UMBC Information Systems Department

Permanent URI for this collectionhttp://hdl.handle.net/11603/51

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Can Generative AI be Egalitarian?
(IEEE, 2024-10) Feldman, Philip; Foulds, James; Pan, Shimei
The recent explosion of “foundation” generative AI models has been built upon the extensive extraction of value from online sources, often without corresponding reciprocation. This pattern mirrors and intensifies the extractive practices of surveillance capitalism [46], while the potential for enormous profit has challenged technology organizations’ commitments to responsible AI practices, raising significant ethical and societal concerns. However, a promising alternative is emerging: the development of models that rely on content willingly and collaboratively provided by users. This article explores this “egalitarian” approach to generative AI, taking inspiration from the successful model of Wikipedia. We explore the potential implications of this approach for the design, development, and constraints of future foundation models. We argue that such an approach is not only ethically sound but may also lead to models that are more responsive to user needs, more diverse in their training data, and ultimately more aligned with societal values. Furthermore, we explore potential challenges and limitations of this approach, including issues of scalability, quality control, and potential biases inherent in volunteercontributed content.
Fair Inference for Discrete Latent Variable Models: An Intersectional Approach
(ACM, 2024-09-04) Islam, Rashidul; Pan, Shimei; Foulds, James
It is now widely acknowledged that machine learning models, trained on data without due care, often exhibit discriminatory behavior. Traditional fairness research has mainly focused on supervised learning tasks, particularly classification. While fairness in unsupervised learning has received some attention, the literature has primarily addressed fair representation learning of continuous embeddings. This paper, however, takes a different approach by investigating fairness in unsupervised learning using graphical models with discrete latent variables. We develop a fair stochastic variational inference method for discrete latent variables. Our approach uses a fairness penalty on the variational distribution that reflects the principles of intersectionality, a comprehensive perspective on fairness from the fields of law, social sciences, and humanities. Intersectional fairness brings the challenge of data sparsity in minibatches, which we address via a stochastic approximation approach. We first show the utility of our method in improving equity and fairness for clustering using naïve Bayes and Gaussian mixture models on benchmark datasets. To demonstrate the generality of our approach and its potential for real-world impact, we then develop a specialized graphical model for criminal justice risk assessments, and use our fairness approach to prevent the inferences from encoding unfair societal biases.
ANSR-DT: An Adaptive Neuro-Symbolic Learning and Reasoning Framework for Digital Twins
(2025-01-15) Hakim, Safayat Bin; Adil, Muhammad; Velasquez, Alvaro; Song, Houbing
In this paper, we propose an Adaptive Neuro-Symbolic Learning Framework for digital twin technology called ``ANSR-DT." Our approach combines pattern recognition algorithms with reinforcement learning and symbolic reasoning to enable real-time learning and adaptive intelligence. This integration enhances the understanding of the environment and promotes continuous learning, leading to better and more effective decision-making in real-time for applications that require human-machine collaboration. We evaluated the \textit{ANSR-DT} framework for its ability to learn and adapt to dynamic patterns, observing significant improvements in decision accuracy, reliability, and interpretability when compared to existing state-of-the-art methods. However, challenges still exist in extracting and integrating symbolic rules in complex environments, which limits the full potential of our framework in heterogeneous settings. Moreover, our ongoing research aims to address this issue in the future by ensuring seamless integration of neural models at large. In addition, our open-source implementation promotes reproducibility and encourages future research to build on our foundational work.
