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The College, Saint Mary's College Of California

Academic Catalog

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The Schools, Saint Mary's College Of California

Academic Catalog

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Undergraduate Curriculum, Saint Mary's College Of California

Academic Catalog

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Research On Legal Text Matching Based On Pre-Training Model, Chuanming Yu, Yifan Jiang

Journal of Scientific Information Research

[Purpose/significance]This study aims to solve the problem of traditional short text matching models being difficult to apply to long text matching tasks such as legal case retrieval. [Method/process]For the task of legal case matching, this paper proposes a Legal Text Matching model based on RoFormer （LTMR）. In the coding layer, the legal case is encoded through the RoFormer model and the legal feature extractor. In the reasoning layer, the context and interactive information of long text are further extracted by using interactive attention and self-attention mechanisms. We conducted the empirical research by applying the proposed model to the CAIL2019-SCM dataset. …

Droiddetectmw: A Hybrid Intelligent Model For Android Malware Detection, Fatma Taher, Omar Alfandi, Mousa Al-Kfairy, Hussam Al Hamadi, Saed Alrabaee

All Works

Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffective. Although prior writers have proposed numerous high-quality approaches, static and dynamic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tailored for multiple-feature data, for …

Empowering Patient Similarity Networks Through Innovative Data-Quality-Aware Federated Profiling, Alramzana Nujum Navaz, Mohamed Adel Serhani, Hadeel T. El Kassabi, Ikbal Taleb

All Works

Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage, the processing and analytics are moved close to where the data are located (e.g., the edge). However, data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, it is crucial to keep an eye on data quality and spot problems as quickly as possible, so that they do not mislead clinical judgments and lead to the …

Article Processing Charges For Open Access Journal Publishing: A Review, Ángel Borrego

Copyright, Fair Use, Scholarly Communication, etc.

Abstract

Some open access (OA) publishers charge authors fees to make their articles freely available online. This paper reviews literature on article processing charges (APCs) that has been published since 2000. Despite praise for diamond OA journals, which charge no fees, most OA articles are published by commercial publishers that charge APCs. Publishers fix APCs depending on the reputation assigned to journals by peers. Evidence shows a relationship between high impact metrics and higher, faster rising APCs. Authors express reluctance about APCs, although this varies by discipline depending on previous experience of paying publication fees and the availability of research …

Asset Cueing Nuclear Radiation Anomaly Detection Using An Embedded Neural Network Resource, April Inamura

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Nuclear radiation detection is inherently a challenging task, coupled with a high background variation or increase in anomalies, the accuracy for detection can plummet. A key factor in the success of nuclear detection hinges on the sensor’s ability to generalize its model and directly leads to the model’s robustness. The goal of this project is to develop algorithms suitable for use on the University of Nebraska-Lincoln’s Pingora chip, a low-power, system-on-chip device with an active neural processing unit (NPU) made for nuclear radiation detection. The thesis aims to improve Pingora’s overall generalization ability in nuclear radiation source detection. A multiphase …

Users’ Perception Of Staff Attitude As A Determinant Of Effective Library Service Delivery In Federal Universities In South-West, Nigeria, Lauretta Nnebuogor Ojei Mrs., Sunday Olanrewaju Popoola

Library Philosophy and Practice (e-journal)

ABSTRACT

Libraries are established purposively to acquire and organize knowledge and information content in different carriers and make them available to the intended audience in the best possible format and at the right time. Considering the customer as the king, this work sets out to investigate the perception of the users on the inherent attitude displayed by library staff in the federal universities in south-western Nigeria while delivering their services to the user community.

To achieve this, a descriptive survey was carried out using a sample size of 2576 out of 43355 population of registered users. A well-structured SERVQUAL questionnaire …

Transprofessional Competencies Of Information Managers And The Challenges Of The New Normal, Adeoye Augustine Bamgbose, Hassan Mallam Ibrahim, Sa’Ad Madaki Adamu

Library Philosophy and Practice (e-journal)

Librarians and other information professionals are required to possess transprofessional competencies in the form of additional education or knowledge to increase the range of services that could be provided for improved efficiency of information service delivery in the new normal. This study investigates the transprofessional competencies of librarians and their perceptions of what barriers prevent librarians from developing transprofessional knowledge in this changing and knowledge-driven environment. The study is a qualitative research, and the phenomenography method was adopted for this study; The participants constitute librarians and information managers in four academic libraries in Lagos and Oyo states, Nigeria. A semi-structured …

Finding Causally Different Tests For An Industrial Control System, Christopher M. Poskitt, Yuqi Chen, Jun Sun, Yu Jiang

