Develops a deep learning framework to predict future tech directions for high-tech companies.
problem Difficult task in predicting future R&D trends for high-tech companies due to complexity and variety of factors.
method Deep Technology Forecasting (DTF) framework with three components: PCR, CTR, and DTT neural network.
result DTF framework precisely predicts future tech emphasis of companies using hybrid factors.
Machine learning and blockchain are two of the most noticeable technologies in recent years. The first one is the foundation of artificial intelligence and big data, and the second one has significantly disrupted the financial industry. Both technologies are data-driven, and thus there are rapidly growing interests in …
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.
This paper describes the Pressure Ulcers Online Website, which is a first step solution towards a new and innovative platform for helping people to detect, understand and manage pressure ulcers. It outlines the reasons why the project has been developed and provides a central point of contact for pressure ulcer analysi…
Shai-am simplifies ML for finance, solving code structure and scalability issues.
problem Challenges in integrating ML for investment strategies, including code structure and scalability.
method Integrates a Python framework with modern open-source technologies to manage containerized pipelines and unified interfaces.
result Facilitates collaborative work in quantitative finance by enhancing reusability and readability.
Framework for responsible LLM deployment with human involvement and decentralized technologies.
problem Challenges in deploying LLMs for high-stakes decisions, including data security and accountability.
method Interactive human involvement through multiple iterations, decentralized technologies, and automated auditing.
result Enhanced security and accountability in LLM deployment for financial decisions.
Study shows market volatility affects optimal communication design for trading strategies.
problem Investigating how communication impacts trading strategy performance in multi-agent systems.
method 5-agent LLM-based trading systems across 450 experiments spanning 21 months, comparing 5 organizational structures.
result Communication improves performance but depends on market characteristics, with competitive conversation excelling in volatile tech stocks.
Study examines machine learning competitions' impact on AI development.
problem Fostering innovation and skill development in AI.
method Analysis of major competition platforms, workflows, and participant demographics.
result MLCs promote collaboration, reproducibility, and continuous innovation in AI.
Examines AI regulation in finance, highlighting risks and gaps in current laws.
problem Rapid AI adoption in finance introduces risks and compliance challenges.
method Reviews current legislation, industry guidelines, and real-world use cases.
result Need for adaptive, technology-neutral policies to balance innovation and consumer protection.
DeFi TrustBoost uses blockchain and AI to assess small business loans.
problem Assessing small business loans from low-wealth households.
method Combines blockchain and Explainable AI to ensure confidentiality, compliance, and security.
result Tamper-proof auditing and on-chain/off-chain data storage for financial organizations.
The recent advances in sensor technologies and smart devices enable the collaborative collection of a sheer volume of data from multiple information sources. As a promising tool to efficiently extract useful information from such big data, machine learning has been pushed to the forefront and seen great success in a wi…
Financial crime is a large and growing problem, in some way touching almost every financial institution. Financial institutions are the front line in the war against financial crime and accordingly, must devote substantial human and technology resources to this effort. Current processes to detect financial misconduct h…
Kaggle chronicles 15 years of competitions, innovation, and data science.
problem Exploring 15 years of data science competitions and innovations.
method Longitudinal trend analysis and exploratory data analysis of millions of kernels and discussion threads.
result Kaggle is a growing platform with diverse use cases and adaptable Kagglers.
Recommender systems (RS) help users navigate large sets of items in the search for "interesting" ones. One approach to RS is Collaborative Filtering (CF), which is based on the idea that similar users are interested in similar items. Most model-based approaches to CF seek to train a machine-learning/data-mining model b…
Paper presents a risk management framework for blockchain protocols.
problem Blockchain protocol risks affecting DLT and digital assets.
method Developed a comprehensive risk management framework using traditional taxonomy.
result Structured approach to identify, measure, monitor and report blockchain protocol risks.
AI could democratize education but risks inequality.
problem AI risks creating educational inequality.
method Speculation and posing questions on AI in education.
result AI could exacerbate educational inequality.
