Crowdsourcing insect observations helps assess demographic shifts.
problem Difficulty in collecting sufficient scale insect census data.
method Gather observations from bystanders and enthusiasts.
result Interest exists for collaboration between enthusiasts and researchers.
Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated by assessing the potential environmental threat of coal mining through increased sulfate concentrations in river networks, which do not belong to any simple …
Paper detects adversarial attacks in sound classification models.
problem Adversarial attacks threaten data-driven models, especially in sound classification.
method Detects adversarial subspaces in unitary vector domain using chordal distance and generalized Schur decomposition.
result Regularized logistic regression detector outperforms other approaches on benchmark datasets.
Why do nations produce scientific research? This is a fundamental problem in the field of social studies of science. The paper confronts this question here by showing vital determinants of science to explain the sources of social power and wealth creation by nations. Firstly, this study suggests a new general definitio…
In this paper, we provide an integrated systems modeling approach to analyzing global externalities from a microeconomic perspective. Various forms of policy (fiscal, monetary, etc.) have addressed flaws and market failures in models, but few have been able to successfully eliminate modern externalities that remain an …
Framework detects cyber threats from Twitter tweets.
problem Time-consuming manual extraction of cyber threat intelligence.
method Novelty detection model trained on CVE data.
result F1-score of 0.643 for classifying cyber threat tweets.
Enhanced network threat detection using KG, LLM, and imbalanced learning.
problem Challenges in network threat detection due to complex attack patterns and limited historical data.
method Integrated framework combining Knowledge Graph, Imbalanced Learning, and Large Language Model.
result Improved threat capture rate by 3%-4% and increased interpretability of risk predictions.
We improve image perturbation defenses using a better-defined Wasserstein threat model.
problem Real-world image perturbations are not pixel-independent, unlike ℓp threat models. method We rectify flaws in the Wasserstein threat model and explore stronger attacks and defenses.
result Current Wasserstein-robust models are ineffective against real-world perturbations.
Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective fi…
This paper analyzes privacy threats in federated matrix factorization.
problem Privacy threats in federated matrix factorization models.
method Categorizes federated matrix factorization into three types and analyzes privacy threats.
result This is the first study of privacy threats in federated matrix factorization.
New method defends against unseen threat models using perceptual adversarial training.
problem Lack of precise mathematical characterization of human perception in adversarial attacks.
method Adversarial training against the set of all imperceptible adversarial examples approximated by deep neural networks.
result Perceptual Adversarial Training (PAT) achieves state-of-the-art robustness against multiple diverse adversarial attacks.
This article reviews recent advances in secure distributed and decentralized inference and learning against Byzantine threats.
problem Securing distributed and decentralized inference and learning against malicious attacks.
method Review of recent algorithmic approaches under Byzantine threat model.
result A plethora of robust algorithmic approaches have been developed.
Investigates how extreme temperature events affect global equity portfolios.
problem Impact of extreme temperature events on global equity portfolios.
method Panel regression analysis and multi-objective portfolio optimization.
result Extreme temperature events negatively impact most sectors' returns.
This paper uses deep learning to improve network threat detection in finance.
problem Detecting unknown threats in large-scale data applications.
method Uses deep learning for advanced threat detection.
result Improves protective measures in the financial industry.
Study finds environmental liability insurance reduces industrial carbon emissions.
problem Reduction of industrial carbon emissions.
method Two-way fixed effect model using provincial (city) level panel data from 2010 to 2020.
result Environmental liability insurance reduces industrial carbon emissions at both direct and indirect levels, with varying effects.
Research creates a taxonomy to bridge AI security and regulatory gaps.
problem Disciplinary disconnect between technical and legal teams in AI risk assessment.
method Developed an AI System Threat Vector Taxonomy with 9 domains and 53 sub-threats.
result Empirically validated and aligned with ISO/IEC 42001 controls and NIST AI RMF functions.
