Digital Commons Network^™

Compelling Compliance: An Analysis Of Methods For Enforcing New York’S Compulsory Education Law As Applied To Haredi Yeshivas, Kaitlyn Sivertsen

Fordham Law Review

This Note explains recent findings that many of New York’s Haredi yeshivas are failing to comply with the compulsory education laws and advocacy for the state to heighten enforcement. It examines other community members’ opposition to this belief and numerous legal challenges. This Note advances legal scholarship by pointing out that although these debates regarding education and religious freedom are important, in the context of New York’s Haredi yeshivas, they are futile without also recognizing that New York’s statute does not provide for efficient means of enforcement. There is little to no existing literature on this specific issue. Thus, this …

Adan: Adaptive Nesterov Momentum Algorithm For Faster Optimizing Deep Models, Xingyu Xie, Pan Zhou, Huan Li, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To relieve this issue and consistently improve the model training speed across deep networks, we propose the ADAptive Nesterov momentum algorithm, Adan for short. Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point. Then Adan adopts NME to estimate the gradient's first- and second-order moments in adaptive gradient algorithms for convergence acceleration. Besides, we prove that …

3d Snapshot: Invertible Embedding Of 3d Neural Representations In A Single Image, Yuqin Lu, Bailin Deng, Zhixuan Zhong, Tianle Zhang, Yuhui Quan, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

3D neural rendering enables photo-realistic reconstruction of a specific scene by encoding discontinuous inputs into a neural representation. Despite the remarkable rendering results, the storage of network parameters is not transmission-friendly and not extendable to metaverse applications. In this paper, we propose an invertible neural rendering approach that enables generating an interactive 3D model from a single image (i.e., 3D Snapshot). Our idea is to distill a pre-trained neural rendering model (e.g., NeRF) into a visualizable image form that can then be easily inverted back to a neural network. To this end, we first present a neural image distillation method …

Generative Semi-Supervised Graph Anomaly Detection, Hezhe Qiao, Qingsong Wen, Xiaoli Li, Ee-Peng Lim, Guansong Pang

Research Collection School Of Computing and Information Systems

This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph. We reveal that having access to the normal nodes, even just a small percentage of normal nodes, helps enhance the detection performance of existing unsupervised GAD methods when they are adapted to the semi-supervised setting. However, their utilization of these normal nodes is limited. In this paper we propose a novel Generative GAD approach (namely GGAD) for the semi-supervised scenario to better exploit the …

Learning To Handle Complex Constraints For Vehicle Routing Problems, Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song, Zhiguang Cao, Yaoxin Wu, Jie Zhang

Research Collection School Of Computing and Information Systems

Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, especially when obtaining the masking itself is NP-hard. In this paper, we propose a novel Proactive Infeasibility Prevention (PIP) framework to advance the capabilities of neural methods towards more complex VRPs. Our PIP integrates the Lagrangian multiplier as a basis to enhance constraint awareness and introduces preventative infeasibility masking to proactively steer the solution construction process. Moreover, we present PIP-D, which employs an auxiliary decoder and two adaptive …

Inverse Factorized Soft Q-Learning For Cooperative Multi-Agent Imitation Learning, The Viet Bui, Tien Mai, Thanh Nguyen

Research Collection School Of Computing and Information Systems

This paper concerns imitation learning (IL) in cooperative multi-agent systems.The learning problem under consideration poses several challenges, characterized by high-dimensional state and action spaces and intricate inter-agent dependencies. In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process. However, extending this framework to a multi-agent context introduces the need to simultaneously learn both local value functions to capture local observations and individual actions, and a joint value function for exploiting centralized learning.In this work, we introduce a new multi-agent IL algorithm designed to address these challenges. Our approach enables thecentralized learning by leveraging …

Trustworthy Web3 Domains: A Framework For Digital Identity Verification, Yi Meng Lau, Ping Fan Ke

Research Collection School Of Computing and Information Systems

As decentralized applications evolve, digital identities represented through Web3 domain names gained prominence. This study addresses the challenges of establishing trust in Web3 domain names. The decentralized nature of Web3 introduces complexities in verifying domain name authenticity, making them targets for malicious activities such as cybersquatting and phishing. We propose a comprehensive framework that enhances traditional identification, authentication, and authorization processes by incorporating technological and social trust elements. This framework enables organizations and users to systematically assess the trustworthiness of Web3 domain names, offering a structured approach to managing digital identities in decentralized environments.

