Virinchi Roy Surabhi, Rajat Sadhukhan, Md Raz, H. Pearce, P. Krishnamurthy, Joshua Trujillo, Ramesh Karri, F. Khorrami
Field-Programmable Gate Arrays (FPGAs) play a significant and evolving role in various industries and applications in the current technological landscape. They are widely known for their flexibility, rapid prototyping, reconfigurability, and design development features. FPGA designs are often constructed as compositions of interconnected modules that implement the various features/functionalities required in an application. This work develops a novel tool FEINT, which facilitates this module composition process and automates the design-level modifications required when introducing new modules into an existing design. The proposed methodology is architected as a “template” insertion tool that operates based on a user-provided configuration script to introduce dynamic design features as plugins at different stages of the FPGA design process to facilitate rapid prototyping, composition-based design evolution, and system customization. FEINT can be useful in applications where designers need to tailor system behavior without requiring expert FPGA programming skills or significant manual effort. For example, FEINT can help insert defensive monitoring, adversarial Trojan, and plugin-based functionality enhancement features. FEINT is scalable, future-proof, and cross-platform without a dependence on vendor-specific file formats, thus ensuring compatibility with FPGA families and tool versions and being integrable with commercial tools. To assess FEINT’s effectiveness, our tests covered the injection of various types of templates/modules into FPGA designs. For example, in the Trojan insertion context, our tests consider diverse Trojan behaviors and triggers, including key leakage and denial of service Trojans. We evaluated FEINT’s applicability to complex designs by creating an FPGA design that features a MicroBlaze soft-core processor connected to an AES-accelerator via an AXI-bus interface. FEINT can successfully and efficiently insert various templates into this design at different FPGA design stages.
{"title":"FEINT: Automated Framework for Efficient INsertion of Templates/Trojans into FPGAs","authors":"Virinchi Roy Surabhi, Rajat Sadhukhan, Md Raz, H. Pearce, P. Krishnamurthy, Joshua Trujillo, Ramesh Karri, F. Khorrami","doi":"10.3390/info15070395","DOIUrl":"https://doi.org/10.3390/info15070395","url":null,"abstract":"Field-Programmable Gate Arrays (FPGAs) play a significant and evolving role in various industries and applications in the current technological landscape. They are widely known for their flexibility, rapid prototyping, reconfigurability, and design development features. FPGA designs are often constructed as compositions of interconnected modules that implement the various features/functionalities required in an application. This work develops a novel tool FEINT, which facilitates this module composition process and automates the design-level modifications required when introducing new modules into an existing design. The proposed methodology is architected as a “template” insertion tool that operates based on a user-provided configuration script to introduce dynamic design features as plugins at different stages of the FPGA design process to facilitate rapid prototyping, composition-based design evolution, and system customization. FEINT can be useful in applications where designers need to tailor system behavior without requiring expert FPGA programming skills or significant manual effort. For example, FEINT can help insert defensive monitoring, adversarial Trojan, and plugin-based functionality enhancement features. FEINT is scalable, future-proof, and cross-platform without a dependence on vendor-specific file formats, thus ensuring compatibility with FPGA families and tool versions and being integrable with commercial tools. To assess FEINT’s effectiveness, our tests covered the injection of various types of templates/modules into FPGA designs. For example, in the Trojan insertion context, our tests consider diverse Trojan behaviors and triggers, including key leakage and denial of service Trojans. We evaluated FEINT’s applicability to complex designs by creating an FPGA design that features a MicroBlaze soft-core processor connected to an AES-accelerator via an AXI-bus interface. FEINT can successfully and efficiently insert various templates into this design at different FPGA design stages.","PeriodicalId":510156,"journal":{"name":"Information","volume":"120 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141666533","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 advances in machine learning (ML) have shown great promise in detecting heart disease. However, to ensure the clinical adoption of ML models, they must not only be generalizable and robust but also transparent and explainable. Therefore, this research introduces an approach that integrates the robustness of ensemble learning algorithms with the precision of Bayesian optimization for hyperparameter tuning and the interpretability offered by Shapley additive explanations (SHAP). The ensemble classifiers considered include adaptive boosting (AdaBoost), random forest, and extreme gradient boosting (XGBoost). The experimental results on the Cleveland and Framingham datasets demonstrate that the optimized XGBoost model achieved the highest performance, with specificity and sensitivity values of 0.971 and 0.989 on the Cleveland dataset and 0.921 and 0.975 on the Framingham dataset, respectively.
