arXiv subjects

Ya-Wen Teng

Publications and source records attributed to Ya-Wen Teng.

9 recordsLinked to original sources

arXiv2026

Quantum Computing and Data Processing for Frequent Itemset Mining

Frequent Itemset Mining (FIM) is an important task in data analytics, where classical algorithms face scalability bottlenecks from the combinatorial growth of candidates and the memory overhead of their data structures. Inspired by recent developments in quantum computing, in this paper, we propose the Quantum Frequent-itemset Mining (QFM) data-processing framework for FIM. Following the level-wise structure of the itemset lattice, QFM introduces three mechanisms: (1) Bit-Vector Qubit Encoding for quantum data representation, which organizes transaction data into branchless bit-vectors to facilitate systematic uncomputation; (2) Mining-Aware Candidate Superposition, which prepares a quantum superposition over valid candidates at each lattice level rather than the full itemset lattice; and (3) Bit-Parallel Threshold Marking, which constructs a logarithmic-depth threshold-marking oracle for reliable repeated support verification within hardware coherence limits. We provide theoretical time complexity analysis, implement QFM on IBM Qiskit and Amazon Braket, and evaluate it on real-world datasets against representative classical baselines, where QFM achieves 96% improvement on average.

cs.DB↗

arXiv2025

ArtPerception: ASCII Art-based Jailbreak on LLMs with Recognition Pre-test

The integration of Large Language Models (LLMs) into computer applications has introduced transformative capabilities but also significant security challenges. Existing safety alignments, which primarily focus on semantic interpretation, leave LLMs vulnerable to attacks that use non-standard data representations. This paper introduces ArtPerception, a novel black-box jailbreak framework that strategically leverages ASCII art to bypass the security measures of state-of-the-art (SOTA) LLMs. Unlike prior methods that rely on iterative, brute-force attacks, ArtPerception introduces a systematic, two-phase methodology. Phase 1 conducts a one-time, model-specific pre-test to empirically determine the optimal parameters for ASCII art recognition. Phase 2 leverages these insights to launch a highly efficient, one-shot malicious jailbreak attack. We propose a Modified Levenshtein Distance (MLD) metric for a more nuanced evaluation of an LLM's recognition capability. Through comprehensive experiments on four SOTA open-source LLMs, we demonstrate superior jailbreak performance. We further validate our framework's real-world relevance by showing its successful transferability to leading commercial models, including GPT-4o, Claude Sonnet 3.7, and DeepSeek-V3, and by conducting a rigorous effectiveness analysis against potential defenses such as LLaMA Guard and Azure's content filters. Our findings underscore that true LLM security requires defending against a multi-modal space of interpretations, even within text-only inputs, and highlight the effectiveness of strategic, reconnaissance-based attacks. Content Warning: This paper includes potentially harmful and offensive model outputs.

cs.CR↗

arXiv2025

TPSQLi: Test Prioritization for SQL Injection Vulnerability Detection in Web Applications

The rapid proliferation of network applications has led to a significant increase in network attacks. According to the OWASP Top 10 Projects report released in 2021, injection attacks rank among the top three vulnerabilities in software projects. This growing threat landscape has increased the complexity and workload of software testing, necessitating advanced tools to support agile development cycles. This paper introduces a novel test prioritization method for SQL injection vulnerabilities to enhance testing efficiency. By leveraging previous test outcomes, our method adjusts defense strength vectors for subsequent tests, optimizing the testing workflow and tailoring defense mechanisms to specific software needs. This approach aims to improve the effectiveness and efficiency of vulnerability detection and mitigation through a flexible framework that incorporates dynamic adjustments and considers the temporal aspects of vulnerability exposure.

cs.SE↗

arXiv2025

Human-Centric Community Detection in Hybrid Metaverse Networks with Integrated AI Entities

Community detection is a cornerstone problem in social network analysis (SNA), aimed at identifying cohesive communities with minimal external links. However, the rise of generative AI and Metaverse introduce complexities by creating hybrid human-AI social networks (denoted by HASNs), where traditional methods fall short, especially in human-centric settings. This paper introduces a novel community detection problem in HASNs (denoted by MetaCD), which seeks to enhance human connectivity within communities while reducing the presence of AI nodes. Effective processing of MetaCD poses challenges due to the delicate trade-off between excluding certain AI nodes and maintaining community structure. To address this, we propose CUSA, an innovative framework incorporating AI-aware clustering techniques that navigate this trade-off by selectively retaining AI nodes that contribute to community integrity. Furthermore, given the scarcity of real-world HASNs, we devise four strategies for synthesizing these networks under various hypothetical scenarios. Empirical evaluations on real social networks, reconfigured as HASNs, demonstrate the effectiveness and practicality of our approach compared to traditional non-deep learning and graph neural network (GNN)-based methods.

