Digital Commons Network^™

Risurconv : Rotation Invariant Surface Attention-Augmented Convolutions For 3d Point Cloud Classification And Segmentation, Zhiyuan Zhang, Licheng Yang, Xiang Zhiyu

Research Collection School Of Computing and Information Systems

Despite the progress on 3D point cloud deep learning, most prior works focus on learning features that are invariant to translation and point permutation, and very limited efforts have been devoted for rotation invariant property. Several recent studies achieve rotation invariance at the cost of lower accuracies. In this work, we close this gap by proposing a novel yet effective rotation invariant architecture for 3D point cloud classification and segmentation. Instead of traditional pointwise operations, we construct local triangle surfaces to capture more detailed surface structure, based on which we can extract highly expressive rotation invariant surface properties which are …

Desk2desk : Optimization-Based Mixed Reality Workspace Integration For Remote Side-By-Side Collaboration, Ludwig Sidenmark, Tianyu Zhang, Leen Al Lababidi, Jiannan Li, Tovi Grossman

Research Collection School Of Computing and Information Systems

Mixed Reality enables hybrid workspaces where physical and virtual monitors are adaptively created and moved to suit the current environment and needs. However, in shared settings, individual users’ workspaces are rarely aligned and can vary significantly in the number of monitors, available physical space, and workspace layout, creating inconsistencies between workspaces which may cause confusion and reduce collaboration. We present Desk2Desk, an optimization-based approach for remote collaboration in which the hybrid workspaces of two collaborators are fully integrated to enable immersive side-by-side collaboration. The optimization adjusts each user’s workspace in layout and number of shared monitors and creates a mapping …

Direct Range Proofs For Paillier Cryptosystem And Their Applications, Zhikang Xie, Mengling Liu, Haiyang Xue, Man Ho Au, Robert H. Deng, Siu-Ming Yiu

Research Collection School Of Computing and Information Systems

The Paillier cryptosystem is renowned for its applications in electronic voting, threshold ECDSA, multi-party computation, and more, largely due to its additive homomorphism. In these applications, range proofs for the Paillier cryptosystem are crucial for maintaining security, because of the mismatch between the message space in the Paillier system and the operation space in application scenarios. In this paper, we present novel range proofs for the Paillier cryptosystem, specifically aimed at optimizing those for both Paillier plaintext and affine operation. We interpret encryptions and affine operations as commitments over integers, as opposed to solely over ZN. Consequently, we propose direct …

Improving Out-Of-Distribution Detection With Disentangled Foreground And Background Features, Choubo Ding, Guansong Pang

Research Collection School Of Computing and Information Systems

Detecting out-of-distribution (OOD) inputs is a principal task for ensuring the safety of deploying deep-neural-network classifiers in open-set scenarios. OOD samples can be drawn from arbitrary distributions and exhibit deviations from in-distribution (ID) data in various dimensions, such as foreground features (e.g., objects in CIFAR100 images vs. those in CIFAR10 images) and background features (e.g., textural images vs. objects in CIFAR10). Existing methods can confound foreground and background features in training, failing to utilize the background features for OOD detection. This paper considers the importance of feature disentanglement in out-of-distribution detection and proposes the simultaneous exploitation of both foreground and …

Densetrack : Drone-Based Crowd Tracking Via Density-Aware Motion-Appearance Synergy, Yi Lei, Huilin Zhu, Jingling Yuan, Guangli Xiang, Xian Zhong, Shengfeng He

Research Collection School Of Computing and Information Systems

Drone-based crowd tracking faces difficulties in accurately identifying and monitoring objects from an aerial perspective, largely due to their small size and close proximity to each other, which complicates both localization and tracking. To address these challenges, we present the Density-aware Tracking (DenseTrack) framework. DenseTrack capitalizes on crowd counting to precisely determine object locations, blending visual and motion cues to improve the tracking of small-scale objects. It specifically addresses the problem of cross-frame motion to enhance tracking accuracy and dependability. DenseTrack employs crowd density estimates as anchors for exact object localization within video frames. These estimates are merged with motion …

