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FEINT: Automated Framework for Efficient INsertion of Templates/Trojans into FPGAs FEINT：将模板/木马高效插入 FPGA 的自动化框架

Information Pub Date : 2024-07-08 DOI: 10.3390/info15070395

Virinchi Roy Surabhi, Rajat Sadhukhan, Md Raz, H. Pearce, P. Krishnamurthy, Joshua Trujillo, Ramesh Karri, F. Khorrami

{"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}

引用次数: 0

Optimized Ensemble Learning Approach with Explainable AI for Improved Heart Disease Prediction 利用可解释的人工智能优化集合学习法改进心脏病预测

Information Pub Date : 2024-07-08 DOI: 10.3390/info15070394

Ibomoiye Domor Mienye, N. Jere

引用次数: 0

The Effect of Training Data Size on Disaster Classification from Twitter 训练数据量对推特灾害分类的影响

Information Pub Date : 2024-07-08 DOI: 10.3390/info15070393

Dimitrios Effrosynidis, Georgios Sylaios, Avi Arampatzis

引用次数: 0

Research on Resident Behavioral Activities Based on Social Media Data: A Case Study of Four Typical Communities in Beijing 基于社交媒体数据的居民行为活动研究：北京四个典型社区的案例研究

Information Pub Date : 2024-07-05 DOI: 10.3390/info15070392

Zhiyuan Ou, Bingqing Wang, Bin Meng, Changsheng Shi, Dongsheng Zhan

{"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}

引用次数: 0

Use of Knowledge Graphs for Construction Safety Management: A Systematic Literature Review 知识图谱在建筑安全管理中的应用：系统性文献综述

Information Pub Date : 2024-07-03 DOI: 10.3390/info15070390

Fansheng Kong, Seungjun Ahn

引用次数: 0

DIPA: Adversarial Attack on DNNs by Dropping Information and Pixel-Level Attack on Attention DIPA：通过丢弃信息对 DNN 的对抗性攻击和对注意力的像素级攻击

Information Pub Date : 2024-07-03 DOI: 10.3390/info15070391

Jing Liu, Huailin Liu, Pengju Wang, Yang Wu, Keqin Li

{"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}

引用次数: 0

Artificial Neural Network Learning, Attention, and Memory 人工神经网络学习、注意力和记忆力

Information Pub Date : 2024-07-02 DOI: 10.3390/info15070387

Vincenzo Manca

引用次数: 0

The Development of a Prototype Solution for Collecting Information on Cycling and Hiking Trail Users 开发收集自行车道和远足径使用者信息的原型解决方案

Information Pub Date : 2024-07-02 DOI: 10.3390/info15070389

Joaquim Miguel, Pedro Mendonça, Agnelo Quelhas, J. M. L. P. Caldeira, V. N. Soares

{"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}

引用次数: 0

Stability and Motion Patterns of Two Interactive Oscillating Agents 两个互动振荡代理的稳定性和运动模式

Information Pub Date : 2024-07-02 DOI: 10.3390/info15070388

J. Juang

引用次数: 0

CCNN-SVM: Automated Model for Emotion Recognition Based on Custom Convolutional Neural Networks with SVM CCNN-SVM：基于自定义卷积神经网络和 SVM 的自动情感识别模型

Information Pub Date : 2024-07-01 DOI: 10.3390/info15070384

Metwally Rashad, Doaa M. Alebiary, Mohammed Aldawsari, Ahmed A. El-Sawy, Ahmed H. AbuEl-Atta

{"title":"CCNN-SVM: Automated Model for Emotion Recognition Based on Custom Convolutional Neural Networks with SVM","authors":"Metwally Rashad, Doaa M. Alebiary, Mohammed Aldawsari, Ahmed A. El-Sawy, Ahmed H. AbuEl-Atta","doi":"10.3390/info15070384","DOIUrl":"https://doi.org/10.3390/info15070384","url":null,"abstract":"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.","PeriodicalId":510156,"journal":{"name":"Information","volume":"4 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141715340","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