This study delves into the dual nature of artificial intelligence (AI), illuminating its transformative potential that has the power to revolutionize various aspects of our lives. We delve into critical issues such as AI hallucinations, misinformation, and unpredictable behavior, particularly in large language models (LLMs) and AI-powered chatbots. These technologies, while capable of manipulating human decisions and exploiting cognitive vulnerabilities, also hold the key to unlocking unprecedented opportunities for innovation and progress. Our research underscores the need for robust, ethical AI development and deployment frameworks, advocating a balance between technological advancement and societal values. We emphasize the importance of collaboration among researchers, developers, policymakers, and end users to steer AI development toward maximizing benefits while minimizing potential harms. This study highlights the critical role of responsible AI practices, including regular training, engagement, and the sharing of experiences among AI users, to mitigate risks and develop the best practices. We call for updated legal and regulatory frameworks to keep pace with AI advancements and ensure their alignment with ethical principles and societal values. By fostering open dialog, sharing knowledge, and prioritizing ethical considerations, we can harness AI’s transformative potential to drive human advancement while managing its inherent risks and challenges.
{"title":"The Era of Artificial Intelligence Deception: Unraveling the Complexities of False Realities and Emerging Threats of Misinformation","authors":"Steven M. Williamson, Victor Prybutok","doi":"10.3390/info15060299","DOIUrl":"https://doi.org/10.3390/info15060299","url":null,"abstract":"This study delves into the dual nature of artificial intelligence (AI), illuminating its transformative potential that has the power to revolutionize various aspects of our lives. We delve into critical issues such as AI hallucinations, misinformation, and unpredictable behavior, particularly in large language models (LLMs) and AI-powered chatbots. These technologies, while capable of manipulating human decisions and exploiting cognitive vulnerabilities, also hold the key to unlocking unprecedented opportunities for innovation and progress. Our research underscores the need for robust, ethical AI development and deployment frameworks, advocating a balance between technological advancement and societal values. We emphasize the importance of collaboration among researchers, developers, policymakers, and end users to steer AI development toward maximizing benefits while minimizing potential harms. This study highlights the critical role of responsible AI practices, including regular training, engagement, and the sharing of experiences among AI users, to mitigate risks and develop the best practices. We call for updated legal and regulatory frameworks to keep pace with AI advancements and ensure their alignment with ethical principles and societal values. By fostering open dialog, sharing knowledge, and prioritizing ethical considerations, we can harness AI’s transformative potential to drive human advancement while managing its inherent risks and challenges.","PeriodicalId":510156,"journal":{"name":"Information","volume":"22 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141106514","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}
Real estate price prediction is crucial for informed decision making in the dynamic real estate sector. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing prediction accuracy and data-driven decision making. However, the existing literature lacks a cohesive synthesis of methodologies, findings, and research gaps in ML-based real estate price prediction. This study addresses this gap through a comprehensive literature review, examining various ML approaches, including neural networks, ensemble methods, and advanced regression techniques. We identify key research gaps, such as the limited exploration of hybrid ML-econometric models and the interpretability of ML predictions. To validate the robustness of regression models, we conduct generalization testing on an independent dataset. Results demonstrate the applicability of regression models in predicting real estate prices across diverse markets. Our findings underscore the importance of addressing research gaps to advance the field and enhance the practical applicability of ML techniques in real estate price prediction. This study contributes to a deeper understanding of ML’s role in real estate forecasting and provides insights for future research and practical implementation in the real estate industry.