Understanding the Challenges of Maker Entrepreneurship
(ACM, 2025-01-23) Friedman, Natalie; Bremers, Alexandra; Nyanyo, Adelaide; Clark, Ian; Kotturi, Yasmine; Dabbish, Laura; Ju, Wendy; Martelaro, Nikolas
The maker movement embodies a resurgence in DIY creation, merging physical craftsmanship and arts with digital technology support. However, mere technological skills and creativity are insufficient for economically and psychologically sustainable practice. By illuminating and smoothing the path from ``maker" to ``maker entrepreneur," we can help broaden the viability of making as a livelihood. Our research centers on makers who design, produce, and sell physical goods. In this work, we explore the transition to entrepreneurship for these makers and how technology can facilitate this transition online and offline. We present results from interviews with 20 USA-based maker entrepreneurs {(i.e., lamps, stickers)}, six creative service entrepreneurs {(i.e., photographers, fabrication)}, and seven support personnel (i.e., art curator, incubator director). Our findings reveal that many maker entrepreneurs 1) are makers first and entrepreneurs second; 2) struggle with business logistics and learn business skills as they go; and 3) are motivated by non-monetary values. We discuss training and technology-based design implications and opportunities for addressing challenges in developing economically sustainable businesses around making.
RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots
(2024-06-12) Feldman, Philip; Foulds, James; Pan, Shimei
Large language models (LLMs) like ChatGPT demonstrate the remarkable progress of artificial intelligence. However, their tendency to hallucinate -- generate plausible but false information -- poses a significant challenge. This issue is critical, as seen in recent court cases where ChatGPT's use led to citations of non-existent legal rulings. This paper explores how Retrieval-Augmented Generation (RAG) can counter hallucinations by integrating external knowledge with prompts. We empirically evaluate RAG against standard LLMs using prompts designed to induce hallucinations. Our results show that RAG increases accuracy in some cases, but can still be misled when prompts directly contradict the model's pre-trained understanding. These findings highlight the complex nature of hallucinations and the need for more robust solutions to ensure LLM reliability in real-world applications. We offer practical recommendations for RAG deployment and discuss implications for the development of more trustworthy LLMs.
GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models
(2024-12-16) Zhang, Tao; Zeng, Ziqian; Xiao, Yuxiang; Zhuang, Huiping; Chen, Cen; Foulds, James; Pan, Shimei
Large Language Models (LLMs) are prone to generating content that exhibits gender biases, raising significant ethical concerns. Alignment, the process of fine-tuning LLMs to better align with desired behaviors, is recognized as an effective approach to mitigate gender biases. Although proprietary LLMs have made significant strides in mitigating gender bias, their alignment datasets are not publicly available. The commonly used and publicly available alignment dataset, HH-RLHF, still exhibits gender bias to some extent. There is a lack of publicly available alignment datasets specifically designed to address gender bias. Hence, we developed a new dataset named GenderAlign, aiming at mitigating a comprehensive set of gender biases in LLMs. This dataset comprises 8k single-turn dialogues, each paired with a "chosen" and a "rejected" response. Compared to the "rejected" responses, the "chosen" responses demonstrate lower levels of gender bias and higher quality. Furthermore, we categorized the gender biases in the "rejected" responses of GenderAlign into 4 principal categories. The experimental results show the effectiveness of GenderAlign in reducing gender bias in LLMs.
DoubleDistillation: Enhancing LLMs for Informal Text Analysis using Multistage Knowledge Distillation from Speech and Text
(ACM, 2024-11-04) Hasan, Fatema; Li, Yulong; Foulds, James; Pan, Shimei; Bhattacharjee, Bishwaranjan
Traditional large language models (LLMs) leverage extensive text corpora but lack access to acoustic and para-linguistic cues present in speech. There is a growing interest in enhancing text-based models with audio information. However, current models often require an aligned audio-text dataset which is frequently much smaller than typical language model training corpora. Moreover, these models often require both text and audio streams during inference/testing. In this study, we introduce a novel two-stage knowledge distillation (KD) approach that enables language models to (a) incorporate rich acoustic and paralinguistic information from speech, (b) utilize text corpora comparable in size to typical language model training data, and (c) support text-only analysis without requiring an audio stream during inference/testing. Specifically, we employ a pre-trained speech embedding teacher model (OpenAI Whisper) to train a Teacher Assistant (TA) model on an aligned audio-text dataset in the first stage. In the second stage, the TA’s knowledge is transferred to a student language model trained on a conventional text dataset. Thus, our two-stage KD method leverages both the acoustic and paralinguistic cues in the aligned audio-text data and the nuanced linguistic knowledge in a large text-only dataset. Based on our evaluation, this DoubleDistillation system consistently outperforms traditional LLMs in 15 informal text understanding tasks.
EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses
(IEEE, 2025-01-11) Pyreddy, Shireesh Reddy; Zaman, Tarannum Shaila
The widespread adoption of generative AI has generated diverse opinions, with individuals expressing both support and criticism of its applications. This study investigates the emotional dynamics surrounding generative AI by analyzing human tweets referencing terms such as ChatGPT, OpenAI, Copilot, and LLMs. To further understand the emotional intelligence of ChatGPT, we examine its responses to selected tweets, highlighting differences in sentiment between human comments and LLM-generated responses. We introduce EmoXpt, a sentiment analysis framework designed to assess both human perspectives on generative AI and the sentiment embedded in ChatGPT's responses. Unlike prior studies that focus exclusively on human sentiment, EmoXpt uniquely evaluates the emotional expression of ChatGPT. Experimental results demonstrate that LLM-generated responses are notably more efficient, cohesive, and consistently positive than human responses.
Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of Things
(2025-01-17) Li, Mengran; Chen, Junzhou; Yu, Chenyun; Jiang, Guanying; Zhang, Ronghui; Shen, Yanming; Song, Houbing
With the advancement of information technology, the Social Internet of Things (SIoT) has fostered the integration of physical devices and social networks, deepening the study of complex interaction patterns. Text Attribute Graphs (TAGs) capture both topological structures and semantic attributes, enhancing the analysis of complex interactions within the SIoT. However, existing graph learning methods are typically designed for complete attributed graphs, and the common issue of missing attributes in Attribute Missing Graphs (AMGs) increases the difficulty of analysis tasks. To address this, we propose the Topology-Driven Attribute Recovery (TDAR) framework, which leverages topological data for AMG learning. TDAR introduces an improved pre-filling method for initial attribute recovery using native graph topology. Additionally, it dynamically adjusts propagation weights and incorporates homogeneity strategies within the embedding space to suit AMGs' unique topological structures, effectively reducing noise during information propagation. Extensive experiments on public datasets demonstrate that TDAR significantly outperforms state-of-the-art methods in attribute reconstruction and downstream tasks, offering a robust solution to the challenges posed by AMGs. The code is available at https://github.com/limengran98/TDAR.
ENABLING DATA PRIVACY THROUGH ANONYMIZATION IN CENTRALIZED AND DISTRIBUTED ENVIRONMENTS TO SECURELY SHARE NETWORK TRACE & HEALTHCARE DATA
(2024-01-01) Xenakis, Antonios; Karabatis, George; Chen, Zhiyuan; Information Systems; Information Systems
Privacy is the right to control sensitive information and protect it from unauthorized access or disclosure. Anonymization ensures privacy by removing or altering sensitive data, making it difficult to uncover the original information. This dissertation investigates anonymization techniques in both centralized and distributed environments and emphasizes on preserving the privacy of data in two different application domains, namely network trace data, and healthcare data. Organizations collect vast amounts of network trace data for purposes such as network optimization and user behavior analysis but are often hesitant to share this data due to privacy concerns and proprietary information. Existing anonymization tools have significant shortcomings: they lack provable protection and rely heavily on parameter settings without offering adequate guidance. This dissertation proposes a self-adaptive and secure approach for sharing network trace data in order to maintain privacy by removing or obfuscating sensitive information. Additionally, we investigate network trace data anonymization in distributed environments. Organizations often rely on integrating data from multiple sites, presenting challenges in anonymization due to the required communication. This dissertation introduces two new methods for cluster-based distributed anonymization: one based on distributed coordinated anonymization and the other on top-down distributed anonymization. These methods enable each site to anonymize its data in a coordinated manner, allowing the merged anonymized data to be centrally analyzed. Finally, this dissertation examines anonymization and integration of healthcare data. Anonymizing healthcare data is essential for protecting privacy, requiring the removal of personal identifiers while ensuring accurate integration and alignment of distributed patient information. In order to address the anonymization and integration challenge of distributed healthcare data, we introduce a novel approach to anonymize distributed data with limited communication, followed by an integration process for subsequent analysis. This approach ensures consistency across sources so that anonymized data can be directly integrated without expensive procedures. A hash-function generator is used to create consistent noise based on a locally generated seed, which also serves as a unique identifier for data integration. Our approaches overcome these limitations by providing provable protection and automatically optimizing parameter settings. The proposed solutions support differential privacy, k-anonymity and random response. Experimental evaluations demonstrate that the proposed techniques ensure privacy through anonymization, maintain data utility, and enable efficient integration of distributed anonymized data with minimal computational overhead.
