Search Results (302)

Biometric authentication, such as facial recognition and fingerprint scanning, is now standard on mobile devices, offering secure and convenient access. However, the processing of biometric data is tightly regulated under the European Union’s General Data Protection Regulation (GDPR), where such data qualifies as “special category” personal data when used for uniquely identifying individuals. Compliance requires meeting strict conditions, including explicit consent and data protection by design. Passkeys, the modern name for FIDO2-based authentication credentials developed by the FIDO Alliance, enable passwordless login using public key cryptography. Its “match-on-device” architecture stores biometric data locally in secure hardware (e.g., Android’s Trusted Execution Environment, Apple’s Secure Enclave), potentially reducing the regulatory obligations associated with cloud-based biometric processing. This paper examines how Passkeys are implemented on Android and iOS platforms and their differences in architecture, API access, and hardware design, and how those differences affect compliance with the GDPR. Through a comparative analysis, we evaluate the extent to which each platform supports local processing, data minimization, and user control—key principles under GDPR. We find that while both platforms implement strong local protections, differences in developer access, trust models, and biometric isolation can influence the effectiveness and regulatory exposure of Passkeys deployment. These differences have direct implications for privacy risk, legal compliance, and implementation choices by app developers and service providers. Our findings highlight the need for platform-aware design and regulatory interpretation in the deployment of biometric authentication technologies. This work can help inform stakeholders, policymakers, and legal experts in drafting robust privacy and ethical policies—not only in the realm of biometrics but across AI technologies more broadly. By understanding platform-level implications, future frameworks can better align technical design with regulatory compliance and ethical standards. Full article

The integration of Internet of Things (IoT) technologies into public healthcare enables continuous monitoring and sustainable health management. However, conventional frameworks often depend on transmitting and storing raw personal data on centralized servers, posing challenges related to privacy, security, ethical compliance, and long-term sustainability. This study proposes a privacy-preserving framework that avoids the exposure of true health-related data. Sensor nodes encrypt collected measurements and collaborate with a secure computation core to evaluate health indicators under homomorphic encryption, maintaining confidentiality. For example, the system can determine whether a patient’s heart rate within a monitoring window falls inside clinically recommended thresholds, while the framework remains general enough to support a wide range of encrypted computations. A compliance verification client generates zero-knowledge range proofs, allowing external parties to verify whether health indicators meet predefined conditions without accessing actual values. Simulation results confirm the correctness of encrypted computation, controllability of threshold-based compliance judgments, and resistance to inference attacks. The proposed framework provides a practical solution for secure, auditable, and sustainable real-time health assessment in IoT-enabled public healthcare systems. Full article

The General Data Protection Regulation (GDPR) imposes additional demands and obligations on service providers that handle and process personal data. In this paper, we examine how advanced cryptographic techniques can be employed to develop a privacy-preserving solution for ensuring GDPR compliance in Industrial Internet of Things (IIoT) systems. The primary objective is to ensure that sensitive data from IIoT devices is encrypted and accessible only to authorized entities, in accordance with Article 32 of the GDPR. The proposed system combines Decentralized Attribute-Based Encryption (DABE) with smart contracts on a blockchain to create a decentralized way of managing access to IIoT systems. The proposed system is used in an IIoT use case where industrial sensors collect operational data that is encrypted according to DABE. The encrypted data is stored in the IPFS decentralized storage system. The access policy and IPFS hash are stored in the blockchain’s smart contracts, allowing only authorized and compliant entities to retrieve the data based on matching attributes. This decentralized system ensures that information is stored encrypted and secure until it is retrieved by legitimate entities, whose access rights are automatically enforced by smart contracts. The implementation and evaluation of the proposed system have been analyzed and discussed, showing the promising achievement of the proposed system. Full article

