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Malicious Attack Challenges and Mitigation Strategies for Large Code Models: A Survey on Data Poisoning, Adversarial Attacks, and Backdoor Vulnerabilities
Get PDFThe rapid proliferation of Large Code Models (LCMs), driven by Large Language Models (LLMs) advancements, has revolutionized automated code generation and completion. However, their widespread adoption introduces significant security risks like data poisoning, adversarial attacks, and backdoor vulnerabilities perspectives. This survey comprehensively reviews LCMs' security landscape with more than 200 recent papers to identify and categorize threats in code generation techniques, and summarizes five mainstream mitigation strategies: Model Hardening, Data Sanitization, Adversarial Training, Security Alignment, and Evaluation Datasets. Uniquely, this work applies Evolutionary Game Theory (EGT) to conceptualize LCMs' security as a continuous ``arms race" between attackers and defenders, where the effectiveness of specific strategies serves as a fitness indicator. Our analysis reveals that while defense techniques have advanced, balancing the robustness and functionality of LCMs remains a persistent challenge. Our findings underscore the need for standardized security benchmarks and real-time threat monitoring to ensure the safety of LCM-powered software
Designing for Engagement in mHealth: A Patient-Centered DSR Approach to Epilepsy Self-Tracking
Get PDFMobile health seizure diaries offer superior precision compared to paper logs (85% versus 67% in benchmarking), yet suffer from high dropout rates. The literature identifies poor user engagement as a critical limitation. In response, this study prioritizes engagement-centered design. Guided by Design Science Research, we conducted a systematic literature review and stakeholder interviews, yielding 71 functional and 29 non-functional requirements. Differences between literature and patient perspectives were prioritized by frequency and implemented in a cross-platform prototype. Evaluation using the user version of the Mobile App Rating Scale showed higher scores than the average of 26 benchmarked seizure apps: Engagement (+0.09), Functionality (+0.21), Aesthetics (+0.43), and Information (+0.76). Based on user feedback, we propose actionable design principles to support engagement: progressive disclosure, safety affordance, visual summaries, and cross-platform parity. This study contributes (1) a validated prototype, (2) a stakeholder-grounded requirement set, and (3) a replicable process for engagement-oriented mobile health development
Emotions in Collective Risk Dilemmas Using Fuzzy Linguistic Rules
Get PDFThe goal of this paper is to propose how to model emotions in climate change policies through evolutionary game theory (i.e., collective risk dilemmas) using fuzzy linguistic rules. We will first study evolutionary game theory models for exploring climate change policies and decisions to enhance cooperation between the policy players. Using computational agent-based simulations, we will build a framework where we incorporate and extend evolutionary game models with players' emotions in the game dynamics. These emotions, modeled by fuzzy linguistic rules, are a way for players to reconsider their strategies when playing the game. Results show how these rules representing emotions can better control defection in the collective-risk dilemmas while modeling a more realistic way of dealing with players' features
From Patches to Patterns: A Process Perspective on Continuous Innovation in Modular Game Systems
Get PDFDigital products enable continuous innovation through iterative updates; however, the empirical understanding of this process remains limited. This study analyzes 261 patches across two multiplayer games — Defence of the Ancients 2 and Counter-Strike 2 — using Henderson and Clark's innovation framework to examine how system architecture shapes innovation strategies. Defence of the Ancients 2’s highly integrated architecture generates cyclical patterns combining architectural, modular, and incremental innovations in coordinated episodes, while Counter-Strike 2 demonstrates sustained incremental improvements with minimal structural intervention. Statistical analysis confirms significant differences in innovation complexity between the games. We hypothesize that system architecture influences innovation strategies, with incremental improvements forming the foundation of continuous innovation across both games. This research extends digital innovation theory by empirically revealing structured temporal patterns in continuous innovation processes
Introduction to the Minitrack on Organizational Cybersecurity: Advanced Cyber Defense, Cyber Analytics, and Security Operations
No full textIntroduction to the Minitrack on Disaster Information, Resilience, for Emergency and Crisis Technologies
No full textIs Integration the New Incubation? A Systematic Literature Review on the Shift from Supply to Demand Models of Corporate-Startup Engagement
Get PDFAs corporations increasingly adopt open innovation strategies, engaging with startups has become vital. Corporate accelerators (CA) and venture clienting (VCL) offer distinct approaches to startup collaboration: supply-driven incubation and demand-driven integration. While CAs have been widely implemented, their impact on long-term innovation integration remains debated. VCL emphasizes direct business application, but is only emerging in research. Drawing on a systematic literature review of 46 publications, we analyze each model’s key phases and how challenges manifest in design and outcome alignment. Our findings highlight that accelerators support exploratory innovation and ecosystem engagement but lack mechanisms for adoption. VCL promotes problem-driven, measurable innovation with higher demands on startup maturity. Despite its increasing adoption in practice, the lack of academic research on VCL is surprising. This study contributes a conceptual foundation for future empirical studies and calls for deeper investigation into VCL’s mechanisms and startups’ perspectives
Understanding Energy Equity through Smart Meter Data: Insights from Low-to-Moderate-Income Detroit Households
Get PDFUnderstanding household energy use is a complicated problem with individual household use shaped by environmental, cultural, and economic factors. This paper investigates energy use and affordability for 57 mostly low-to-moderate income (LMI) homes in Detroit, MI using smart meter and some limited demographic data collected through surveys. We evaluate energy use through three equity-focused metrics: Energy Use Intensity (EUI), Energy Burden (EB), and the Energy Equity Gap (EEG), which measure efficiency of electrical usage, affordability, and energy limiting behavior through disparities in energy use and thermal comfort across income groups, respectively. Results show that lower income households in our sample face a median electric energy burden of 6.4%, which is considered unaffordable, while the highest income group in our sample has a median burden of 1.6%. The EEG reveals a 7°F cooling gap and a 7°F heating gap, highlighting disparities in energy use and thermal comfort across income groups. Our work demonstrates that these metrics, when used together, can help capture and reveal the many dimensions of energy poverty and insecurity facing LMI homes, which has implications for energy policy, energy rate design, and power system design and operation