Shaping Ownership in Augmented Reality: The Impact of Annotation Control on Psychological Ownership and Collaboration
(2024-01-01) Seo, Jwawon; Mentis, Helena; Information Systems; Human Centered Computing
Augmented Reality (AR) has emerged as a transformative technology, reshaping the way we interact, collaborate, and perceive our surroundings. This dissertation delves into the intricate dynamics of psychological ownership within AR collaborative workspaces, focusing on the influence of annotation control capabilities. Through a series of controlled experiments using the HoloMentor system, this study examines how different levels of annotation control shape Individual and Collective Psychological Ownership (IPO and CPO) across blended workspaces. The research investigates the mediating and moderating roles of five dimensions of psychological ownership (possession, control, identity, responsibility, and territoriality) and explores how CPO mediates the relationship between annotation control and group processes. Key findings reveal that parallel annotation control consistently fosters the strongest sense of CPO while decreasing IPO across all blended workspace types. A notable spillover effect demonstrates that control over virtual annotations influences ownership perceptions beyond the immediate virtual environment. The comprehensive path analysis uncovers contrasting mechanisms through which parallel and full control influence CPO and collaboration outcomes. In parallel control scenarios, CPO emerges primarily through heightened responsibility, while in full control scenarios, it develops through a stronger sense of personal connection and identification with the task. The study also reveals a paradoxical relationship between individual control and CPO in shared environments, where the increased individual sense of control can sometimes attenuate the positive effect of parallel control on CPO. Furthermore, the research demonstrates that CPO significantly mediates the relationship between annotation control and various group process outcomes, particularly in collaboration quality, performance, and satisfaction. This mediation analysis highlights the importance of fostering CPO in enhancing collaboration, while also revealing that different control modes may be optimal for different aspects of group processes. This research contributes to both theory and practice in Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW). It extends our understanding of psychological ownership in AR collaboration, illuminating the complex interplay between system features, psychological factors, and group performance. Additionally, it provides practical design guidelines for creating AR collaboration tools that foster CPO and enhance outcomes. By bridging theoretical insights with design implications, this dissertation advances the development of effective and satisfying AR collaborative systems.
Exploring the Effects of Self-Expression Modalities on Positive and Negative Self-Reflection Toward the Design of an Interactive Technology for Emotional Well-Being
(2024-01-01) Rajendran, Kavya; Kleinsmith, Andrea; Information Systems; Human Centered Computing
Stress and anxiety have heightened among graduate students since the pandemic, challenging their emotional well-being and academic success. Psychology advocates for self-expression techniques like expressive writing, creative arts, and dance/movement to promote mental resilience. However, there is limited research on the integration of these modalities within interactive self-reflection technologies. Considering three self-expression forms—written, visual, and physical movement—this research explores how these modalities influence affect regulation during positive and negative self-reflection. Findings suggest that self-reflection was calming, and participants preferred modalities they were more familiar and comfortable with. While written reflections served as a tool for processing emotions to provide relief, the other modalities distracted them from overwhelming emotions or kept them in check. This research contributes to the field of HCI by investigating how and why integrating diverse modalities into interactive affective technologies can support users in emotional regulation and mental well-being.