Recipes available on the web often lack nutritional transparency and clear indicators of dietary suitability. While searching by title is straightforward, exploring recipes that meet combined dietary needs, nutritional goals, and ingredient-level preferences remains challenging. Most existing recipe search systems do not effectively support flexible multi-dietary reasoning in combination with user preferences and restrictions. For example, users may seek gluten-free and dairy-free dinners with suitable substitutions, or compound goals such as vegan and low-fat desserts. Recent systematic reviews report that most food recommender systems are content-based and often non-personalized, with limited support for dietary restrictions, ingredient-level exclusions, and multi-criteria nutrition goals. This paper introduces DietQA, an end-to-end, language-adaptable chatbot system that integrates a Knowledge Graph (KG), Retrieval-Augmented Generation (RAG), and a Large Language Model (LLM) to support personalized, dietary-aware recipe search and question answering. DietQA crawls Greek-language recipe websites to extract structured information such as titles, ingredients, and quantities. Nutritional values are calculated using validated food composition databases, and dietary tags are inferred automatically based on ingredient composition. All information is stored in a Neo4j-based knowledge graph, enabling flexible querying via Cypher. Users interact with the system through a natural language chatbot friendly interface, where they can express preferences for ingredients, nutrients, dishes, and diets, and filter recipes based on multiple factors such as ingredient availability, exclusions, and nutritional goals. DietQA supports multi-diet recipe search by retrieving both compliant recipes and those adaptable via ingredient substitutions, explaining how each result aligns with user preferences and constraints. An LLM extracts intents and entities from user queries to support rule-based Cypher retrieval, while the RAG pipeline generates contextualized responses using the user query and preferences, retrieved recipes, statistical summaries, and substitution logic. The system integrates real-time updates of recipe and nutritional data, supporting up-to-date, relevant, and personalized recommendations. It is designed for language-adaptable deployment and has been developed and evaluated using Greek-language content. DietQA provides a scalable framework for transparent and adaptive dietary recommendation systems powered by conversational AI. Full article

The primary goal of Transportation systems is to provide transportation accessibility to opportunities. Equitable access to essential destinations encompassing social, recreational, educational, and civic opportunities needs to be more consistent across different social groups. This study evaluates the disparities in social justice using social equity as a measure of transit access and walk access to non-work amenities. These non-work amenities include grocery stores, personal services, retail outlets, recreational venues, entertainment centers, and healthcare facilities in the U.S. Logistic regression models are developed using the 2017 National Community Livability Survey data. The results indicate regressive public transit access for socially disadvantaged groups, including older citizens, non-drivers, Medicare/Medicaid beneficiaries, and non-metropolitan residents. Walk access inequities similarly affect older individuals, non-drivers, the physically disabled, the unemployed, students, women, and non-metropolitan residents. This research emphasizes the importance of addressing transit and walk-access inequities to non-work amenities within transportation systems. By acknowledging the disparities in transportation equity, decision-makers and communities can foster more inclusive and equitable access to essential destinations, thereby promoting social cohesion and overall community well-being. Full article

Background/Objectives: As convenience store meals become a major dietary source for modern society, the reliability of their nutrition labels is increasingly scrutinized. With advances in artificial intelligence (AI), large language models (LLMs) have been explored for automated nutrition estimation. Aim: To evaluate the accuracy and clinical applicability of AI-assessed nutrition data by comparing outputs from five AI models with professional dietitian estimations and labeled nutrition facts. Methods: Eight ready-to-eat convenience store meals were analyzed. Four experienced dietitians independently estimated the meals’ calories, macronutrients, and sodium content based on measured food weights. Five AI chatbots were queried multiple times with identical input prompts to assess intra- and inter-assay variability. All results were compared to the official nutrition labels to quantify discrepancies and cross-model consistency. Results: Dietitian estimations showed strong internal consistency (CV < 15%), except for fat, saturated fat and sodium (CVs up to 33.3 ± 37.6%, 24.5 ± 11.7%, and 40.2 ± 30.3%, respectively). Among AI models, ChatGPT4.o showed relatively consistent calory, protein, fat, saturated fat and carbohydrate estimates (CV < 15%), and Claude3.7, Grok3, Gemini, and Copilot showed caloric and protein content as consistent (CV < 15%). Sodium values were consistently underestimated across all AI models, with CVs ranging from 20% to 70%. The accuracy of nutritional fact estimation over the five AI models for calories, protein, fat, saturated fat and carbohydrates was between 70 and 90%; when compared to the nutritional labels of RTE, the sodium content and saturated fat estimated were severely underestimated. Conclusions: Current AI chat models provide rapid estimates for basic nutrients and can aid public education or preliminary assessment; GPT-4 outperforms peers in calorie and potassium-related estimations but remains suboptimal in micronutrient prediction. Professional dietitian oversight remains essential for safe and personalized dietary planning. Full article

