Digital Commons Network^™

Exploring Undergraduate Experiences In Physical Education And Health: A Photovoice Study Of Indonesian Students, Sunarno Basuki, Perdinanto Perdinanto, Akhmad Amirudin

Indonesian Journal of Research in Physical Education, Sport, and Health

Background: Research on undergraduate students in Physical Education (PE) and Health particularly in Indonesia and the Global South has predominantly relied on quantitative indicators, often neglecting students’ lived, embodied, and emotional experiences within demanding academic and physical environments. Objective: This study aimed to explore the lived experiences of Indonesian undergraduate students enrolled in PE and Health programs and to examine how these experiences can inform more responsive and inclusive educational practices. Methods: A qualitative participatory design was employed using photovoice. Eight undergraduate students documented their academic and personal experiences through photography over a two-week period, followed by reflective writing and …

Identification Of Characteristics And Incidence Of Injuries In Pencak Silat Athletes In The Adult Competition Category In East Java, Dinda Farida Putri Bahari, Kurniati Rahayuni

Indonesian Journal of Research in Physical Education, Sport, and Health

Background: Pencak silat, as a competitive sport, has a high risk of injury. However, data on its characteristics and causative factors during pre-match and matches in East Java are still limited. Objective: This study aims to identify the characteristics and incidence of injuries in adult sparring category pencak silat athletes in East Java during training and matches. Methods: Using a quantitative descriptive method with a cross-sectional survey approach. Data were collected through a survey distributed to 100 athletes (61 men and 31 women) aged 17-22 years from 11 cities/districts in East Java, including information on injury characteristics and causal factors. …

Neuromuscular Hamstring Profile Based On Strength Imbalance As A Basis For Injury Prevention In Youth Football Players, Yulingga Nanda Hanief, Sri Sumartiningsih, Setya Rahayu, Heny Setyawati, Hadi Hadi, Karol Karol Jaskulski

Indonesian Journal of Research in Physical Education, Sport, and Health

Background: Hamstring strength imbalance between the left and right sides has been identified as a factor contributing to increased injury risk in athletes. However, few studies have evaluated not only bilateral strength differences but also the magnitude of clinically significant imbalances in various hamstring strength parameters. Objective: This study aims to analyze bilateral hamstring muscle asymmetry in terms of maximum, average, and maximum impulse strength, and to evaluate whether the distribution of high imbalances reaches clinically relevant thresholds associated with potential injury risk. Methods: This study is a cross-sectional observational study. Twenty athletes (n = 20) underwent hamstring strength measurements …

The Current State Of Diagnostics For Neuroimmunological Disorders In Africa, Malya Sahu, Mashina Chomba, Fiifi Duodu, Monica Diaz, Dilraj Sokhi, Deanna Saylor

Internal Medicine, East Africa

The epidemiology of neuroimmunological disorders in Africa remains poorly understood. Regional variation in diagnostics and provider knowledge has led to challenges in real-world application of diagnostic criteria. We conducted a cross-sectional survey of clinicians practicing in Africa to characterize the current state of clinical diagnostic resources and developed a clinician-driven prioritized action plan. 130 respondents from 24 out of 54 countries in Africa participated (44% national response rate). Most respondents specialized in neurology (n = 86, 66%) and practiced in an urban setting (n = 84, 65%). While all participating countries had access to a neurologist and ophthalmologist, only half …

Lost In Translation: Chomskyan Analysis Of Language Variation In Ai, Post-Critical Learners, And Native Speakers, Hannah Van Rijnswou

Holster Scholar Projects

Artificial Intelligence (AI) and Large Language Models (LLMs) have revolutionized linguistic theory, raising questions about human language acquisition. Some argue AI's language generation challenges the concept of innate linguistic ability, while others contend it reinforces it. This research examines AI's "Surplus of the Stimulus" learning—relying on vast datasets—against human language acquisition, particularly within Chomskyan frameworks of Universal Grammar and the "Poverty of the Stimulus." By analyzing language production across three groups—Native English Speakers, Post-Critical Period Second Language Learners, and LLMs—this study compares linguistic complexity using the Lexical Opulence Index (LOI). Data is collected through structured prompts across four linguistic registers: …

Common Grammatical Errors And Conventions In Academic Writing, Dawn Atkinson

Writing

No abstract provided.