FedSyn generates synthetic data from multiple organizations' datasets.
problem Generating diverse synthetic data from limited datasets.
method Federated learning and GAN for privacy-preserving synthetic data generation.
result Synthetic data can be generated from diverse datasets without accessing individual data.
New approach improves cross-domain recommendation for sparse target domains.
problem Cross-domain recommendation challenges with sparse target domains.
method Guided neural collaborative filtering with domain-invariant components across dense and sparse domains.
result Effective and scalable approach demonstrated on public and Visa datasets.
New algorithms reduce communication costs in collaborative learning.
problem Reducing communication costs in collaborative learning.
method Distributed boosting and adaptation to classification noise.
result Communication-efficient algorithms for collaborative PAC learning robust to noise.
This study uses deep learning to improve the accuracy of raw data denoising in ProtoDUNE experiments.
problem Improving the accuracy of raw data denoising in ProtoDUNE experiments.
method Investigates two graph neural network architectures to enhance the receptive field of convolutional neural networks for raw data denoising.
result Graph neural network architectures outperform traditional algorithms in denoising raw ProtoDUNE data.
Object detection in streaming images is a major step in different detection-based applications, such as object tracking, action recognition, robot navigation, and visual surveillance applications. In mostcases, image quality is noisy and biased, and as a result, the data distributions are disturbed and imbalanced. Most…
Supply Chain Management often requires independent organizations to work together to achieve shared objectives. This collaboration is necessary when coordinated actions benefit the group more than the uncoordinated efforts of individual firms. Despite the commonly reported benefits that can be gained in close relations…
This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the Fusion Center (FC). We design optimal collaboration strategies w…
The excellent performance of representation learning of autoencoders have attracted considerable interest in various applications. However, the structure and multi-local collaborative relationships of unlabeled data are ignored in their encoding procedure that limits the capability of feature extraction. This paper pre…
Paper tackles unobserved confounding in human-AI collaborations.
problem Unobserved confounding undermines human-AI collaboration effectiveness.
method Combines sensitivity analysis from causal inference with AI-driven statistical modeling.
result Enhances robustness and reliability of collaborative outcomes.
Post-Quantum Secure Federated DeFi for Inclusive Banking
problem Financial systems and DeFi ecosystems are vulnerable to quantum computing threats.
method Post-Quantum Secure Federated DeFi framework using lattice-based FHE.
result End-to-end homomorphic computation enables inter-bank collaboration.
Firms' collaboration networks can decline but remain resilient.
problem Resilience of firms' collaboration networks during decline.
method Analysis of 21,500 R&D collaborations over 25 years, simulating drop-out cascades.
result Firms' collaboration networks can adapt to mitigate decline and recover.
Data science teams collaborate extensively, using various tools and stakeholders.
problem Lack of understanding in how data science workers collaborate in practice.
method Conducted an online survey with 183 data science workers.
result Data science teams are highly collaborative and use multiple tools and stakeholders.
Study collaborative learning among multi-agents in multi-armed bandits.
problem Minimizing group cumulative regret in a heterogeneous multi-agent setting.
method Developed decentralized algorithms for collaboration between N agents learning M stochastic multi-armed bandits. result Proved near-optimal behavior of proposed algorithms for group regret.
Study of dynamic competition between old and new technologies using a nonlinear map.
problem Dynamic competition between old and new technologies in market share.
method Simulations with three key parameters: scientific-technological, purely technological, and economic.
result Different outcomes in terms of technology prevalence based on interaction of key parameters.
DEMVC improves multi-view clustering with collaborative training and deep autoencoders.
problem Existing multi-view clustering methods have high computation and space complexities or lack representation capability.
method DEMVC learns embedded representations of multiple views individually using deep autoencoders and collaboratively trains all views.
result DEMVC achieves significant improvements over state-of-the-art methods on multi-view datasets.
The paper analyzes tech specialization and diversification at various scales.
problem Trade-offs between specialization and diversification in economic development.
method Patent data and Economic Complexity framework.
result Technological Coherence positively impacts growth at metropolitan areas but negatively at larger scales.