{"title":"Optimized Ensemble Learning Approach with Explainable AI for Improved Heart Disease Prediction","authors":"Ibomoiye Domor Mienye, N. Jere","doi":"10.3390/info15070394","DOIUrl":"https://doi.org/10.3390/info15070394","url":null,"abstract":"Recent advances in machine learning (ML) have shown great promise in detecting heart disease. However, to ensure the clinical adoption of ML models, they must not only be generalizable and robust but also transparent and explainable. Therefore, this research introduces an approach that integrates the robustness of ensemble learning algorithms with the precision of Bayesian optimization for hyperparameter tuning and the interpretability offered by Shapley additive explanations (SHAP). The ensemble classifiers considered include adaptive boosting (AdaBoost), random forest, and extreme gradient boosting (XGBoost). The experimental results on the Cleveland and Framingham datasets demonstrate that the optimized XGBoost model achieved the highest performance, with specificity and sensitivity values of 0.971 and 0.989 on the Cleveland dataset and 0.921 and 0.975 on the Framingham dataset, respectively.","PeriodicalId":510156,"journal":{"name":"Information","volume":" 390","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141669625","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}
Dimitrios Effrosynidis, Georgios Sylaios, Avi Arampatzis
In the realm of disaster-related tweet classification, this study presents a comprehensive analysis of various machine learning algorithms, shedding light on crucial factors influencing algorithm performance. The exceptional efficacy of simpler models is attributed to the quality and size of the dataset, enabling them to discern meaningful patterns. While powerful, complex models are time-consuming and prone to overfitting, particularly with smaller or noisier datasets. Hyperparameter tuning, notably through Bayesian optimization, emerges as a pivotal tool for enhancing the performance of simpler models. A practical guideline for algorithm selection based on dataset size is proposed, consisting of Bernoulli Naive Bayes for datasets below 5000 tweets and Logistic Regression for larger datasets exceeding 5000 tweets. Notably, Logistic Regression shines with 20,000 tweets, delivering an impressive combination of performance, speed, and interpretability. A further improvement of 0.5% is achieved by applying ensemble and stacking methods.
{"title":"The Effect of Training Data Size on Disaster Classification from Twitter","authors":"Dimitrios Effrosynidis, Georgios Sylaios, Avi Arampatzis","doi":"10.3390/info15070393","DOIUrl":"https://doi.org/10.3390/info15070393","url":null,"abstract":"In the realm of disaster-related tweet classification, this study presents a comprehensive analysis of various machine learning algorithms, shedding light on crucial factors influencing algorithm performance. The exceptional efficacy of simpler models is attributed to the quality and size of the dataset, enabling them to discern meaningful patterns. While powerful, complex models are time-consuming and prone to overfitting, particularly with smaller or noisier datasets. Hyperparameter tuning, notably through Bayesian optimization, emerges as a pivotal tool for enhancing the performance of simpler models. A practical guideline for algorithm selection based on dataset size is proposed, consisting of Bernoulli Naive Bayes for datasets below 5000 tweets and Logistic Regression for larger datasets exceeding 5000 tweets. Notably, Logistic Regression shines with 20,000 tweets, delivering an impressive combination of performance, speed, and interpretability. A further improvement of 0.5% is achieved by applying ensemble and stacking methods.","PeriodicalId":510156,"journal":{"name":"Information","volume":" 1239","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141668894","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}
Zhiyuan Ou, Bingqing Wang, Bin Meng, Changsheng Shi, Dongsheng Zhan
With the support of big data mining techniques, utilizing social media data containing location information and rich semantic text information can construct large-scale daily activity OD flows for urban populations, providing new data resources and research perspectives for studying urban spatiotemporal structures. This paper employs the ST-DBSCAN algorithm to identify the residential locations of Weibo users in four communities and then uses the BERT model for activity-type classification of Weibo texts. Combined with the TF-IDF method, the results are analyzed from three aspects: temporal features, spatial features, and semantic features. The research findings indicate: ① Spatially, residents’ daily activities are mainly centered around their residential locations, but there are significant differences in the radius and direction of activity among residents of different communities; ② In the temporal dimension, the activity intensities of residents from different communities exhibit uniformity during different time periods on weekdays and weekends; ③ Based on semantic analysis, the differences in activities and venue choices among residents of different communities are deeply influenced by the comprehensive characteristics of the communities. This study explores methods for OD information mining based on social media data, which is of great significance for expanding the mining methods of residents’ spatiotemporal behavior characteristics and enriching research on the configuration of public service facilities based on community residents’ activity spaces and facility demands.