Research Collection School Of Computing and Information Systems

Industrial control systems (ICSs) are types of cyber-physical systems in which programs, written in languages such as ladder logic or structured text, control industrial processes through sensing and actuating. Given the use of ICSs in critical infrastructure, it is important to test their resilience against manipulations of sensor/actuator inputs. Unfortunately, existing methods fail to test them comprehensively, as they typically focus on finding the simplest-to-craft manipulations for a testing goal, and are also unable to determine when a test is simply a minor permutation of another, i.e. based on the same causal events. In this work, we propose a guided …

Impact Of Difficult Negatives On Twitter Crisis Detection, Yuhao Zhang, Siaw Ling Lo, Phyo Yi Win Myint

Research Collection School Of Computing and Information Systems

Twitter has become an alternative information source during a crisis. However, the short, noisy nature of tweets hinders information extraction. While models trained with standard Twitter crisis datasets accomplished decent performance, it remained a challenge to generalize to unseen crisis events. Thus, we proposed adding “difficult” negative examples during training to improve model generalization for Twitter crisis detection. Although adding random noise is a common practice, the impact of difficult negatives, i.e., negative data semantically similar to true examples, was never examined in NLP. Most of existing research focuses on the classification task, without considering the primary information need of …

Plan-And-Solve Prompting: Improving Zero-Shot Chain-Of-Thought Reasoning By Large Language Models, Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, few-shot chain-of-thought (CoT) prompting includes a few manually crafted step-by-step reasoning demonstrations which enable LLMs to explicitly generate reasoning steps and improve their reasoning task accuracy. To eliminate the manual effort, Zeroshot-CoT concatenates the target problem statement with “Let’s think step by step” as an input prompt to LLMs. Despite the success of Zero-shot-CoT, it still suffers from three pitfalls: calculation errors, missing-step errors, and semantic misunderstanding errors. To address the missing-step errors, we propose Planand-Solve (PS) Prompting. It …

Conference Report On 2022 Ieee Symposium Series On Computational Intelligence (Ieee Ssci 2022), Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao

Research Collection School Of Computing and Information Systems

On behalf of the organizing committee, we are delighted to deliver this conference report for the 2022 IEEE Symposium Series on Computational Intelligence (SSCI 2022), which was held in Singapore from 4th to 7th December 2022. IEEE SSCI is an established flagship annual international series of symposia on computational intelligence (CI) sponsored by the IEEE Computational Intelligence Society (CIS) to promote and stimulate discussions on the latest theory, algorithms, applications, and emerging topics on computational intelligence. After two years of virtual conferences due to the global pandemic, IEEE SSCI returned as an in-person meeting with online elements in 2022.

Qebverif: Quantization Error Bound Verification Of Neural Networks, Yedi Zhang, Fu Song, Jun Sun

Research Collection School Of Computing and Information Systems

To alleviate the practical constraints for deploying deep neural networks (DNNs) on edge devices, quantization is widely regarded as one promising technique. It reduces the resource requirements for computational power and storage space by quantizing the weights and/or activation tensors of a DNN into lower bit-width fixed-point numbers, resulting in quantized neural networks (QNNs). While it has been empirically shown to introduce minor accuracy loss, critical verified properties of a DNN might become invalid once quantized. Existing verification methods focus on either individual neural networks (DNNs or QNNs) or quantization error bound for partial quantization. In this work, we propose …

Augmenting Low-Resource Text Classification With Graph-Grounded Pre-Training And Prompting, Zhihao Wen, Yuan Fang

Research Collection School Of Computing and Information Systems

ext classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with few or no labeled samples, poses a serious concern for supervised learning. Meanwhile, many text data are inherently grounded on a network structure, such as a hyperlink/citation network for online articles, and a user-item purchase network for e-commerce products. These graph structures capture rich semantic relationships, which can potentially augment low-resource text classification. In this paper, we propose a novel model called Graph-Grounded Pre-training and Prompting (G2P2) …

Do-Good: Towards Distribution Shift Evaluation For Pre-Trained Visual Document Understanding Models, Jiabang He, Yi Hu, Lei Wang, Xing Xu, Ning Liu, Hui Liu

Research Collection School Of Computing and Information Systems

Numerous pre-training techniques for visual document understanding (VDU) have recently shown substantial improvements in performance across a wide range of document tasks. However, these pre-trained VDU models cannot guarantee continued success when the distribution of test data differs from the distribution of training data. In this paper, to investigate how robust existing pre-trained VDU models are to various distribution shifts, we first develop an out-of-distribution (OOD) benchmark termed Do-GOOD for the fine-Grained analysis on Document image-related tasks specifically. The Do-GOOD benchmark defines the underlying mechanisms that result in different distribution shifts and contains 9 OOD datasets covering 3 VDU related …