QUANTUM SOFTWARE TESTING AND QUANTUM CRYPTOGRAPHY EDUCATION
(2024-01-01) Kaur, Khushdeep; Zhang, Lei; Information Systems; Information Systems
Quantum computers bring in a transformative technology with the potential to revolutionize various sectors, such as healthcare, chemistry, and data science. The functioning of quantum computers relies on both hardware and software. This leads to the emergence of “Quantum Software Engineering” involving numerous opportunities for discovery and research. While the Quantum Software Lifecycle may be similar to that of classical software, it holds unique challenges due to the principles of quantum mechanics involved in quantum computing. This thesis focuses on quantum software quality assurance and quantum education through three key contributions. First, it discusses existing state-of-the-art quantum software testing and debugging strategies and provides our visions and insights into testing and debugging quantum software in terms of challenges and opportunities for improving quantum software quality. Second, it presents a novel machine-learning platform that leverages a combination of multiple machine-learning models (e.g., support vector machine and random forests) to automatically detect flaky tests in quantum programs. Third, it introduces a novel active learning approach and assesses the best practices for teaching post-quantum cryptography to undergraduate and graduate students in the discipline of information systems. These contributions aim to highlight the importance of quantum software quality and quantum education to harness the full potential of quantum computers and help future generations develop the necessary skills in the field.
Learning View-invariant and Novel Spatio-temporal Features Under Uncertainty from Video
(2024-01-01) Hasan, Zahid; Roy, Nirmalya Dr.; Information Systems; Information Systems
Video understanding research enables machines to interpret activities, objects, and contextual situations, and measure physiological signals from humans using video data. It has various applications in human-computer interaction, robotics, contactless health monitoring, security, computer vision, search and rescue, autonomous navigation, and surveillance. Deep learning (DL) has emerged as the standard method in video understanding research, achieving state-of-the-art results in close label space due to their data-driven learning capability from large-scale datasets. However, traditional supervised DL models exhibit suboptimal performance when developed using noisy labeled data and therefore, fail to incorporate open-world unlabeled and unknown novel data during the model development. Moreover, the performance of such models degrades when encountering novel data patterns in open-set scenarios. In this thesis, we address these challenges by introducing robust DL algorithms to learn under data uncertainty in two real-world video understanding applications: contactless physiological health sensing for remote heart rate monitoring referred to as the remote photoplethysmograph (rPPG), and video action recognition (VAR). First, we proposed generalized DL approaches that utilize large-scale rPPG data containing inherent aleatoric uncertainty in labels to learn to extract the micro PPG signals from skin videos for remote heart rate monitoring applications. We made three key algorithmic contributions to design the DL-based rPPG systems: (i) introducing multi-task learning for noise separation, (2) leveraging the self-supervised learning to reduce reliance on labeled data, and (3) designing a self-supervised, adversarial framework for refining rPPG estimation using large-scale unlabeled data. Further, we developed the rPPG model pruning technique to reduce DL model size for real-time edge deployment and released an open-source large-scale rPPG dataset. Next, to broaden the scope of contactless health sensing from physiological signals to human action and activity recognition, we postulated uncertainty-aware DL-based video action recognition (VAR) models. Our proposed VAR model to comprehend spatiotemporal action patterns amidst epistemic data uncertainty due to the knowledge gaps in data from unlabeled novel angular viewpoints, and partially annotated open-world action space. In particular, we proposed self-supervision and adversarial optimization to learn view-invariant VAR models, addressing unlabeled viewpoints. We depicted two novel algorithms in novel category discovery (NCD) research: (i) negative learning, variance, and entropy constraints, and (ii) uncertainty-aware generalized statistical constraints to facilitate learning novel action categories. We demonstrated the scaleability of our NCD algorithms across image and 1-D time-series classification tasks. In this thesis, we posited a novel video understanding framework under data uncertainty by introducing novel DL algorithms for learning pixel-level information in rPPG and uncovering visual classes.