“Sometimes I Have To Figure Things Out By Talking About Them”: Conceptualizing Think-Aloud Protocols As Spaces For Reflexivity During Open Textbook Construction, Dawn Atkinson, Stacey Corbitt

Writing

Abstract Open textbooks, freely available open educational resources, have an ever-growing presence in learning environments around the world, though detailed studies of their development are rare. The challenges of tracking textbook writing activity in a precise manner over time may account for this sparsity. To highlight how process-tracing research can contribute insight to the textbook development literature, particularly that focused on open textbook writing, this study addresses the roles that think-aloud protocols (TAPs) played during the construction of an open corequisite writing textbook. The volume, which was designed with underprepared university students in mind, stresses the growth of academic and …

Associations Among Human Milk Polyunsaturated Fatty Acids And Infant Sleep Patterns: A Cross-Sectional Study, Ana M Palacios, Dominick J Lemas, Bridget E Young, Erik Parker, Stephanie Dickinson, Nandi Marshall, Kelly L Sullivan, Haley Wilt, Michelle I Cardel, David B Allison

Children’s Nutrition Research Center Staff Publications

Background: Infant sleep is critical for cognitive, emotional, and long-term health outcomes. Although diet-sleep relationships are established, limited research has explored how polyunsaturated fatty acids (PUFAs) in human milk (HM) relate to infant sleep.

Objectives: This study aims to examine associations between PUFAs in HM and sleep patterns in 2-mo-old infants exclusively fed with HM.

Methods: This cross-sectional secondary analysis used data from a lactation cookie trial involving 131 parents of healthy, term infants. Participants provided a fasting HM sample and completed sociodemographic and Brief Infant Sleep Questionnaire surveys. Multivariate linear models were adjusted for infant sex assigned at birth, …

All-Cause And Cause-Specific Mortality Among Patients With Narcolepsy., Chih-Wei Hsu, Yen-Shan Yang, Yang-Chieh Brian Chen, Liang-Jen Wang, Mu-Hong Chen, Yao-Hsu Yang, Chih-Sung Liang, Edward Chia-Cheng Lai

Children’s Nutrition Research Center Staff Publications

Importance: Narcolepsy is a sleep disorder potentially affecting mortality, yet evidence on this association remains sparse.

Objective: To examine whether narcolepsy is associated with an increased risk of all-cause and cause-specific mortality.

Design, setting, and participants: This retrospective cohort study used data from the Taiwan National Health Insurance Research Database (NHIRD) from 2001 to 2021, with patients followed up until death or December 31, 2022. Patients were aged 6 years or older with 2 or more narcolepsy diagnoses from psychiatrists or neurologists. Controls were selected from the NHIRD as a population-based sample. Controls without narcolepsy were matched in a 1:4 …

Menopause Factors And Alterations In Gut Microbiota And Insulin Homeostasis: A Cross-Sectional Analysis Of The Microbiome And Insulin Longitudinal Evaluation Study (Miles), Vincent J Maffei, Alain G Bertoni, Alexis C Wood, Jerome I Rotter, Osa Crago, Yii-Der I Chen, Joseph F Petrosino, Kristi L Hoffman, Mark O Goodarzi, Elizabeth T Jensen

Children’s Nutrition Research Center Staff Publications

Aim: To assess the risk for impaired insulin homeostasis as a function of menopause-related factors and gut microbiota dysbiosis in non-diabetic, post-menopausal women.

Materials and methods: Baseline data (n = 175 women) from the Microbiome and Insulin Longitudinal Evaluation Study (MILES) were used, including insulin and dysglycaemia indices calculated from a 2-h oral glucose tolerance test, untargeted peripheral metabolomics, targeted peripheral short chain fatty-acid levels and faecal bacterial microbiota surveyed by whole-metagenomic sequencing.

Results: After adjustment for covariates, menopause age < 50 years and use of hormone replacement therapy (HRT) were associated with lower Matsuda et al. insulin sensitivity index levels (β = -0.232, confidence interval (CI) = [-0.450, -0.014] and β = -0.275, CI = [-0.444, -0.107], respectively) but not pre-menopausal gynaecologic surgery. Pre-menopausal gynaecologic surgery was significantly associated with faecal microbiota beta diversity driven by a relative increase in diabetogenic Ruminococcus gnavus and Clostridium species and a decrease in protective Alistipes species and Akkermansia muciniphila relative abundances. A reduction in two glycerophospholipids in the plasmalogen class significantly statistically mediated an inverse association between gynaecologic surgery before menopause and insulin sensitivity.