cs.SI↗

arXiv2023

CDGraph: Dual Conditional Social Graph Synthesizing via Diffusion Model

The social graphs synthesized by the generative models are increasingly in demand due to data scarcity and concerns over user privacy. One of the key performance criteria for generating social networks is the fidelity to specified conditionals, such as users with certain membership and financial status. While recent diffusion models have shown remarkable performance in generating images, their effectiveness in synthesizing graphs has not yet been explored in the context of conditional social graphs. In this paper, we propose the first kind of conditional diffusion model for social networks, CDGraph, which trains and synthesizes graphs based on two specified conditions. We propose the co-evolution dependency in the denoising process of CDGraph to capture the mutual dependencies between the dual conditions and further incorporate social homophily and social contagion to preserve the connectivity between nodes while satisfying the specified conditions. Moreover, we introduce a novel classifier loss, which guides the training of the diffusion process through the mutual dependency of dual conditions. We evaluate CDGraph against four existing graph generative methods, i.e., SPECTRE, GSM, EDGE, and DiGress, on four datasets. Our results show that the generated graphs from CDGraph achieve much higher dual-conditional validity and lower discrepancy in various social network metrics than the baselines, thus demonstrating its proficiency in generating dual-conditional social graphs.

cs.SI↗

arXiv2022

Targeted Influence with Community and Gender-Aware Seeding

When spreading information over social networks, seeding algorithms selecting users to start the dissemination play a crucial role. The majority of existing seeding algorithms focus solely on maximizing the total number of reached nodes, overlooking the issue of group fairness, in particular, gender imbalance. To tackle the challenge of maximizing information spread on certain target groups, e.g., females, we introduce the concept of the community and gender-aware potential of users. We first show that the network's community structure is closely related to the gender distribution. Then, we propose an algorithm that leverages the information about community structure and its gender potential to iteratively modify a seed set such that the information spread on the target group meets the target ratio. Finally, we validate the algorithm by performing experiments on synthetic and real-world datasets. Our results show that the proposed seeding algorithm achieves not only the target ratio but also the highest information spread, compared to the state-of-the-art gender-aware seeding algorithm.

cs.SI↗

arXiv2021

A Unified Seeding Framework

Online social networks have become a crucial medium to disseminate the latest political, commercial, and social information. Users with high visibility are often selected as seeds to spread information and affect their adoption in target groups. We study how gender differences and similarities can impact the information spreading process. Using a large-scale Instagram dataset and a small-scale Facebook dataset, we first conduct a multi-faceted analysis taking the interaction type, directionality and frequency into account. To this end, we explore a variety of existing and new single and multihop centrality measures. Our analysis unveils that males and females interact differently depending on the interaction types, e.g., likes or comments, and they feature different support and promotion patterns. We complement prior work showing that females do not reach top visibility (often referred to as the glass ceiling effect) jointly factoring in the connectivity and interaction intensity, both of which were previously mainly discussed independently. Inspired by these observations, we propose a novel seeding framework, called Disparity Seeding, which aims at maximizing spread while reaching a target user group, e.g., a certain percentage of females -- promoting the influence of under-represented groups. Disparity Seeding ranks influential users with two gender-aware measures, the Target HI-index and the Embedding index. Extensive simulations comparing Disparity Seeding with target-agnostic algorithms show that Disparity Seeding meets the target percentage while effectively maximizing the spread. Disparity Seeding can be generalized to counter different types of inequality, e.g., race, and proactively promote minorities in the society.

cs.SI↗

arXiv2021

Influence Maximization Based on Dynamic Personal Perception in Knowledge Graph

Viral marketing on social networks, also known as Influence Maximization (IM), aims to select k users for the promotion of a target item by maximizing the total spread of their influence. However, most previous works on IM do not explore the dynamic user perception of promoted items in the process. In this paper, by exploiting the knowledge graph (KG) to capture dynamic user perception, we formulate the problem of Influence Maximization with Dynamic Personal Perception (IMDPP) that considers user preferences and social influence reflecting the impact of relevant item adoptions. We prove the hardness of IMDPP and design an approximation algorithm, named Dynamic perception for seeding in target markets (Dysim), by exploring the concepts of dynamic reachability, target markets, and substantial influence to select and promote a sequence of relevant items. We evaluate the performance of Dysim in comparison with the state-of-the-art approaches using real social networks with real KGs. The experimental results show that Dysim effectively achieves up to 6.7 times of influence spread in large datasets over the state-of-the-art approaches.

cs.SI↗

arXiv2015

An Effective Marketing Strategy for Revenue Maximization with a Quantity Constraint

Recently the influence maximization problem has received much attention for its applications on viral marketing and product promotions. However, such influence maximization problems have not taken into account the monetary effect on the purchasing decision of individuals. To fulfill this gap, in this paper, we aim for maximizing the revenue by considering the quantity constraint on the promoted commodity. For this problem, we not only identify a proper small group of individuals as seeds for promotion but also determine the pricing of the commodity. To tackle the revenue maximization problem, we first introduce a strategic searching algorithm, referred to as Algorithm PRUB, which is able to derive the optimal solutions. After that, we further modify PRUB to propose a heuristic, Algorithm PRUB+IF, for obtaining feasible solutions more effciently on larger instances. Experiments on real social networks with different valuation distributions demonstrate the effectiveness of PRUB and PRUB+IF.

cs.SI↗