Towards General Conceptual Model Editing Via Adversarial Representation Engineering, Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun

Research Collection School Of Computing and Information Systems

Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging. In this work, we explore how to leverage insights from representation engineering to guide the editing of LLMs by deploying a representation sensor as an editing oracle. We first identify the importance of a robust and reliable sensor during editing, then propose an …

Question-Attentive Review-Level Explanation For Neural Rating Regression, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Recommendation explanations help to improve their acceptance by end users. Explanations come in many different forms. One that is of interest here is presenting an existing review of the recommended item as the explanation. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance or “attention” of each review to the recommendation objective. Our focus is improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution …

Custom Permission Misconfigurations In Android: A Large-Scale Security Analysis, Rui Li, Wenrui Diao, Debin Gao

Research Collection School Of Computing and Information Systems

Android’s popularity is due to its openness and vast app ecosystem. Global developers can use Android Studio and rich Android APIs to create their apps. Within this ecosystem, Android permissions play a crucial role in managing access to resources, with system permissions controlled by system apps and custom permissions declared by third-party apps. However, the security of custom permissions has not received enough attention from the mobile security community, resulting in a lack of thorough evaluation of security practices for app developers using custom permissions. This study systematically evaluated the misconfiguration of custom permissions by Android app developers. It is …

Long-Tailed Out-Of-Distribution Detection Via Normalized Outlier Distribution Adaptation, Wenjun Miao, Guansong Pang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Onekeychallenge in Out-of-Distribution (OOD) detection is the absence of groundtruth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers (i.e., pseudo OOD samples) to train OOD detectors. However, we find empirically that the outlier samples often present a distribution shift compared to the true OOD samples, especially in LongTailed Recognition (LTR) scenarios, where ID classes are heavily imbalanced, i.e., the true OOD samples exhibit very different probability distribution to the head and tailed ID classes from the outliers. In this work, we propose a novel approach, namely normalized outlier …

Chain Of Preference Optimization: Improving Chain-Of-Thought Reasoning In Llms, Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin

Research Collection School Of Computing and Information Systems

The recent development of chain-of-thought (CoT) decoding has enabled large language models (LLMs) to generate explicit logical reasoning paths for complex problem-solving. However, research indicates that these paths are not always deliberate and optimal. The tree-of-thought (ToT) method employs tree-searching to extensively explore the reasoning space and find better reasoning paths that CoT decoding might overlook. This deliberation, however, comes at the cost of significantly increased inference complexity. In this work, we demonstrate that fine-tuning LLMs leveraging the search tree constructed by ToT allows CoT to achieve similar or better performance, thereby avoiding the substantial inference burden. This is achieved …

Gotcha ! This Model Uses My Code ! Evaluating Membership Leakage Risks In Code Models, Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsum Kim, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Leveraging large-scale datasets from open-source projects and advances in large language models, recent progress has led to sophisticated code models for key software engineering tasks, such as program repair and code completion. These models are trained on data from various sources, including public open-source projects like GitHub and private, confidential code from companies, raising significant privacy concerns. This paper investigates a crucial but unexplored question: What is the risk of membership information leakage in code models? Membership leakage refers to the vulnerability where an attacker can infer whether a specific data point was part of the training dataset. We present …

User Acceptance Of Advice By Ai Agents: Expectation-System Fit Perspective, Jingyuan Cai, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Algorithms have increasing influence on our daily decisions, especially when the recommendations are presented by human-like AI agents. This study applies the Theory of Effective Use to investigate how the fit between the user’s role expectation for an AI agent and the agent’s interaction style impacts AI advice adoption. We proposed a new concept termed Perceived Expectation-System Fit (PESF) and empirically examined its impact on user perceptions and advice acceptance. We found that low PESF reduces advice acceptance by diminishing cognitive and affective trust in the AI agent. Furthermore, increased algorithm transparency increases PESF's impact on decision-making. Our findings provide …

