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2026

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Potent But Stealthy: Rethink Profile Pollution Against Sequential Recommendation Via Bi-Level Constrained Reinforcement Paradigm, Jiajie Su, Zihan Nan, Yunshan Ma, Xiaobo Xia, Xiaohua Feng, Weiming Liu, Xiang Chen, Xiaolin Zheng, Chaochao Chen Jan 2026

Potent But Stealthy: Rethink Profile Pollution Against Sequential Recommendation Via Bi-Level Constrained Reinforcement Paradigm, Jiajie Su, Zihan Nan, Yunshan Ma, Xiaobo Xia, Xiaohua Feng, Weiming Liu, Xiang Chen, Xiaolin Zheng, Chaochao Chen

Research Collection School Of Computing and Information Systems

Sequential Recommenders, which exploit dynamic user intents through interaction sequences, are vulnerable to adversarial attacks. While existing attacks primarily rely on data poisoning, they require large-scale user access or fake profiles, thus lacking practicality. In this paper, we focus on the Profile Pollution Attack that subtly contaminates partial user interactions to induce targeted mispredictions. Previous PPA methods suffer from two limitations, i.e., i) overreliance on sequence horizon impact restricts fine-grained perturbations on item transitions, and ii) holistic modifications cause detectable distribution shifts. To address these challenges, we propose a constrained reinforcement driven attack CREAT that synergizes a bi-level optimization framework …

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Nondeterministic Polynomial-Time Problem Challenge: An Ever-Scaling Reasoning Benchmark For Llms, Chang Yang, Ruiyu Wang, Junzhe Jiang, Qi Jiang, Qinggang Zhang, Yanchen Deng, Shuxin Li, Shuyue Hu, Bo Li, Florian T. Pokorny, Xiao Huang, Xinrun Wang Jan 2026

Nondeterministic Polynomial-Time Problem Challenge: An Ever-Scaling Reasoning Benchmark For Llms, Chang Yang, Ruiyu Wang, Junzhe Jiang, Qi Jiang, Qinggang Zhang, Yanchen Deng, Shuxin Li, Shuyue Hu, Bo Li, Florian T. Pokorny, Xiao Huang, Xinrun Wang

Research Collection School Of Computing and Information Systems

Reasoning is the fundamental capability of large language models (LLMs). Due to the rapid progress of LLMs, there are two main issues of current benchmarks: i) these benchmarks can be crushed in a short time (less than 1 year), and ii) these benchmarks may be easily hacked. To handle these issues, we propose the ever-scalingness for building the benchmarks which are scaling over complexity against crushing, instance against hacking and exploitation, oversight for easy verification, and coverage for real-world relevance. This paper presents Nondeterministic Polynomial-time Problem Challenge (NPPC), an ever-scaling reasoning benchmark for LLMs. Specifically, the NPPC has three main …

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Compendia: Automated Visual Storytelling Generation From Online Article Collection, Manusha Imesh Karunathilaka Gamage Vidana, Litian Lei, Yiming Gao, Yong Wang, Jiannan Li Jan 2026

Compendia: Automated Visual Storytelling Generation From Online Article Collection, Manusha Imesh Karunathilaka Gamage Vidana, Litian Lei, Yiming Gao, Yong Wang, Jiannan Li

Research Collection School Of Computing and Information Systems

In the digital age, readers value quantitative journalism that is clear, concise, analytical, and humancentred. To understand complex topics, they often piece together scattered facts from multiple articles. Visual storytelling can transform fragmented information into clear, engaging narratives, yet its use with unstructured online articles remains largely unexplored. To fill this gap, we present Compendia, an automated system that analyzes online articles in response to a user’s query and generates a coherent data story tailored to the user’s informational needs. Compendia addresses key challenges of storytelling from unstructured text through two modules covering: Online Article Retrieval, which gathers relevant articles; …

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Evaluating Large Language Models For Line-Level Vulnerability Localization, Jian Zhang, Chong Wang, Anran Li, Weisong Sun, Cen Zhang, Wei Ma, Yang Liu Jan 2026

Evaluating Large Language Models For Line-Level Vulnerability Localization, Jian Zhang, Chong Wang, Anran Li, Weisong Sun, Cen Zhang, Wei Ma, Yang Liu