房地产价格预测对于动态房地产行业的明智决策至关重要。近年来，机器学习（ML）技术已成为提高预测准确性和数据驱动决策的有力工具。然而，现有文献缺乏对基于 ML 的房地产价格预测的方法、发现和研究差距的综合分析。本研究通过全面的文献综述弥补了这一不足，研究了各种 ML 方法，包括神经网络、集合方法和高级回归技术。我们发现了一些关键的研究空白，例如对混合 ML 计量经济学模型和 ML 预测可解释性的探索有限。为了验证回归模型的稳健性，我们在独立数据集上进行了泛化测试。结果表明，回归模型适用于预测不同市场的房地产价格。我们的研究结果凸显了解决研究空白的重要性，从而推动该领域的发展，并提高 ML 技术在房地产价格预测中的实际应用性。本研究有助于加深对 ML 在房地产预测中的作用的理解，并为房地产行业的未来研究和实际应用提供了见解。
{"title":"Advanced Machine Learning Techniques for Predictive Modeling of Property Prices","authors":"Kanchana Vishwanadee Mathotaarachchi, Raza Hasan, Salman Mahmood","doi":"10.3390/info15060295","DOIUrl":"https://doi.org/10.3390/info15060295","url":null,"abstract":"Real estate price prediction is crucial for informed decision making in the dynamic real estate sector. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing prediction accuracy and data-driven decision making. However, the existing literature lacks a cohesive synthesis of methodologies, findings, and research gaps in ML-based real estate price prediction. This study addresses this gap through a comprehensive literature review, examining various ML approaches, including neural networks, ensemble methods, and advanced regression techniques. We identify key research gaps, such as the limited exploration of hybrid ML-econometric models and the interpretability of ML predictions. To validate the robustness of regression models, we conduct generalization testing on an independent dataset. Results demonstrate the applicability of regression models in predicting real estate prices across diverse markets. Our findings underscore the importance of addressing research gaps to advance the field and enhance the practical applicability of ML techniques in real estate price prediction. This study contributes to a deeper understanding of ML’s role in real estate forecasting and provides insights for future research and practical implementation in the real estate industry.","PeriodicalId":510156,"journal":{"name":"Information","volume":"76 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141111802","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}
José Miguel Domingues, V. Filipe, André Carita, Vítor Carvalho
This paper explores the intricate interplay between perceived challenge and narrative immersion within role-playing game (RPG) video games, motivated by the escalating influence of game difficulty on player choices. A quantitative methodology was employed, utilizing three specific questionnaires for data collection on player habits and experiences, perceived challenge, and narrative immersion. The study consisted of two interconnected stages: an initial research phase to identify and understand player habits, followed by an in-person intervention involving the playing of three distinct RPG video games. During this intervention, selected players engaged with the chosen RPG video games separately, and after each session, responded to two surveys assessing narrative immersion and perceived challenge. The study concludes that a meticulous adjustment of perceived challenge by video game studios moderately influences narrative immersion, reinforcing the enduring prominence of the RPG genre as a distinctive choice in narrative.
{"title":"Understanding the Impact of Perceived Challenge on Narrative Immersion in Video Games: The Role-Playing Game Genre as a Case Study","authors":"José Miguel Domingues, V. Filipe, André Carita, Vítor Carvalho","doi":"10.3390/info15060294","DOIUrl":"https://doi.org/10.3390/info15060294","url":null,"abstract":"This paper explores the intricate interplay between perceived challenge and narrative immersion within role-playing game (RPG) video games, motivated by the escalating influence of game difficulty on player choices. A quantitative methodology was employed, utilizing three specific questionnaires for data collection on player habits and experiences, perceived challenge, and narrative immersion. The study consisted of two interconnected stages: an initial research phase to identify and understand player habits, followed by an in-person intervention involving the playing of three distinct RPG video games. During this intervention, selected players engaged with the chosen RPG video games separately, and after each session, responded to two surveys assessing narrative immersion and perceived challenge. The study concludes that a meticulous adjustment of perceived challenge by video game studios moderately influences narrative immersion, reinforcing the enduring prominence of the RPG genre as a distinctive choice in narrative.","PeriodicalId":510156,"journal":{"name":"Information","volume":"57 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141108475","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}
Barbara Simon, Ádám Hartvég, Lehel Dénes-Fazakas, György Eigner, László Szilágyi
In recent times, the prevalence of chatbot technology has notably increased, particularly in the realm of medical assistants. However, there is a noticeable absence of medical chatbots that cater to the Hungarian language. Consequently, Hungarian-speaking people currently lack access to an automated system capable of providing assistance with their health-related inquiries or issues. Our research aims to establish a competent medical chatbot assistant that is accessible through both a website and a mobile app. It is crucial to highlight that the project’s objective extends beyond mere linguistic localization; our goal is to develop an official and effectively functioning Hungarian chatbot. The assistant’s task is to answer medical questions, provide health advice, and inform users about health problems and treatments. The chatbot should be able to recognize and interpret user-provided text input and offer accurate and relevant responses using specific algorithms. In our work, we put a lot of emphasis on having steady input so that it can detect all the diseases that the patient is dealing with. Our database consisted of sentences and phrases that a user would type into a chatbot. We assigned health problems to these and then assigned the categories to the corresponding cure. Within the research, we developed a website and mobile app, so that users can easily use the assistant. The app plays a particularly important role for users because it allows them to use the assistant anytime and anywhere, taking advantage of the portability of mobile devices. At the current stage of our research, the precision and validation accuracy of the system is greater than 90%, according to the selected test methods.
