Pub Date : 2024-02-15DOI: 10.48550/arXiv.2402.09721
Tao Lin, Yiling Chen
We study a repeated Bayesian persuasion problem (and more generally, any generalized principal-agent problem with complete information) where the principal does not have commitment power and the agent uses algorithms to learn to respond to the principal's signals. We reduce this problem to a one-shot generalized principal-agent problem with an approximately-best-responding agent. This reduction allows us to show that: if the agent uses contextual no-regret learning algorithms, then the principal can guarantee a utility that is arbitrarily close to the principal's optimal utility in the classic non-learning model with commitment; if the agent uses contextual no-swap-regret learning algorithms, then the principal cannot obtain any utility significantly more than the optimal utility in the non-learning model with commitment. The difference between the principal's obtainable utility in the learning model and the non-learning model is bounded by the agent's regret (swap-regret). If the agent uses mean-based learning algorithms (which can be no-regret but not no-swap-regret), then the principal can do significantly better than the non-learning model. These conclusions hold not only for Bayesian persuasion, but also for any generalized principal-agent problem with complete information, including Stackelberg games and contract design.
{"title":"Persuading a Learning Agent","authors":"Tao Lin, Yiling Chen","doi":"10.48550/arXiv.2402.09721","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09721","url":null,"abstract":"We study a repeated Bayesian persuasion problem (and more generally, any generalized principal-agent problem with complete information) where the principal does not have commitment power and the agent uses algorithms to learn to respond to the principal's signals. We reduce this problem to a one-shot generalized principal-agent problem with an approximately-best-responding agent. This reduction allows us to show that: if the agent uses contextual no-regret learning algorithms, then the principal can guarantee a utility that is arbitrarily close to the principal's optimal utility in the classic non-learning model with commitment; if the agent uses contextual no-swap-regret learning algorithms, then the principal cannot obtain any utility significantly more than the optimal utility in the non-learning model with commitment. The difference between the principal's obtainable utility in the learning model and the non-learning model is bounded by the agent's regret (swap-regret). If the agent uses mean-based learning algorithms (which can be no-regret but not no-swap-regret), then the principal can do significantly better than the non-learning model. These conclusions hold not only for Bayesian persuasion, but also for any generalized principal-agent problem with complete information, including Stackelberg games and contract design.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.48550/arXiv.2402.10082
Enrique Mármol Campos, Aurora González-Vidal, José Luis Hernández Ramos, A. Gómez-Skarmeta
Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is vulnerable to security attacks such as Byzantine behaviors and poisoning attacks, which can significantly degrade model performance and hinder convergence. The effectiveness of existing approaches to mitigate complex attacks, such as median, trimmed mean, or Krum aggregation functions, has been only partially demonstrated in the case of specific attacks. Our study introduces a novel robust aggregation mechanism utilizing the Fourier Transform (FT), which is able to effectively handling sophisticated attacks without prior knowledge of the number of attackers. Employing this data technique, weights generated by FL clients are projected into the frequency domain to ascertain their density function, selecting the one exhibiting the highest frequency. Consequently, malicious clients' weights are excluded. Our proposed approach was tested against various model poisoning attacks, demonstrating superior performance over state-of-the-art aggregation methods.
{"title":"FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning","authors":"Enrique Mármol Campos, Aurora González-Vidal, José Luis Hernández Ramos, A. Gómez-Skarmeta","doi":"10.48550/arXiv.2402.10082","DOIUrl":"https://doi.org/10.48550/arXiv.2402.10082","url":null,"abstract":"Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is vulnerable to security attacks such as Byzantine behaviors and poisoning attacks, which can significantly degrade model performance and hinder convergence. The effectiveness of existing approaches to mitigate complex attacks, such as median, trimmed mean, or Krum aggregation functions, has been only partially demonstrated in the case of specific attacks. Our study introduces a novel robust aggregation mechanism utilizing the Fourier Transform (FT), which is able to effectively handling sophisticated attacks without prior knowledge of the number of attackers. Employing this data technique, weights generated by FL clients are projected into the frequency domain to ascertain their density function, selecting the one exhibiting the highest frequency. Consequently, malicious clients' weights are excluded. Our proposed approach was tested against various model poisoning attacks, demonstrating superior performance over state-of-the-art aggregation methods.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.48550/arXiv.2402.10172
Ali AhmadiTeshnizi, Wenzhi Gao, Madeleine Udell
Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than optimally by state-of-the-art solvers because the expertise required to formulate and solve these problems limits the widespread adoption of optimization tools and techniques. This paper introduces OptiMUS, a Large Language Model (LLM)-based agent designed to formulate and solve (mixed integer) linear programming problems from their natural language descriptions. OptiMUS can develop mathematical models, write and debug solver code, evaluate the generated solutions, and improve its model and code based on these evaluations. OptiMUS utilizes a modular structure to process problems, allowing it to handle problems with long descriptions and complex data without long prompts. Experiments demonstrate that OptiMUS outperforms existing state-of-the-art methods on easy datasets by more than $20%$ and on hard datasets (including a new dataset, NLP4LP, released with this paper that features long and complex problems) by more than $30%$.