Adversarial attacks have exposed a significant security vulnerability in state-of-the-art machine learning models. Among these models include deep reinforcement learning agents. The existing methods for attacking reinforcement learning agents assume the adversary either has access to the target agent's learned paramete…
Higher environmental performance linked to more tax avoidance, especially for financially constrained firms.
problem Tax avoidance practices in relation to environmental performance.
method Entropy balancing, propensity score matching, instrumental variable method, Heckman test.
result Higher environmental performance correlates with increased tax avoidance, particularly for financially constrained firms.
The paper introduces a new financial market for environmental indices to attract investors.
problem Inherent risks and sustainability concerns in environmental investments.
method Quantitative measures, econometric analysis, dynamic asset pricing tools, and financial options.
result Monetization and construction of country-specific environmental indices as dollar-denominated assets.
New approach detects and ranks novel and developing cyber threats in Twitter.
problem Detecting and ranking novel and developing cyber threats in Twitter streams.
method Unsupervised machine learning approach focusing on novelty and trendiness.
result Ranking of cyber threat events based on importance score using extracted terms.
Study shows environmental spending positively impacts company profitability.
problem Impact of environmental spending on company profitability.
method Panel data regression analysis using E-Views.
result Environmental spending positively impacts profitability metrics.
Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model. In practice, the threat model for real-world systems is often more restrictive than the typical black-box model where the adversary can observe the full …
Insider threat detection is getting an increased concern from academia, industry, and governments due to the growing number of malicious insider incidents. The existing approaches proposed for detecting insider threats still have a common shortcoming, which is the high number of false alarms (false positives). The chal…
Proposes a new framework for environmental CVA with robust wrong-way risk.
problem Limited operational implementations of translating environmental scenarios into CVA.
method Three components: hazard rate mapping, tail generators, and KL divergence-based wrong-way risk bound.
result Nature CVAs can vary significantly across different ecosystem generators.
Survey on security and privacy in decentralized federated learning.
problem New threats in decentralized federated learning due to the removal of the server.
method Thorough security analysis and overview of defense mechanisms.
result Challenges and threats in decentralized federated learning.
AI methods are energy-intensive, but efficiency alone isn't enough for sustainability.
problem AI methods are energy-intensive and contribute to climate change.
method Critically examines the limitations of efficiency in improving environmental sustainability of AI.
result Efficiency alone is insufficient to address the environmental impacts of AI.
ADSAGE detects anomalies in graph edge sequences for insider threat detection.
problem Detecting insider threats in fine-grained audit logs using graph and text features.
method Anomaly detection at edge level, supporting numeric, categorical, and text attributes.
result ADSAGE detects anomalies in authentications and email communications effectively.
Study assesses environmental management accounting practices in Bangladesh.
problem Low environmental management accounting practices in Bangladeshi manufacturing companies.
method Developed a compliance checklist and evaluated practices using binary scoring.
result Environmental management accounting practices are poor in Bangladeshi manufacturing companies.
Hedonic models predict 84-92% of U.S. real estate prices, highlighting environmental factors' impact.
problem Predicting real estate prices using hedonic models with environmental factors.
method P-spline generalized additive models for real estate prices, contrasting with linear and polynomial models.
result GAM models explain 84-92% of U.S. real estate price variance, with environmental factors contributing minimally.
Better methods to detect insider threats need new anticipatory analytics to capture risky behavior prior to losing data. In search of the best overall classifier, this work empirically scores 88 machine learning algorithms in 16 major families. We extract risk features from the large CERT dataset, which blends real net…
Most of the existing solutions to enterprise threat management are preventive approaches prescribing means to prevent policy violations with varying degrees of success. In this paper we consider the complementary scenario where a number of security violations have already occurred, or security threats, or vulnerabiliti…
TinyML models detect RF and cyber threats in spacecraft with low latency.
problem Detecting cyber-RF threats in autonomous spacecraft with low latency.
method Analysis of classical models (RF, LR, SVM, MLP) for latency-accuracy trade-offs.
result Logistic Regression achieves microsecond-level inference with minimal accuracy loss.