This study analyzes the determinants of impulsive buying behavior in low-cost retail stores in Metropolitan Lima, with particular emphasis on psychological, economic, social, and personal factors. The research draws on survey data collected from 380 consumers aged 18 to 39 belonging to socioeconomic levels B and C who had made recent purchases in discount stores. Data were gathered through a structured and validated instrument and examined using ordinal logistic regression and multinomial discrete choice models. The dependent variable, impulsive buying, was measured through three dimensions—remembered, suggested, and pure—while explanatory variables were classified into low, medium, and high categories. The empirical results demonstrate that psychological and economic dimensions exert a strong and positive influence on impulsive consumption, whereas social factors show no significant effect. Personal factors, though less consistent, also reveal a positive role. Diagnostic tests, including robustness checks, confirm the stability of the estimations. Beyond its marketing relevance, the findings contribute to the sustainability debate by highlighting how understanding impulsive behavior can guide the design of retail strategies that foster responsible consumption, reduce the risks of over-spending in vulnerable households, and support inclusive and resilient consumption practices. Thus, the study links the analysis of changing consumption patterns with broader sustainability goals in emerging urban contexts. Full article

Recommender systems are essential tools in the digital age, helping users discover products, content, and services across platforms like streaming services, online stores, and social networks. Traditionally, these systems have relied on methods such as collaborative filtering, content-based, and knowledge-based approaches, using data like user–item interactions and demographic details. With the rise of big data, an increasing amount of “side information”, like contextual data, social behavior, and metadata, has become available, enabling more personalized and effective recommendations. This work provides a comparative analysis of traditional recommender systems and newer models incorporating side information, particularly visual features, to determine whether integrating such data improves recommendation quality. By evaluating the benefits and limitations of using complex formats like visual content, this work aims to contribute to the development of more robust and adaptive recommender systems, offering insights for future research in the field. Full article

Interest in improving roadside services for long-haul truckers’ health, safety, and well-being has led to an effort to describe the services offered at truck stop/rest areas. This study aimed to describe services offered in truck stop and rest areas and to determine, based on what was available, their implications for the health of long-haul truck drivers. A systematic and structured direct observation of thirteen truck stop and rest areas was undertaken within one state in the US on a major north–south interstate highway from October 2023 to June 2024. The categories of services observed included food, physical activity, rest, personal hygiene and health, and safety. A descriptive analysis of the data was performed. Seventeen visits were carried out in 13 truck stop and rest areas. All sites offered paved parking areas, with lighting and signage; 92% offered internet access; more than 85% offered food, safety, and personal hygiene services; 69% offered laundry services; 54% had a convenience store and hotel nearby; and 15% had green/natural areas with benches. The services offered at the truck stop and rest areas in this study meet the basic needs of food, personal hygiene, and safety of truckers and can serve as lessons for other states and countries to consider. Full article

This article describes the hardware and software implementation of an application for monitoring parameters in a room. The reported values are visualized in a web application, which allows for the data to be viewed from anywhere in the world. The development made and the sensor used allow for measuring temperature, humidity, and pressure in a room. This is of particular importance for people who work with food products, materials that are dependent on temperature conditions, for families with children who want to maintain a certain home temperature and humidity, and many others. Each parameter is of particular importance for a person’s health. All measurement data is stored in a database and can be used for statistics and analysis of changes in the room. The components used in the hardware implementation of the project are a Wi-Fi board ESP8266, Arduino UNO, and a sensor for simultaneous measurement of temperature, humidity, and pressure—BME280. The technologies used in the design of the software implementation of the project are JAVA, PHP, MySQL, Arduino IDE, hosting server, and domain name. Full article

In recent years, indoor user identification via Wi-Fi signals has emerged as a vibrant research area in smart homes and the Internet of Things, thanks to its privacy preservation, immunity to lighting conditions, and ease of large-scale deployment. Conventional deep-learning classifiers, however, suffer from poor generalization and demand extensive pre-collected data for every new scenario. To overcome these limitations, we introduce SimID, a few-shot Wi-Fi user recognition framework based on identity-similarity learning rather than conventional classification. SimID embeds user-specific signal features into a high-dimensional space, encouraging samples from the same individual to exhibit greater pairwise similarity. Once trained, new users can be recognized simply by comparing their Wi-Fi signal “query” against a small set of stored templates—potentially as few as a single sample—without any additional retraining. This design not only supports few-shot identification of unseen users but also adapts seamlessly to novel movement patterns in unfamiliar environments. On the large-scale XRF55 dataset, SimID achieves average accuracies of 97.53%, 93.37%, 92.38%, and 92.10% in cross-action, cross-person, cross-action-and-person, and cross-person-and-scene few-shot scenarios, respectively. These results demonstrate SimID’s promise for robust, data-efficient indoor identity recognition in smart homes, healthcare, security, and beyond. Full article