{"title":"OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models","authors":"Ali AhmadiTeshnizi, Wenzhi Gao, Madeleine Udell","doi":"10.48550/arXiv.2402.10172","DOIUrl":"https://doi.org/10.48550/arXiv.2402.10172","url":null,"abstract":"Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than optimally by state-of-the-art solvers because the expertise required to formulate and solve these problems limits the widespread adoption of optimization tools and techniques. This paper introduces OptiMUS, a Large Language Model (LLM)-based agent designed to formulate and solve (mixed integer) linear programming problems from their natural language descriptions. OptiMUS can develop mathematical models, write and debug solver code, evaluate the generated solutions, and improve its model and code based on these evaluations. OptiMUS utilizes a modular structure to process problems, allowing it to handle problems with long descriptions and complex data without long prompts. Experiments demonstrate that OptiMUS outperforms existing state-of-the-art methods on easy datasets by more than $20%$ and on hard datasets (including a new dataset, NLP4LP, released with this paper that features long and complex problems) by more than $30%$.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.48550/arXiv.2402.10210
Huizhuo Yuan, Zixiang Chen, Kaixuan Ji, Quanquan Gu
Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI), especially when compared with the remarkable progress made in fine-tuning Large Language Models (LLMs). While cutting-edge diffusion models such as Stable Diffusion (SD) and SDXL rely on supervised fine-tuning, their performance inevitably plateaus after seeing a certain volume of data. Recently, reinforcement learning (RL) has been employed to fine-tune diffusion models with human preference data, but it requires at least two images ("winner"and"loser"images) for each text prompt. In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion), where the diffusion model engages in competition with its earlier versions, facilitating an iterative self-improvement process. Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment. Our experiments on the Pick-a-Pic dataset reveal that SPIN-Diffusion outperforms the existing supervised fine-tuning method in aspects of human preference alignment and visual appeal right from its first iteration. By the second iteration, it exceeds the performance of RLHF-based methods across all metrics, achieving these results with less data.
{"title":"Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation","authors":"Huizhuo Yuan, Zixiang Chen, Kaixuan Ji, Quanquan Gu","doi":"10.48550/arXiv.2402.10210","DOIUrl":"https://doi.org/10.48550/arXiv.2402.10210","url":null,"abstract":"Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI), especially when compared with the remarkable progress made in fine-tuning Large Language Models (LLMs). While cutting-edge diffusion models such as Stable Diffusion (SD) and SDXL rely on supervised fine-tuning, their performance inevitably plateaus after seeing a certain volume of data. Recently, reinforcement learning (RL) has been employed to fine-tune diffusion models with human preference data, but it requires at least two images (\"winner\"and\"loser\"images) for each text prompt. In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion), where the diffusion model engages in competition with its earlier versions, facilitating an iterative self-improvement process. Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment. Our experiments on the Pick-a-Pic dataset reveal that SPIN-Diffusion outperforms the existing supervised fine-tuning method in aspects of human preference alignment and visual appeal right from its first iteration. By the second iteration, it exceeds the performance of RLHF-based methods across all metrics, achieving these results with less data.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.48550/arXiv.2402.09896
Anastasios K. Papazafeiropoulos, Hanxiao Ge, P. Kourtessis, T. Ratnarajah, S. Chatzinotas, S. Papavassiliou
Simultaneously transmitting and reflecting textcolor{black}{reconfigurable intelligent surface} (STAR-RIS) is a promising implementation of RIS-assisted systems that enables full-space coverage. However, STAR-RIS as well as conventional RIS suffer from the double-fading effect. Thus, in this paper, we propose the marriage of active RIS and STAR-RIS, denoted as ASTARS for massive multiple-input multiple-output (mMIMO) systems, and we focus on the energy splitting (ES) and mode switching (MS) protocols. Compared to prior literature, we consider the impact of correlated fading, and we rely our analysis on the two timescale protocol, being dependent on statistical channel state information (CSI). On this ground, we propose a channel estimation method for ASTARS with reduced overhead that accounts for its architecture. Next, we derive a textcolor{black}{closed-form expression} for the achievable sum-rate for both types of users in the transmission and reflection regions in a unified approach with significant practical advantages such as reduced complexity and overhead, which result in a lower number of required iterations for convergence compared to an alternating optimization (AO) approach. Notably, we maximize simultaneously the amplitudes, the phase shifts, and the active amplifying coefficients of the ASTARS by applying the projected gradient ascent method (PGAM). Remarkably, the proposed optimization can be executed at every several coherence intervals that reduces the processing burden considerably. Simulations corroborate the analytical results, provide insight into the effects of fundamental variables on the sum achievable SE, and present the superiority of 16 ASTARS compared to passive STAR-RIS for a practical number of surface elements.