Delidar: Decoupling Lidars For Pervasive Spatial Computing, Kanatta Gamage Ramesh Darshana Rathnayake, Razat Sutradhar, Abbaas A. M. Nishar, Weerakoon Dulaj S., Ashwin Ashok, Archan Misra

Research Collection School Of Computing and Information Systems

Unbounded proliferation of LiDAR-equipped pervasive devices generates two challenges: (a) mutual interference among emitters and (b) significantly higher sensing energy overhead. We propose a fundamentally different approach for LiDAR sensing, in indoor spaces, that decouples the sensor’s emitter and receiver components. Our proposed approach, called DeLiDAR, centralizes the emitter functionality in one or more stationary nodes that continually emit pulses; this decoupling allows each mobile LiDAR sensor to be an ultra-low power, pure receiver unit consisting solely of passive multiple photodiodes. We explain how the emitter can utilize VLC-based encoding of its pulses to convey parameter settings that allow a …

A Full-History Network Dataset For Btc Asset Decentralization Profiling, Ling Cheng, Qian Shao, Fengzhu Zeng, Feida Zhu

Research Collection School Of Computing and Information Systems

Since its advent in 2009, Bitcoin (BTC) has garnered increasing attention from both academia and industry. However, due to the massive transaction volume, no systematic study has quantitatively measured the asset decentralization degree specifically from a network perspective.In this paper, by conducting a thorough analysis of the BTC transaction network, we first address the significant gap in the availability of full-history BTC graph and network property dataset, which spans over 15 years from the genesis block (1st March, 2009) to the 845651-th block (29, May 2024). We then present the first systematic investigation to profile BTC's asset decentralization and design …

Shield-U: Safeguarding Traffic Sign Recognition Against Perturbation Attacks, Shengmin Xu, Jianfei Sun, Hangcheng Cao, Yulan Gao, Ziyang He, Cong Wu

Research Collection School Of Computing and Information Systems

Traffic sign recognition systems are crucial for the navigation and situation awareness of autonomous vehicles. They leverage deep learning technologies to swiftly and accurately identify traffic signs, even in the most challenging traffic environments. However, security researchers have uncovered a critical vulnerability in these systems: learning-based TSRs are particularly susceptible to physical-world perturbation attacks. Through subtle modifications (i.e., attaching well-designed patches on traffic signs), attackers can deceive the recognition system into making erroneous judgments, which can further lead to serious traffic accidents. Although several defense mechanisms have been proposed to enhance the security of sign recognition systems, these solutions generally …

Ohss: Optimizing Homomorphic Secret Sharing To Support Fast Matrix Multiplication, Shuguang Zhang, Jianli Bai, Kun Tu, Ziyue Yin, Chan Liu

Research Collection School Of Computing and Information Systems

Homomorphic Secret Sharing (HSS) has evolved as a state-of-the-art methodology for achieving secure two-party computation, synthesizing the advantages of secret sharing and homomorphic encryption. This amalgamation ensures minimal computational and communicational overhead, making it particularly adept at arithmetic operations. However, HSS faces challenges in scalability and efficiency when confronted with extensive matrix operations, including both matrix-vector and matrix-matrix multiplications, which are fundamental in numerous privacy-preserving computations, notably within the realm of privacy-preserving machine learning. In this research, we introduce Optimized Homomorphic Secret Sharing (OHSS), a refined version of HSS, crafted to address these limitations. Our contributions include enhancements to the …

An Aggregate Matching And Pick-Up Model For Mobility-On-Demand Services, Xinwei Li, Jintao Ke, Hai Yang, Hai Wang, Yaqian Zhou

Research Collection School Of Computing and Information Systems

This paper presents an Aggregate Matching and Pick-up (AMP) model to delineate the matching and pick-up processes in mobility-on-demand (MoD) service markets by explicitly considering the matching mechanisms in terms of matching intervals and matching radii. With passenger demand rate, vehicle fleet size and matching strategies as inputs, the AMP model can well approximate drivers’ idle time and passengers’ waiting time for matching and pick-up by considering batch matching in a stationary state. Properties of the AMP model are then analyzed, including the relationship between passengers’ waiting time and drivers’ idle time, and their changes with market thickness, which is …