Research Collection School Of Computing and Information Systems

Recently, Automated Vulnerability Localization (AVL) has attracted growing attention, aiming to facilitate diagnosis by pinpointing the specific lines of code responsible for vulnerabilities. Large Language Models (LLMs) have shown potential in various domains, yet their effectiveness in line-level vulnerability localization remains underexplored. In this work, we present the first comprehensive empirical evaluation of LLMs for AVL. Our study examines 19 leading LLMs suitable for code analysis, including ChatGPT and multiple open-source models, spanning encoder-only, encoder-decoder, and decoder-only architectures, with model sizes from 60M to 70B parameters. We evaluate three paradigms including few-shot prompting, discriminative fine-tuning, and generative fine-tuning with and …

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From Circuits To Hardware: Benchmarking Standard And Qubit-Efficient Quantum Optimization On Real Hardware, Monit Sharma, Hoong Chuin Lau Jan 2026

From Circuits To Hardware: Benchmarking Standard And Qubit-Efficient Quantum Optimization On Real Hardware, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Despite rapid progress in quantum optimization, the field lacks broad real-hardware benchmarks comparing multiple algorithmic families across diverse classically hard combinatorial problems under one protocol. We present a hardware-aware benchmark of gate-based quantum optimization across four NP-hard problems: the multi-dimensional knapsack problem (MDKP), maximum independent set (MIS), quadratic assignment problem (QAP), and market-share problem, spanning variational methods (VQE, CVaR-VQE), standard, multi-angle, and warm-start QAOA, and qubit-efficient encodings (Pauli correlation encoding (PCE), QRAO), executed on IBM Heron r1/r2 processors under resilience-level-2 mitigation. To our knowledge this includes the first real-hardware QRAO results and the first multi-problem PCE hardware benchmark. Across 247 …

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Scale-Net: A Hierarchical U-Net Framework For Cross-Scale Generalization In Multi-Task Vehicle Routing, Suyu Liu, Zhiguang Cao, Nan Yin, Yew-Soon Ong Jan 2026

Scale-Net: A Hierarchical U-Net Framework For Cross-Scale Generalization In Multi-Task Vehicle Routing, Suyu Liu, Zhiguang Cao, Nan Yin, Yew-Soon Ong

Research Collection School Of Computing and Information Systems

Neural solvers for Vehicle Routing Problems (VRPs) have shown great advantages in solving various kinds of problem types. However, they also face critical challenges in generalizing from small-scale training to large-scale problems and in identifying the most salient topological information for decision-making. To mitigate these gaps, we introduce ScaleNet, a novel hierarchical framework that integrates a U-Net architecture into a unified, multi-task VRP solver. Scale-Net explicitly captures multi-scale structural patterns by processing a nested hierarchy of input graph instances. This enriched, coarse-to-fine representation is extracted by the encoder and fed directly into the decoder, empowering decoder module with superior topological …

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Instance Generation For Meta-Black-Box Optimization Through Latent Space Reverse Engineering, Chen Wang, Yue-Jiao Gong, Zhiguang Cao, Zeyuan Ma Jan 2026

Instance Generation For Meta-Black-Box Optimization Through Latent Space Reverse Engineering, Chen Wang, Yue-Jiao Gong, Zhiguang Cao, Zeyuan Ma

Research Collection School Of Computing and Information Systems

To relieve intensive human-expertise required to design optimization algorithms, recent Meta-Black-Box Optimization (MetaBBO) researches leverage generalization strength of meta-learning to train neural network-based algorithm design policies over a predefined training problem set, which automates the adaptability of the low-level optimizers on unseen problem instances. Currently, a common training problem set choice in existing MetaBBOs is well-known benchmark suites CoCo-BBOB. Although such choice facilitates the MetaBBO's development, problem instances in CoCo-BBOB are more or less limited in diversity, raising the risk of overfitting of MetaBBOs, which might further results in poor generalization. In this paper, we propose an instance generation approach, …

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Bridging Synthetic And Real Routing Problems Via Llm-Guided Instance Generation And Progressive Adaptation, Jianghan Zhu, Yaoxin Wu, Zhuoyi Lin, Zhengyuan Zhang, Haiyan Yin, Zhiguang Cao, Senthilnath Jayavelu, Xiaoli Li Jan 2026