In the context of modern healthcare, the integration of sensor networks into electronic health record (EHR) systems introduces new opportunities and challenges related to data privacy, security, and interoperability. This paper proposes a secure distributed web system architecture that integrates real-time sensor data with a custom customer relationship management (CRM) module to optimize patient monitoring and clinical decision-making. The architecture leverages IoT-enabled medical sensors to capture physiological signals, which are transmitted through secure communication channels and stored in a modular EHR system. Security mechanisms such as data encryption, role-based access control, and distributed authentication are embedded to address threats related to unauthorized access and data breaches. The CRM system enables personalized healthcare management while respecting strict privacy constraints defined by current healthcare standards. Experimental simulations validate the scalability, latency, and data protection performance of the proposed system. The results confirm the potential of combining CRM, sensor data, and distributed technologies to enhance healthcare delivery while ensuring privacy and security compliance. Full article

The objective of this manuscript is to describe the iterative user-centered development of the Mobile Device Assessment Tool (MoDAT) and to present early usability results involving persons with disabilities and assistive technology (AT) professionals. Smartphones have become a ubiquitous tool for use in everyday life. However, there are limited tools and resources available for AT providers to assess the needs of persons with disabilities in using smartphones. The MoDAT is being developed to help determine the most effective accessibility and AT options for smartphone use by individuals with functional limitations. A user-centered approach has been implemented, including preliminary guidance by advisory committees, focus groups, and usability testing by persons with disabilities and providers who recommend AT solutions. This process has guided the development of a pilot system that can generate personalized recommendations on smartphone device setup and configuration. The MoDAT consists of a series of simulated typical tasks completed on a smartphone application. Individuals complete these tasks to assess their functional capacity, with data regarding their performance gathered and sent to the provider portal. Data is securely stored in the portal for review to help determine accessibility settings and AT that may improve smartphone use. These results and the iterative process are described in this manuscript. Future research will focus on establishing the psychometric properties of the MoDAT as an assessment tool and outcomes. Full article

Current learning and development approaches often struggle to capture dynamic individual capabilities, particularly the skills they acquire informally every day on the job. This dynamic creates a significant gap between what traditional models think people know and their actual performance, leading to an incomplete and often outdated understanding of how ready the workforce truly is, which can hinder organizational adaptability in rapidly evolving environments. This paper proposes a novel dynamic learner-state ecosystem—an AI-driven solution designed to bridge this gap. Our approach leverages specialized AI agents, orchestrated via the Model Context Protocol (MCP), to continuously track and evolve an individual’s multi-dimensional state (e.g., mastery, confidence, context, and decay). The seamless integration of in-workflow performance data will transform daily work activities into granular and actionable data points through AI-powered dynamic xAPI generation into Learning Record Stores (LRSs). This system enables continuous, authentic performance-based assessment, precise skill gap identification, and highly personalized interventions. The significance of this ecosystem lies in its ability to provide a real-time understanding of everyone’s capabilities, enabling more accurate workforce planning for the future and cultivating a workforce that is continuously learning and adapting. It ultimately helps to transform learning from a disconnected, occasional event into an integrated and responsive part of everyday work. Full article

Nowadays, a pedestrian navigation system using a smartphone has become popular as a useful tool to reach an unknown destination. When the destination is the office of a person, a detailed map information is necessary on the target area such as the room number and location inside the building. The information can be collected from various sources including Google maps, websites for the building, and images of signs. In this paper, we propose a map information collection tool for a pedestrian navigation system. To improve the accuracy and completeness of information, it works with the four steps: (1) a user captures building and room images manually, (2) an OCR software using Google ML Kit v2 processes them to extract the sign information from images, (3) web scraping using Scrapy (v2.11.0) and crawling with Apache Nutch (v1.19) software collects additional details such as room numbers, facilities, and occupants from relevant websites, and (4) the collected data is stored in the database to be integrated with a pedestrian navigation system. For evaluations of the proposed tool, the map information was collected for 10 buildings at Okayama University, Japan, a representative environment combining complex indoor layouts (e.g., interconnected corridors, multi-floor facilities) and high pedestrian traffic, which are critical for testing real-world navigation challenges. The collected data is assessed in completeness and effectiveness. A university campus was selected as it presents a complex indoor and outdoor environment that can be ideal for testing pedestrian navigations in real-world scenarios. With the obtained map information, 10 users used the navigation system to successfully reach destinations. The System Usability Scale (SUS) results through a questionnaire confirms the high usability. Full article