{"title":"Advancing Medical Assistance: Developing an Effective Hungarian-Language Medical Chatbot with Artificial Intelligence","authors":"Barbara Simon, Ádám Hartvég, Lehel Dénes-Fazakas, György Eigner, László Szilágyi","doi":"10.3390/info15060297","DOIUrl":"https://doi.org/10.3390/info15060297","url":null,"abstract":"In recent times, the prevalence of chatbot technology has notably increased, particularly in the realm of medical assistants. However, there is a noticeable absence of medical chatbots that cater to the Hungarian language. Consequently, Hungarian-speaking people currently lack access to an automated system capable of providing assistance with their health-related inquiries or issues. Our research aims to establish a competent medical chatbot assistant that is accessible through both a website and a mobile app. It is crucial to highlight that the project’s objective extends beyond mere linguistic localization; our goal is to develop an official and effectively functioning Hungarian chatbot. The assistant’s task is to answer medical questions, provide health advice, and inform users about health problems and treatments. The chatbot should be able to recognize and interpret user-provided text input and offer accurate and relevant responses using specific algorithms. In our work, we put a lot of emphasis on having steady input so that it can detect all the diseases that the patient is dealing with. Our database consisted of sentences and phrases that a user would type into a chatbot. We assigned health problems to these and then assigned the categories to the corresponding cure. Within the research, we developed a website and mobile app, so that users can easily use the assistant. The app plays a particularly important role for users because it allows them to use the assistant anytime and anywhere, taking advantage of the portability of mobile devices. At the current stage of our research, the precision and validation accuracy of the system is greater than 90%, according to the selected test methods.","PeriodicalId":510156,"journal":{"name":"Information","volume":"53 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141108768","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}
Simeon Karpuzov, George H. Petkov, Sylvia Ilieva, Alexander Petkov, S. Kalitzin
Rationale. Object tracking has significance in many applications ranging from control of unmanned vehicles to autonomous monitoring of specific situations and events, especially when providing safety for patients with certain adverse conditions such as epileptic seizures. Conventional tracking methods face many challenges, such as the need for dedicated attached devices or tags, influence by high image noise, complex object movements, and intensive computational requirements. We have developed earlier computationally efficient algorithms for global optical flow reconstruction of group velocities that provide means for convulsive seizure detection and have potential applications in fall and apnea detection. Here, we address the challenge of using the same calculated group velocities for object tracking in parallel. Methods. We propose a novel optical flow-based method for object tracking. It utilizes real-time image sequences from the camera and directly reconstructs global motion-group parameters of the content. These parameters can steer a rectangular region of interest surrounding the moving object to follow the target. The method successfully applies to multi-spectral data, further improving its effectiveness. Besides serving as a modular extension to clinical alerting applications, the novel technique, compared with other available approaches, may provide real-time computational advantages as well as improved stability to noisy inputs. Results. Experimental results on simulated tests and complex real-world data demonstrate the method’s capabilities. The proposed optical flow reconstruction can provide accurate, robust, and faster results compared to current state-of-the-art approaches.