Zero-Shot Object Counting With Good Exemplars, Huilin Zhu, Jingling Yuan, Zhengwei Yang, Yu Guo, Zheng Wang, Xian Zhong, Shengfeng He

Research Collection School Of Computing and Information Systems

Zero-shot object counting (ZOC) aims to enumerate objects in images using only the names of object classes during testing, without the need for manual annotations. However, a critical challenge in current ZOC methods lies in their inability to identify high-quality exemplars effectively. This deficiency hampers scalability across diverse classes and undermines the development of strong visual associations between the identified classes and image content. To this end, we propose the Visual Association-based Zero-shot Object Counting (VA-Count) framework. VACount consists of an Exemplar Enhancement Module (EEM) and a Noise Suppression Module (NSM) that synergistically refine the process of class exemplar identification …

Beat-It : Beat-Synchronized Multi-Condition 3d Dance Generation, Zikai Huang, Xuemiao Xu, Cheng Xu, Huaidong Zhang, Chenxi Zheng, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Dance, as an art form, fundamentally hinges on the precise synchronization with musical beats. However, achieving aesthetically pleasing dance sequences from music is challenging, with existing methods often falling short in controllability and beat alignment. To address these shortcomings, this paper introduces Beat-It, a novel framework for beat-specific, key pose-guided dance generation. Unlike prior approaches, Beat-It uniquely integrates explicit beat awareness and key pose guidance, effectively resolving two main issues: the misalignment of generated dance motions with musical beats, and the inability to map key poses to specific beats, critical for practical choreography. Our approach disentangles beat conditions from music …

Onerestore : A Universal Restoration Framework For Composite Degradation, Yu Guo, Yuan Gao, Yuxu Lu, Huilin Zhu, Ryan Wen Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

In real-world scenarios, image impairments often manifest as composite degradations, presenting a complex interplay of elements such as low light, haze, rain, and snow. Despite this reality, existing restoration methods typically target isolated degradation types, thereby falling short in environments where multiple degrading factors coexist. To bridge this gap, our study proposes a versatile imaging model that consolidates four physical corruption paradigms to accurately represent complex, composite degradation scenarios. In this context, we propose OneRestore, a novel transformer-based framework designed for adaptive, controllable scene restoration. The proposed framework leverages a unique cross-attention mechanism, merging degraded scene descriptors with image features, …

Documenting Ethical Considerations In Open Source Ai Models, Haoyu Gao, Mansooreh Zahedi, Christoph Treude, Sarita Rosenstock, Marc Cheong

Research Collection School Of Computing and Information Systems

Background: The development of AI-enabled software heavily depends on AI model documentation, such as model cards, due to different domain expertise between software engineers and model developers. From an ethical standpoint, AI model documentation conveys critical information on ethical considerations along with mitigation strategies for downstream developers to ensure the delivery of ethically compliant software. However, knowledge on such documentation practice remains scarce. Aims: The objective of our study is to investigate how developers document ethical aspects of open source AI models in practice, aiming at providing recommendations for future documentation endeavours. Method: We selected three sources of documentation on …

An Empirical Study Of Api Misuses Of Data-Centric Libraries, Akalanda Galappaththi, Sarah Nadi, Christoph Treude

Research Collection School Of Computing and Information Systems

Developers rely on third-party library Application Programming Interfaces (APIs) when developing software. However, libraries typically come with assumptions and API usage constraints, whose violation results in API misuse. API misuses may result in crashes or incorrect behavior. Even though API misuse is a well-studied area, a recent study of API misuse of deep learning libraries showed that the nature of these misuses and their symptoms are different from misuses of traditional libraries, and as a result highlighted potential shortcomings of current misuse detection tools. We speculate that these observations may not be limited to deep learning API misuses but may …