Learning De-Biased Representations For Remote-Sensing Imagery, Zichen Tian, Zhaozheng Chen, Qianru Sun

Research Collection School Of Computing and Information Systems

Remote sensing (RS) imagery, requiring specialized satellites to collect and being difficult to annotate, suffers from data scarcity and class imbalance in certain spectrums. Due to data scarcity, training any large-scale RS models from scratch is unrealistic, and the alternative is to transfer pre-trained models by fine-tuning or a more data-efficient method LoRA. Due to class imbalance, transferred models exhibit strong bias, where features of the major class dominate over those of the minor class. In this paper, we propose debLoRA---a generic training approach that works with any LoRA variants to yield debiased features. It is an unsupervised learning approach …

Mvgamba : Unify 3d Content Generation As State Space Sequence Modeling, Xuanyu Yi, Zike Wu, Qiuhong Shen, Qingshan Xu, Pan Zhou, Joo-Hwee Lim, Shuicheng Yan, Xinchao Wang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Recent 3D large reconstruction models (LRMs) can generate high-quality 3D content in sub-seconds by integrating multi-view diffusion models with scalable multi-view reconstructors. Current works further leverage 3D Gaussian Splatting as 3D representation for improved visual quality and rendering efficiency. However, we observe that existing Gaussian reconstruction models often suffer from multi-view inconsistency and blurred textures. We attribute this to the compromise of multi-view information propagation in favor of adopting powerful yet computationally intensive architectures (e.g., Transformers). To address this issue, we introduce MVGamba, a general and lightweight Gaussian reconstruction model featuring a multi-view Gaussian reconstructor based on the RNN-like State …

Lova3 : Learning To Visual Question Answering, Asking And Assessment, Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Bai Shou, Mike Zheng Shou

Research Collection School Of Computing and Information Systems

Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge. By enhancing these capabilities, humans can more effectively utilize data, leading to better comprehension and learning outcomes. Current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills. Inspired by the human learning mechanism, we introduce LOVA3 , an innovative framework named “Learning tO Visual question Answering, Asking and Assessment,” designed to equip MLLMs with these additional capabilities. Our approach involves the creation of two supplementary training tasks GenQA and EvalQA, aiming …

Thinking And Doing Beyond Science And Risk Communication As Mere Transmission, Inez Z. Ponce De Leon

Department of Communication Faculty Publications

I look back on my professional experience as both bench and social scientist, and examine my misun derstandings of science communication as a field, which were fueled by my ideologically-driven stance on the primacy of the bench sciences over all other fields of study. I begin with my work as a molecular biologist, continue with my socialization into the social sciences at the PhD level, and narrate my ex periences as a researcher attempting to break down the notion of science and risk communication as mere vessels for sharing scientific information. I do this by discussing my research findings, and …

A Pan-Cancer Single-Cell Analysis Of Intratumoral Copy Number Diversity And Evolution, Hanghui Ye

Dissertations and Theses (Open Access)

Aneuploidy is a hallmark of human cancers, with many copy number aberrations (CNAs) being associated with disease progression. Previous studies have revealed extensive inter-patient heterogeneity (IPH) in copy number profiles. However, the extent of intratumoral heterogeneity (ITH) and its evolutionary dynamics remain poorly understood.

To address these gaps, we developed Acoustic Cell Tagmentation (ACT), an advanced single-cell single-molecule DNA sequencing (scDNA-seq) technology, to resolve the copy number substructure of human tumors and investigate the evolution of aneuploidy. Applying ACT to eight triple-negative breast cancer (TNBC) patients to profile 9,765 aneuploid tumor cells, we discovered that following initial punctuated copy number …

The Life And Discoveries Of Yellapragada Subbarow, Nikhil R, Varuni Pragya, Subramanian Ramaswamy, Shiva Prasad B N, Mahabaleshwar Mamadapur

Digital Journal of Clinical Medicine

Dr. Yellapragada Subbarow was a researcher, teacher, and a wizard of wonder drugs who constantly made efforts in the discoveries/ inventions such as vitamins, anti-vitamins, antibiotics, and many more by disentangling the mysteries of nature for the betterment of the people. He inspired the youngsters to take up the challenges and to stand up above mediocrity. The reward for his achievement was delayed. The article describes a true humanist's journey, discoveries, and his achievements that are largely unknown to date.