Silent Compiler Bug De-Duplication Via Three-Dimensional Analysis, Chen Yang, Junjie Chen, Xingyu Fan, Jiajun Jiang, Jun Sun

Research Collection School Of Computing and Information Systems

Compiler testing is an important task for assuring the quality of compilers, but investigating test failures is very time-consuming. This is because many test failures are caused by the same compiler bug (known as bug duplication problem). In particular, this problem becomes much more challenging on silent compiler bugs (also called wrong code bugs), since these bugs can provide little information (unlike crash bugs that can produce error messages) for bug de-duplication. In this work, we propose a novel technique (called D3) to solve the duplication problem on silent compiler bugs. Its key insight is to characterize the silent bugs …

Fesa: Automatic Federated Swarm Attestation On Dynamic Large-Scale Iot Devices, Boyu Kuang, Anmin Fu, Yansong Gao, Yuqing Zhang, Jianying Zhou, Robert H. Deng

Research Collection School Of Computing and Information Systems

Swarm attestation, as an important branch of Remote Attestation (RA), enables a trusted party (verifier) to verify the security states of multiple devices (provers) in a large network (swarm) simultaneously via a challenge-response mechanism. However, swarm attestation suffers from significant redundancy overhead since all devices in the swarm need to be attested in each attestation round. Besides, it faces challenges such as verifier-impersonation Denial of Service (DoS) attacks, highly dynamic networks, transient & self-relocating malware, and Time-Of-Check-Time-Of-Use (TOCTOU) attacks. In this paper, considering not only the detection accuracy but also the privacy of swarm owners in real Internet of Things …

Discriminative Reasoning With Sparse Event Representation For Document-Level Event-Event Relation Extraction, Changsen Yuan, Heyan Huang, Yixin Cao, Yonggang Wen

Research Collection School Of Computing and Information Systems

Document-level Event-Event Relation Extraction (DERE) aims to extract relations between events in a document. It challenges conventional sentence-level task (SERE) with difficult long-text understanding. In this paper, we propose a novel DERE model (SENDIR) for better document-level reasoning. Different from existing works that build an event graph via linguistic tools, SENDIR does not require any prior knowledge. The basic idea is to discriminate event pairs in the same sentence or span multiple sentences by assuming their different information density: 1) low density in the document suggests sparse attention to skip irrelevant information. Our module 1 designs various types of attention …

Marketing In Metaverse: Sales-Avatar Visual Features On Customer Trust, Yuxin Liu, Keng Siau

Research Collection School Of Computing and Information Systems

The metaverse wave is sweeping across the world. Virtual shopping promises a bright future and has the potential to provide customers with an immersive and realistic shopping experience anytime and anywhere. Salesavatars are critical in customer trust development in virtual shopping in the metaverse by directly interacting with customers. Considering the virtual characteristics of the metaverse and the anonymity of salesavatars, this research investigates the effect of salesavatars' genders and ages on customers' trust in the metaverse through the lens of first impression formation. Two dimensions of facial first impression, perceived facial trustworthiness and perceived facial dominance, will be studied …

Learning Deep Time-Index Models For Time Series Forecasting, Jiale Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Research Collection School Of Computing and Information Systems

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historicalvalue models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while naive deep timeindex models are far more expressive than the manually predefined function representations of classical time-index models, they are inadequate for forecasting, being unable to generalize to unseen time steps due to the lack of inductive bias. In this paper, we propose DeepTime, a …

Contrastive Video Question Answering Via Video Graph Transformer, Junbin Xiao Xiao, Pan Zhou, Angela Yao, Yicong Li, Richang Hong, Shuicheng Yan, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

We propose to perform video question answering (VideoQA) in a Contrastive manner via a Video Graph Transformer model (CoVGT). CoVGT’s uniqueness and superiority are three-fold: 1) It proposes a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations and dynamics, for complex spatio-temporal reasoning. 2) It designs separate video and text transformers for contrastive learning between the video and text to perform QA, instead of multi-modal transformer for answer classification. Fine-grained video-text communication is done by additional cross-modal interaction modules. 3) It is optimized by the joint fully- and self-supervised contrastive objectives between the …

Peerda: Data Augmentation Via Modeling Peer Relation For Span Identification Tasks, Weiwen Xu, Xin Li, Yang Deng, Wai Lam, Lidong Bing