A Survey Of Protocol Fuzzing, Xiaohan Zhang, Cen Zhang, Xinghua Li, Zhengjie Du, Bing Mao, Yeting Li, Pan Li

Research Collection School Of Computing and Information Systems

Communication protocols form the bedrock of our interconnected world, yet vulnerabilities within their implementations pose significant security threats. Recent developments have seen a surge in fuzzing-based research dedicated to uncovering these vulnerabilities within protocol implementations. However, there still lacks a systematic overview of protocol fuzzing for answering the essential questions such as what the unique challenges are, how existing works solve them, and so on. To bridge this gap, we conducted a comprehensive investigation of related works from both academia and industry. Our study includes a detailed summary of the specific challenges in protocol fuzzing and provides a systematic categorization …

An Empirical Study Of Automatic Program Repair Techniques For Injection Vulnerabilities, Tingwei Zhu, Tongtong Xu, Kui Liu, Jiayuan Zhou, Xing Hu, Xin Xia, Tian Zhang, David Lo

Research Collection School Of Computing and Information Systems

Injection vulnerabilities are among the most serious and dangerous security defects, as they can be exploited by attackers to inject malicious inputs and carry out cybercrimes. Timely fixing of injection vulnerabilities is crucial. However, manual repairs of injection vulnerabilities often require specialized knowledge and are prone to errors, posing a challenge and a heavy burden on developers. In recent years, Automated Program Repair (APR) techniques have shown promising momentum in automatically fixing general defects. Yet, there has been no research on how APR techniques perform in repairing injection vulnerabilities. Therefore, in this paper, we conduct an empirical study. We first …

Graph Continual Learning With Debiased Lossless Memory Replay, Chaoxi Niu, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Real-life graph data often expands continually, rendering the learning of graph neural networks (GNNs) on static graph data impractical. Graph continual learning (GCL) tackles this problem by continually adapting GNNs to the expanded graph of the current task while maintaining the performance over the graph of previous tasks. Memory replay-based methods, which aim to replay data of previous tasks when learning new tasks, have been explored as one principled approach to mitigate the forgetting of the knowledge learned from the previous tasks. In this paper we extend this methodology with a novel framework, called Debiased Lossless Memory replay (DeLoMe). Unlike …

Generative Ai In Software Engineering Must Be Human-Centered: The Copenhagen Manifesto, D. Russo, S. Van Berkel Baltes, Christoph Treude

Research Collection School Of Computing and Information Systems

The advent of Generative Artificial Intelligence—systems that can produce human-like content such as text, music, visual art, or source code—marks not only a significant leap for Artificial Intelligence (AI) but also a pivotal moment for software practitioners and researchers. The role of software engineering researchers and practitioners in adopting the technologies that shape our world is critical. Historically, the human aspects of developing software have been treated as secondary to more technical innovations. However, the emergence of Generative AI will simultaneously enhance human capabilities while surfacing complex ethical, social, legal, and technical challenges.While primarily aimed at software engineering (SE) researchers …

Does Ceo Agreeableness Personality Mitigate Real Earnings Management?, Shan Liu, Xingying Wu, Nan Hu

Research Collection School Of Computing and Information Systems

Despite efforts to mitigate aggressive financial reporting, earnings management remains challenging to parties interested in inhibiting its dysfunctional effects. Using linguistic algorithms to assess CEO agreeableness personality from their unscripted texts in conference calls, we find that it is a determinant that mitigates a firm's real earnings management. Furthermore, such an effect is more pronounced when firms confront intensive market competition and financial distress and have weaker managerial entrenchment or when CEOs face stronger internal governance. Our findings persist even after we utilize several alternative real earnings management metrics and control other confounding personalities in prior earnings management studies. The …

On The Lossiness Of 2k-Th Power And The Instantiability Of Rabin-Oaep, Haiyang Xue, Bao Li, Xianhui Lu, Kunpeng Wang, Yamin Liu

