Study finds Indian stock market inefficient, suggesting undervalued securities offer returns.
problem Determining market efficiency and identifying undervalued securities.
method Analyzed five BSE indices, tested random walk model, and identified biased random time series.
result Indian stock market is inefficient, with undervalued securities offering higher returns.
New method corrects Markowitz variance for trading volume fluctuations.
problem Incorrect risk estimates from Markowitz variance in trading environments.
method Modeling portfolio variance based on trade volume fluctuations.
result Market-based variance can significantly differ from Markowitz variance.
Develops objective metrics to evaluate NFL offensive linemen performance.
problem Objective evaluation of NFL offensive linemen performance is lacking.
method Uses statistical analysis of performance metrics to objectively evaluate offensive linemen.
result Identifies overvalued and undervalued offensive linemen.
Underrepresented scientists produce more novel work but it's undervalued.
problem Underrepresented groups in science face undervaluation of their innovations.
method Text analysis and machine learning of career data of over 1 million US doctoral recipients.
result Underrepresented groups produce more scientific novelty but their contributions are undervalued.
Be it for taking advantage of stock undervaluation or in order to distribute part of their profits to shareholders, firms may buy back their own shares. One of the way they proceed is by including Accelerated Share Repurchases (ASR) as part of their repurchase programs. In this article, we study the pricing and optimal…
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
problem Investor preference for a set of securities over a reference security with constraints.
method Mean-variance optimization with Sharpe Ratio performance measurement.
result Derives an optimal portfolio that maximizes returns while minimizing risk.
Study shows Bitcoin security tied to mining rewards and prices.
problem Understanding Bitcoin security's dependency on market outcomes.
method Used ARDL approach with daily blockchain and Bitcoin data from 2014-2019.
result Bitcoin security outcomes linked to Bitcoin price and mining rewards.
A pair of points in a riemannian manifold makes a secure configuration if the totality of geodesics connecting them can be blocked by a finite set. The manifold is secure if every configuration is secure. We investigate the security of compact, locally symmetric spaces.
New method corrects correlation bias in feature importance.
problem Correlations between features in high-dimensional data bias statistical and machine learning models.
method Pairwise Permutation Algorithm (PPA)
result PPA corrects correlation bias, identifying biological relevant biomarkers.
This paper explores security threats in ML systems and proposes mitigation techniques.
problem Security vulnerabilities in ML-based systems during training and inference.
method Overview of security threats, demonstrations using LeNet and VGGNet, proposed attack.
result Demonstrated security threats and proposed mitigation techniques.
We say that a pair of points x and y is secure if there exist a finite set of blocking points such that any geodesic between x and y passes through one of the blocking points. The main point of this paper is to exhibit new examples of blocking phenomena both in the manifold and the billiard table setting. As an approac…
Paper proposes a recursive PLS model for optimal response to security threats.
problem Optimal response to security threats after violations have occurred.
method Recursive Partial Least Squares (PLS) model with factorial analysis of security events.
result The model optimally estimates security administrators' responses to threats.
Industry lacks tools to secure ML systems, study finds.
problem Insufficient security tools for ML systems in industry.
method Interviews with 28 organizations to identify gaps.
result Need revised Security Development Lifecycle for ML.
Back cover text: Megaprojects and Risk provides the first detailed examination of the phenomenon of megaprojects. It is a fascinating account of how the promoters of multibillion-dollar megaprojects systematically and self-servingly misinform parliaments, the public and the media in order to get projects approved and b…
Sparse oblique decision tree improves security rules for renewable power systems.
problem Identifying secure operating conditions in power systems with high renewable energy.
method Sparse weighted oblique decision tree to learn and embed linear security rules.
result The method significantly increases secure states and reduces solution time.
RL models improve target control in SSGs for security applications.
problem Improving RL algorithms for target control in SSGs.
method Investigates improvements to target representations in RL algorithms.
result Enhanced RL models control targets better in SSGs.
The paper evaluates methods for explaining deep learning in security.
problem Understanding the predictions of deep learning models in security applications.
method Developed criteria to compare and evaluate six explanation methods.
result Significant differences exist between the methods, leading to recommendations.
