Human activity recognition (HAR) problems have traditionally been solved by using engineered features obtained by heuristic methods. These methods ignore the time information of the streaming sensor data and cannot achieve sequential human activity recognition. With the use of traditional statistical learning methods, results could easily plunge into the local minimum other than the global optimal and also face the problem of low efficiency. Therefore, we propose a hybrid deep framework based on convolution operations, LSTM recurrent units, and ELM classifier; the advantages are as follows: (1) does not require expert knowledge in extracting features; (2) models temporal dynamics of features; and (3) is more suitable to classify the extracted features and shortens the runtime. All of these unique advantages make it superior to other HAR algorithms. We evaluate our framework on OPPORTUNITY dataset which has been used in OPPORTUNITY challenge. Results show that our proposed method outperforms deep nonrecurrent networks by 6%, outperforming the previous reported best result by 8%. When compared with neural network using BP algorithm, testing time reduced by 38%.

National Materials Data Management and Service platform (NMDMS) is a materials data repository for the publication and sharing of heterogeneous materials scientific data and follows the FAIR principles: Findable, Accessible, Interoperable, and Reusable. To ensure data are ‘Interoperable, NMDMS uses a user-friendly semi-structured scientific data model, named dynamic container’, to define, exchange, and store heterogeneous scientific data. Then, a personalized yet standardized data submission subsystem, a rigorous project data review and publication subsystem, and a multi-granularity data query and retrieval subsystem collaboratively make data ‘Reusable’, ‘Findable’, and ‘Accessible’. Finally, China’s “National Key R&D Program: Material Genetic Engineering Key Special Project” has adopted NMDMS to publish and share its project data. There are 12,251,040 pieces of data published in NMDMS since 2018, under 87 categories and 1,912 user-defined schemas from 45 projects. The platform has been accessed 908875 times, and 2403,208 pieces of data have been downloaded. In short, NMDMS effectively accelerates the publication and sharing of material project data in China.

Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors and drift. In this paper, we propose a multi-sensor hybrid method to solve this problem. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. Secondly, Cramér–Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Furthermore, proposed method is verified in 3D practical application scenarios. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications.

A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation.

The cold-start problem has always been a key challenge in the recommendation research field. As a popular method to learn a learner that can rapidly adapt to a new task through a small number of updates, meta-learning is considered to be a feasible algorithm to reduce the error of cold-start recommendation. However, meta-learning does not take the diverse interests of users into account, which limits the performance improvement in cold-start scenarios. In this paper, we proposed a new model for a cold-start recommendation, which combines the attention mechanism and meta learning. This method enhances the ability of modeling the personalized user interest by learning the weights between users and items based on the attention mechanism and then improves the performance of the cold-start recommendation. We validated the model with two publicly available datasets in the recommendation field. Compared with the three benchmark methods, the proposed model reduces the mean absolute error by at least 2.3% and the root mean square error of 2.5%.