Nursing Facility Characteristics Are Differentially Associated With Family Satisfaction and Regulatory Star Ratings
(Southern Gerontological Society, 2025-01-08) Millar, Roberto J.; Diehl, Christin; Kusmaul, Nancy; Stockwell, Ian
Research suggests that nursing facility structural characteristics are important contributors toward residents’ quality of care. We use 2021 data from 220 Maryland nursing facilities to examine associations between two different quality-of-care metrics: family satisfaction and Care Compare five-star quality ratings. We used descriptive statistics to explore differences in quality metrics across facility ownership (for-profit vs. non-profit), geographic location (urban vs. rural), and resident census (1–60, 61–120, and 121+). Relationships were examined across overall ratings, as well as across subdomains of the two frameworks (e.g., staffing). Family members of residents in non-profit, rural, and low-census facilities rated facilities higher. Non-profit and low-resident census facilities were more likely to be rated four or five stars, while no significant association was observed across geographic location, or interactions across structural factors. Findings emphasize the need for comprehensive quality-of-care frameworks that explore quality care across stakeholders and types of facilities.
KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion
(2025-01-07) Jeon, Dong Hyun; Sun, Wenbo; Song, Houbing; Liu, Dongfang; Alvaro, Velasquez; Xie, Yixin Chloe; Niu, Shuteng
While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-item relationship data and insufficient transparency in recommendation generation. Traditional collaborative filtering approaches fail to integrate multifaceted item attributes, and although Factorization Machines account for item-specific details, they overlook broader relational patterns. Collaborative knowledge graph-based models have progressed by embedding user-item interactions with item-attribute relationships, offering a holistic perspective on interconnected entities. However, these models frequently aggregate attribute and interaction data in an implicit manner, leaving valuable relational nuances underutilized. This study introduces the Knowledge Graph Attention Network with Information Fusion (KGIF), a specialized framework designed to merge entity and relation embeddings explicitly through a tailored self-attention mechanism. The KGIF framework integrates reparameterization via dynamic projection vectors, enabling embeddings to adaptively represent intricate relationships within knowledge graphs. This explicit fusion enhances the interplay between user-item interactions and item-attribute relationships, providing a nuanced balance between user-centric and item-centric representations. An attentive propagation mechanism further optimizes knowledge graph embeddings, capturing multi-layered interaction patterns. The contributions of this work include an innovative method for explicit information fusion, improved robustness for sparse knowledge graphs, and the ability to generate explainable recommendations through interpretable path visualization.
Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study
(2024-12-17) Jamshidi, Ainaz; Arif, Muhammad; Kalhoro, Sabir Ali; Gelbukh, Alexander
The generation of high-quality medical time series data is essential for advancing healthcare diagnostics and safeguarding patient privacy. Specifically, synthesizing realistic phonocardiogram (PCG) signals offers significant potential as a cost-effective and efficient tool for cardiac disease pre-screening. Despite its potential, the synthesis of PCG signals for this specific application received limited attention in research. In this study, we employ and compare three state-of-the-art generative models from different categories - WaveNet, DoppelGANger, and DiffWave - to generate high-quality PCG data. We use data from the George B. Moody PhysioNet Challenge 2022. Our methods are evaluated using various metrics widely used in the previous literature in the domain of time series data generation, such as mean absolute error and maximum mean discrepancy. Our results demonstrate that the generated PCG data closely resembles the original datasets, indicating the effectiveness of our generative models in producing realistic synthetic PCG data. In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs, in order to address the current scarcity of abnormal data. We hope to improve the robustness and accuracy of diagnostic tools in cardiology, enhancing their effectiveness in detecting heart murmurs.
