanul; Phillips, Tyler; Luo, Xiao; Al Hasan, Mohammad; Computer Science, Luddy School of Informatics, Computing, and EngineeringSemantic relationships, such as hyponym–hypernym, cause–effect, meronym–holonym etc., between a pair of entities in a sentence are usually reflected through syntactic patterns. Automatic extraction of such patterns benefits several downstream tasks, including, entity extraction, ontology building, and question answering. Unfortunately, automatic extraction of such patterns has not yet received much attention from NLP and information retrieval researchers. In this work, we propose an attention-based supervised deep learning model, ASPER, which extracts syntactic patterns between entities exhibiting a given semantic relation in the sentential context. We validate the performance of ASPER on three distinct semantic relations—hyponym–hypernym, cause–effect, and meronym–holonym on six datasets. Experimental results show that for all these semantic relations, ASPER can automatically identify a collection of syntactic patterns reflecting the existence of such a relation between a pair of entities in a sentence. In comparison to the existing methodologies of syntactic pattern extraction, ASPER’s performance is substantially superior.