{"title":"Object Tracking Based on Optical Flow Reconstruction of Motion-Group Parameters","authors":"Simeon Karpuzov, George H. Petkov, Sylvia Ilieva, Alexander Petkov, S. Kalitzin","doi":"10.3390/info15060296","DOIUrl":"https://doi.org/10.3390/info15060296","url":null,"abstract":"Rationale. Object tracking has significance in many applications ranging from control of unmanned vehicles to autonomous monitoring of specific situations and events, especially when providing safety for patients with certain adverse conditions such as epileptic seizures. Conventional tracking methods face many challenges, such as the need for dedicated attached devices or tags, influence by high image noise, complex object movements, and intensive computational requirements. We have developed earlier computationally efficient algorithms for global optical flow reconstruction of group velocities that provide means for convulsive seizure detection and have potential applications in fall and apnea detection. Here, we address the challenge of using the same calculated group velocities for object tracking in parallel. Methods. We propose a novel optical flow-based method for object tracking. It utilizes real-time image sequences from the camera and directly reconstructs global motion-group parameters of the content. These parameters can steer a rectangular region of interest surrounding the moving object to follow the target. The method successfully applies to multi-spectral data, further improving its effectiveness. Besides serving as a modular extension to clinical alerting applications, the novel technique, compared with other available approaches, may provide real-time computational advantages as well as improved stability to noisy inputs. Results. Experimental results on simulated tests and complex real-world data demonstrate the method’s capabilities. The proposed optical flow reconstruction can provide accurate, robust, and faster results compared to current state-of-the-art approaches.","PeriodicalId":510156,"journal":{"name":"Information","volume":"37 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141112974","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}
Conventional few-shot learning (FSL) mainly focuses on knowledge transfer from a single source dataset to a recognition scenario with only a few training samples available but still similar to the source domain. In this paper, we consider a more practical FSL setting where multiple semantically different datasets are available to address a wide range of FSL tasks, especially for some recognition scenarios beyond natural images, such as remote sensing and medical imagery. It can be referred to as multi-source cross-domain FSL. To tackle the problem, we propose a two-stage learning scheme, termed learning and adapting multi-source representations (LAMR). In the first stage, we propose a multi-head network to obtain efficient multi-domain representations, where all source domains share the same backbone except for the last parallel projection layers for domain specialization. We train the representations in a multi-task setting where each in-domain classification task is taken by a cosine classifier. In the second stage, considering that instance discrimination and class discrimination are crucial for robust recognition, we propose two contrastive objectives for adapting the pre-trained representations to be task-specialized on the few-shot data. Careful ablation studies verify that LAMR significantly improves representation transferability, showing consistent performance boosts. We also extend LAMR to single-source FSL by introducing a dataset-splitting strategy that equally splits one source dataset into sub-domains. The empirical results show that LAMR can achieve SOTA performance on the BSCD-FSL benchmark and competitive performance on mini-ImageNet, highlighting its versatility and effectiveness for FSL of both natural and specific imaging.
