Move to Samsung Health Data SDK as Samsung Health SDK for Android Deprecates
Samsung Health SDK for Android was deprecated on July 31, 2025. The SDK will remain available for an additional two years and will reach the end of its service in 2028. To ensure continuity and gain access to additional health data types, please migrate to the Samsung Health Data SDK as soon as possible. Migrating to the Samsung Health Data SDK will allow you to continue using existing data types and access new ones. For detailed instructions for the migration, please refer to the Migration Guide.
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SmartThings Q3 Updates: Next-Gen Hub, Thread, & Analytics
SmartThings introduced significant new products for users and developers in Q3. Highlights include two-way thread unification, and enhanced analytics providing WWST partners with deeper insights into product usage. The next-generation Aeotec Smart Home Hub 2 offers faster performance with double the RAM, enabling it to connect to more devices with support for Matter, Thread, and Zigbee. The WWST ecosystem also continues to grow, incorporating more brands and products. Read more to discover the key SmartThings updates in Q3 here. |
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Samsung Health Accessory SDK v3.2 Released
The latest version of Samsung Health Accessory SDK, version 3.2, now supports integration with indoor bikes and cross trainers, enhancing the fitness experience for users. Additionally, the Verification Tool’s testing scope has been expanded to include these devices, offering a more reliable testing environment. Utilize the upgraded SDK today to create innovative health solutions. Explore more features and detailed information here.
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Deprecation of Tizen Studio
The Tizen Studio will be deprecated with the next SDK release (Platform 10, SDK 6.5). The primary IDE for Tizen application development will transition to the Tizen Extension for Visual Studio Code. This change streamlines development, leverages Visual Studio Code’s robust ecosystem, and introduces enhanced features for a smoother, more efficient Tizen application development experience. Resources will be provided to ensure a seamless transition. Discover more about this change here.
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Easy Steps to Capture and Collect Logs from Galaxy Watch
Have you used the dumpstate log on Galaxy Watch? It’s a comprehensive system diagnostic report that captures everything happening on the watch at a specific moment. Developers can utilize it to identify issues such as application crashes, excessive battery drain, and sensor failures that cannot be diagnosed from application logs alone. It is also useful for deep debugging, capturing everything from application behavior to hardware status in one file. This tutorial demonstrates the step-by-step process for collecting the log, whether or not your smartphone is connected to a Galaxy Watch. Generate the dumpstate log on your connected smartphone, wearable applications, and Galaxy Watch to more quickly and accurately assess the watch’s status.
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Create Samsung Wallet Card Templates Using the Server API
Samsung Wallet partners can create and update card templates to meet their business needs through the Wallet Partners Portal. However, managing a large number of cards on the website can be challenging. Samsung offers server APIs that allow partners to easily create and modify Samsung Wallet card templates without accessing the Wallet Partners Portal. This tutorial guides you through creating a card template for coupons using the Add Wallet Card Templates API. The API enables an expanded card management via an independent UI or dashboard, with secure and flexible integration through a base URL, authentication headers, and a structured request body based on JWT. Discover how to create new card templates on the server rather than the portal, enhancing operational efficiency for your wallet on our blog.
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Samsung’s Breakthrough Wearable Technologies Driven by Innovation and Collaboration
Samsung Electronics has developed the world’s first smartwatch feature to detect heart failures early, which received approval from the Ministry of Food and Drug Safety in September. The AI algorithm for early detection of LVSD, developed in partnership with the Korean medical device company Medical AI, has proven reliable in real-world use, now serving more than 100,000 people each month across over 100 hospitals worldwide. Samsung also recently collaborated with the Department of Biomedical Engineering at Hanyang University to create an around-the-ear wearable device capable of measuring brain waves (EEG), advancing innovation in brain-computer interface (BCI) technology. The ergonomically designed device detects high-quality signals through electrodes around the ear and has demonstrated real-world application potential, such as drowsiness detection and video preference analysis. Explore how Samsung is shaping the future of healthcare through constant innovation and collaboration with leading experts and institutions on our Newsroom.
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Clustering-based Hard Negative Sampling for Supervised Contrastive Speaker Verification
In recent years, researchers have employed various deep learning approaches to enhance the performance of speaker verification (SV) systems. Contrastive learning methods for SV are gaining attention as alternatives to traditional classification-based approaches because they effectively utilize hard negative pairs, which are different-class samples that are particularly challenging for a verification model to distinguish due to their similarity.
Samsung R&D Institute Poland introduces a clustering-based hard negative sampling (CHNS) method to improve the efficiency of supervised contrastive learning models for SV. The CHNS approach clusters embeddings of similar speakers and adjusts batch composition to obtain an optimal ratio of hard and easy negatives during contrastive loss calculation. Learn more about how CHNS enables high-performance SV even in environments with limited computing capabilities on the Samsung Research blog.
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SemEval-2025 Task 8: LLM Ensemble Methods for QA over Tabular Data
Large language models (LLMs) have shown exceptional capabilities in understanding and generating natural language, making them widely used in question answering (QA) tasks. However, they still encounter difficulties when processing and reasoning over tabular data, particularly in understanding relationships, identifying relevant columns, and answering complex queries.
To tackle these challenges, the Samsung Research team presented a new tabular QA solution at the SemEval-2025 Task 8 competition. This solution is based on an ensemble approach using generative LLMs, where each model contributes to question interpretation, column selection, code generation, and result verification. The final answer is produced through a voting mechanism. The team also introduced automated pandas/SQL code generation with iterative correction and cross-verification between LLMs, significantly improving both accuracy and reliability. The solution achieved 86.21% accuracy and ranked second amongst 100 teams participating in the competition. This new tabular QA solution helps LLMs better understand and utilize structured data, paving the way for broader applications such as automated data analysis, AI assistants, and search systems. Discover more about this innovation on the Samsung Research blog.
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