Measuring Social Influence In Online Social Networks - Focused Literature Review On Human Behaviors, Shyamala N. Chalakudi, Gnana Bharathy, Dildar Hussain, Dakshinamurthy Kolluru

Journal of Applied Marketing Theory

Measuring an individual's social influence (SI) capability is gaining traction with the rapid growth and expansive reach of online social networks (OSN). However, most existing frameworks and methods are confined to graphical theories or the structural properties of these networks. Even behavioral investigations are often limited to a narrow set of influencing behaviors and confront significant challenges in encompassing the breadth of human psychological characteristics. This study aims to address this gap through an exhaustive and focused literature review, concentrating on the definitions of human behavior characteristics rooted in established sociology and psychology theories. Beyond providing a comprehensive overview, the …

Machine Learning Optimized Depolymerization Of Pet (Polyethylene Terephthalate) Via Glycolysis, Mehwash Aamir

Theses and Dissertations

Over 90% of the approximately 1 million PET bottles sold every minute end up in landfills or oceans, where they can persist for centuries. Plastic pollution urgently needs sustainable management and recycling solutions to mitigate the environmental impact of PET waste. From all techniques to recycle plastic waste, catalytic glycolysis stands out for its rapid reaction time, high monomer yields, lower costs, and enhanced durability.

In this work glycolysis of PET was evaluated under microwave conditions using 1,5,7-Triazabicyclo [4.4.0] dec5-ene (TBD) and 1,8-Diazabicyclo [5.4.0] undec-7-ene (DBU) as catalysts, being DBU as the best catalyst for further analysis. The effect of …

Three Decades Of Rat Genomics: Approaching The Finish(Ed) Line, Theodore S Kalbfleisch, Melissa L Smith, Julia L Ciosek, Kai Li, Peter A Doris

The Brown Foundation: Institute of Molecular Medicine

The rat, Rattus norvegicus, has provided an important model for investigation of a range of characteristics of biomedical importance. Here we survey the origins of this species, its introduction into laboratory research, and the emergence of genetic and genomic methods that utilize this model organism. Genomic studies have yielded important progress and provided new insight into several biologically important traits. However, some studies have been impeded by the lack of a complete and accurate reference genome for this species. New sequencing and genome assembly methods applied to the rat have resulted in a new reference genome assembly, GRCr8, which …

Classifying Supersonic Frequencies For Active Acoustic Side-Channel Exploitation, Destin Hinkel

Graduate Theses and Dissertations (2019 - present)

Computing side-channel research explores the manner in which physical emanations from systems can be used to reconstruct data. Acoustic side-channels are those physical emanations that produce a sonic frequency that is subsonic, supersonic, or considered in the range of human hearing [1]. Acoustic side-channel attacks (SCAs) are typically performed passively: a listening device captures aural frequencies from a machine via a microphone that are transmitted to the attacker for analysis [1]–[3]. Machine learning models have been presented to classify individual keystrokes according to variations in acoustic frequency [4]. Furthermore, the SonarSnoop framework presents a novel active approach that involves both …

Hybrid Deep Learning-Based Model For Eclipse Attack Detection On Ethereum Network, Dhanasak Bhumichai

Graduate Theses and Dissertations (2019 - present)

An eclipse attack is a significant cyber threat targeting the network layer of blockchain platforms. Detecting eclipse attacks is challenging for several reasons. First, there are no available datasets for training and testing models. Second, comprehensive studies identifying features to detect eclipse attacks are lacking. Additionally, the amount of eclipse network traffic is much smaller than that of normal network traffic, which leads to imbalanced samples. Moreover, the characteristics of eclipse network traffic closely resemble those of normal traffic, causing overlapping samples, which makes it challenging for traditional classifiers to learn how to identify eclipse attacks. To address these challenges, …