{"title":"Task-Adaptive Multi-Source Representations for Few-Shot Image Recognition","authors":"Ge Liu, Zhongqiang Zhang, Xiangzhong Fang","doi":"10.3390/info15060293","DOIUrl":"https://doi.org/10.3390/info15060293","url":null,"abstract":"Conventional few-shot learning (FSL) mainly focuses on knowledge transfer from a single source dataset to a recognition scenario with only a few training samples available but still similar to the source domain. In this paper, we consider a more practical FSL setting where multiple semantically different datasets are available to address a wide range of FSL tasks, especially for some recognition scenarios beyond natural images, such as remote sensing and medical imagery. It can be referred to as multi-source cross-domain FSL. To tackle the problem, we propose a two-stage learning scheme, termed learning and adapting multi-source representations (LAMR). In the first stage, we propose a multi-head network to obtain efficient multi-domain representations, where all source domains share the same backbone except for the last parallel projection layers for domain specialization. We train the representations in a multi-task setting where each in-domain classification task is taken by a cosine classifier. In the second stage, considering that instance discrimination and class discrimination are crucial for robust recognition, we propose two contrastive objectives for adapting the pre-trained representations to be task-specialized on the few-shot data. Careful ablation studies verify that LAMR significantly improves representation transferability, showing consistent performance boosts. We also extend LAMR to single-source FSL by introducing a dataset-splitting strategy that equally splits one source dataset into sub-domains. The empirical results show that LAMR can achieve SOTA performance on the BSCD-FSL benchmark and competitive performance on mini-ImageNet, highlighting its versatility and effectiveness for FSL of both natural and specific imaging.","PeriodicalId":510156,"journal":{"name":"Information","volume":"122 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141115489","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}
In-lab elicitation studies inform the design of gestures by having the participants suggest actions to activate the system functions. Conversely, crowd-sourced identification studies follow the opposite path, asking the users to associate the control actions with functions. Identification studies have been used to validate the gestures produced by elicitation studies, but not to design interactive systems. In this paper, we show that identification studies can be combined with in situ observations to design the gestures for data exploration with public displays. To illustrate this method, we developed two versions of a gesture-controlled system for data exploration with 368 users: one designed through an elicitation study, and one designed through in situ observations followed by an identification study. Our results show that the users discovered the majority of the gestures with similar accuracy across the two prototypes. Additionally, the in situ approach enabled the direct recruitment of target users, and the crowd-sourced approach typical of identification studies expedited the design process.
{"title":"Designing Gestures for Data Exploration with Public Displays via Identification Studies","authors":"Adina Friedman, Francesco Cafaro","doi":"10.3390/info15060292","DOIUrl":"https://doi.org/10.3390/info15060292","url":null,"abstract":"In-lab elicitation studies inform the design of gestures by having the participants suggest actions to activate the system functions. Conversely, crowd-sourced identification studies follow the opposite path, asking the users to associate the control actions with functions. Identification studies have been used to validate the gestures produced by elicitation studies, but not to design interactive systems. In this paper, we show that identification studies can be combined with in situ observations to design the gestures for data exploration with public displays. To illustrate this method, we developed two versions of a gesture-controlled system for data exploration with 368 users: one designed through an elicitation study, and one designed through in situ observations followed by an identification study. Our results show that the users discovered the majority of the gestures with similar accuracy across the two prototypes. Additionally, the in situ approach enabled the direct recruitment of target users, and the crowd-sourced approach typical of identification studies expedited the design process.","PeriodicalId":510156,"journal":{"name":"Information","volume":"32 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141118116","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 aims to determine whether there is a case for promoting a new benchmark for forecasting practice via the innovative application of generative artificial intelligence (Gen-AI) for predicting the future. Today, forecasts can be generated via Gen-AI models without the need for an in-depth understanding of forecasting theory, practice, or coding. Therefore, using three datasets, we present a comparative analysis of forecasts from Gen-AI models against forecasts from seven univariate and automated models from the forecast package in R, covering both parametric and non-parametric forecasting techniques. In some cases, we find statistically significant evidence to conclude that forecasts from Gen-AI models can outperform forecasts from popular benchmarks like seasonal ARIMA, seasonal naïve, exponential smoothing, and Theta forecasts (to name a few). Our findings also indicate that the accuracy of forecasts from Gen-AI models can vary not only based on the underlying data structure but also on the quality of prompt engineering (thus highlighting the continued importance of forecasting education), with the forecast accuracy appearing to improve at longer horizons. Therefore, we find some evidence towards promoting forecasts from Gen-AI models as benchmarks in future forecasting practice. However, at present, users are cautioned against reliability issues and Gen-AI being a black box in some cases.