A riemannian manifold is secure if the geodesics between any pair of points in the manifold can be blocked by a finite number of point obstacles. Compact, flat manifolds are secure. A standing conjecture says that these are the only secure, compact riemannian manifolds. The conjecture claims, in particular, that a riem…
New model values equity-linked securities with guaranteed return.
problem Valuation of equity-linked securities with guaranteed return.
method Replicate security price as sum of guaranteed amount and Asian style option price on basket.
result Analytical formulas derived for security price and hedge ratios.
Securely trains regression models with secret sharing for data collaboration.
problem Balancing data collaboration for technological improvements with security concerns.
method Secret sharing scheme for scalable and efficient secure multiparty training.
result Scalable and efficient protocols for training linear and logistic regression models.
Abstract: Cyber-security challenges tackled with machine learning.
problem Cyber-security problems and lack of labeled data.
method Provided novel data sets and a method to generate labels via pivoting.
result Solutions for generating labels in cyber-security.
Secure linear regression at speed of plaintext methods.
problem Secure multiparty linear regression and feature selection.
method Distributed algorithms combining geometric ideas.
result Efficient and secure genome-wide association studies.
Paper proposes a secure protocol for federated learning.
problem Combining robustness, privacy, and security in federated learning.
method Secure two-server protocol for federated learning.
result Offers both input privacy and Byzantine-robustness.
This paper reviews ML and DL for IoT security, highlighting gaps and future directions.
problem Security and privacy issues in IoT networks due to resource constraints and dynamic behavior.
method Systematic review of current security solutions and ML/ DL approaches.
result ML and DL are essential for IoT security due to resource constraints and dynamic behavior.
Proposes a secure communication method independent of eavesdropper's decoder.
problem Lack of practical security constraints in existing methods.
method Dual MINE-based neural secure communications model.
result Security performance is not affected by eavesdropper's decoding means.
New method defends machine learning models from black-box attacks.
problem Protecting machine learning models from black-box transfer attacks.
method Randomized ensemble technique with provable security guarantee.
result Empirical evidence of security for random binary classifiers.
Explores security challenges of machine learning in real-world systems.
problem Vulnerabilities in machine learning models deployed in safety-critical systems.
method Broadens systems security view of ML vulnerabilities, identifies novel challenges, proposes mitigation suggestions.
result Highlights novel challenges and proposes mitigation strategies for securing ML systems.
In this paper are presented methods of impact analysis on informatics system security accidents, qualitative and quantitative methods, starting with risk and informational system security definitions. It is presented the relationship between the risks of exploiting vulnerabilities of security system, security level of …
Unified market-based description of returns and variances of trades.
problem Market-based variance of trades and market portfolio.
method Unified market-based approach to describe returns and variances of trades and market portfolio.
result Market-based variance accounts for random volumes of trades and differs from Markowitz's portfolio variance.
Optimizes crypto-oriented neural architectures for faster secure inference.
problem Privacy conflicts between model users and providers in neural network applications.
method Proposes a novel Partial Activation layer to optimize the initial design of crypto-oriented neural architectures.
result Significant improvement in the efficiency of secure inference on common evaluation metrics.
This paper discusses adversarial attacks on cyber security systems using machine learning.
problem Adversarial attacks limit the use of machine learning in cyber security.
method Characterizes adversarial attack methods and their applications in cyber security.
result Highlights unique challenges and future research directions for adversarial attacks in cyber security.
Investigates the cost-effectiveness of security features in smart card chips.
problem Costs of adding security features to smart card chips.
method Examines production phases, costs, and security features.
result Security features are worth the cost due to potential damages from attacks.
We develop a secure aggregation protocol for federated learning that reduces communication and computation costs.
problem Expensive communication and privacy concerns in federated learning.
method Adapting compression-based federated techniques to additive secret sharing.
result Our protocol achieves high accuracy with low communication costs and is more efficient than prior work.