{"title":"Research on Resident Behavioral Activities Based on Social Media Data: A Case Study of Four Typical Communities in Beijing","authors":"Zhiyuan Ou, Bingqing Wang, Bin Meng, Changsheng Shi, Dongsheng Zhan","doi":"10.3390/info15070392","DOIUrl":"https://doi.org/10.3390/info15070392","url":null,"abstract":"With the support of big data mining techniques, utilizing social media data containing location information and rich semantic text information can construct large-scale daily activity OD flows for urban populations, providing new data resources and research perspectives for studying urban spatiotemporal structures. This paper employs the ST-DBSCAN algorithm to identify the residential locations of Weibo users in four communities and then uses the BERT model for activity-type classification of Weibo texts. Combined with the TF-IDF method, the results are analyzed from three aspects: temporal features, spatial features, and semantic features. The research findings indicate: ① Spatially, residents’ daily activities are mainly centered around their residential locations, but there are significant differences in the radius and direction of activity among residents of different communities; ② In the temporal dimension, the activity intensities of residents from different communities exhibit uniformity during different time periods on weekdays and weekends; ③ Based on semantic analysis, the differences in activities and venue choices among residents of different communities are deeply influenced by the comprehensive characteristics of the communities. This study explores methods for OD information mining based on social media data, which is of great significance for expanding the mining methods of residents’ spatiotemporal behavior characteristics and enriching research on the configuration of public service facilities based on community residents’ activity spaces and facility demands.","PeriodicalId":510156,"journal":{"name":"Information","volume":" 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141673952","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}
Effective safety management is crucial in the construction industry. The growing interest in employing Knowledge Graphs (KGs) for safety management in construction is driven by the need for efficient computing-aided safety practices. This paper systematically reviews the literature related to automating safety management processes through knowledge base systems, focusing on the creation and utilization of KGs for construction safety. It captures current methodologies for developing and using KGs in construction safety management, outlining the techniques for each phase of KG development, including scope identification, integration of external data, ontological modeling, data extraction, and KG completion. This provides structured guidance on building a KG for safety management. Moreover, this paper discusses the challenges and limitations that hinder the wider adoption of KGs in construction safety management, leading to the identification of goals and considerations for future research.
有效的安全管理对建筑行业至关重要。由于需要高效的计算辅助安全实践，人们对采用知识图谱（KG）进行建筑安全管理的兴趣与日俱增。本文系统回顾了与通过知识库系统实现安全管理流程自动化相关的文献，重点关注建筑安全知识图谱的创建和使用。它总结了当前在建筑安全管理中开发和使用知识库的方法，概述了知识库开发各阶段的技术，包括范围确定、外部数据集成、本体建模、数据提取和知识库完成。这为构建安全管理 KG 提供了结构化指导。此外，本文还讨论了阻碍在建筑安全管理中更广泛地采用 KG 的挑战和限制，从而确定了未来研究的目标和考虑因素。
{"title":"Use of Knowledge Graphs for Construction Safety Management: A Systematic Literature Review","authors":"Fansheng Kong, Seungjun Ahn","doi":"10.3390/info15070390","DOIUrl":"https://doi.org/10.3390/info15070390","url":null,"abstract":"Effective safety management is crucial in the construction industry. The growing interest in employing Knowledge Graphs (KGs) for safety management in construction is driven by the need for efficient computing-aided safety practices. This paper systematically reviews the literature related to automating safety management processes through knowledge base systems, focusing on the creation and utilization of KGs for construction safety. It captures current methodologies for developing and using KGs in construction safety management, outlining the techniques for each phase of KG development, including scope identification, integration of external data, ontological modeling, data extraction, and KG completion. This provides structured guidance on building a KG for safety management. Moreover, this paper discusses the challenges and limitations that hinder the wider adoption of KGs in construction safety management, leading to the identification of goals and considerations for future research.","PeriodicalId":510156,"journal":{"name":"Information","volume":"57 S271","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141683270","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}
Jing Liu, Huailin Liu, Pengju Wang, Yang Wu, Keqin Li
Deep neural networks (DNNs) have shown remarkable performance across a wide range of fields, including image recognition, natural language processing, and speech processing. However, recent studies indicate that DNNs are highly vulnerable to well-crafted adversarial samples, which can cause incorrect classifications and predictions. These samples are so similar to the original ones that they are nearly undetectable by human vision, posing a significant security risk to DNNs in the real world due to the impact of adversarial attacks. Currently, the most common adversarial attack methods explicitly add adversarial perturbations to image samples, often resulting in adversarial samples that are easier to distinguish by humans. To address this issue, we are motivated to develop more effective methods for generating adversarial samples that remain undetectable to human vision. This paper proposes a pixel-level adversarial attack method based on attention mechanism and high-frequency information separation, named DIPA. Specifically, our approach involves constructing an attention suppression loss function and utilizing gradient information to identify and perturb sensitive pixels. By suppressing the model’s attention to the correct classes, the neural network is misled to focus on irrelevant classes, leading to incorrect judgments. Unlike previous studies, DIPA enhances the attack of adversarial samples by separating the imperceptible details in image samples to more effectively hide the adversarial perturbation while ensuring a higher attack success rate. Our experimental results demonstrate that under the extreme single-pixel attack scenario, DIPA achieves higher attack success rates for neural network models with various architectures. Furthermore, the visualization results and quantitative metrics illustrate that the DIPA can generate more imperceptible adversarial perturbation.