Research Collection School Of Computing and Information Systems

Seurin PKC 2014 proposed the 2-ï /4-hiding assumption which asserts the indistinguishability of Blum Numbers from pseudo Blum Numbers. In this paper, we investigate the lossiness of 2 k -th power based on the 2 k -ï /4-hiding assumption, which is an extension of the 2-ï /4-hiding assumption. And we prove that 2 k -th power function is a lossy trapdoor permutation over Quadratic Residuosity group. This new lossy trapdoor function has 2 k -bits lossiness for k -bits exponent, while the RSA lossy trapdoor function given by Kiltz et al. Crypto 2010 has k -bits lossiness for k -bits …

Demystifying And Extracting Fault-Indicating Information From Logs For Failure Diagnosis, Junjie Huang, Zhihan Jiang, Jinyang Liu, Yintong Huo, Jiazhen Gu, Zhuangbin Chen, Cong Feng, Hui Dong, Zengyin Yang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Logs are imperative in the maintenance of online service systems, which often encompass important information for effective failure mitigation. While existing anomaly detection methodologies facilitate the identification of anomalous logs within extensive runtime data, manual investigation of log messages by engineers remains essential to comprehend faults, which is labor-intensive and error-prone. Upon examining the log-based troubleshooting practices at CloudA 1, we find that engineers typically prioritize two categories of log information for diagnosis. These include fault-indicating descriptions, which record abnormal system events, and fault-indicating parameters, which specify the associated entities. Motivated by this finding, we propose an approach to automatically …

Privacy Risks And Regulatory Challenges In Smart Grids And Renewable Energy Systems, Mikołaj Rajca

internetowy Kwartalnik Antymonopolowy i Regulacyjny (internet Quarterly on Antitrust and Regulation)

Smart grid technologies are central to the global shift towards a more efficient and sustainable energy infrastructure, integrating advanced digital systems with traditional power networks. While these technologies offer significant benefits, including enhanced energy management and the seamless integration of renewable energy sources, they also introduce complex privacy challenges. The extensive data collection and real-time communication capabilities inherent in smart grids raise concerns over consumer privacy, data breaches, and cybersecurity threats. This paper critically examines these privacy risks within the context of evolving regulatory frameworks such as the GDPR, NIS2 Directive, and the forthcoming EU AI Act. The discussion emphasizes …

A Framework For Human Evaluation Of Large Language Models In Healthcare Derived From Literature Review, Thomas Yu Chow Tam, Sonish Sivarajkumar, Sumit Kapoor, Alisa V Stolyar, Katelyn Polanska, Karleigh R Mccarthy, Hunter Osterhoudt, Xizhi Wu, Shyam Visweswaran, Sunyang Fu, Piyush Mathur, Giovanni E Cacciamani, Cong Sun, Yifan Peng, Yanshan Wang

Faculty, Staff and Student Publications

With generative artificial intelligence (GenAI), particularly large language models (LLMs), continuing to make inroads in healthcare, assessing LLMs with human evaluations is essential to assuring safety and effectiveness. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare across various medical specialties and addresses factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Our literature review of 142 studies shows gaps in reliability, generalizability, and applicability of current human evaluation practices. To overcome such significant obstacles to healthcare LLM developments and deployments, we …

Architecting Standards: Leveraging Ia To Drive Emerging Technology Standards​, Noreen Y. Whysel

Publications and Research

In this talk for the annual meeting of the W3C Information Architecture Community Group, I discuss how standards bodies can learn from the Information Architecture field to improve accessibility and usability. By adopting user-centered design principles and engaging in iterative feedback loops, standards organizations can create more relevant and practical standards that align with real-world applications. Emphasizing collaboration with industry experts and end-users will help ensure that standards evolve along with technological advancements.

