Kendall, Susan L.

University of Nevada, Las Vegas, History, 1980 M.A. University of Denver, Library Science, 1972 M.A. St. Louis University, History, 1969 B.A.https://scholarworks.sjsu.edu/erfa_bios/1334/thumbnail.jp

Fountain, Anne O.

B.A. With Honors in Spanish, Indiana University, 1966 M.A. in Spanish, Indiana University, 1968 Ph.D. in Spanish, Columbia University, 1973 Graduate Area Certificate, Latin American Studies, Columbia University, School of International Affairs, 1973https://scholarworks.sjsu.edu/erfa_bios/1035/thumbnail.jp

Hill, Patricia Evridge

University of Texas at Dallas, Humanities, Emphasis in History, Ph.D. 1990 University of Texas at Dallas, Humanities, Emphasis in History, M.A. 1984 Southern Methodist University, History Major; Spanish Minor, B.A. 1979 Lifetime Texas Teaching Credential Grades 6 through 12, History and Spanish, 1980https://scholarworks.sjsu.edu/erfa_bios/1374/thumbnail.jp

iSchool Student Research Journal, Vol.14, Iss.1

Volume 14, Issue 1 of the School of Information Student Research Journal explores the multifaceted impacts of artificial intelligence (AI) on academia, particularly within library and information science (LIS) education and scholarly publishing. Highlighting the proactive measures taken by San Jose State University\u27s iSchool, this issue underscores the necessity of integrating AI competencies, such as data privacy and ethical AI use, into curricula. Through detailed case studies and policy reviews, the issue examines the ethical and societal implications of AI, including biases and inequalities, advocating for adaptive and responsible AI integration. Odin Halvorson\u27s paper emphasizes the transformative potential of Large Language Models (LLMs) and how revised curriculum and updated policies can address AI\u27s impact, and ensure equitable access and ethical use of AI technologies. Marc Hoffeditz\u27s editorial delves into the Student Research Journal\u27s development of its AI disclosure policy, offering a comprehensive literature review and practical guidelines for other scholarly journals to navigate the challenges posed by AI. Souvick \u27Vic\u27 Ghosh and Denise McCoy\u27s article evaluates the evolution of SJSU iSchool\u27s Master of Library and Information Science (MLIS) program, proposing updates to core competencies to incorporate advancements in AI, ensuring graduates are prepared for an AI-integrated future. Madelaine Russell\u27s book review of Christine Pawley\u27s Organizing Women: Home, Work, and the Institutional Infrastructure of Print in Twentieth-Century America provides a historical perspective on women\u27s roles in print culture, highlighting the intersections of gender, race, and information access. This issue provides valuable insights into the ongoing integration of AI in LIS education and scholarly publishing, emphasizing the importance of ethical considerations, policy development, and curricular advancements to foster a future-ready academic landscape

Assumptions, Resources, and Inputs to Case Management: Implications for California’s Regional Center System

This project adds to knowledge of case management assumptions, resources, and inputs for California’s Regional Center system by surveying members of the Service Access and Equity working group, formed by the Department of Developmental Services (DDS). It recommends development of a logic model to evaluate case management activities because their intended societal impacts are difficult to directly measure. Additionally, it adds to the debate on health equity and racial disparities in Medicaid long-term services and supports (LTSS). In 1969, passage of the Lanterman Developmental Disabilities Services Act (The Lanterman Act) led to the first and still only entitlement to community-based services that is granted to people with developmental and intellectual disabilities (I/DD) by a state. Twenty-one private, nonprofit Regional Centers have exclusive rights to provide case management and to purchase community- based services for eligible consumers within their catchment area. By contracting with DDS, Regional Centers receive reimbursement for case management operations, pass-through rates to purchase community-based services, and administer various grants, projects, and funds. This project contributes to understanding whether and how knowledge gaps in Regional Center case management affect expenditures of home- and community-based services (HCBS). In addition, Vogel et al. (2019) lay out systemic LTSS and demographic challenges in California: a higher percentage of people require services and have autism; a growing unpaid caregiver and adult consumer population aging-in-place at home; a struggle with rising labor costs to recruit and retain qualified personnel, high cost of housing for community living, and non-compliance with Medicaid HCBS regulation that may restrict federal funding. Surveying working group members’ knowledge of case management in home- and community-based services improves understanding of the disparities in service access for racial and non-English speaking consumers

