ethicalabs/Research-Intent-Collab huggingface dataset:
Jailbreak Large Vision-Language Models Through Multi-Modal Linkage

With the significant advancement of Large Vision-Language Models (VLMs), concerns about their potential misuse and abuse have grown rapidly. Previous studies have highlighted VLMs' vulnerability to jailbreak attacks, where carefully crafted inputs can lead the model to produce content that violates ethical and legal standards. However, existing methods struggle against state-of-the-art VLMs like GPT-4o, due to the over-exposure of harmful content and lack of stealthy malicious guidance. In this work, we propose a novel jailbreak attack framework: Multi-Modal Linkage (MML) Attack. Drawing inspiration from cryptography, MML utilizes an encryption-decryption process across text and image modalities to mitigate over-exposure of malicious information. To align the model's output with malicious intent covertly, MML employs a technique called "evil alignment", framing the attack within a video game production scenario. Comprehensive experiments demonstrate MML's effectiveness. Specifically, MML jailbreaks GPT-4o with attack success rates of 97.80% on SafeBench, 98.81% on MM-SafeBench and 99.07% on HADES-Dataset. Our code is available at https://github.com/wangyu-ovo/MML.
Health Insurance Coverage Disruptions and Access to Care and Affordability among Cancer Survivors in the United States

Abstract                                   Background:                   Lack of health insurance is associated with having problems with access to high-quality care. We estimated prevalence and evaluated associations of insurance coverage disruptions and access to health care and affordability among cancer survivors in the United States.                                                   Methods:                   Adult cancer survivors ages 18 to 64 years with current private or public health insurance were identified from the 2011 to 2018 National Health Interview Survey (n = 7,186). Health insurance coverage disruption was measured as self-reports of any time in the prior year without coverage. Outcomes included preventive services use, problems with care affordability, and cost-related medication nonadherence in the prior year. We used separate multivariable logistic models to evaluate associations between coverage disruptions and study outcomes by current insurance coverage.                                                   Results:                   Among currently insured survivors, 3.7% [95% confidence interval (95% CI), 3.0%–4.4%] with private, and 7.8% (95% CI, 6.5%–9.4%) with public insurance reported coverage disruptions in 2011 to 2018. We estimated that approximately 260,000 survivors ages 18 to 64 years had coverage disruptions in 2018. Among privately and publicly insured survivors, those with coverage disruptions were less likely to report all preventive services use (16.9% vs. 36.2%; 14.6% vs. 25.3%, respectively) and more likely to report any problems with care affordability (55.0% vs. 17.7%; 71.1% vs. 38.4%, respectively) and any cost-related medication nonadherence (39.4% vs. 10.1%; 36.5% vs. 16.3%, respectively) compared with those continuously insured (all P &amp;lt; 0.05).                                                   Conclusions:                   Coverage disruptions in the prior year were associated with problems with health care access and affordability among currently insured survivors.                                                   Impact:                   Reducing coverage disruptions may help improve access and affordability for survivors.
MODELS AND TECHNOLOGIES OF INTELLIGENT PROTECTION OF INFORMATION SYSTEMS OF CRITICAL INFRASTRUCTURE FOR ENHANCING RESILIENCE

The article substantiates the feasibility of using modern information technologies to ensure the sustainable functioning of critical infrastructure (CI) facilities, with a focus on protecting their information systems, which are a key factor in national security and the resilience of the state against hybrid threats. It is demonstrated that enhancing the protection and recoverability of CI systems is possible through the establishment of an extensive network of crisis centers integrated with platforms for monitoring, detection, and real-time response to cyber incidents. Particular attention is paid to scenario modeling, which enables forecasting possible developments of cyberattacks, designing security management models, and supporting the decision-making process. This approach makes it possible to identify likely channels of impact on systems in advance, assess the consequences of their disruption, and generate optimal strategies for threat neutralization. An architecture of a multi-level system for managing the protection and recovery of CI information systems is proposed, taking into account both physical and cyber risks. It is based on the integration of intelligent technologies capable of providing adaptive responses to environmental changes and automated support for backup and recovery procedures. The feasibility of applying artificial intelligence in crisis centers is substantiated, particularly through agent-based systems that enhance the efficiency of analyzing large datasets, detecting anomalies in traffic, assessing risks, and generating managerial recommendations. The use of intelligent agents ensures speed and accuracy in the localization of cyber threats, significantly increasing the resilience of critical infrastructure information systems and forming the foundation for proactive cybersecurity mechanisms.
AromAtom, an olfactory approach to science communication and public engagement

