Digital Commons Network^™

Quadripartitioned Neutrosophic Soft Pre - Open And Pre - Closed Sets, S. Ramesh Kumar, Dr. A. Stanis Arul Mary

Neutrosophic Sets and Systems

No abstract provided.

Integrating Industry Needs With Language Training From Classroom To Profession: English Translation Courses Measurement In Neutrosophic Number Operator, Yujia Guo

Neutrosophic Sets and Systems

No abstract provided.

Construction Of Almost Unbiased Estimator For Population Median Using Neutrosophic Information,, Rajesh Singh, Anamika Kumari, Florentin Smarandache, Sunil Kumar Yadav

Neutrosophic Sets and Systems

No abstract provided.

Neutrosophic Model For Analyzing The Effect Of The Enterprise Resource Planning Functions On Business Processes, Samah Ibrahim Abdel Aal, Mahmoud M. A. Abdellatif

Neutrosophic Sets and Systems

No abstract provided.

Exploring Neutrosophic Contra Alpha Generalized Semi-Continuous Maps, V. Banu Priya, M. Suresh, S. Chandrasekar, A. Atkinswestley

Neutrosophic Sets and Systems

No abstract provided.

Pentapartitioned Single-Valued Neutrosophic Z-Numbers: Theory And Applications, A. Mohammed Shapique, R. Sudharani, K. Karuppiah, H. Prathab, M. Karthigeyan, S. Senthil

Neutrosophic Sets and Systems

No abstract provided.

Properties Of Quadripartitioned Neutrosophic Interval-Valued Set To The Reciprocal Fraction Function Via Various Operators, M. Palanikumar, Nasreen Kausar, Cuauhtemoc Samaniego

Neutrosophic Sets and Systems

No abstract provided.

Neutrosophic E-Open Sets In A Neutrosophic Topological Spaces, Thangaraja P, Vadivel A, Bobin A, Thayalan S, John Sundar C, Manivannan P

Neutrosophic Sets and Systems

No abstract provided.

A Comparative Review Of The Fuzzy, Intuitionistic And Neutrosophic Numbers In Solving Uncertainty Matrix Equations, Myra Suphelea Asmizal, Wan Suhana Wan

Neutrosophic Sets and Systems

No abstract provided.

, Generalized Neutrosophic Sets And Its Application In Ku-Algebras University Of New Mexico, Ramesh Kumar D, Vasu M

Neutrosophic Sets and Systems

No abstract provided.

Bijective Single Valued Neutrosophic Graph And Its Application In Fraud Detection Analysis In Social Networks, D. Rajalaxmi, R. Shivaragavi, Said Broumi

Neutrosophic Sets and Systems

No abstract provided.

Modest Insights From Grammar To Growth: A Neutrosophic Numbers Model For Measuring English Teaching Outcomes In University Contexts In A Preliminary Approach, Yisen Zhao

Neutrosophic Sets and Systems

No abstract provided.

Pentapartitioned Neutrosophic Pythagorean Connectedness, R. Radha, Annapoorna M. S, S. Vasundhara, Parul Arora, Ankita Tiwari, R. Prema

Neutrosophic Sets and Systems

No abstract provided.

A Novel Approach In Heptapartitioned Neutrosophic Sets With Its Weighted Arithmetic Averaging Operator, Sudharani R, Chitra Devi D, Mahimairaj P, Thirunavukkarasu J, Jeyanthi L, Nagalakshmi T

Neutrosophic Sets and Systems

No abstract provided.

Probabilistic Interval Neutrosophic Hesitant Fuzzy Set For Strategic Estimation Of Communication Instruction And Critical Analysis Of Journalism Teaching Practices In Higher Education, Yuan Heng

Neutrosophic Sets and Systems

No abstract provided.

Toward Educational Excellence: A Comprehensive Measuring Of Ideological And Political Theory Courses In Universities Using Neutrosophic Z-Rough Numbers, Dongxia Tian

Neutrosophic Sets and Systems

No abstract provided.

Comparison Of Gravity Waves Observed By Atmospheric Waves Experiment (Awe) And Simulated By High-Resolution Waccm-X, Jiarong Zhang, Han-Li Liu, Yucheng Zhao, Dominique Pautet, Ludger Scherliess, Michael Taylor

Space Dynamics Laboratory Publications

Outline

Data: AWE and High-Resolution Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (HR-WACCMX) simulation

• Quantify gravity wave (GW) activity
• Compare GW activity observed by AWE and simulated by high-resolution WACCMX
• GW activity over Western Europe

Building Narratives And Probing Concepts: Preparing Materials For Co-Design With Autistic Livestreamers, Terrance Mok, Tyson Hartley, Anthony Tang, Adam Mccrimmon, Lora Oehlberg