{"title":"Two-Timescale Design for Active STAR-RIS Aided Massive MIMO Systems","authors":"Anastasios K. Papazafeiropoulos, Hanxiao Ge, P. Kourtessis, T. Ratnarajah, S. Chatzinotas, S. Papavassiliou","doi":"10.48550/arXiv.2402.09896","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09896","url":null,"abstract":"Simultaneously transmitting and reflecting textcolor{black}{reconfigurable intelligent surface} (STAR-RIS) is a promising implementation of RIS-assisted systems that enables full-space coverage. However, STAR-RIS as well as conventional RIS suffer from the double-fading effect. Thus, in this paper, we propose the marriage of active RIS and STAR-RIS, denoted as ASTARS for massive multiple-input multiple-output (mMIMO) systems, and we focus on the energy splitting (ES) and mode switching (MS) protocols. Compared to prior literature, we consider the impact of correlated fading, and we rely our analysis on the two timescale protocol, being dependent on statistical channel state information (CSI). On this ground, we propose a channel estimation method for ASTARS with reduced overhead that accounts for its architecture. Next, we derive a textcolor{black}{closed-form expression} for the achievable sum-rate for both types of users in the transmission and reflection regions in a unified approach with significant practical advantages such as reduced complexity and overhead, which result in a lower number of required iterations for convergence compared to an alternating optimization (AO) approach. Notably, we maximize simultaneously the amplitudes, the phase shifts, and the active amplifying coefficients of the ASTARS by applying the projected gradient ascent method (PGAM). Remarkably, the proposed optimization can be executed at every several coherence intervals that reduces the processing burden considerably. Simulations corroborate the analytical results, provide insight into the effects of fundamental variables on the sum achievable SE, and present the superiority of 16 ASTARS compared to passive STAR-RIS for a practical number of surface elements.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.48550/arXiv.2402.09897
M. Bishal, Md. Rakibul Hassan Chowdory, Anik Das, Muhammad Ashad Kabir
The COVID-19 pandemic has had adverse effects on both physical and mental health. During this pandemic, numerous studies have focused on gaining insights into health-related perspectives from social media. In this study, our primary objective is to develop a machine learning-based web application for automatically classifying COVID-19-related discussions on social media. To achieve this, we label COVID-19-related Twitter data, provide benchmark classification results, and develop a web application. We collected data using the Twitter API and labeled a total of 6,667 tweets into five different classes: health risks, prevention, symptoms, transmission, and treatment. We extracted features using various feature extraction methods and applied them to seven different traditional machine learning algorithms, including Decision Tree, Random Forest, Stochastic Gradient Descent, Adaboost, K-Nearest Neighbour, Logistic Regression, and Linear SVC. Additionally, we used four deep learning algorithms: LSTM, CNN, RNN, and BERT, for classification. Overall, we achieved a maximum F1 score of 90.43% with the CNN algorithm in deep learning. The Linear SVC algorithm exhibited the highest F1 score at 86.13%, surpassing other traditional machine learning approaches. Our study not only contributes to the field of health-related data analysis but also provides a valuable resource in the form of a web-based tool for efficient data classification, which can aid in addressing public health challenges and increasing awareness during pandemics. We made the dataset and application publicly available, which can be downloaded from this link https://github.com/Bishal16/COVID19-Health-Related-Data-Classification-Website.