{"title":"Predictions from Generative Artificial Intelligence Models: Towards a New Benchmark in Forecasting Practice","authors":"Hossein Hassani, E. Silva","doi":"10.3390/info15060291","DOIUrl":"https://doi.org/10.3390/info15060291","url":null,"abstract":"This paper aims to determine whether there is a case for promoting a new benchmark for forecasting practice via the innovative application of generative artificial intelligence (Gen-AI) for predicting the future. Today, forecasts can be generated via Gen-AI models without the need for an in-depth understanding of forecasting theory, practice, or coding. Therefore, using three datasets, we present a comparative analysis of forecasts from Gen-AI models against forecasts from seven univariate and automated models from the forecast package in R, covering both parametric and non-parametric forecasting techniques. In some cases, we find statistically significant evidence to conclude that forecasts from Gen-AI models can outperform forecasts from popular benchmarks like seasonal ARIMA, seasonal naïve, exponential smoothing, and Theta forecasts (to name a few). Our findings also indicate that the accuracy of forecasts from Gen-AI models can vary not only based on the underlying data structure but also on the quality of prompt engineering (thus highlighting the continued importance of forecasting education), with the forecast accuracy appearing to improve at longer horizons. Therefore, we find some evidence towards promoting forecasts from Gen-AI models as benchmarks in future forecasting practice. However, at present, users are cautioned against reliability issues and Gen-AI being a black box in some cases.","PeriodicalId":510156,"journal":{"name":"Information","volume":"45 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141113792","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}
Manar Aldaoud, Dawood Al-Abri, F. Kausar, M. Awadalla
Named Data Networking (NDN) stands out as a prominent architectural framework for the future Internet, aiming to address deficiencies present in IP networks, specifically in the domain of security. Although NDN packets containing requested content are signed with the publisher’s signature which establishes data provenance for content, the NDN domain still requires more holistic frameworks that address consumers’ identity verification while accessing protected contents or services using producer/publisher-preapproved authentication servers. In response, this paper introduces the NDN One-Time Authentication (NDNOTA) framework, designed to authenticate NDN online services, applications, and data in real time. NDNOTA comprises three fundamental elements: the consumer, producer, and authentication server. Employing a variety of security measures such as single sign-on (SSO), token credentials, certified asymmetric keys, and signed NDN packets, NDNOTA aims to reinforce the security of NDN-based interactions. To assess the effectiveness of the proposed framework, we validate and evaluate its impact on the three core elements in terms of time performance. For example, when accessing authenticated content through the entire NDNOTA process, consumers experience an additional time overhead of 70 milliseconds, making the total process take 83 milliseconds. In contrast, accessing normal content that does not require authentication does not incur this delay. The additional NDNOTA delay is mitigated once the authentication token is generated and stored, resulting in a comparable time frame to unauthenticated content requests. Additionally, obtaining private content through the authentication process requires 10 messages, whereas acquiring public data only requires two messages.
{"title":"NDNOTA: NDN One-Time Authentication","authors":"Manar Aldaoud, Dawood Al-Abri, F. Kausar, M. Awadalla","doi":"10.3390/info15050289","DOIUrl":"https://doi.org/10.3390/info15050289","url":null,"abstract":"Named Data Networking (NDN) stands out as a prominent architectural framework for the future Internet, aiming to address deficiencies present in IP networks, specifically in the domain of security. Although NDN packets containing requested content are signed with the publisher’s signature which establishes data provenance for content, the NDN domain still requires more holistic frameworks that address consumers’ identity verification while accessing protected contents or services using producer/publisher-preapproved authentication servers. In response, this paper introduces the NDN One-Time Authentication (NDNOTA) framework, designed to authenticate NDN online services, applications, and data in real time. NDNOTA comprises three fundamental elements: the consumer, producer, and authentication server. Employing a variety of security measures such as single sign-on (SSO), token credentials, certified asymmetric keys, and signed NDN packets, NDNOTA aims to reinforce the security of NDN-based interactions. To assess the effectiveness of the proposed framework, we validate and evaluate its impact on the