{"title":"DIPA: Adversarial Attack on DNNs by Dropping Information and Pixel-Level Attack on Attention","authors":"Jing Liu, Huailin Liu, Pengju Wang, Yang Wu, Keqin Li","doi":"10.3390/info15070391","DOIUrl":"https://doi.org/10.3390/info15070391","url":null,"abstract":"Deep neural networks (DNNs) have shown remarkable performance across a wide range of fields, including image recognition, natural language processing, and speech processing. However, recent studies indicate that DNNs are highly vulnerable to well-crafted adversarial samples, which can cause incorrect classifications and predictions. These samples are so similar to the original ones that they are nearly undetectable by human vision, posing a significant security risk to DNNs in the real world due to the impact of adversarial attacks. Currently, the most common adversarial attack methods explicitly add adversarial perturbations to image samples, often resulting in adversarial samples that are easier to distinguish by humans. To address this issue, we are motivated to develop more effective methods for generating adversarial samples that remain undetectable to human vision. This paper proposes a pixel-level adversarial attack method based on attention mechanism and high-frequency information separation, named DIPA. Specifically, our approach involves constructing an attention suppression loss function and utilizing gradient information to identify and perturb sensitive pixels. By suppressing the model’s attention to the correct classes, the neural network is misled to focus on irrelevant classes, leading to incorrect judgments. Unlike previous studies, DIPA enhances the attack of adversarial samples by separating the imperceptible details in image samples to more effectively hide the adversarial perturbation while ensuring a higher attack success rate. Our experimental results demonstrate that under the extreme single-pixel attack scenario, DIPA achieves higher attack success rates for neural network models with various architectures. Furthermore, the visualization results and quantitative metrics illustrate that the DIPA can generate more imperceptible adversarial perturbation.","PeriodicalId":510156,"journal":{"name":"Information","volume":"97 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681726","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}
The learning equations of an ANN are presented, giving an extremely concise derivation based on the principle of backpropagation through the descendent gradient. Then, a dual network is outlined acting between synapses of a basic ANN, which controls the learning process and coordinates the subnetworks selected by attention mechanisms toward purposeful behaviors. Mechanisms of memory and their affinity with comprehension are considered, by emphasizing the common role of abstraction and the interplay between assimilation and accommodation, in the spirit of Piaget’s analysis of psychological acquisition and genetic epistemology. Learning, comprehension, and knowledge are expressed as different levels of organization of informational processes inside cognitive systems. It is argued that formal analyses of cognitive artificial systems could shed new light on typical mechanisms of “natural intelligence” and, in a specular way, that models of natural cognition processes could promote further developments of ANN models. Finally, new possibilities of chatbot interaction are briefly discussed.
