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The Era of Artificial Intelligence Deception: Unraveling the Complexities of False Realities and Emerging Threats of Misinformation 人工智能欺骗时代：揭示虚假现实的复杂性和误导信息的新威胁

Information Pub Date : 2024-05-23 DOI: 10.3390/info15060299

Steven M. Williamson, Victor Prybutok

{"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}

引用次数: 0

Advanced Machine Learning Techniques for Predictive Modeling of Property Prices 用于房地产价格预测建模的先进机器学习技术

Information Pub Date : 2024-05-22 DOI: 10.3390/info15060295

Kanchana Vishwanadee Mathotaarachchi, Raza Hasan, Salman Mahmood

{"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}

引用次数: 0

Understanding the Impact of Perceived Challenge on Narrative Immersion in Video Games: The Role-Playing Game Genre as a Case Study 了解认知挑战对电子游戏叙事沉浸感的影响：角色扮演游戏类型案例研究

Information Pub Date : 2024-05-22 DOI: 10.3390/info15060294

José Miguel Domingues, V. Filipe, André Carita, Vítor Carvalho

引用次数: 0

Advancing Medical Assistance: Developing an Effective Hungarian-Language Medical Chatbot with Artificial Intelligence 推进医疗援助：利用人工智能开发有效的匈牙利语医疗聊天机器人

Information Pub Date : 2024-05-22 DOI: 10.3390/info15060297

Barbara Simon, Ádám Hartvég, Lehel Dénes-Fazakas, György Eigner, László Szilágyi

{"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}

引用次数: 0

Object Tracking Based on Optical Flow Reconstruction of Motion-Group Parameters 基于运动组参数光流重构的物体跟踪

Information Pub Date : 2024-05-22 DOI: 10.3390/info15060296

Simeon Karpuzov, George H. Petkov, Sylvia Ilieva, Alexander Petkov, S. Kalitzin

{"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}

引用次数: 0

Task-Adaptive Multi-Source Representations for Few-Shot Image Recognition 用于少量图像识别的任务自适应多源表示法

Information Pub Date : 2024-05-21 DOI: 10.3390/info15060293

Ge Liu, Zhongqiang Zhang, Xiangzhong Fang

{"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}

引用次数: 0

Designing Gestures for Data Exploration with Public Displays via Identification Studies 通过识别研究设计手势，利用公共显示器进行数据探索

Information Pub Date : 2024-05-21 DOI: 10.3390/info15060292

Adina Friedman, Francesco Cafaro

引用次数: 0

Predictions from Generative Artificial Intelligence Models: Towards a New Benchmark in Forecasting Practice 生成式人工智能模型的预测：迈向预测实践的新基准

Information Pub Date : 2024-05-21 DOI: 10.3390/info15060291

Hossein Hassani, E. Silva

{"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}

引用次数: 0

NDNOTA: NDN One-Time Authentication NDNOTA： NDN 一次性身份验证

Information Pub Date : 2024-05-20 DOI: 10.3390/info15050289

Manar Aldaoud, Dawood Al-Abri, F. Kausar, M. Awadalla

{"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}

引用次数: 0

A Lightweight Face Detector via Bi-Stream Convolutional Neural Network and Vision Transformer 通过双流卷积神经网络和视觉变换器实现的轻量级人脸检测器

Information Pub Date : 2024-05-20 DOI: 10.3390/info15050290

Zekun Zhang, Qingqing Chao, Shijie Wang, Teng Yu

{"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}

引用次数: 0