Conclusions: Menopause age and history of HRT are more strongly associated with insulin resistance than gynaecologic surgery before menopause. However, …

Is Your Data Good, Bad, Or Neutral?: Redefining Concreteness For Data Breach Harms, Jeeyoon Lim

Fordham Law Review

In recent years, the Supreme Court has tightened federal court standing requirements for intangible harms, including statutory harms. In TransUnion LLC v. Ramirez, the Court held that a mere statutory violation, absent a common-law analogue, is not sufficiently concrete to grant standing. The Court did not provide clear guidance as to how stringent the common-law analogue analysis must be, other than that it does not require an “exact duplicate.” Because of the ever-evolving nature of digital data, data breach victims attempting to enforce their statutory right to privacy in federal court have struggled to rely on tradition and history …

Adolescent Executive Function As A Resilience Factor In The Family Stress Model Among Mexican-Origin Families, Su Yeong Kim, Yayu Du, Tianlu Zhang, Jingyi Shen, Kiera M. Coulter, Lester Sim, Wen Wen, Belem G. Lopez, Maria M. Arredondo, Shen Yishan, Hou Yang

Research Collection School of Social Sciences

Mexican-origin families often face economic hardship due to systemic oppression, increasing the likelihood of adolescent marijuana use. While the family stress model provides insight into the mechanism of the association between family economic hardship and adolescent marijuana use, resilience factors are relatively unknown. The present study uses a three-wave longitudinal data set of Mexican immigrant families in the United States (Wave 1—adolescents: Mage = 12.29, SD = 0.93; mothers: Mage = 38.57, SD = 5.71) to investigate the mechanism underlying the association between family income and adolescents’ likelihood of marijuana use, guided by the family stress model. The protective role …

A 3-Item Short Version Of The Fear Of Missing Out (Fomo) Scale For Trait And Time-Intensive Designs: Measurement Invariance And Psychometric Properties, Eva M. Hisham, Andree Hartanto, Nadyanna M. Majeed

Research Collection School of Social Sciences

Research on Fear of Missing Out (FoMO) has gained prominence alongside the rise of digital connectivity and social media, which offer individuals continuous access to others’ experiences in real time. Importantly, it has been increasingly recognised as an important psychological construct, closely associated with various problematic digital behaviours. As the fields of media psychology and human-computer interaction increasingly adopt time-intensive methodologies (e.g., daily and weekly studies) to capture dynamic experiences of technology use, there is a growing need for brief yet psychometrically-sound measures of FoMO suitable for such designs. The present work reports the development of a 3-item FoMO scale …

Lightweight Population-Based Policy Optimization For Pickup And Delivery Problems, Yizhou Liu, Li Li, Yixin Xu, Tang Liu, Rong Cheng, Die Wu, Jilin Yang, Jingwen Li

Research Collection School Of Computing and Information Systems

In recent years, applying deep models to automatically learn construction heuristics for vehicle routing problems has achieved remarkable advancements. However, they are less effective in searching solutions due to two primary limitations: relying on deterministic probability distributions and overlooking the strategic advantage of prioritizing nearby unvisited nodes during the route construction process, resulting in suboptimal policies In this paper, we propose a novel lightweight population-based policy optimization (LPPO) framework that learns a diverse population of solution strategies through the utilization of innovative perturbation factors, in order to facilitate search exploration. Moreover, we design a localized attention synthesis (LAS) network to …

Look Before You Decide: Prompting Active Deduction Of Mllms For Assumptive Reasoning, Yian Li, Wentao Tian, Yang Jiao, Jingjing Chen, Tianwen Qian, Bin Zhu, Na Zhao, Yu‑Gang Jiang