本文介绍了智能网络的学习方程，并根据梯度下降的反向传播原理进行了极为简洁的推导。然后，概述了在基本 ANN 的突触之间起作用的双重网络，该网络控制学习过程，并协调由注意机制选择的子网络，以实现有目的的行为。通过强调抽象的共同作用以及同化与调适之间的相互作用，并借鉴皮亚杰对心理习得和遗传认识论的分析精神，研究了记忆的机制及其与理解的关系。学习、理解和知识表现为认知系统内部信息过程的不同组织层次。本文认为，对人工认知系统的形式分析可以为 "自然智能 "的典型机制提供新的启示，而且自然认知过程的模型可以促进人工智能网络模型的进一步发展。最后，简要讨论了聊天机器人交互的新可能性。
{"title":"Artificial Neural Network Learning, Attention, and Memory","authors":"Vincenzo Manca","doi":"10.3390/info15070387","DOIUrl":"https://doi.org/10.3390/info15070387","url":null,"abstract":"The learning equations of an ANN are presented, giving an extremely concise derivation based on the principle of backpropagation through the descendent gradient. Then, a dual network is outlined acting between synapses of a basic ANN, which controls the learning process and coordinates the subnetworks selected by attention mechanisms toward purposeful behaviors. Mechanisms of memory and their affinity with comprehension are considered, by emphasizing the common role of abstraction and the interplay between assimilation and accommodation, in the spirit of Piaget’s analysis of psychological acquisition and genetic epistemology. Learning, comprehension, and knowledge are expressed as different levels of organization of informational processes inside cognitive systems. It is argued that formal analyses of cognitive artificial systems could shed new light on typical mechanisms of “natural intelligence” and, in a specular way, that models of natural cognition processes could promote further developments of ANN models. Finally, new possibilities of chatbot interaction are briefly discussed.","PeriodicalId":510156,"journal":{"name":"Information","volume":"5 5‐6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141686578","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}
Joaquim Miguel, Pedro Mendonça, Agnelo Quelhas, J. M. L. P. Caldeira, V. N. Soares
Hiking and cycling have gained popularity as ways of promoting well-being and physical activity. This has not gone unnoticed by Portuguese authorities, who have invested in infrastructure to support these activities and to boost sustainable and nature-based tourism. However, the lack of reliable data on the use of these infrastructures prevents us from recording attendance rates and the most frequent types of users. This information is important for the authorities responsible for managing, maintaining, promoting and using these infrastructures. In this sense, this study builds on a previous study by the same authors which identified computer vision as a suitable technology to identify and count different types of users of cycling and hiking routes. The performance tests carried out led to the conclusion that the YOLOv3-Tiny convolutional neural network has great potential for solving this problem. Based on this result, this paper describes the proposal and implementation of a prototype demonstrator. It is based on a Raspberry Pi 4 platform with YOLOv3-Tiny, which is responsible for detecting and classifying user types. An application available on users’ smartphones implements the concept of opportunistic networks, allowing information to be collected over time, in scenarios where there is no end-to-end connectivity. This aggregated information can then be consulted on an online platform. The prototype was subjected to validation and functional tests and proved to be a viable low-cost solution.
徒步旅行和骑自行车作为促进身心健康和体育锻炼的方式，越来越受到人们的欢迎。葡萄牙政府也注意到了这一点，投资建设了基础设施，以支持这些活动，促进可持续发展和以自然为基础的旅游业。然而，由于缺乏有关这些基础设施使用情况的可靠数据，我们无法记录这些设施的使用率和最常使用的用户类型。这些信息对于负责管理、维护、推广和使用这些基础设施的部门非常重要。从这个意义上说，本研究以同一作者之前的一项研究为基础，该研究认为计算机视觉是一种合适的技术，可用于识别和统计自行车和徒步路线的不同类型用户。通过性能测试得出的结论是，YOLOv3-Tiny 卷积神经网络在解决这一问题方面具有巨大潜力。基于这一结果，本文介绍了原型验证器的提案和实施。它基于 Raspberry Pi 4 平台和 YOLOv3-Tiny，YOLOv3-Tiny 负责检测和分类用户类型。用户智能手机上的一个应用程序实现了机会主义网络的概念，允许在没有端到端连接的情况下长期收集信息。然后，可以在一个在线平台上查询这些汇总信息。对原型进行了验证和功能测试，证明这是一个可行的低成本解决方案。
{"title":"The Development of a Prototype Solution for Collecting Information on Cycling and Hiking Trail Users","authors":"Joaquim Miguel, Pedro Mendonça, Agnelo Quelhas, J. M. L. P. Caldeira, V. N. Soares","doi":"10.3390/info15070389","DOIUrl":"https://doi.org/10.3390/info15070389","url":null,"abstract":"Hiking and cycling have gained popularity as ways of promoting well-being and physical activity. This has not gone unnoticed by Portuguese authorities, who have invested in infrastructure to support these activities and to boost sustainable and nature-based tourism. However, the lack of reliable data on the use of these infrastructures prevents us from recording attendance rates and the most frequent types of users. This information is important for the authorities responsible for managing, maintaining, promoting and using these infrastructures. In this sense, this study builds on a previous study by the same authors which identified computer vision as a suitable technology to identify and count different types of users of cycling and hiking routes. The performance tests carried out led to the conclusion that the YOLOv3-Tiny convolutional neural network has great potential for solving this problem. Based on this result, this paper describes the proposal and implementation of a prototype demonstrator. It is based on a Raspberry Pi 4 platform with YOLOv3-Tiny, which is responsible for detecting and classifying user types. An application available on users’ smartphones implements the concept of opportunistic networks, allowing information to be collected over time, in scenarios where there is no end-to-end connectivity. This aggregated information can then be consulted on an online platform. The prototype was subjected to validation and functional tests and proved to be a viable low-cost solution.","PeriodicalId":510156,"journal":{"name":"Information","volume":"28 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685241","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}
This paper investigates the stability and motion of two interactive oscillating agents. Multiple agents can be controlled in a centralized and/or distributed manner to form specific patterns in cooperative tracking, pursuit, and evasion games, as well as environmental exploration. This paper studies the behavior of two oscillating agents due to their interaction. It shows that, through a combination of selecting oscillation centers and interaction gain, a variety of motions, including limit-cycles and stationary behavior, can be realized.