Creating valid adversarial examples of malware

Because of its world-class results, machine learning (ML) is becoming increasingly popular as a go-to solution for many tasks. As a result, antivirus developers are incorporating ML models into their toolchains. While these models improve malware detection capabilities, they also carry the disadvantage of being susceptible to adversarial attacks. Although this vulnerability has been demonstrated for many models in white-box settings, a black-box scenario is more applicable in practice for the domain of malware detection. We present a method of creating adversarial malware examples using reinforcement learning algorithms. The reinforcement learning agents utilize a set of functionality-preserving modifications, thus creating valid adversarial examples. Using the proximal policy optimization (PPO) algorithm, we achieved an evasion rate of 53.84% against the gradient-boosted decision tree (GBDT) detector. The PPO agent previously trained against the GBDT classifier scored an evasion rate of 11.41% against the neural network-based classifier MalConv and an average evasion rate of 2.31% against top antivirus programs. Furthermore, we discovered that random application of our functionality-preserving portable executable modifications successfully evades leading antivirus engines, with an average evasion rate of 11.65%. These findings indicate that ML-based models used in malware detection systems are sensitive to adversarial attacks and that better safeguards need to be taken to protect these systems

Observations of a rotating pyroconvective plume

Background: There is an ongoing need for improved understanding of wildfire plume dynamics. Aims: To improve process-level understanding of wildfire plume dynamics including strong (\u3e10 m s-1) fire-generated winds and pyrocumulus (pyroCu) development. Methods: Ka-band Doppler radar and two Doppler lidars were used to quantify plume dynamics during a high-intensity prescribed fire and airborne laser scanning (ALS) to quantify the fuel consumption. Key results: We document the development of a strongly rotating (\u3e10 m s-1) pyroCu-topped plume reaching 10 km. Plume rotation develops during merging of discrete plume elements and is characterised by inflow and rotational winds an order of magnitude stronger than the ambient flow. Deep pyroCu is initiated after a sequence of plume-deepening events that push the plume top above its condensation level. The pyroCu exhibits a strong central updraft (35 m s-1) flanked by mechanically and evaporative forced downdrafts. The downdrafts do not reach the surface and have no impact on fire behaviour. ALS data show plume development is linked to large fuel consumption (20 kg m-2). Conclusions: Interactions between discrete plume elements contributed to plume rotation and large fuel consumption led to strong updrafts triggering deep pyroCu. Implications: These results identify conditions conducive to strong plume rotation and deep pyroCu initiation