Not that long ago, the term science communication would have merely implied communication between scientists, but nowadays the term has a much wider context. Science today needs to be made accessible to a more diverse and inclusive audience; it needs to be presented in a way that is engaging, innovative and fun. The formal talk and slides combination favoured by academia is not necessarily the best way to communicate with the public. We need to rethink the way in which we engage with non-scientific audiences who have little interest in what we do, and especially with those with very low science capital. How can we ignite their curiosity and encourage them to ask questions about science? How can we help them to feel involved and interested in our work?
Deep Learning for Interpretable ADHD Diagnosis from fMRI: ConvLSTM with Enhanced Augmentation,  Grad-CAM Explainability, and External Validation

The diagnosis of attention-deficit/hyperactivity disorder (ADHD) using resting-state functional MRI (rs-fMRI) remains difficult due to the high dimensionality of the data, short sample sizes, and interpretability issues with most deep learning models. In this work, based on a modified ConvLSTM architecture, we provide a robust and interpretable framework to extract important spatiotemporal biomarkers from rs-fMRI series. A targeted augmentation approach based on temporal jittering and regulated Gaussian noise, both tuned to the temporal dynamics of fMRI data, is presented to address data scarcity and class imbalance. With stratified evaluation, the model obtained an F1-score of 0.89, an accuracy of 0.90, and an AUC of 0.96 after undergoing extensive validation on the ADHD-200 dataset. Its generalizability was further validated on external cohorts that were completely independent, achieving perfect specificity and precision and a balanced accuracy of 83.3%. While these results are promising, the limited size and scope of the external cohort necessitate further validation in larger, multi-center studies. To address important transparency issues in medical AI, we integrated Grad-CAM explainability to visually identify brain regions influencing model predictions, supporting clinical applicability. In brief, this work advances the usage of deep learning in neuroimaging-based ADHD diagnosis by imparting a pipeline that is clinically relevant, reproducible, and interpretable. Overall, this study proposes a clinically significant, reproducible, and interpretable deep learning pipeline that improves diagnostic overall performance and addresses agreement and transparency, key conditions for real-world medical adoption of AI in neuroimaging
Automated Secure DevOps: An Intelligent CI/CD Pipeline with SAST Integration and Agentic AI-Based Vulnerability Patching

The increasing complexity of software supply chains and the rising frequency of security breaches necessitate robust securityintegration in CI/CD pipelines. This paper presents an intelligent CI/CD pipelinethat integrates Static Application Security Testing (SAST) tools with agentic AI for automated vulnerability patching. Our framework automatically detects code-level security issues upon code push, generates comprehensive security reports, employs AI agents to generate and apply patches, performs regression security testing, and automates deployment upon vulnerability resolution. The system employs an ensemble of four SAST tools (Bandit, Pyre, Pylint, Semgrep) with weighted aggregation to achieve 94.7% vulnerability detection rate across 1,247 real-world vulnerabilities from 15 open-source Python Flask applications. The agentic AI framework, comprising three specialized agents (Code Understanding, Security Knowledge, and Patch Synthesis), achieves 87.3% successful automated patching rate while maintaining code functionality. The proposed system reduces mean-time-to-remediation (MTTR) from 3.7 days to 4.2 hours (95.3% reduction) with a false positive rate of only 8.7%. We demonstrate statistical significance (p < 0.001) across all performance metrics through rigorous evaluation. The system's integration with existing CI/CD workflows (Jenkins, GitLab CI, GitHub Actions) enables seamless adoption in enterprise environments. Our work represents a significant advancement toward fully autonomous DevSecOps pipelines, addressing critical gaps in current security automation approaches.
Examining user information disclosure on artificial intelligence-generated content platforms: A privacy calculus perspective

Users need to disclose personal information in order to obtain accurate services during their interactions with artificial intelligence-generated content (AIGC) platforms. This may increase their privacy concerns and lower the disclosure intention, which may undermine the continuous development of platforms. Based on the privacy calculus theory, this study aims to explore AIGC user information disclosure intention. We conducted an online survey to obtain 446 valid responses, and adopted a mixed method of structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to conduct data analysis. The results show that perceived anonymity and privacy statements negatively affect privacy concerns, while perceived anthropomorphism has a positive effect on privacy concerns. Perceived anthropomorphism, perceived anonymity and privacy statements positively affect privacy benefits. Both privacy concerns and privacy benefits predict disclosure intention. In addition, AI literacy strengthens the effects of both privacy concerns and privacy benefits on disclosure intention. The results imply that AIGC platforms need to mitigate privacy concerns and increase privacy benefits in order to promote users’ disclosure and ensure the continuous development of platforms.
Battery Storage Optimization on the German Continuous Intraday Market: An Experimental Analysis under Consideration of Price Uncertainties