This article asks how planning scholarship may effectively gain impact in planning practice through media exposure. In liberal democracies the public sphere is dominated by mass media. Therefore, working with such media is a prerequisite for effective public impact of planning research. Using the example of megaproject…
Advances in collaborative filtering and ranking methods.
problem Improving recommendation systems efficiency and accuracy.
method Graph information encoding, pairwise and listwise approaches, regularization techniques, personalization.
result New methods significantly improve recommendation system performance.
PCL tackles collaborative learning for diverse agents, reducing sample complexity.
problem Balancing collaborative speedup with personalization for heterogeneous agents.
method AffPCL, with bias and importance correction mechanisms.
result AffPCL reduces sample complexity by a factor of max{n−1,δ}, where n is the number of agents and δ∈[0,1] measures heterogeneity. How technology affects growth or employment has long been debated. With a hiatus, the debate revived once again in the form of how Information and Communications Technology, as a form of new technology, exerts on productivity and employment. Information and Communications Technology perceived as General Purpose Technol…
In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We pr…
Collaborative recommendation is an information-filtering technique that attempts to present information items (movies, music, books, news, images, Web pages, etc.) that are likely of interest to the Internet user. Traditionally, collaborative systems deal with situations with two types of variables, users and items. In…
Collaborative filtering (CF) has been successfully employed by many modern recommender systems. Conventional CF-based methods use the user-item interaction data as the sole information source to recommend items to users. However, CF-based methods are known for suffering from cold start problems and data sparsity proble…
The way developers collaborate inside and particularly across teams often escapes management's attention, despite a formal organization with designated teams being defined. Observability of the actual, organically formed engineering structure provides decision makers invaluable additional tools to manage their talent p…
Meta clustering categorizes learners for collaborative learning.
problem Filtering out unqualified collaborators in collaborative learning.
method Select-Exchange-Cluster (SEC) method to classify learners by their supervised functions.
result SEC can cluster learners into accurate collaboration sets and enhance single-learner performance.
Proposes a deep latent factor model for better recommendation systems.
problem Improving collaborative filtering in recommendation systems.
method Introduces a deeper latent factor model using deep learning.
result Significantly outperforms state-of-the-art techniques in experiments.
Optimal algorithm found for collaborative learning in bandits with optimal regret bounds.
problem Minimizing regret in collaborative multi-agent bandit problems.
method Proposed an algorithm with optimal regret bounds for collaborative multi-agent multi-armed bandit model.
result First algorithm with order optimal regret bounds for collaborative bandit model.
Quantum crypto-economics models price risks in blockchain technology.
problem Quantum technology's potential to undermine blockchain security.
method Building financial models to price quantum risk in blockchain scenarios.
result Quantum crypto-economics models can assess and price quantum risks in blockchain.
Partner-aware algorithms improve AI collaboration in multi-agent settings.
problem Improving AI cooperation in teams with shared rewards.
method Proposed Partner-Aware strategy extending Upper Confidence Bound for decentralized MAB.
result Achieves logarithmic regret in collaborative decision-making.
A new privacy-preserving deep learning scheme for asymmetrically collaborative machine learning.
problem Privacy and efficiency in collaborative machine learning across different data owners.
method Decomposes neural network steps for privacy-preserving training; novel protocol for information leakage.
result Efficient training with stable performance and significant speedup.
A new method for online personalized learning reduces gradient variance by dynamically selecting peers.
problem Online personalized decentralized learning with statistically heterogeneous clients.
method Gradient-based collaboration criterion allowing clients to dynamically select peers with similar gradients.
result The method acts as a variance reduction method, achieving optimal performance in certain conditions.
New algorithms for collaborative learning in uncertain, decentralized environments.
problem Byzantine collaborative learning in uncertain, decentralized environments.
method Two asynchronous solutions to averaging agreement, each optimal according to some dimension.
result New algorithms achieve optimal Byzantine resilience and collaborative learning.