CASA: A Compact and Scalable Accelerator for Approximate Homomorphic Encryption

Approximate arithmetic-based homomorphic encryption (HE) scheme CKKS [CKKS17] is arguably the most suitable one for real-world data-privacy applications due to its wider computation range than other HE schemes such as BGV [BGV14], FV and BFV [Bra12, FV12]. However, the most crucial homomorphic operation of CKKS called key-switching induces a great amount of computational burden in actual deployment situations, and creates scalability challenges for hardware acceleration. In this paper, we present a novel Compact And Scalable Accelerator (CASA) for CKKS on the field-programmable gate array (FPGA) platform. The proposed CASA addresses the aforementioned computational and scalability challenges in homomorphic operations, including key-exchange, homomorphic multiplication, homomorphic addition, and rescaling. On the architecture layer, we propose a new design methodology for efficient acceleration of CKKS. We design this novel hardware architecture by carefully studying the homomorphic operation patterns and data dependency amongst the primitive oracles. The homomorphic operations are efficiently mapped into an accelerator with simple control and smooth operation, which brings benefits for scalable implementation and enhanced pipeline and parallel processing (even with the potential for further improvement). On the component layer, we carry out a detailed and extensive study and present novel micro-architectures for primitive function modules, including memory bank, number theoretic transform (NTT) module, modulus switching bank, and dyadic multiplication and accumulation. On the arithmetic layer, we develop a new partially reduction-free modular arithmetic technique to eliminate part of the reduction cost over different prime moduli within the moduli chain of the Residue Number System (RNS). The proposed structure can support arbitrary numbers of security primes of CKKS during key exchange, which offers better security options for adopting the scalable design methodology. As a proof-of-concept, we implement CASA on the FPGA platform and compare it with state-of-the-art designs. The implementation results showcase the superior performance of the proposed CASA in many aspects such as compact area, scalable architecture, and overall better area-time complexities. In particular, we successfully implement CASA on a mainstream resource-constrained Artix-7 FPGA. To the authors’ best knowledge, this is the first compact CKKS accelerator implemented on an Artix-7 device, e.g., CASA achieves a 10.8x speedup compared with the state-of-the-art CPU implementations (with power consumption of only 5.8%). Considering the power-delay product metric, CASA also achieves 138x and 105x improvement compared with the recent GPU implementation

Ensemble Model with Meta-Learning for DDoS Attack Classification in SDN

In response to the security threats posed by Distributed Denial of Service (DDoS) attacks, this paper presents an intrusion detection framework with a high-accuracy multi-class classification model. In addition to detecting the existence of DDoS attacks, our framework aims to identify the type of attack (e.g., protocol or message type) so that the system can select the most appropriate countermeasure against the DDoS type. We leverage a meta-learner to build an ensemble model of multiple machine learning models such as LSTM, RF, and KNN to enhance detection and classification accuracy. Tested on the CIC-DDoS 2019 dataset, the proposed model achieves 96% accuracy in the type identification task, while a simple combination of existing detection classifiers only achieves 92% accuracy in the same type identification. To demonstrate a practical implementation of the DDoS classification model, we integrated the model with the Ryu SDN controller running on a Mininet network testbed, which emulates different types of DDoS attacks and benign traffic. The integrated ensemble model achieved 93% accuracy in identifying the DDoS types in the testbed experiment

Multiclass lung disease classification in chest X-ray images: A fine-tuned hybrid CNN-GNN approach using transfer learning and feature extraction

In clinical practice, it is still difficult to accurately diagnose lung diseases from chest X-ray (CXR) images. In this study, we propose a new hybrid method for identifying several types of lung diseases using CXR images by combining Convolutional Neural Networks (CNNs) with Graph Neural Networks (GNNs). The framework of our proposed methodology takes advantage of CNN’s ability to extract detailed visual features and GNN’s capacity to understand complex relationships between these features, enabling comprehensive analysis in a multi-class classification setting of COVID-19, pneumonia, and normal lung conditions. We used multiple transfer learning models such as DenseNet201, VGG16, VGG19, MobileNetV2, and ResNet50 as backbones for extracting rich feature representations from the CXR images. We used K-Means clustering on the extracted CNN feature vectors to transform them into a graph structure in which clusters were nodes with edges representing similarity based on cluster association for images sharing similar pathological features. Instead of selecting a single CNN model, we experimented with various CNN-GNN hybrid combinations to capture local visual characteristics as well as global relational patterns. As a part of our GNN model, we have developed a Graph Convolutional Network (GCN) hybridized with the Graph Attention Network (GAT). This GCN-GAT hybrid model used the graph structure to uncover complex patterns by analyzing the connections between features to perform lung disease classification. Our best performing hybrid CNN-GNN model delivered impressive performance metrics, with an accuracy of 98.53%, an F1 score of 98.52%, an ROC-AUC of 99.81%, a recall of 98.53%, and a specificity of 99.26% in the multi-class classification task