Study predicts academic achievement using students' support networks.
problem Predicting academic achievement in college students.
method Decision tree and random forest algorithms applied to Ties data.
result Different types of support are important for different demographics and genders.
Study evaluates the impact of academic support center's face-to-face assistance on student performance.
problem Underestimation of Academic Support Center's true impact due to group bias.
method Applied causal inference theory and T-learner to evaluate conditional average treatment effect (CATE) of F2F personal assistance.
result Developed a new CATE function that depends on the number of F2F sessions, predicting improved CATE performance.
Predicts academic risk in college students using interpretable machine learning.
problem Predicting academic risk from high-dimensional, unbalanced student data.
method Binary classification task using LightGBM model and Shapley value.
result 8 predictors for academic risk identified, including quality of academic partners and dormitory study atmosphere.
This reviews the econophysics activities in Belgium from my admittedly biased point of view. Unknown historical notes or facts are presented for the first time explaining the aims, whence evolution of the research papers and friendly connections with colleagues. Comments on endeavors are also provided. The lack of offi…
Paper discusses how financial institutions' model risk management can benefit academic research.
problem Improving academic research process and mitigating limitations.
method Adopting financial institutions' model risk management practices.
result Lessons from financial institutions can enhance academic research reliability.
Over the last 23 years, the U.S. Securities and Exchange Commission has required over 34,000 companies to file over 165,000 annual reports. These reports, the so-called "Form 10-Ks," contain a characterization of a company's financial performance and its risks, including the regulatory environment in which a company op…
This report was originally written as an industry white paper on Hedge Funds. This paper gives an overview to Hedge Funds, with a focus on risk management issues. We define and explain the general characteristics of Hedge Funds, their main investment strategies and the risk models employed. We address the problems in H…
ELM detects mislabels in Finnish academic publication ranks.
problem Detecting mislabeled academic publication ranks in Finland.
method Used Extreme Learning Machine (ELM) with features characterizing publication channels.
result ELM-based approach accurately detected mislabels compared to reference results.
Paper presents deep learning and ML for automated student performance estimation.
problem Evaluation of students' performance during the pandemic.
method In-depth analysis of deep learning and machine learning approaches.
result Better performance across different prediction tasks with fully data-driven approach.
Study on academic finance evolution over 30 years.
problem Understanding changes in academic finance research over time.
method Analysis of 32 finance journals from 1992 to 2021 using Structural Topic Model.
result Most journals have become more generalist over time, covering more topics.
Study uses ML and causal analysis to predict student performance factors.
problem Understanding socio-academic and economic factors affecting student performance.
method Employed machine learning techniques and causal analysis on 1,050 student profiles.
result Ridge Regression achieved robust predictions with MAE of 0.12 and MSE of 0.024.
System identifies high impact research from academic papers.
problem Identifying high impact research amidst a growing volume of papers.
method Combines visual and text classifiers on a large dataset of PDFs and citation counts.
result Improved accuracy in predicting high impact research.
Tool to estimate research impact for low-resource institutions.
problem Costly databases limit access for third-world institutions.
method Machine Learning for data analysis and panel regression.
result Approximation of SCOPUS Impact Factor for free.
We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suit…
SMM preserves matrix data structure for SVM classification.
problem Preserving spatial correlations in matrix data for SVM.
method SMM uses spectral elastic net combining nuclear and Frobenius norms.
result SMM improves SVM performance on matrix data.
Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterat…
Paper uses PSM to improve fake news detection generalizability.
problem Confounding variables in fake news features.
method Propensity Score Matching (PSM) to select features.
result Generalizability of fake news detection methods improved significantly.
Geospatial framework assesses climate risks for California's banking and exposed sectors.
problem Evaluating climate risks on banking and exposed sectors in California.
method Integrates hazard mapping, exposure analysis, and scenario-based financial risk assessment.
result Framework supports portfolio monitoring and institutional readiness under new standards.
KT models struggle with student concept drift, but BKT remains the most stable.
problem Impact of student concept drift on KT models.
method Applied four KT models to five academic years of data.
result KT models generally degrade in performance with concept drift, BKT remains stable.
Social media enhances or diminishes scientific status, depending on usage.
problem Impact of social media on scientific stratification and mobility.
method Logistic Attribution Analysis combining statistical and machine learning methods.
result Social media promotes stratification and mobility, but beyond a threshold, it negatively impacts status.
The study uses Hidden Markov Models to analyze student enrollment patterns and academic performance.
problem Limited understanding of how enrollment patterns affect academic performance.
method Applied Hidden Markov Models to categorize enrollment strategies and compare academic outcomes.
result Mixed enrollment strategies lead to better academic performance, especially during part-time semesters.
National statistical systems are the enterprises tasked with collecting, validating and reporting societal attributes. These data serve many purposes - they allow governments to improve services, economic actors to traverse markets, and academics to assess social theories. National statistical systems vary in quality, …
Study simulates liquidity in fractional ownership markets using ABM.
problem Understanding liquidity dynamics in illiquid markets.
method Agent-based modeling (ABM) with empirical data.
result Simulation reveals insights into market structures and trading behaviors.
Study shows conventional data prep fails for insurance data, proposing new methods.
problem Challenges in data preparation for insurance data lead to unreliable models.
method Proposes a new data preparation framework using support points and Chatterjee correlation coefficient.
result New methods significantly enhance model robustness and reduce computational resource requirements.
Network analysis techniques remain rarely used for understanding international management strategies. Our paper highlights their value as research tool in this field of social science using a large set of micro-data (20,000) to investigate the presence of networks of subsidiaries overseas. The research question is the …
A new method for name disambiguation in academic networks using multi-view attention and recurrent neural networks.
problem Disambiguating authors with the same name in large-scale academic networks.
method Multi-view Attention-based Pairwise Recurrent Neural Network (MA-PairRNN) that divides papers into blocks based on author attributes and merges blocks of the same author.
result MA-PairRNN significantly improves name disambiguation performance on real-world datasets.