Research Collection School Of Computing and Information Systems

Based on ten semi-structured interviews with autistic Twitch streamers, we introduce a series of scenario-based design narratives coupled with technology design concepts as a starting point for co-design discussion about autistic streaming. This work builds on prior thematic analysis of the unique intersection between autism and livestreaming. Our user-centered scenarios highlight the needs, goals, and challenges of autistic individuals in livestreaming contexts. By using evocative narratives, the scenarios serve to facilitate empathy and deeper engagement with the needs of autistic users, and help facilitate and support co-creative dialogues and discussions about new technology designs. We contribute this starting point for …

Irhunter: Universal Detection Of Instruction Reordering Vulnerabilities For Enhanced Concurrency In Distributed And Parallel Systems, Guohua Xin, Guangquan Xu, Yao Zhang, Cheng Wen, Cen Zhang, Xiaofei Xie, Neal N. Xiong, Shaoying Liu, Pan Gao

Research Collection School Of Computing and Information Systems

Instruction reordering is an essential optimization technique used in both compilers and multi-core processors to enhance parallelism and resource utilization. Although the original intent of this technique is to benefit the program, some improper reordering can significantly impact the program correctness, which we call instruction reordering vulnerability (IRV). However, existing methods detect IRV by defining CPU instruction reordering rules to schedule execution paths while neglecting compiler reordering, and thus generate false positives that require manual filtering and resulting in inefficiency. To bridge this gap, in this paper, we propose the IRV detection method, , which analyzes IRV characteristics and extracts …

Contested: Consistency-Aided Tested Code Generation With Llm, Jinhao Dong, Jun Sun, Wenjie Zhang, Jinsong Dong, Dan Hao

Research Collection School Of Computing and Information Systems

Recent advancements in large language models (LLMs) have significantly improved code generation, which generates code snippets automatically based on natural language requirements. Despite achieving state-of-the-art performance, LLMs often struggle to generate accurate and reliable code, requiring developers to spend substantial effort debugging and evaluating the generated output. Researchers have proposed leveraging Consistency to select code that passes more tests (inter-consistency) and demonstrates consistent behavior across more counterparts (intra-consistency). However, since the tests themselves are also generated by LLMs, relying on majority voting based on incorrect tests leads to unreliable results. To address this, we propose a lightweight interaction framework that …

Enhancing Vulnerability Detection Via Inter-Procedural Semantic Completion, Bozhi Wu, Chengjie Liu, Zhiming Li, Yushi Cao, Jun Sun, Shang-Wei Lin

Research Collection School Of Computing and Information Systems

Inspired by advances in deep learning, numerous learning-based approaches for vulnerability detection have emerged, primarily operating at the function level for scalability. However, this design choice has a critical limitation: many vulnerabilities span multiple functions, causing function-level approaches to lose the semantics of called functions and fail to capture true vulnerability patterns. To address this issue, we propose VulnSC, a novel framework designed to enhance learning-based approaches by complementing inter-procedural semantics. VulnSC retrieves the source code of called functions for datasets and leverages large language models (LLMs) with well-designed prompts to generate summaries for these functions. The datasets, enhanced with …

Reaccept: Automated Co-Evolution Of Production And Test Code Based On Dynamic Validation And Large Language Models, Jianlei Chi, Xiaotian Wang, Yuhan Huang, Lechen Yu, Di Cui, Jianguo Sun, Jun Sun

Research Collection School Of Computing and Information Systems

Synchronizing production and test code, known as PT co-evolution, is critical for software quality. Given the significant manual effort involved, researchers have tried automating PT co-evolution using predefined heuristics and machine learning models. However, existing solutions are still incomplete. Most approaches only detect and flag obsolete test cases, leaving developers to manually update them. Meanwhile, existing solutions may suffer from low accuracy, especially when applied to real-world software projects. In this paper, we propose ReAccept, a novel approach leveraging large language models (LLMs), retrievalaugmented generation (RAG), and dynamic validation to fully automate PT co-evolution with high accuracy. ReAccept employs an …

Lessons Learned From Sandboxing, Piloting And Policy Experimentation With Ai And Other Digital Initiatives: Part 1, Summary Report, Steven M. Miller

Research Collection School Of Computing and Information Systems

This report, "Lessons Learned from Sandboxing, Piloting and Policy Experimentation with AI and Other Digital Initiatives," captures insights and experiences from project experts involved in recent digital innovation initiatives with the governments of Bangladesh, Maldives, and Kazakhstan, and from project experts actively involved with the use of AI for delivering government digital services in the EU, New Zealand, Rwanda, Singapore, United States, and Uzbekistan. The ten in-depth interview write-ups produced from these nine different country settings provide a small but highly informative sample of rich descriptions of some of the important realities, approaches, nuances, issues and challenges related to testing …

Cashift: Benchmarking Log-Based Cloud Attack Detection Under Normality Shift, Jiongchi Yu, Xiaofei Xie, Qiang Hu, Bowen Zhang, Ziming Zhao, Yun Lin, Lei Ma, Ruitao Feng, Frank Liau