three core elements in terms of time performance. For example, when accessing authenticated content through the entire NDNOTA process, consumers experience an additional time overhead of 70 milliseconds, making the total process take 83 milliseconds. In contrast, accessing normal content that does not require authentication does not incur this delay. The additional NDNOTA delay is mitigated once the authentication token is generated and stored, resulting in a comparable time frame to unauthenticated content requests. Additionally, obtaining private content through the authentication process requires 10 messages, whereas acquiring public data only requires two messages.","PeriodicalId":510156,"journal":{"name":"Information","volume":"56 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121452","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}
Lightweight convolutional neural networks are widely used for face detection due to their ability to learn local representations through spatial induction bias and translational invariance. However, convolutional face detectors have limitations in detecting faces under challenging conditions like occlusion, blurring, or changes in facial poses, primarily attributed to fixed-size receptive fields and a lack of global modeling. Transformer-based models have advantages on learning global representations but are insensitive to capture local patterns. To address these limitations, we propose an efficient face detector that combines convolutional neural network and transformer architectures. We introduce a bi-stream structure that integrates convolutional neural network and transformer blocks within the backbone network, enabling the preservation of local pattern features and the extraction of global context. To further preserve the local details captured by convolutional neural networks, we propose a feature enhancement convolution block in a hierarchical backbone structure. Additionally, we devise a multiscale feature aggregation module to enhance obscured and blurred facial features. Experimental results demonstrate that our method has achieved improved lightweight face detection accuracy with an average precision of 95.30%, 94.20%, and 87.56% across the easy, medium, and hard subdatasets of WIDER FACE, respectively. Therefore, we believe our method will be a useful supplement to the collection of current artificial intelligence models and benefit the engineering applications of face detection.
轻量级卷积神经网络能够通过空间诱导偏差和平移不变性学习局部表征，因此被广泛用于人脸检测。然而，卷积人脸检测器在检测遮挡、模糊或面部姿势变化等挑战性条件下的人脸时存在局限性，这主要归因于固定大小的感受野和缺乏全局建模。基于变换器的模型在学习全局表征方面具有优势，但对捕捉局部模式不敏感。为了解决这些局限性，我们提出了一种结合卷积神经网络和变换器架构的高效人脸检测器。我们引入了一种双流结构，将卷积神经网络和变压器模块整合到主干网络中，从而能够保留局部模式特征并提取全局上下文。为了进一步保留卷积神经网络捕捉到的局部细节，我们在分层主干结构中提出了特征增强卷积块。此外，我们还设计了一个多尺度特征聚合模块，以增强模糊不清的面部特征。实验结果表明，我们的方法提高了轻量级人脸检测的准确率，在 WIDER FACE 的易、中、难子数据集中的平均准确率分别为 95.30%、94.20% 和 87.56%。因此，我们相信我们的方法将成为当前人工智能模型集合的有益补充，并有利于人脸检测的工程应用。
{"title":"A Lightweight Face Detector via Bi-Stream Convolutional Neural Network and Vision Transformer","authors":"Zekun Zhang, Qingqing Chao, Shijie Wang, Teng Yu","doi":"10.3390/info15050290","DOIUrl":"https://doi.org/10.3390/info15050290","url":null,"abstract":"Lightweight convolutional neural networks are widely used for face detection due to their ability to learn local representations through spatial induction bias and translational invariance. However, convolutional face detectors have limitations in detecting faces under challenging conditions like occlusion, blurring, or changes in facial poses, primarily attributed to fixed-size receptive fields and a lack of global modeling. Transformer-based models have advantages on learning global representations but are insensitive to capture local patterns. To address these limitations, we propose an efficient face detector that combines convolutional neural network and transformer architectures. We introduce a bi-stream structure that integrates convolutional neural network and transformer blocks within the backbone network, enabling the preservation of local pattern features and the extraction of global context. To further preserve the local details captured by convolutional neural networks, we propose a feature enhancement convolution block in a hierarchical backbone structure. Additionally, we devise a multiscale feature aggregation module to enhance obscured and blurred facial features. Experimental results demonstrate that our method has achieved improved lightweight face detection accuracy with an average precision of 95.30%, 94.20%, and 87.56% across the easy, medium, and hard subdatasets of WIDER FACE, respectively. Therefore, we believe our method will be a useful supplement to the collection of current artificial intelligence models and benefit the engineering applications of face detection.","PeriodicalId":510156,"journal":{"name":"Information","volume":"69 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121432","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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