Research Collection School Of Computing and Information Systems

Recently, Multimodal Large Language Models (MLLMs) have achieved significant success across multiple disciplines due to their exceptional instruction-following capabilities and extensive world knowledge. However, whether these MLLMs possess human-like compositional reasoning abilities remains an open problem. To unveil their reasoning behaviors, we first curate a Multimodal Assumptive Reasoning Benchmark (MARS-Bench) in this paper. Interestingly, we find that most prevalent MLLMs can be easily fooled by the introduction of a presupposition into the question, whereas such presuppositions appear naive to human reasoning. Besides, we also propose a simple yet effective method, Active Deduction (AD), a novel reinforcement learning paradigm to encourage …

Memory-Efficient 4-Bit Preconditioned Stochastic Optimization, Jingyang Li, Kuangyu Ding, Kim-Chuan Toh, Pan Zhou

Research Collection School Of Computing and Information Systems

Preconditioned stochastic optimization algorithms, exemplified by Shampoo, outperform first-order optimizers by offering theoretical convergence benefits and practical gains in large-scale neural network training. However, they incur substantial memory overhead due to the storage demands of non-diagonal preconditioning matrices. To address this, we introduce 4-bit quantization for Shampoo’s preconditioners. We introduce two key methods: First, we apply Cholesky decomposition followed by quantization of the Cholesky factors, reducing memory usage by leveraging their lower triangular structure while better preserving spectral properties to minimize information loss. To our knowledge, this is the first quantization approach applied to Cholesky factors of preconditioners. Second, we …

Probabilistic Prototype Calibration Of Vision-Language Models For Generalized Few-Shot Semantic Segmentation, Jie Liu, Jiayi Shen, Pan Zhou, Jan-Jakob Sonke, Stratis Gavves

Research Collection School Of Computing and Information Systems

Generalized Few-Shot Semantic Segmentation (GFSS) aims to extend a segmentation model to novel classes with only a few annotated examples while maintaining performance on base classes. Recently, pretrained vision-language models (VLMs) such as CLIP have been leveraged in GFSS to improve generalization on novel classes through multi-modal prototypes learning. However, existing prototype-based methods are inherently deterministic, limiting the adaptability of learned prototypes to diverse samples, particularly for novel classes with scarce annotations. To address this, we propose FewCLIP, a probabilistic prototype calibration framework over multi-modal prototypes from the pretrained CLIP, thus providing more adaptive prototype learning for GFSS. Specifically, FewCLIP …

From Holistic To Localized: Local Enhanced Adapters For Efficient Visual Instruction Fine-Tuning, Pengkun Jiao, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yugang Jiang

Research Collection School Of Computing and Information Systems

Efficient Visual Instruction Fine-Tuning (EVIT) seeks to adapt Multimodal Large Language Models (MLLMs) to downstream tasks with minimal computational overhead. However, as task diversity and complexity increase, EVIT faces significant challenges in resolving data conflicts. To address this limitation, we propose the Dual Low-Rank Adaptation (Dual-LoRA), a holistic-to-local framework that enhances the adapter’s capacity to address data conflict through dual structural optimization. Specifically, we utilize two subspaces: a skill space for stable, holistic knowledge retention, and a rank-rectified task space that locally activates the holistic knowledge. Additionally, we introduce Visual Cue Enhancement (VCE), a multi-level local feature aggregation module designed …

Exploring Object Status Recognition For Recipe Progress Tracking In Non-Visual Cooking, Franklin Mingzhe Li, Kaitlyn Ng, Bin Zhu, Patrick Carrington

Research Collection School Of Computing and Information Systems

Cooking plays a vital role in everyday independence and well-being, yet remains challenging for people with vision impairments due to limited support for tracking progress and receiving contextual feedback. Object status — the condition or transformation of ingredients and tools — offers a promising but underexplored foundation for context-aware cooking support. In this paper, we present OSCAR (Object Status Context Awareness for Recipes), a technical pipeline that explores the use of object status recognition to enable recipe progress tracking in non-visual cooking. OSCAR integrates recipe parsing, object status extraction, visual alignment with cooking steps, and time-causal modeling to support real-time …

Reproducibility Debt In Scientific Software, Zara Hassan, Christoph Treude, Graham Williams, Michael Norrish, Alex Potanin

Research Collection School Of Computing and Information Systems

Reproducibility Debt (RpD) refers to accumulated technical and organisational issues in scientific software that hinder the ability to reproduce research results. While reproducibility is essential to scientific integrity, RpD remains poorly defined and under-addressed. This study introduces a formal definition of RpD and investigates its causes, effects, and mitigation strategies using a mixed-methods approach involving a systematic literature review (214 papers), interviews (23 practitioners), and a global survey (59 participants). We identify seven categories of contributing issues, 75 causes, 110 effects, and 61 mitigation strategies. Findings are synthesised into a cause-effect model and supported by taxonomies of team roles and …