Research Collection School Of Computing and Information Systems

With the rapid advancement of cloud-native computing, securing cloud environments has become an important task. Log-based Anomaly Detection (LAD) is the most representative technique used in different systems for attack detection and safety guarantee, where multiple LAD methods and relevant datasets have been proposed. However, even though some of these datasets are specifically prepared for cloud systems, they only cover limited cloud behaviors and lack information from a whole-system perspective. Another critical issue to consider is normality shift, which implies that the test distribution could differ from the training distribution and highly affect the performance of LAD. Unfortunately, existing works …

Regtrieve: Reducing System-Level Regression Errors For Machine Learning Systems Via Retrieval-Enhanced Ensemble, Junming Cao, Xuwen Xiang, Mingfei Cheng, Bihuan Chen, Xinyan Wang, You Lu, Chaofeng Sha, Xiaofei Xie, Xin Peng

Research Collection School Of Computing and Information Systems

Multiple machine learning (ML) models are often incorporated into real-world ML systems. However, updating an individual model in these ML systems frequently results in regression errors, where the new model performs worse than the old model for some inputs. While model-level regression errors have been widely studied, little is known about how regression errors propagate at system level. To address this gap, we propose RegTrieve, a novel retrieval-enhanced ensemble approach to reduce regression errors at both model and system level. Our evaluation across various model update scenarios shows that RegTrieve reduces system-level regression errors with almost no impact on system …

Dupin: A Parallel Framework For Densest Subgraph Discovery In Fraud Detection On Massive Graphs, Jiaxin Jiang, Siyuan Yao, Yuchen Li, Qiange Wang, Bingsheng He, Min Chen

Research Collection School Of Computing and Information Systems

Detecting fraudulent activities in financial and e-commerce transaction networks is crucial. One effective method for this is Densest Subgraph Discovery (DSD). However, deploying DSD methods in production systems faces substantial scalability challenges due to the predominantly sequential nature of existing methods, which impedes their ability to handle large-scale transaction networks and results in significant detection delays. To address these challenges, we introduce Dupin, a novel parallel processing framework designed for efficient DSD processing in billion-scale graphs. Dupin is powered by a processing engine that exploits the unique properties of the peeling process, with theoretical guarantees on detection quality and efficiency. …

Unsupervised Recognition Of Unknown Objects For Open-World Object Detection, Ruohuan Fang, Guansong Pang, Wenjun Miao, Xiao Bai, Jin Zheng, Xin Ning

Research Collection School Of Computing and Information Systems

Open-world object detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models detect the unknowns that exhibit similar features to the known objects, but they suffer from a severe label bias problem, i.e., they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background. To eliminate the label bias, this article proposes a novel module, namely reconstruction error-based Weibull (REW) model, that …

Keep The Balance: A Parameter-Efficient Symmetrical Framework For Rgb+X Semantic Segmentation, Jiaxin Cai, Jingze Su, Qi Li, Wenjie Yang, Shu Wang, Tiesong Zhao, Shengfeng He, Wenxi Liu

Research Collection School Of Computing and Information Systems

Multimodal semantic segmentation is a critical challenge in computer vision, with early methods suffering from high computational costs and limited transferability due to full fine-tuning of RGB-based pre-trained parameters. Recent studies, while leveraging additional modalities as supplementary prompts to RGB, still predominantly rely on RGB, which restricts the full potential of other modalities. To address these issues, we propose a novel symmetric parameter-efficient fine-tuning framework for multimodal segmentation, featuring with a modality-aware prompting and adaptation scheme, to simultaneously adapt the capabilities of a powerful pre-trained model to both RGB and X modalities. Furthermore, prevalent approaches use the global cross-modality correlations …

Hvi: A New Color Space For Low-Light Image Enhancement, Qingsen Yan, Yixu Feng, Cheng Zhang, Guansong Pang, Kangbiao Shi, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang

Research Collection School Of Computing and Information Systems

Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods are based on standard RGB (sRGB) space, which often produce color bias and brightness artifacts due to inherent high color sensitivity in sRGB. While converting the images using Hue, Saturation and Value (HSV) color space helps resolve the brightness issue, it introduces significant red and black noise artifacts. To address this issue, we propose a new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable inten sity. The former enforces …

Why Does My Transaction Fail? A First Look At Failed Transactions On The Solana Blockchain, Xiaoye Zheng, Zhiyuan Wan, David Lo, Difan Xie, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Solana is an emerging blockchain platform, recognized for its high throughput and low transaction costs, positioning it as a preferred infrastructure for Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs), and other Web 3.0 applications. In the Solana ecosystem, transaction initiators submit various instructions to interact with a diverse range of Solana smart contracts, among which are decentralized exchanges (DEXs) that utilize automated market makers (AMMs), allowing users to trade cryptocurrencies directly on the blockchain without the need for intermediaries. Despite the high throughput and low transaction costs of Solana, the advantages have exposed Solana to bot spamming for financial exploitation, resulting …