Research Collection School Of Computing and Information Systems

Span identification aims at identifying specific text spans from text input and classifying them into pre-defined categories. Different from previous works that merely leverage the Subordinate (SUB) relation (i.e. if a span is an instance of a certain category) to train models, this paper for the first time explores the Peer (PR) relation, which indicates that two spans are instances of the same category and share similar features. Specifically, a novel Peer Data Augmentation (PeerDA) approach is proposed which employs span pairs with the PR relation as the augmentation data for training. PeerDA has two unique advantages: (1) There are …

Product Question Answering In E-Commerce: A Survey, Yang Deng, Wenxuan Zhang, Qian Yu, Wai Lam

Research Collection School Of Computing and Information Systems

Product question answering (PQA), aiming to automatically provide instant responses to customer’s questions in E-Commerce platforms, has drawn increasing attention in recent years. Compared with typical QA problems, PQA exhibits unique challenges such as the subjectivity and reliability of user-generated contents in E-commerce platforms. Therefore, various problem settings and novel methods have been proposed to capture these special characteristics. In this paper, we aim to systematically review existing research efforts on PQA. Specifically, we categorize PQA studies into four problem settings in terms of the form of provided answers. We analyze the pros and cons, as well as present existing …

Groundwater Budgeting And Climate Change Vulnerability Assessment Of Water Supply At Bosque Del Apache National Wildlife Refuge, Maximiliano Trujillo

Water Resources Professional Project Reports

Water resources management is becoming increasingly complex in the Middle Rio Grande Valley, where the negative impacts of climate change and growing demand for a fully allocated river put further strain on diminishing water supply. The precarity of future water availability emphasizes the importance of fully accounting for existing water rights allocations and understanding how ever changing social and ecological processes might hinder those rights from being fully realized. This study aimed at improving knowledge of the groundwater budget at Bosque del Apache National Wildlife Refuge, New Mexico by utilizing experimental methods of Darcy Flow analysis to estimate groundwater flow …

Capacity And Communication: Addressing Barriers To Agricultural Innovations In The Middle Rio Grande Basin, Corina Michelle Gomez

Water Resources Professional Project Reports

Farmers and ranchers across the American Southwest are battling intense droughts, leaving them with decreasing water supplies to irrigate their fields or perform the necessary activities their operations require. These shortages have placed mounting pressures on agricultural water users and water managers alike to seek techniques and innovations that will stretch water supplies further and help them survive into the future. In an area like the Middle Rio Grande (MRG) basin in central New Mexico, for many farmers agriculture is more than a way to generate income, it’s a way of life. It is closely intertwined with the history of …

Irrigation And Innovation: Understanding Barriers To Innovative Actions To Manage Drought On Middle Rio Grande Farms, Eleanor C. Hasenbeck

Water Resources Professional Project Reports

The Rio Grande has irrigated agricultural land in central New Mexico since time immemorial. Today, agriculture in New Mexico’s Middle Rio Grande valley is adapting to an increasingly scarce water supply due to long-term drought and climate change (Dunbar et al., 2022). As supply is decreasing, demands are increasing because of several factors: an increase in evapotranspiration (Dunbar et al., 2022); population growth in the Middle Rio Grande valley (NM LFC, 2021); and legal obligations to provide water for endangered species and to Texas and Mexico (NM ISC, 2017). Agriculture is largest water use by sector in the Rio Grande …

Motivations And Barriers To Participation In Community Outreach And Engagement Among Environmental And Water Resources Students And Postdocs, Sydney Donohue

Water Resources Professional Project Reports

There is a need to utilize knowledge exchange avenues between academia and non-academic communities in order to advance environmental science and engineering education. Many universities and research centers attempt to enhance knowledge sharing by organizing broader impact outreach events such as lab tours, demonstrations, hands-on activities, and public presentations. Anecdotally, at two NSF-funded centers, the Transformation Network and Center for Water and the Environment, we have seen that only the same small subset of students and postdocs frequently participate while over 70% of those invited never volunteer. This study aims to survey environmentally-focused undergraduate students, graduate students, and postdocs’ motivations …

Long-Term Monitoring And Maintenance Plan For The Mixed Waste Landfill – March 2012, Revision 2, July 2023, Sandia National Laboratories/Nm Snl

Facilities-Units

The New Mexico Environment Department approved the Class 2 Permit Modification Request (23-024) to plug and abandon monitoring well MWL-MW4. The proposed revisions to the LTMMP have been incorporated as Revision 2 (July 2023) of the Long-Term Monitoring and Maintenance Plan for the Mixed Waste Landfill and replaces Revision 1 (dated February 2022). 268 pages.