This article uses an experimental approach to examine the extent to which price fluctuations influence the profitability of electricity storages operating on the German Continuous Intraday Market. For this reason, we are extending our previous research and analyzing the trade-off between storage capacity and price forecast uncertainty with regard to arbitrage profitability. Using a genetic algorithm, we optimize buy and sell decisions for different battery storage sizes and simulated price forecast uncertainties over the year 2021. Our results show that a storage management strategy generated by the genetic algorithm enables significant arbitrage revenues, which rise in particular with increasing storage capacity. However, with an increasing battery size, decreasing marginal profits are to be expected. Price uncertainties due to forecast errors also reduce the gains, but their influence on profit remains moderate compared to the storage size. The genetic algorithm used provides an intelligent strategy for optimizing storage usage even under uncertain market conditions.
Legislative Approaches to Addressing the Issue of Genetic Discrimination in Insurance

Genetic testing is a very attractive source of information for insurers, who often associate this opportunity with improving the risk assessment of personal insurance, as it is a key factor in determining whether insurers are willing to offer coverage to a particular individual and at what price. At the same time, statements are increasingly being made about the possibility of discrimination against policyholders and insured persons due to the expansion of the practice of using such information, which required the adoption of appropriate legislative decisions by a number of countries, although it is too early to talk about the formation of a systematic approach to solving this problem. An analysis of the legislation of a number of states revealed several approaches to solving this problem. The author highlights the countries that have established a complete ban on the use of genetic information in insurance (Austria, Norway, France), including in the context of protecting the rights to protect personal data of third parties (Spain, Portugal), as well as differentiated conditions for the use of genetic data depending on the amounts insurance coverage for life insurance, accident insurance (including at work); on the specific circumstances, determined by the legislator, and the reasons for the genetic study (Switzerland); from the participation of the insured in the program for the detection and prevention of congenital diseases (Israel).
Exploring the effects of mental workload on professional well-being of AI‑empowered translators and      interpreters

Abstract                   Artificial intelligence (AI) has significantly improved the efficiency of translation and interpreting (T&amp;I),      yet its cognitive and psychological impacts remain underexplored. To address this, 299 Chinese translators and interpreters were      surveyed, and interviews were conducted with 12 respondents, focusing on mental workload, anxiety, adaptation, and professional      well-being. The results show that AI adoption reduces mental workload and improves professional well-being, but also increases the      anxiety induced by AI use. However, this anxiety does not directly affect professional well-being. Effective adaptation strategies      and continual training are essential for optimizing AI’s benefits. While AI streamlines initial T&amp;I tasks, it increases      cognitive demands during post-editing. These findings emphasize the need for a human-centered approach that balances AI’s      efficiency gains with its negative cognitive effects, so as to foster a collaborative relationship between T&amp;I professionals      and AI.
The Impact of Digital Surveillance and Artificial Intelligence on Privacy Rights, Freedom of Expression, and Human Rights Accountability

The study examined the impact of digital surveillance and artificial intelligence on privacy rights, freedom of expression, and human rights accountability in digital environments. The rapid expansion of AI-based surveillance systems increased concerns regarding continuous monitoring, data extraction, and algorithmic profiling, which influenced individuals’ digital behavior and perceptions of rights protection. A quantitative research design was applied, and data were collected from a sample of 300 respondents, including students, professionals, and regular digital platform users. A structured questionnaire based on a five-point Likert scale was used, and data analysis was conducted using descriptive statistics and regression techniques. The results showed that AI-driven surveillance significantly affected privacy rights (β = -0.62, p &lt; 0.001), indicating a strong negative relationship. Freedom of expression was also negatively influenced (β = -0.58, p &lt; 0.001), suggesting that increased surveillance reduced individuals’ willingness to express opinions openly. In addition, human rights accountability showed a significant negative effect (β = -0.54, p &lt; 0.001), reflecting reduced trust in transparency and governance mechanisms. Descriptive findings further revealed moderate to high awareness of surveillance practices (M = 3.89) and concerns regarding privacy protection (M = 3.76). The findings confirmed that AI surveillance systems significantly undermined key human rights dimensions in digital societies. The study concluded that although AI surveillance enhances efficiency and security, it simultaneously poses serious challenges to privacy protection, expressive freedom, and institutional accountability. The research recommended stronger regulatory frameworks and ethical AI governance to mitigate risks associated with surveillance technologies.
Assessing the Influence of Community- Led Strategies on Conflict Resolution and Peace Building in Selected Communities in the Bole District