{"title":"Stability and Motion Patterns of Two Interactive Oscillating Agents","authors":"J. Juang","doi":"10.3390/info15070388","DOIUrl":"https://doi.org/10.3390/info15070388","url":null,"abstract":"This paper investigates the stability and motion of two interactive oscillating agents. Multiple agents can be controlled in a centralized and/or distributed manner to form specific patterns in cooperative tracking, pursuit, and evasion games, as well as environmental exploration. This paper studies the behavior of two oscillating agents due to their interaction. It shows that, through a combination of selecting oscillation centers and interaction gain, a variety of motions, including limit-cycles and stationary behavior, can be realized.","PeriodicalId":510156,"journal":{"name":"Information","volume":"106 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141686965","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}
Metwally Rashad, Doaa M. Alebiary, Mohammed Aldawsari, Ahmed A. El-Sawy, Ahmed H. AbuEl-Atta
The expressions on human faces reveal the emotions we are experiencing internally. Emotion recognition based on facial expression is one of the subfields of social signal processing. It has several applications in different areas, specifically in the interaction between humans and computers. This study presents a simple CCNN-SVM automated model as a viable approach for FER. The model combines a Convolutional Neural Network for feature extraction, certain image preprocessing techniques, and Support Vector Machine (SVM) for classification. Firstly, the input image is preprocessed using face detection, histogram equalization, gamma correction, and resizing techniques. Secondly, the images go through custom single Deep Convolutional Neural Networks (CCNN) to extract deep features. Finally, SVM uses the generated features to perform the classification. The suggested model was trained and tested on four datasets, CK+, JAFFE, KDEF, and FER. These datasets consist of seven primary emotional categories, which encompass anger, disgust, fear, happiness, sadness, surprise, and neutrality for CK+, and include contempt for JAFFE. The model put forward demonstrates commendable performance in comparison to existing facial expression recognition techniques. It achieves an impressive accuracy of 99.3% on the CK+ dataset, 98.4% on the JAFFE dataset, 87.18% on the KDEF dataset, and 88.7% on the FER.
人脸上的表情揭示了我们内心正在经历的情绪。基于面部表情的情绪识别是社会信号处理的子领域之一。它在不同领域都有一些应用，特别是在人与计算机的交互中。本研究提出了一个简单的 CCNN-SVM 自动模型，作为 FER 的可行方法。该模型结合了用于特征提取的卷积神经网络、某些图像预处理技术和用于分类的支持向量机（SVM）。首先，使用人脸检测、直方图均衡化、伽玛校正和大小调整技术对输入图像进行预处理。其次，图像经过定制的单一深度卷积神经网络（CCNN）来提取深度特征。最后，SVM 使用生成的特征进行分类。建议的模型在 CK+、JAFFE、KDEF 和 FER 四个数据集上进行了训练和测试。这些数据集包含七个主要情感类别，CK+ 包含愤怒、厌恶、恐惧、快乐、悲伤、惊讶和中立，JAFFE 包含蔑视。与现有的面部表情识别技术相比，所提出的模型表现出了值得称赞的性能。它在 CK+ 数据集上达到了令人印象深刻的 99.3%，在 JAFFE 数据集上达到了 98.4%，在 KDEF 数据集上达到了 87.18%，在 FER 数据集上达到了 88.7%。
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