In the meantime, key standards are examined, such as ISO 9241, which provides guidelines for usability and user-centered design, and the W3C's Web Content Accessibility Guidelines (WCAG), …

An Edge Platform Streamlining Connectivity Between Modern Edge Devices And Cloud, Anderson Carvalho

Theses

The numerous uses of cloud, fog, and edge computing in a variety of industries are thoroughly examined in this thesis, with a special emphasis on smart factories, smart cities, and smart agriculture. It starts with an introduction to cloud computing, going over its history, importance in contemporary digital infrastructures, and related security concerns. It emphasizes the value of cloud computing for processing and storing data while addressing issues like guaranteeing low latency applications and possible improvements from container technologies. The conversation then shifts to fog computing, going over its history, designs, and uses. It focuses on how fog computing might …

Prompt And Accurate Grb Source Localization Aboard The Advanced Particle Astrophysics Telescope (Apt) And Its Antarctic Demonstrator (Adapt), Ye Htet, Marion Sudvarg, Jeremy Buhler, Roger Chamberlain, Wenlei Chen, James Buckley, Roger D. Chamberlain, Corrado Altomare, Matthew Andrew, Blake Bal, Richard G. Bose, Dana Braun, Eric Burns, Michael L. Cherry, Leonardo Di Venere, Jeffrey Dumonthier, Manel Errando, Stefan Funk

Computer Science Faculty Research & Creative Works

We characterize the performance of our computational pipeline for real-time gamma-ray burst (GRB) detection and localization aboard the Advanced Particle-astrophysics Telescope (APT) – a space-based observatory for MeV to TeV gamma-ray astronomy – and its smaller, balloon-borne prototype, the Antarctic Demonstrator for APT (ADAPT), whose scientific focus will be the detection of MeV transients. These instruments observe scintillation light from multiple Compton scattering and photoabsorption of gamma-ray photons across a series of CsI detector layers. We infer the incident angle of each photon's first scattering to localize its source direction to a Compton ring about the vector defined by its …

Front-End Computational Modeling And Design For The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope, Marion Sudvarg, Ye Htet, Roger Chamberlain, Jeremy Buhler, Blake Bal, Blake Bal, Corrado Altomare, Corrado Altomare, Davide Serini, Davide Serini, Mario Nicola Mazziotta, Mario Nicola Mazziotta, Leonardo Di Venere, Leonardo Di Venere, Wenlei Chen, Wenlei Chen, James H. Buckley, Roger D. Chamberlain

Computer Science Faculty Research & Creative Works

The Advanced Particle-astrophysics Telescope (APT) is a planned space-based observatory designed to localize MeV to TeV transients such as gamma-ray bursts in real time using onboard computational hardware. The Antarctic Demonstrator for APT (ADAPT) is a prototype high-altitude balloon mission scheduled to fly during the 2025–26 season. Gamma-ray-induced scintillations in CsI tiles will be captured by perpendicular arrays of optical fibers running across both tile surfaces, as well as SiPM-based edge detectors to improve light collection and calorimetry. Signal samples are captured by analog waveform digitizer ASICs then sent to the front end of the computational pipeline, which is designed …

Optical Lens Attack On Deep Learning Based Monocular Depth Estimation, Ce Zhou, Qiben Yan, Daniel Kent, Guangjing Wang, Ziqi Zhang, Haydar Radha

Computer Science Faculty Research & Creative Works

Monocular Depth Estimation (MDE) plays a crucial role in vision-based Autonomous Driving (AD) systems. It utilizes a singlecamera image to determine the depth of objects, facilitating driving decisions such as braking a few meters in front of a detected obstacle or changing lanes to avoid collision. In this paper, we investigate the security risks associated with monocular vision-based depth estimation algorithms utilized by AD systems. By exploiting the vulnerabilities of MDE and the principles of optical lenses, we introduce 𝐿𝑒𝑛𝑠𝐴𝑡𝑡𝑎𝑐𝑘, a physical attack that involves strategically placing optical lenses on the camera of an autonomous vehicle to manipulate the perceived …