This study examined how community-led strategies support conflict resolution and peacebuilding in selected communities in the Bole District. The findings show that local strategies, such as mediation by elders, community meetings, and peace committees, play a central role in resolving disputes, especially those related to land and intergroup relations. These methods are widely trusted because they reflect local customs, encourage participation, and rely on community knowledge. The results also indicate that these strategies have helped improve peace and unity among community members by creating spaces where disagreements can be resolved without violence.  However, challenges such as mistrust, unresolved past conflicts, and land-related tensions continue to hinder their full effectiveness.  The study recommends a bottom-up approach to conflict management and peacebuilding.  This approach should deliberately use existing local systems, structures, and traditional practices to reduce conflict and promote peaceful coexistence. It should also involve the many community members who have previously participated in peace initiatives, strengthen local strategies for conflict resolution,  and create stronger links with formal law enforcement and justice institutions.   The study further recommends that widespread availability of small arms is a significant obstacle to community-driven peace-building. Because firearms significantly contribute to insecurity, the government needs to strengthen efforts to identify, recover, and control illicit weapons in the Bole district. Then again, women and youth should be actively encouraged and empowered to participate in conflict resolution and peacebuilding. Their involvement is essential in preventing peace efforts from being viewed as the responsibility of only men. Providing them with support and opportunities to serve as peace ambassadors will strengthen community cohesion and long-term stability. Several aspects remain worthy of further investigation despite the important insights this study has provided.  First, the current study focused on one specific area. Future studies could include more communities or districts in the Savannah region to allow for comparative analysis and strengthen the generalisation of results.  Then again, since illegal firearms are a significant concern, future research could examine how disarmament programmes influence community safety and peace-building initiatives. Besides the challenges identified, further research could explore additional factors that fuel conflict, such as political influence, climate change, or external interventions. Future researchers could investigate how community-led structures can work more effectively with formal security and justice systems, and what obstacles hinder such collaboration. Further studies could explore how to increase women's and young people's involvement in peace-building efforts and the impact of their participation on conflict resolution.
Whole Genome Messenger RNA Profiling Identifies a Novel Signature to Predict Gastric Cancer Survival

OBJECTIVES:             Molecular prognostic biomarkers for gastric cancer (GC) are still limited. We aimed to identify potential messenger RNAs (mRNAs) associated with GC prognosis and further establish an mRNA signature to predict the survival of GC based on the publicly accessible databases.                                   METHODS:             Discovery of potential mRNAs associated with GC survival was undertaken for 441 patients with GC based on the Cancer Genome Atlas (TCGA), with information on clinical characteristics and vital status. Gene ontology functional enrichment analysis and pathway enrichment analysis were conducted to interrogate the possible biological functions. We narrowed down the list of mRNAs for validation study based on a significance level of 1.00 × 10−4, also integrating the information from the methylation analysis and constructing the protein–protein interaction network for elucidating biological processes. A total of 54 mRNAs were further studied in the validation stage, using the Gene Expression Omnibus (GEO) database (GSE84437, n = 433). The validated mRNAs were used to construct a risk score model predicting the prognosis of GC.                                   RESULTS:             A total of 13 mRNAs were significantly associated with survival of GC, after the validation stage, including DCLK1, FLRT2, MCC, PRICKLE1, RIMS1, SLC25A15, SLCO2A1, CDO1, GHR, CD109, SELP, UPK1B, and CD36. Except CD36, DCLK1, and SLCO2A1, other mRNAs are newly reported to be associated with GC survival. The 13 mRNA-based risk score had good performance on distinguishing GC prognosis, with a higher score indicating worse survival in both TCGA and GEO datasets.                                   CONCLUSIONS:             We established a 13-mRNA signature to potentially predict the prognosis of patients with GC, which might be useful in clinical practice for informing patient stratification.
The Clever Hans Effect in Unsupervised Learning

Unsupervised learning has become an essential building block of AI systems. The representations it produces, e.g. in foundation models, are critical to a wide variety of downstream applications. It is therefore important to carefully examine unsupervised models to ensure not only that they produce accurate predictions, but also that these predictions are not "right for the wrong reasons", the so-called Clever Hans (CH) effect. Using specially developed Explainable AI techniques, we show for the first time that CH effects are widespread in unsupervised learning. Our empirical findings are enriched by theoretical insights, which interestingly point to inductive biases in the unsupervised learning machine as a primary source of CH effects. Overall, our work sheds light on unexplored risks associated with practical applications of unsupervised learning and suggests ways to make unsupervised learning more robust.