Bridging Synthetic And Real Routing Problems Via Llm-Guided Instance Generation And Progressive Adaptation, Jianghan Zhu, Yaoxin Wu, Zhuoyi Lin, Zhengyuan Zhang, Haiyan Yin, Zhiguang Cao, Senthilnath Jayavelu, Xiaoli Li

Research Collection School Of Computing and Information Systems

Recent advances in Neural Combinatorial Optimization (NCO) methods have significantly improved the capability of neural solvers to handle synthetic routing instances. Nonetheless, existing neural solvers typically struggle to generalize effectively from synthetic, uniformly-distributed training data to real-world VRP scenarios, including widely recognized benchmark instances from TSPLib and CVRPLib. To bridge this generalization gap, we present Evolutionary Realistic Instance Synthesis (EvoReal), which leverages an evolutionary module guided by large language models (LLMs) to generate synthetic instances characterized by diverse and realistic structural patterns. Specifically, the evolutionary module produces synthetic instances whose structural attributes statistically mimics those observed in authentic real-world instances. …

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Navigating School Re-Entry For Teen Mothers In Arua District, Uganda, Flavia Mane Eriku, Mweru Mwingi Jan 2026

Navigating School Re-Entry For Teen Mothers In Arua District, Uganda, Flavia Mane Eriku, Mweru Mwingi

Institute for Educational Development, East Africa

Uganda's 2020 Education Re-entry Policy supports teenage mothers to resume schooling after pregnancy, yet re-enrollment remains low. In a qualitative case study in two public secondary schools in Arua District, we identified key barriers to be inadequate guidance, stigma, discrimination, irregular attendance, limited knowledge and resources, and forced marriage. We recommend boosting teen mothers’ self-esteem through personalized counseling, mental health support, and community awareness of the value of education. Strengthening these areas can enhance policy implementation and help policymakers and practitioners improve monitoring and educational reintegration for teenage mothers.

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Knowing The User: Representation, Contextualization, And Predictive Internalization In Personalized Models, Zhongyu Ouyang Jan 2026

Knowing The User: Representation, Contextualization, And Predictive Internalization In Personalized Models, Zhongyu Ouyang

Dartmouth College Ph.D Dissertations

Intelligent systems increasingly anticipate which items a person will prefer, adapt generated text to a familiar style, and carry relevant information across interactions. These capabilities invite the impression that a model knows its user. In the technical sense studied here, a model knows a user when it carries information specific to that user, uses that information in the relevant context, and generalizes beyond the observations used to build the user state. Knowing is thus a measurable relation among user evidence, model state, context, and held-out user-specific behavior. The research community generally studies this family of systems under the term personalization, …

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Freshman Record (Year Unknown), Bemidji State Unversity Jan 2026

Freshman Record (Year Unknown), Bemidji State Unversity

Freshman Record

No abstract provided.

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Artificial Neural Network Based Protection For Dc Bus Using Solid State Dc Breaker, Eswaraiah Giddalur, Askani Jaya Laxmi Jan 2026

Artificial Neural Network Based Protection For Dc Bus Using Solid State Dc Breaker, Eswaraiah Giddalur, Askani Jaya Laxmi

ASEAN Journal on Science and Technology for Development

Low voltage Direct Current (LVDC) microgrids have been extensively used as a hopeful technology with high access to distributed energy resources (DERs) and DC loads, due to the absence of multiple conversions. A high amplitude Fault current together with the vulnerability of electronic devices, poses a great challenge to the line protection in LVDC microgrids. Electromechanical switches are slow and take long tripping time to clear the short circuit fault. Power electronic circuit breakers are used to achieve fast fault interruption recent progress in areas like machine learning and natural language processing have affected nearly every assiduity and area of …

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Extraction And Characterization Of Fresh And Dehydrated Cactus (Opuntia Ficus Indica) Polysaccharide For Hydrogel Preparation, Aye Thwe Thwesoe, May Myat Khine Jan 2026

Extraction And Characterization Of Fresh And Dehydrated Cactus (Opuntia Ficus Indica) Polysaccharide For Hydrogel Preparation, Aye Thwe Thwesoe, May Myat Khine