Improving spleen segmentation in ultrasound images using a hybrid deep learning framework
(Springer Nature, 2025-01-11) Karimi, Ali; Seraj, Javad; Mirzadeh Sarcheshmeh, Fatemeh; Fazli, Kasra; Seraj, Amirali; Eslami, Parisa; Khanmohamadi, Mohamadreza; Sajjadian Moosavi, Helia; Ghattan Kashani, Hadi; Sajjadian Moosavi, Abdoulreza; Shariat Panahi, Masoud
This paper introduces a novel method for spleen segmentation in ultrasound images, using a two-phase training approach. In the first phase, the SegFormerB0 network is trained to provide an initial segmentation. In the second phase, the network is further refined using the Pix2Pix structure, which enhances attention to details and corrects any erroneous or additional segments in the output. This hybrid method effectively combines the strengths of both SegFormer and Pix2Pix to produce highly accurate segmentation results. We have assembled the Spleenex dataset, consisting of 450 ultrasound images of the spleen, which is the first dataset of its kind in this field. Our method has been validated on this dataset, and the experimental results show that it outperforms existing state-of-the-art models. Specifically, our approach achieved a mean Intersection over Union (mIoU) of 94.17% and a mean Dice (mDice) score of 96.82%, surpassing models such as Splenomegaly Segmentation Network (SSNet), U-Net, and Variational autoencoder based methods. The proposed method also achieved a Mean Percentage Length Error (MPLE) of 3.64%, further demonstrating its accuracy. Furthermore, the proposed method has demonstrated strong performance even in the presence of noise in ultrasound images, highlighting its practical applicability in clinical environments.
Analysis of Teacher Readiness to Implement Bioinformatics to Biology Learning in Senior High School
(GMPI, 2024-12-31) Sari, Indah Juwita; Setiawati, Eli; Aisy, Rihadatul; Bayoh, Alpha Jr.
This study aimed爐o analyze teachers' readiness in applying bioinformatics to biology learning in senior high schools. Bioinformatics is a combination of computer science, statistics, and biology to analyze and interpret complex biological data. The application of bioinformatics into learning can foster students' realization of the real-life commitment of bioinformatics, enhance their understanding, and expand their curiosity about bioinformatics. The application of bioinformatics into biology learning needs to be supported by teacher readiness in terms of understanding, skills and factors that influence teacher readiness in applying bioinformatics. This research used a case study method with a qualitative descriptive approach to analyze the readiness of biology teachers from several senior high schools in Banten province in implementing bioinformatics into biology learning. The results of this study show that most teachers understand and realize the importance of bioinformatics in learning, on the other hand some teachers also feel not fully confident and able to apply bioinformatics into learning effectively. Although most teachers have adequate access to technology, teachers are not familiar with bioinformatics concepts and software such as BLAST and NCBI. 燭herefore, to fulfill the aspect of teachers' understanding and skills in applying bioinformatics to learning, focused training is needed that discusses the basic concepts of bioinformatics, the use of bioinformatics software, and its application in learning to improve teacher competence and create interactive and relevant biology learning.
Transcending Transit Deserts: Participatory Design of Interactive Bicycling Advocacy Tools in an American City
(ACM, 2025-01-10) Prottoy, Hasan Mahmud; Gyi, Serena; Hamidi, Foad
Recent HCI research has focused on developing interactive technologies for cyclists and using technology to promote this activity. However, the community-building and advocacy aspects of cycling remain relatively unexplored. In this study, we explored the integration of urban cycling and participatory technology design to address transportation inequities and promote cycling advocacy in a US city characterized by infrastructural and social disparities. We engaged with nine bicycling advocates from five organizations in Baltimore, USA in participatory design activities to understand their motivations for promoting cycling in urban areas and how interactive technology can aid in creating a cyclist-friendly city. Along with the various practical motivations, we found the participants also promote cycling with a desire to challenge historical discriminatory practices and infrastructural inequalities and as a means of community building and identity expression. We identify three interconnected directions for future change: supporting ancillary bicycle infrastructure and DIY repair practices, changing perceptions through community engagement, and using technology to support community awareness and inclusion. We argue cycling can be a tool for resistance and identity shaping in this context, and participatory design can offer innovative directions for urban designers, policymakers, and system designers to strengthen efforts toward creating inclusive and equitable urban environments.

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