COVID-19 大流行对身心健康都产生了不利影响。在这一流行病期间，许多研究都侧重于从社交媒体中获得与健康相关的观点。在本研究中，我们的主要目标是开发一款基于机器学习的网络应用程序，用于自动分类社交媒体上与 COVID-19 相关的讨论。为此，我们标注了 COVID-19 相关的 Twitter 数据，提供了基准分类结果，并开发了一款网络应用程序。我们使用 Twitter API 收集数据，并将总共 6,667 条推文标记为五个不同的类别：健康风险、预防、症状、传播和治疗。我们使用各种特征提取方法提取特征，并将其应用于七种不同的传统机器学习算法，包括决策树、随机森林、随机梯度下降、Adaboost、K-近邻、逻辑回归和线性 SVC。此外，我们还使用了四种深度学习算法：LSTM、CNN、RNN 和 BERT 进行分类。总体而言，在深度学习中，我们使用 CNN 算法取得了 90.43% 的最高 F1 分数。线性 SVC 算法的 F1 得分最高，达到 86.13%，超过了其他传统机器学习方法。我们的研究不仅为健康相关数据分析领域做出了贡献，还以基于网络的高效数据分类工具的形式提供了宝贵的资源，有助于应对公共卫生挑战和提高对流行病的认识。我们公开了数据集和应用程序，可从以下链接下载：https://github.com/Bishal16/COVID19-Health-Related-Data-Classification-Website。
{"title":"COVIDHealth: A Benchmark Twitter Dataset and Machine Learning based Web Application for Classifying COVID-19 Discussions","authors":"M. Bishal, Md. Rakibul Hassan Chowdory, Anik Das, Muhammad Ashad Kabir","doi":"10.48550/arXiv.2402.09897","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09897","url":null,"abstract":"The COVID-19 pandemic has had adverse effects on both physical and mental health. During this pandemic, numerous studies have focused on gaining insights into health-related perspectives from social media. In this study, our primary objective is to develop a machine learning-based web application for automatically classifying COVID-19-related discussions on social media. To achieve this, we label COVID-19-related Twitter data, provide benchmark classification results, and develop a web application. We collected data using the Twitter API and labeled a total of 6,667 tweets into five different classes: health risks, prevention, symptoms, transmission, and treatment. We extracted features using various feature extraction methods and applied them to seven different traditional machine learning algorithms, including Decision Tree, Random Forest, Stochastic Gradient Descent, Adaboost, K-Nearest Neighbour, Logistic Regression, and Linear SVC. Additionally, we used four deep learning algorithms: LSTM, CNN, RNN, and BERT, for classification. Overall, we achieved a maximum F1 score of 90.43% with the CNN algorithm in deep learning. The Linear SVC algorithm exhibited the highest F1 score at 86.13%, surpassing other traditional machine learning approaches. Our study not only contributes to the field of health-related data analysis but also provides a valuable resource in the form of a web-based tool for efficient data classification, which can aid in addressing public health challenges and increasing awareness during pandemics. We made the dataset and application publicly available, which can be downloaded from this link https://github.com/Bishal16/COVID19-Health-Related-Data-Classification-Website.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advancements in multi-modal artificial intelligence (AI) have revolutionized the fields of stock market forecasting and heart rate monitoring. Utilizing diverse data sources can substantially improve prediction accuracy. Nonetheless, additional data may not always align with the original dataset. Interpolation methods are commonly utilized for handling missing values in modal data, though they may exhibit limitations in the context of sparse information. Addressing this challenge, we propose a Modality Completion Deep Belief Network-Based Model (MC-DBN). This approach utilizes implicit features of complete data to compensate for gaps between itself and additional incomplete data. It ensures that the enhanced multi-modal data closely aligns with the dynamic nature of the real world to enhance the effectiveness of the model. We conduct evaluations of the MC-DBN model in two datasets from the stock market forecasting and heart rate monitoring domains. Comprehensive experiments showcase the model's capacity to bridge the semantic divide present in multi-modal data, subsequently enhancing its performance. The source code is available at: https://github.com/logan-0623/DBN-generate
{"title":"MC-DBN: A Deep Belief Network-Based Model for Modality Completion","authors":"Zihong Luo, Haochen Xue, Mingyu Jin, Chengzhi Liu, Zile Huang, Chong Zhang, Shuliang Zhao","doi":"10.48550/arXiv.2402.09782","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09782","url":null,"abstract":"Recent advancements in multi-modal artificial intelligence (AI) have revolutionized the fields of stock market forecasting and heart rate monitoring. Utilizing diverse data sources can substantially improve prediction accuracy. Nonetheless, additional data may not always align with the original dataset. Interpolation methods are commonly utilized for handling missing values in modal data, though they may exhibit limitations in the context of sparse information. Addressing this challenge, we propose a Modality Completion Deep Belief Network-Based Model (MC-DBN). This approach utilizes implicit features of complete data to compensate for gaps between itself and additional incomplete data. It ensures that the enhanced multi-modal data closely aligns with the dynamic nature of the real world to enhance the effectiveness of the model. We conduct evaluations of the MC-DBN model in two datasets from the stock market forecasting and heart rate monitoring domains. Comprehensive experiments showcase the model's capacity to bridge the semantic divide present in multi-modal data, subsequently enhancing its performance. The source code is available at: https://github.com/logan-0623/DBN-generate","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.48550/arXiv.2402.10122
