The International FLAIRS Conference Proceedings最新文献_第4页

ARCHIMEDES: Active Reasoning Conducted with Heterogeneous Information Monitors for Evading Dangers and Extended Surveillance 阿基米德:主动推理与异质信息监测器进行逃避危险和扩展监视

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133361

Simon Schoenbeck, U. Kuter

引用次数: 0

Impact of Artificial Intelligence Regulations on Organizational Risks 人工智能法规对组织风险的影响

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133263

Misti Jo Payton

引用次数: 0

MOD3NN: A Framework for Automatic Signal Modulation Detection Using 3D CNN MOD3NN:一种基于3D CNN的自动信号调制检测框架

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133383

Vishal Perekadan, Chaity Banerjee, Tathagata Mukherjee, E. Pasiliao, Hovannes Kulhandjian, Michel Kulhandjian

引用次数: 0

Mitigating Age Biases in Resume Screening AI Models 减轻简历筛选人工智能模型中的年龄偏见

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133236

Christopher Harris

引用次数: 0

Choosing the Task Allocator: Effect on Performance and Satisfaction in Human-Agent Team 任务分配器的选择:对人代理团队绩效和满意度的影响

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133310

Sami Abuhaimed, Selim Karaoglu, S. Sen

{"title":"Choosing the Task Allocator: Effect on Performance and Satisfaction in Human-Agent Team","authors":"Sami Abuhaimed, Selim Karaoglu, S. Sen","doi":"10.32473/flairs.36.133310","DOIUrl":"https://doi.org/10.32473/flairs.36.133310","url":null,"abstract":"Ad hoc human-agent teams, where team members interact without prior experience with teammates and only for a limited number of interactions, will be commonplace in dynamic environments with opportunity windows for collaboration between diverse groups. We study the efficacy of virtual ad-hoc teams, consisting of a human and an agent, collaborating to complete tasks in each of a few episodes. To maximize team potential, the relative expertise of team members must be measured and utilized in allocating tasks. As team members are not initially aware of each other's task competence and as humans often cannot accurately estimate their competencies, adapting allocation over the episodes is critical to team performance. Human team member satisfaction with allocations is also critical to determining team viability. We therefore use both these criteria to measure the effectiveness of task allocation procedures with varying degree of flexibility and human teammate control: (a) alternating, (b) performance adaptive, (c) agent-guided, (d) human-selected. We report on the relative strengths of these allocation procedures based on results from experiments with MTurk workers.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123812171","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}

引用次数: 0

Capturing Preferences of New Users in Generative Tasks with Minimal Interactions Collaborative Filtering Using Siamese Networks and Soft Clustering 基于Siamese网络和软聚类的协同过滤在生成任务中捕获新用户的偏好

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133318

Subharag Sarkar, M. Huber

{"title":"Capturing Preferences of New Users in Generative Tasks with Minimal Interactions Collaborative Filtering Using Siamese Networks and Soft Clustering","authors":"Subharag Sarkar, M. Huber","doi":"10.32473/flairs.36.133318","DOIUrl":"https://doi.org/10.32473/flairs.36.133318","url":null,"abstract":"Prediction of user preferences is a challenge, in particular when the objective is to learn them without requiring the user to provide a profile or a significant number of interactions. Many collaborative filtering algorithms exist but all of them require the availability of huge datasets of user information and expensive computations. In this paper, a novel architecture is introduced which aims to predict a new user’s interests in the context of previous users’ interactions with minimal feedback interactions. Here, a Siamese Network is used to generate an embedding space for data from existing users. This information is then used in a Gaussian Mixture Model to generate multiple soft clusters. Based on the embedding space, system responses to the user are generated using a Conditional Generative Adversarial Network which uses a vector drawn from the Gaussian Mixture in embedding space from the Siamese Network as the conditional input. The predictive model then interacts with the new user and based on their feedback adjusts the Gaussian Mixture to find the distribution with the highest probability of generating the user’s preferred data. The approach is applied in the context of an image generation task where the goal is to learn to generate images that match the preferences of the user using only a minimal number of direct user interactions. Testing in this domain has shown promising results that exemplify the ability of the approach to capture the user’s preferences while presenting only a minimal number of image examples.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129178739","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}

引用次数: 0

Group Bias and the Complexity/Accuracy Tradeoff in Machine Learning-Based Trauma Triage Models 基于机器学习的创伤分类模型中的群体偏见和复杂性/准确性权衡

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133358

Katherine Phillips, Katherine E. Brown, Steve Talbert, Douglas A. Talbert

引用次数: 0

Answering Student Queries with a Supervised Memory Conversational Agent 用监督记忆会话代理回答学生查询

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133195

Florian Baud, A. Aussem

引用次数: 0

How Does a Minority Opinion Spread? - An Agent-Based Model on the Opposition Between a Silent Majority and a Loud Minority 少数派意见是如何传播的?——沉默的多数和大声的少数对立的基于主体的模型

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133330

Luca Baccino, S. Villata

引用次数: 0

Causanom: Anomaly Detection With Flexible Causal Graphs 因果关系:灵活因果图的异常检测

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133298

Sasha Strelnikoff, Aruna Jammalamadaka, Tsai-Ching Lu

{"title":"Causanom: Anomaly Detection With Flexible Causal Graphs","authors":"Sasha Strelnikoff, Aruna Jammalamadaka, Tsai-Ching Lu","doi":"10.32473/flairs.36.133298","DOIUrl":"https://doi.org/10.32473/flairs.36.133298","url":null,"abstract":"Causality-based anomaly detection methods provide at least two significant theoretical benefits over purely statistical methods: 1. Improved robustness to non-anomalous out-of-distribution data, which implies a reduction in false-alarms; 2. A potential for failure localization due to the topological ordering of the causal graph. Recent studies have considered the utilization of causality-based methods for time series anomaly detection, however, these methods require the causal graph to be fixed; resultingly, such methods are not robust to incorrectly estimated causal graphs and are not able to natively model counterfactual scenarios. To address these limitations, we introduce Causanom: a graph-based encoder-decoder neural network for time series anomaly detection. Causanom utilizes a node conditional data-stream representation in conjunction with a weighted graph aggregation function in order to efficiently capture heterogeneous node dynamics whilst allowing for a flexible graphical structure. We show that Causanom can be trained along with auxiliary constraints in order to tune the causal graph and improve performance. Additionally, we show that Causanom can be used to produce counterfactual data, which we leverage to identify violated causal relationships. Using real and synthetic time series data respectively, we show that Causanom performs at least as well as state-of-the-art baselines in the anomaly detection task and outperforms existing methods in a causal attribution task.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"363 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134113821","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}

引用次数: 0