Contextual bandit framework improves revenue optimization in securities lending market.
problem Optimizing revenue for agent lenders in a dynamic securities lending market.
method Utilized contextual bandit frameworks to address dynamic pricing problems in an e-commerce-like securities lending market.
result Contextual bandit approach consistently outperforms traditional methods by at least 15% in total revenue generated.
Study assesses how much security restaking protocols need to pay for.
problem Determining the optimal security level for restaking protocols using token incentives.
method Expanding a model by Durvasula and Roughgarden to include strategic attackers and node operators, constructing an approximation algorithm for token-based incentives.
result Restaking protocols can be secure with proper incentive management, even against strategic adversaries.
Survey examines ML for IoT security, addressing new challenges.
problem IoT security challenges due to rapid growth and diverse attacks.
method Comprehensive literature review of ML-based security solutions.
result ML provides dynamic and efficient security for IoT.
The paper explores how mining costs, rewards, and blockchain security are interconnected.
problem Understanding the interdependencies between mining costs, mining rewards, and blockchain security.
method Theoretical derivation and empirical analysis using daily crypto market data and autoregressive distributed lag approach.
result Cryptocurrency price and mining rewards are intrinsically linked to blockchain security outcomes.
The study calculates securities lending haircuts and indemnification costs.
problem Managing borrower default risk in securities markets.
method Repo haircut model applied to securities lending transactions; quantifies haircuts and indemnification costs.
result Computed borrower-dependent haircuts and indemnification costs for US Treasuries and equities.
Secure XGB for privacy-preserving machine learning in federated learning.
problem Privacy-preserving machine learning in federated learning with practical gradient tree boosting models.
method Secure multi-party computation, distributed model storage, secure permutation protocols.
result Our XGB models provide competitive accuracy and practical performance.
The paper reviews defenses against adversarial examples in deep learning.
problem Adversarial examples in deep learning models.
method Summarizes existing defenses and future research directions.
result Recommendations for enhancing security in deep learning models.
MaxMax Q-Learning improves coordination in multi-agent reinforcement learning by refining action selection.
problem Relative over-generalization in decentralized multi-agent reinforcement learning.
method MaxMax Q-Learning employs iterative sampling and evaluation of potential next states to refine approximations of ideal state transitions.
result MaxMax Q-Learning frequently outperforms existing baselines, demonstrating enhanced convergence and sample efficiency.
Memory affects how we perceive time and make decisions about the future.
problem Temporal distortions and intertemporal choice preferences in humans and non-human subjects.
method Combining information theory and artificial intelligence, the study explains these phenomena through sensorimotor representation coding efficiency.
result Memory constraints lead to a renormalization of perceived timescales, resulting in different discount functions.
This paper improves federated learning efficiency by auto-tuning secure aggregation parameters.
problem Efficient communication in federated learning with quantized updates.
method Auto-tuning secure aggregation parameters based on random rotation properties.
result Improved communication efficiency in federated learning with secure aggregation.
This paper investigates the effects of the "uptick rule" (a short selling regulation formally known as rule 10a-1) by means of a simple stock market model, based on the ARED (adaptive rational equilibrium dynamics) modeling framework, where heterogeneous and adaptive beliefs on the future prices of a risky asset were f…
Automated system forecasts safety and security issues in internet-connected devices.
problem Challenges in real-time large-scale forecasting by human experts.
method Developed a scalable and versatile forecasting system using models and methods.
result Automated short and long-term forecasts detect safety and security issues in internet-connected devices.
This paper optimizes a dynamic portfolio using novel dynamic programming.
problem Maximizing a portfolio's value over time with changing prices.
method Novel theoretical approach based on dynamic programming for both deterministic and stochastic cases.
result Theoretical approach successfully maximizes portfolio value using dynamic programming.
HASP enhances mobile security against ASR by adding imperceptible noise.
problem Security leakage from ASR on mobile devices.
method Adversarial noise to perturb WER and enhance security.
result HASP reduces WER by 84.55% and speeds up data processing.
New algorithms secure IoT edge computing from jamming attacks.
problem Secure mobile edge computing in IoT under jamming attacks.
method Online learning tools for developing SAVE-S and SAVE-A algorithms.
result Achieves sublinear regret without extra resources.