B. S. Campello, G. D. Pelegrina, R. Pelissari, Ricardo Suyama, L. T. Duarte
Countries worldwide have been implementing different actions national strategies for Artificial Intelligence (AI) to shape policy priorities and guide their development concerning AI. Several AI indices have emerged to assess countries' progress in AI development, aiding decision-making on investments and policy choices. Typically, these indices combine multiple indicators using linear additive methods such as weighted sums, although they are limited in their ability to account for interactions among indicators. Another limitation concerns the use of deterministic weights, which can be perceived as subjective and vulnerable to debate and scrutiny, especially by nations that feel disadvantaged. Aiming at mitigating these problems, we conduct a methodological analysis to derive AI indices based on multiple criteria decision analysis. Initially, we assess correlations between different AI dimensions and employ the Choquet integral to model them. Thus, we apply the Stochastic Multicriteria Acceptability Analysis (SMAA) to conduct a sensitivity analysis using both weighted sum and Choquet integral in order to evaluate the stability of the indices with regard the weights. Finally, we introduce a novel ranking methodology based on SMAA, which considers several sets of weights to derive the ranking of countries. As a result, instead of using predefined weights, in the proposed approach, the ranking is achieved based on the probabilities of countries in occupying a specific position. In the computational analysis, we utilize the data employed in The Global AI Index proposed by Tortoise. Results reveal correlations in the data, and our approach effectively mitigates bias. In the sensitivity analysis, we scrutinize changes in the ranking resulting from weight adjustments. We demonstrate that our proposal rankings closely align with those derived from weight variations, proving to be more robust.
{"title":"Mitigating subjectivity and bias in AI development indices: A robust approach to redefining country rankings","authors":"B. S. Campello, G. D. Pelegrina, R. Pelissari, Ricardo Suyama, L. T. Duarte","doi":"10.48550/arXiv.2402.10122","DOIUrl":"https://doi.org/10.48550/arXiv.2402.10122","url":null,"abstract":"Countries worldwide have been implementing different actions national strategies for Artificial Intelligence (AI) to shape policy priorities and guide their development concerning AI. Several AI indices have emerged to assess countries' progress in AI development, aiding decision-making on investments and policy choices. Typically, these indices combine multiple indicators using linear additive methods such as weighted sums, although they are limited in their ability to account for interactions among indicators. Another limitation concerns the use of deterministic weights, which can be perceived as subjective and vulnerable to debate and scrutiny, especially by nations that feel disadvantaged. Aiming at mitigating these problems, we conduct a methodological analysis to derive AI indices based on multiple criteria decision analysis. Initially, we assess correlations between different AI dimensions and employ the Choquet integral to model them. Thus, we apply the Stochastic Multicriteria Acceptability Analysis (SMAA) to conduct a sensitivity analysis using both weighted sum and Choquet integral in order to evaluate the stability of the indices with regard the weights. Finally, we introduce a novel ranking methodology based on SMAA, which considers several sets of weights to derive the ranking of countries. As a result, instead of using predefined weights, in the proposed approach, the ranking is achieved based on the probabilities of countries in occupying a specific position. In the computational analysis, we utilize the data employed in The Global AI Index proposed by Tortoise. Results reveal correlations in the data, and our approach effectively mitigates bias. In the sensitivity analysis, we scrutinize changes in the ranking resulting from weight adjustments. We demonstrate that our proposal rankings closely align with those derived from weight variations, proving to be more robust.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.48550/arXiv.2402.09951
Tom'avs Nagy, M. Pinsker
We investigate the `local consistency implies global consistency' principle of strict width among structures within the scope of the Bodirsky-Pinsker dichotomy conjecture for infinite-domain Constraint Satisfaction Problems (CSPs). Our main result implies that for certain CSP templates within the scope of that conjecture, having bounded strict width has a concrete consequence on the expressive power of the template called implicational simplicity. This in turn yields an explicit bound on the relational width of the CSP, i.e., the amount of local consistency needed to ensure the satisfiability of any instance. Our result applies to first-order expansions of any homogeneous $k$-uniform hypergraph, but more generally to any CSP template under the assumption of finite duality and general abstract conditions mainly on its automorphism group. In particular, it overcomes the restriction to binary signatures in the pioneering work of Wrona.