Recently, Basel Committee for Banking Supervision proposed to replace all approaches, including Advanced Measurement Approach (AMA), for operational risk capital with a simple formula referred to as the Standardised Measurement Approach (SMA). This paper discusses and studies the weaknesses and pitfalls of SMA such as …
Machine learning methods tend to outperform traditional statistical models at prediction. In the prediction of academic achievement, ML models have not shown substantial improvement over logistic regression. So far, these results have almost entirely focused on college achievement, due to the availability of administra…
Estimates citation impact to recommend best publication venue.
problem Choosing optimal publication venue for academic papers.
method Treatment effect estimation and bias correction method.
result Effective recommendation of publication venues based on citation potential.
Game theory shows miners' hardware improvements don't centralize mining.
problem Decentralization of cryptocurrency mining.
method Game-theoretical model of mining efficiency and competition.
result Advancements in mining hardware efficiency do not lead to centralization.
Develops a Bonus-Malus model for cyber risk insurance to incentivize cybersecurity.
problem Lack of effective insurance strategies to incentivize cybersecurity.
method Proposes a Bonus-Malus model and a mathematical model with a numerical algorithm.
result Demonstrates how a Bonus-Malus system resolves moral hazard and benefits the insurer.
This paper critiques the Standardized Measurement Approach (SMA) for operational risk and recommends maintaining Advanced Measurement Approach (AMA).
problem Weaknesses and failures of the Standardized Measurement Approach (SMA) in operational risk.
method Critical review and analysis of SMA and AMA approaches.
result SMA is unstable, insensitive to risk, and implicitly related to systemic risk in the banking sector.
One of the important measures of quality of education is the performance of students in the academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and how to improve their performance ahead of time using data mining…
Bitcoin price prediction models fail to outperform a simple 'today's price' baseline, especially at longer horizons.
problem Lack of robust models that consistently outperform a naive price predictor at various horizons.
method Surveyed peer-reviewed papers, categorized by evaluation methodology, contrasted with social media discourse, and proposed methodological standards.
result No peer-reviewed study has shown robust superiority over the naive baseline across multiple market regimes at short-to-medium horizons.
Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whose attributions have certain desirable properties -- most frequently, that particular features are important or unimportant. These attributio…
Efficiently trains BERT on academic GPUs in 12 days.
problem Training large-scale BERT models is expensive and time-consuming.
method Optimizes training on multiple GPUs and nodes, reducing costs.
result Trains BERT on academic GPUs in 12 days, not requiring expensive hardware.
LiteMORT reduces memory usage for GBDT models by 30% with improved accuracy.
problem Memory inefficiency in GBDT models, especially with adaptive compact distributions.
method Share memory, implicit merging, adaptive histogram resizing.
result Significant reduction in memory usage with improved accuracy.
On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts. Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool. In practice, fundam…
Shifu2 discovers advisor-advisee relationships in collaboration networks.
problem Discovering hidden advisor-advisee relationships in scientific collaboration networks.
method Network Representation Learning (NRL) model, considering both network structure and node/edge semantics.
result Improved stability and effectiveness compared to state-of-the-art methods.
Kaggle competitions offer valuable insights for business forecasting.
problem Lack of attention to Kaggle competitions in academic forecasting studies.
method Review of results from six Kaggle competitions featuring real-life business forecasting tasks.
result Global ensemble models outperform local single models in Kaggle competitions.
This study synthesizes stablecoin systems and develops a performance evaluation framework.
problem Fragmented academic research on stablecoins across economics, law, and computer science.
method Multi-method research design including literature synthesis, performance evaluation framework, and case study.
result Unified taxonomy and performance evaluation framework for stablecoin design.
Computer generated academic papers have been used to expose a lack of thorough human review at several computer science conferences. We assess the problem of classifying such documents. After identifying and evaluating several quantifiable features of academic papers, we apply methods from machine learning to build a b…
PhysicsFormer improves TSF models for GSWF with WEATHER-5K dataset.
problem Lack of comprehensive datasets for GSWF.
method PhysicsFormer combines dynamic core and Transformer, enforcing physical consistency.
result PhysicsFormer outperforms TSF models in operational forecasting.
Enhances insurance loss models using InsurTech data and machine learning.
problem Traditional insurance loss models lack predictive accuracy due to limited data sources.
method Combining proprietary claims data with InsurTech data and applying machine learning techniques.
result Improved predictive accuracy of the loss model through machine learning.
Because the choice and tuning of the optimizer affects the speed, and ultimately the performance of deep learning, there is significant past and recent research in this area. Yet, perhaps surprisingly, there is no generally agreed-upon protocol for the quantitative and reproducible evaluation of optimization strategies…
Autonomous driving is getting a lot of attention in the last decade and will be the hot topic at least until the first successful certification of a car with Level 5 autonomy. There are many public datasets in the academic community. However, they are far away from what a robust industrial production system needs. Ther…
The professional services sector is at a turning point, with some industries showing growth opportunities.
problem Identifying growth opportunities in the professional services sector after decades of growth.
method A simple framework applied to the US economic context to diagnose growth opportunities.
result The professional services sector is expected to stall at a national level, but some industries still offer growth opportunities.
Classifier detects LLM-generated text with guarantees.
problem Detecting fake LLM-generated text to prevent misuse.
method Trained classifier without auxiliary info, distinguishing human and LLM text.
result Achieves higher accuracy than existing detectors with type-I error control.