ASEAN Journal on Science and Technology for Development

In this research, extraction of polysaccharide compounds from Cactus (Opuntia Ficus Indica) leaves in both fresh and dehydrated condition by solvent precipitation method using three types of water, acid (HCL) and NaOH. Before extraction, the physicochemical properties were examined to determine optimum yield (%) of extracted polysaccharide by optimization of Box-Behnken Design (BBD) of response surface methodology (RSM). The polysaccharide-based acrylamide hydrogel was prepared by free radical polymerization. The functional and structural characterization was done by FTIR, XRD and SEM for examination of extracted polysaccharide as raw polymer backbone in hydrogel preparation and prepared hydrogel as adsorbent for metal removal. …

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Systematic Study On Preparation And Characterizations Of High Performance Bimetallic Pt-Co/Sba-15 Catalyst In N-Heptane Isomerization, Talib M. Albayati, Khalid A. Sukkar, Nisreen S. Ali, Mahir A. Abdulrahman, Basheer A. Abdulhussein, Issam K. Salih, Narges Elmi Fard Jan 2026

Systematic Study On Preparation And Characterizations Of High Performance Bimetallic Pt-Co/Sba-15 Catalyst In N-Heptane Isomerization, Talib M. Albayati, Khalid A. Sukkar, Nisreen S. Ali, Mahir A. Abdulrahman, Basheer A. Abdulhussein, Issam K. Salih, Narges Elmi Fard

ASEAN Journal on Science and Technology for Development

Bimetallic catalysts were prepared by deposition of 1% of loading of (Pt-Co) onto SBA 15 support using heterogeneous reaction for enhancing hydroisomerization and promoting hydrocracking for the decomposition of n-heptane. The catalytic performance of the catalysts was subsequently evaluated thereafter. Characterization of the prepared catalysts was done by various techniques, which include XRD, FTIR, BETBJH, SEM/EDS, and TEM. The temperature range studied for this catalytic activity was 250 to 400 °C, at atmospheric pressure in a plug-flow reactor. The experimental setup kept a very rigorous control over the temperature, flow rate for the reactants, and pressure, while gas chromatographic techniques …

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Hybrid Model For Phishing Website Detection Using Transfer Learning, Atul Dubal, Mansi Subhedar, Santosh Dhamala, Manasi Patil Jan 2026

Hybrid Model For Phishing Website Detection Using Transfer Learning, Atul Dubal, Mansi Subhedar, Santosh Dhamala, Manasi Patil

ASEAN Journal on Science and Technology for Development

The rapid digitization of human activities has intensified reliance on internet-based platforms, creating fertile ground for cybercriminal exploits such as phishing. Despite advancements in detection mechanisms, phishing attacks continue to evolve, leveraging sophisticated visual mimicry to deceive users. This paper proposes a robust vision-based phishing detection system using ensemble deep learning to analyse webpage screenshots. The framework integrates transfer learning with pre-trained VGG16 and DenseNet121 models, extracting complementary low-level texture features (edges, gradients) and high-level hierarchical patterns (logos, layouts). These features are fused through a custom classifier with dropout regularization to mitigate overfitting. A balanced dataset of 3,000 webpage screenshots …

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Multiclass Skin Lesions Classification And Early Skin Cancer Detection Using An Attention-Enhanced Xception Architecture, Shamira Arshad Shaikh, Yogita Mistry Jan 2026

Multiclass Skin Lesions Classification And Early Skin Cancer Detection Using An Attention-Enhanced Xception Architecture, Shamira Arshad Shaikh, Yogita Mistry

ASEAN Journal on Science and Technology for Development

Skin disorders are considered one of the major healthcare problems globally and need proper, prompt, effective and early diagnosis to enhance clinical practice. Traditional machine learning and deep learning approaches often exhibit limited generalization capability and reduced effectiveness in extracting discriminative features from complex multiclass dermatological datasets. For this purpose, an interpretable attention-enhanced Xception framework for automatic multiclass skin disease diagnosis based on dermoscopic images has been designed through the use of the International Skin Imaging Collaboration (ISIC) dataset. The dataset includes 2,357 dermoscopic images that belong to nine classes of skin lesions. The proposed framework integrates channel attention and …

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