Viewsrd: 3d Visual Grounding Via Structured Multi-View Decomposition, Ronggang Huang, Haoxin Yang, Yan Cai, Xuemiao Xu, Huaidong Zhang, Shengfeng He

Research Collection School Of Computing and Information Systems

3Dvisual grounding aims to identify and localize objects in a 3Dspacebasedontextualdescriptions. However, existing methods struggle with disentangling targets from anchors in complex multi-anchor queries and resolving inconsisten cies in spatial descriptions caused by perspective variations. To tackle these challenges, we propose ViewSRD, a frame work that formulates 3D visual grounding as a structured multi-view decomposition process. First, the Simple Rela tion Decoupling (SRD) module restructures complex multi anchor queries into a set of targeted single-anchor state ments, generating a structured set of perspective-aware de scriptions that clarify positional relationships. These de composed representations serve as the foundation for the Multi-view …

Visual-Enhanced Multimodal Framework For Flexible Job Shop Scheduling Problem, Peng Zhao, Zhiguang Cao, Di Wang, Wen Song, Wei Pang, You Zhou, Yuan Jiang

Research Collection School Of Computing and Information Systems

Multimodal models leverage complementary information across modalities to enrich feature representations. While visual information shows potential in representing structure for some combinatorial optimization problems (COPs), its application to complex scheduling like the Flexible Job Shop Scheduling Problem (FJSP) remains underexplored. Current learning-based FJSP solvers predominantly rely on handcrafted state features. This dependence can lead to inconsistencies and may not fully capture the problem's intricate dynamics. Crucially, these methods overlook visual modalities. Visual representations offer a distinct advantage by inherently capturing the global topological structure and complex resource interactions within the FJSP state. Unlike localized handcrafted features, this holistic, structural view …

Mitigating Cross-Modal Representation Bias For Multicultural Image-To-Recipe Retrieval, Qing Wang, Chong-Wah Ngo, Yu Cao, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Existing approaches for image-to-recipe retrieval have the implicit assumption that a food image can fully capture the details textually documented in its recipe. However, a food image only reflects the visual outcome of a cooked dish and not the underlying cooking process. Consequently, learning cross-modal representations to bridge the modality gap between images and recipes tends to ignore subtle, recipe-specific details that are not visually apparent but are crucial for recipe retrieval. Specifically, the representations are biased to capture the dominant visual elements, resulting in difficulty in ranking similar recipes with subtle differences in use of ingredients and cooking methods. …

Art4math: Handwritten Mathematical Expression Recognition Via Multimodal Sketch Grounding, Yang Zhou, Jin Wang, Yuxiao Zhang, Kaixiang Huang, Guodong Lu, Jingru Yang, Shengfeng He

Research Collection School Of Computing and Information Systems

Handwritten Mathematical Expression Recognition (HMER) remains a challenging task due to the structural complexity of mathematical notation and the ambiguity of handwritten symbols-e.g., ''ρ'' vs. ''p'' or ''B'' vs. ''β''. While stroke-based models offer disambiguation via temporal cues, most existing methods are constrained by coarse modality fusion and a lack of fine-grained cross-modal alignment, further hindered by limited annotated data. We introduce Art for Math (Art4Math), a novel framework that leverages the structural richness of human sketches to enhance HMER through fine-grained, modality-aware learning. Art4Math follows a two-stage training paradigm: Art Grounding (A-Grd) and Math Decoding (M-Dec). In A-Grd, the …

Emoshortcuts: Emotionally Expressive Body Augmentation For Social Mixed Reality Avatars, Hyuna Seo, Youngki Lee, Rajesh Krishna Balan, Thivya Kandappu

Research Collection School Of Computing and Information Systems

We present EmoShortcuts1, a novel social Mixed Reality (MR) framework that enhances emotional expression by dynamically augmenting avatar body gestures to reflect users’ emotional states. While social MR enables immersive remote interactions through avatars, conveying emotions remains challenging due to limitations in head-mounted display (HMD) tracking (e.g., missing lower-body movements, such as stomping or defensive postures), and users’ tendency to deprioritize nonverbal expressions during multitasking. EmoShortcuts addresses these challenges by introducing an augmentation framework that generates expressive body gestures even when users’ physical movements are restricted. We conducted a formative study with 12 participants to identify key challenges in emotional …