Enhancing Resilience And Reducing Waste In Food Supply Chains: A Systematic Review And Future Directions Leveraging Emerging Technologies, Asmaa Seyam, May Ei Barachi, Cheng Zhang, Bo Du, Jun Shen, Sujith Samuel Mathew

All Works

The sustainability of food supply chains is gaining increasing attention, particularly after the COVID-19 pandemic. A food supply system that simultaneously prioritises resilience and minimises wastage is crucial. It is found that many studies have explored reducing food waste and increasing supply chain resilience as separate objectives, but research is scarce investigating both objectives in conjunction. This paper presents a comprehensive systematic review focusing on existing solutions to reducing food waste and enhancing resilience. It discusses future directions, particularly leveraging emerging technologies such as the Internet of Things, blockchain, artificial intelligence, and machine learning. The studies are categorised into three …

Mention Detection In Turkish Coreference Resolution, Şeni̇z Demi̇r, Hani̇fi̇ İbrahi̇m Akdağ

Turkish Journal of Electrical Engineering and Computer Sciences

A crucial step in understanding natural language is detecting mentions that refer to real-world entities in a text and correctly identifying their boundaries. Mention detection is commonly considered a preprocessing step in coreference resolution which is shown to be helpful in several language processing applications such as machine translation and text summarization. Despite recent efforts on Turkish coreference resolution, no standalone neural solution to mention detection has been proposed yet. In this article, we present two models designed for detecting Turkish mentions by using feed-forward neural networks. Both models extract all spans up to a fixed length from input text …

Educating With Ai, Grace Seo, David Wicks

AI and the Future of Work

This conference track explores the integration of AI within teaching and learning, with a focus on practical approaches that leverage AI technologies to optimize teaching practices and enhance students’ learning experience. The topics include the essential AI literacies in educational contexts, collaborative learning with AI, and the use of AI for enhanced learning assessments.

Detecting Lgbtq+ Instances Of Cyberbullying, Arslan Bisharat, Manuel Madrigal, Mohammed Abuhamad, Deborah Hall, Yasin Silva

Computer Science: Faculty Publications and Other Works

Social media continues to have an impact on the trajectory of humanity. However, its introduction has also weaponized keyboards, allowing the abusive language normally reserved for in-person bullying to jump onto the screen, i.e., cyberbullying. Cyberbullying poses a significant threat to adolescents globally, affecting the mental health and well-being of many. A group that is particularly at risk is the LGBTQ+ community, as researchers have uncovered a strong correlation between identifying as LGBTQ+ and suffering from greater online harassment. Therefore, it is critical to develop machine learning models that can accurately discern cyberbullying incidents as they happen to LGBTQ+ members. …

Impact Of An Online Decision Support Tool For Ductal Carcinoma In Situ (Dcis) Using A Pre-Post Design (Aft-25), Elissa Ozanne, Kellyn Maves, Angela Tramontano, Thomas Lynch, Alastair Thompson, Ann Partridge, Elizabeth Frank, Deborah Collyar, Desiree Basila, Donna Pinto, Terry Hyslop, Marc Ryser, Shoshana Rosenberg, E. Shelley Hwang, Rinaa Punglia

Department of Pharmacology and Experimental Therapeutics Faculty Papers

BACKGROUND: The heterogeneous biology of ductal carcinoma in situ (DCIS), as well as the variable outcomes, in the setting of numerous treatment options have led to prognostic uncertainty. Consequently, making treatment decisions is challenging and necessitates involved communication between patient and provider about the risks and benefits. We developed and investigated an interactive decision support tool (DST) designed to improve communication of treatment options and related long-term risks for individuals diagnosed with DCIS.

FINDINGS: The DST was developed for use by individuals aged > 40 years with DCIS and is based on a disease simulation model that integrates empirical data and …

Investigating The Intersection Of Ai And Cybercrime: Risks, Trends, And Countermeasures, Sanaika Shetty, Kyung-Shick Choi, Insun Park

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.