CCAT improves model robustness to various adversarial attacks.
problem Robustness to adversarial attacks does not generalize to unseen threat models.
method CCAT biases models towards low confidence predictions on adversarial examples.
result CCAT increases robustness against multiple adversarial attack norms and types.
Study identifies contagion in aggregated defaults despite environmental changes.
problem Identify contagion in aggregated default counts with fluctuating probabilities.
method Compare three contagion mechanisms (Davis-Lo, Torri, Vasicek) under i.i.d. and hierarchical specifications.
result Threshold contagion is largely absorbed into environmental heterogeneity, while cumulative contagion leaves a persistent signature.
MMformer improves forecasting of environmental time series data.
problem Accurately forecasting environmental change trends for policy-making.
method Meta-learning MTS model combining self-attention and adaptive transferable multi-head attention.
result MMformer outperforms other models in air quality and climate datasets, reducing prediction errors by 50% in MSE and 20% in MAE.
Article evaluates AI security threats and proposes multiple measures.
problem Threats to AI integrity and security.
method Literature review, analysis of AI supply chain, discussion of mitigations.
result Multiple protective measures are necessary for AI security.
RST improves environmental time series classification accuracy using randomized B-spline trees.
problem Improving accuracy in classifying complex environmental time series.
method Randomized Spline Trees (RST) integrates randomized functional representations into ensemble learning.
result RST variants outperform standard Random Forests and Gradient Boosting on most environmental time series datasets.
Bayesian model assesses criminal threat escalation.
problem Evaluate threats posed by potential violent criminals.
method Customised three-level Bayesian hierarchical model.
result Model calibrates to expert judgments and updates in real time.
Survey on threats to federated learning models.
problem Vulnerabilities in federated learning protocols.
method Taxonomy of threat models and attacks.
result Important future research directions.
Bayesian optimization adapted for experiments with changing environmental conditions.
problem Optimizing experiments influenced by uncontrollable environmental factors.
method Extends Bayesian optimization to handle both controllable and uncontrollable parameters, fitting a global surrogate model and optimizing only controllable parameters conditionally on measurements of uncontrollable variables.
result The proposed ENVBO algorithm finds solutions for the full domain of the environmental variable more efficiently and cost-effectively than traditional methods.
Bayesian system helps identify and thwart terrorist plans.
problem Identifying and thwarting terrorist plans before they occur.
method Develops a Bayesian decision support system to integrate member and group activities.
result Estimates the combined threat posed by a terrorist group.
The exponential increase in dependencies between the cyber and physical world leads to an enormous amount of data which must be efficiently processed and stored. Therefore, computing paradigms are evolving towards machine learning (ML)-based systems because of their ability to efficiently and accurately process the eno…
Data poisoning attacks -- where an adversary can modify a small fraction of training data, with the goal of forcing the trained classifier to high loss -- are an important threat for machine learning in many applications. While a body of prior work has developed attacks and defenses, there is not much general understan…
Study uses ML and statistical models to analyze climate impacts of industrial growth.
problem Understanding and predicting environmental impacts of industrial activities.
method Comparative analysis of ML and statistical models on time series data.
result ML models outperform statistical models in predicting environmental impacts.
A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a …
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light lev…
Novel Orlicz regrets consistently bound environmental variable statistics.
problem Consistent evaluation of stochastic environmental variables like water quality indices.
method Proposed novel Orlicz regrets for upper and lower bounds.
result Explicit linkage between Orlicz regrets and divergence risk measures.
We analyse a multiplex of networks between OECD countries during the decade 2002-2010, which consists of five financial layers, given by foreign direct investment, equity securities, short-term, long-term and total debt securities, and five environmental layers, given by emissions of N O x, P M 10 SO 2, CO 2 equivalent…