Language-Driven 3d Human Pose Estimation In Multi-Person Scenarios: A New Dataset And Approach, Tingrui Shen, Bangzhen Liu, Zhirun Fan, Shiting Zhang, Weifeng Pan, Sun Fan, Dan Cao, Shengfeng He

Research Collection School Of Computing and Information Systems

In an NBA game scenario, consider the challenge of locating and analyzing the 3D poses of players performing a user-specified action, such as attempting a shot. Traditional 3D human pose estimation (3DHPE) methods often fall short in such complex, multi-person scenes due to their lack of semantic integration and reliance on isolated pose data. To address these limitations, we introduce Language-Driven 3D Human Pose Estimation (L3DHPE), a novel approach that extends 3DHPE to general multi-person contexts by incorporating detailed language descriptions. We present Panoptic-L3D, the first dataset designed for L3DHPE, featuring 3,838 linguistic annotations for 1,476 individuals across 588 videos, …

Advances In Iot, Ai, And Sensor‑Based Technologies For Disease Treatment, Health Promotion, Successful Ageing, And Ageing Well, Yuzhou Qian, Keng Siau

Research Collection School Of Computing and Information Systems

Recent advancements in the Internet of Things (IoT) and artificial intelligence (AI) are unlocking transformative opportunities across society. One of the most critical challenges addressed by these technologies is the ageing population, which presents mounting concerns for healthcare systems and quality of life worldwide. By supporting continuous monitoring, personal care, and data-driven decision-making, IoT and AI are shifting healthcare delivery from a reactive approach to a proactive one. This paper presents a comprehensive overview of IoT-based systems with a particular focus on the Internet of Healthcare Things (IoHT) and their integration with AI, referred to as the Artificial Intelligence of …

Website Owner Identification Through Multi-Level Contrastive Representation Learning, Cheng Tu, Yunshan Ma, Yang Li, Min Zhang, Miao Hu, Fan Shi, Xiang Wang

Research Collection School Of Computing and Information Systems

Website owner identification aims to recognize the organization or individual who owns a given website that is served on the web. It is a crucial step for cyberspace surveying and mapping, playing a significant role in cyberspace administration and governance. Existing widely employed solutions for website owner identification mainly fall into two paradigms: (1) querying the public information databases such as WHOIS, which store the Internet resource’s registered users or assignees; and (2) directly extracting the organization or individual name of the website owner from the webpage using the technique of named entity recognition. However, the former is less reliable …

Unsupervised Visual Chain-Of-Thought Reasoning Via Preference Optimization, Kesen Zhao, Beier Zhu, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Chain-of-thought (CoT) reasoning greatly improves the interpretability and problem-solving abilities of multimodal large language models (MLLMs). However, existing ap proaches focus on text CoT, limiting their ability to lever age visual cues. Visual CoT remains underexplored, and the only work [35] is based on supervised fine-tuning that relies on extensive labeled bounding-box data and is hard to generalize to unseen cases. In this paper, we introduce Unsupervised Visual CoT (UV-CoT), a novel framework for image-level CoT reasoning via preference optimization. UV-CoTperforms preference comparisons between model generated bounding boxes (one is preferred and the other is dis-preferred), eliminating the need for …

Spd: Shallow Backdoor Protecting Deep Backdoor Against Backdoor Detection, Shunjie Yuan, Xinghua Li, Xuelin Cao, Haiyan Zhang, Mengyao Zhu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Backdoor attacks have revealed the vulnerability of deep neural networks (DNNs), which motivates the development of secure deep learning systems. However, existing backdoor attacks often fail to bypass backdoor detection and human visual inspection, resulting in the exposure of the backdoor implanted in DNNs, which can subsequently be significantly mitigated through pruning or fine-tuning on benign data. To address this issue, in this paper, we propose a novel backdoor attack called SPD (Shallow Protecting Deep), which consists of a deep backdoor in the frequency domain and a shallow backdoor in the pixel domain, where the shallow backdoor acts as a …