{"title":"Strict width for Constraint Satisfaction Problems over homogeneous strucures of finite duality","authors":"Tom'avs Nagy, M. Pinsker","doi":"10.48550/arXiv.2402.09951","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09951","url":null,"abstract":"We investigate the `local consistency implies global consistency' principle of strict width among structures within the scope of the Bodirsky-Pinsker dichotomy conjecture for infinite-domain Constraint Satisfaction Problems (CSPs). Our main result implies that for certain CSP templates within the scope of that conjecture, having bounded strict width has a concrete consequence on the expressive power of the template called implicational simplicity. This in turn yields an explicit bound on the relational width of the CSP, i.e., the amount of local consistency needed to ensure the satisfiability of any instance. Our result applies to first-order expansions of any homogeneous $k$-uniform hypergraph, but more generally to any CSP template under the assumption of finite duality and general abstract conditions mainly on its automorphism group. In particular, it overcomes the restriction to binary signatures in the pioneering work of Wrona.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.48550/arXiv.2402.09724
Shaojie Zhang, Yinghui Wang, Peixuan Liu, Jinlong Yang, Tao Yan, Liangyi Huang, Mingfeng Wang
To address the issue of feature descriptors being ineffective in representing grayscale feature information when images undergo high affine transformations, leading to a rapid decline in feature matching accuracy, this paper proposes a region feature descriptor based on simulating affine transformations using classification. The proposed method initially categorizes images with different affine degrees to simulate affine transformations and generate a new set of images. Subsequently, it calculates neighborhood information for feature points on this new image set. Finally, the descriptor is generated by combining the grayscale histogram of the maximum stable extremal region to which the feature point belongs and the normalized position relative to the grayscale centroid of the feature point's region. Experimental results, comparing feature matching metrics under affine transformation scenarios, demonstrate that the proposed descriptor exhibits higher precision and robustness compared to existing classical descriptors. Additionally, it shows robustness when integrated with other descriptors.
{"title":"Region Feature Descriptor Adapted to High Affine Transformations","authors":"Shaojie Zhang, Yinghui Wang, Peixuan Liu, Jinlong Yang, Tao Yan, Liangyi Huang, Mingfeng Wang","doi":"10.48550/arXiv.2402.09724","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09724","url":null,"abstract":"To address the issue of feature descriptors being ineffective in representing grayscale feature information when images undergo high affine transformations, leading to a rapid decline in feature matching accuracy, this paper proposes a region feature descriptor based on simulating affine transformations using classification. The proposed method initially categorizes images with different affine degrees to simulate affine transformations and generate a new set of images. Subsequently, it calculates neighborhood information for feature points on this new image set. Finally, the descriptor is generated by combining the grayscale histogram of the maximum stable extremal region to which the feature point belongs and the normalized position relative to the grayscale centroid of the feature point's region. Experimental results, comparing feature matching metrics under affine transformation scenarios, demonstrate that the proposed descriptor exhibits higher precision and robustness compared to existing classical descriptors. Additionally, it shows robustness when integrated with other descriptors.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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