Open-EASE: A Cloud-Based Knowledge Service for Autonomous Learning

The human entorhinal-hippocampal circuitry is characterized by an information-specific functional organization where two routes, that are preferentially connected to the parahippocampal cortex or the perirhinal and retrosplenial cortices, divide the entorhinal cortex as well as hippocampal subiculum and CA1 subregions.

Net ecosystem productivity (NEP) is an important index for the quantitative evaluation of carbon sources and sinks in terrestrial ecosystems. Based on MOD17A3 and meteorological data, the vegetation NEP was estimated from 2000 to 2021 in the Loess Plateau (LP) and its six ecological subregions of the LP (loess sorghum gully subregions:A1, A2; loess hilly and gully subregions:B1, B2; sandy land and agricultural irrigation subregion:C; and earth-rock mountain and river valley plain subregion:D). Combined with the terrain, remote sensing, and human activity data, Theil-Sen Median trend analysis, correlation analysis, multiple regression residual analysis, and geographic detector were used, respectively, to explore the spatio-temporal characteristics of NEP and its response mechanism to climate, terrain, and human activity. The results showed that:① On the temporal scale, from 2000 to 2021 the annual mean NEP of the LP region (in terms of C) was 104.62 g·(m2·a)-1. The annual mean NEP for both the whole LP and each of the ecological subregions showed a significant increase trend, and the NEP of the LP increased by 6.10 g·(m2·a)-1 during the study period. The highest growth rate of the NEP was 9.04 g·(m2·a)-1, occurring in the A2 subregion of the loess sorghum gully subregions. The subregion C had the lowest growth rate of 2.74 g·(m2·a)-1. Except for the C subregion, all other ecological subregions (A1, A2, B1, B2, and D) were carbon sinks. ② On the spatial scale, the spatial distribution of annual NEP on the LP was significantly different, with the higher NEP distribution in the southeast of the LP and the lower in the northwest of the LP. The high carbon sink area was mainly distributed in the southern part of the loess sorghum gully subregions, and the carbon source area was mainly distributed in the northern part of the loess sorghum gully subregions and most of the C subregion. The high growth rate was mainly distributed in the central and the southern part of the A2 subregion and the southwest part of the B2 subregion. ③ Human activities had the greatest influence on the temporal variation in NEP in the LP and all the ecological subregions, with the correlation coefficient between human activity data and NEP being above 0.80, and the relative contribution rates of human factors was greater than 50%. The spatial distribution was greatly affected by meteorological factors, among which the precipitation and solar radiation were the main factors affecting the spatial changes in the NEP of the LP. The temporal and spatial variations in the NEP in the LP were influenced by natural and human social factors. To some extent, these results can provide a reference for the terrestrial ecosystem in the LP to reduce emissions and increase sinks and to achieve the goal of double carbon.

This paper investigates the application of ALBERT, a lightweight transformer model, to address the challenge of differentiating episodic and semantic memories in natural language processing (NLP). Episodic memory, which involves recalling personal experiences tied to specific times, places, and emotions, contrasts with semantic memory's focus on general facts. We propose an innovative method that integrates sentence classification with role-entity binding extraction to boost AI's capacity for human-like processing and recall of past interactions. By fine-tuning ALBERT for a five-way classification task, our model accurately sorts sentences into Order, Semantic Information, Semantic Query, Episodic Information, and Episodic Query, achieving over 98% accuracy with low computational demands. We also incorporate a memory-aware forgetting mechanism that dynamically prioritizes retention of significant events while discarding less relevant ones. Rooted in psychological memory theories and cutting-edge computation, this work narrows the divide between cognitive processes and AI, fostering more context-sensitive systems. Our results demonstrate that transformer-based models like ALBERT outperform traditional recurrent networks such as LSTM and GRU, delivering enhanced recall and efficiency for practical use.

Integrated data analysis and management for the problem solving environment

According to modern educational thoughts and theories, the paper uses the new information technology to study the personalized learning service based on big data for education and artificial intelligence technology, so as to provide learners with intelligent services of personalized learning. Under the technical support represented by big data, the paper builds dynamic and diverse learning resources, continuously collects and analyzes learner-related data, and provides differentiated teaching, personalized learning and accurate services in real time. It can meet the individual social and autonomous learning needs of each learner, match learning requirements intelligently and provide adaptive learning services. It intelligently builds a learning model suitable for each learner and realizes personalized learning in a real sense.

Service learning (SL) is a methodology that combines academic training with social commitment, favoring the development of pedagogical and social competencies in music education. Its implementation faces challenges related to planning, sustainability, and teacher training. This study aimed to analyze the SL models in music education and their influence on students’ professional development, and examine their role in connecting music education and the community. A literature review followed the PRISMA protocol, systematically searching the Web of Science (WoS) and Scopus databases. Studies published in the last decade exploring SL’s application in music education were selected. The results showed that SL contributed significantly to developing pedagogical, social, and leadership competencies in future teachers, strengthening student motivation, autonomous learning, and diversity. SL has proven to be a transformative tool in music education, although it still requires more substantial institutional commitment to become a sustainable and accessible practice.

The diffusion policy has introduced the excellent multimodal data modeling capability of the diffusion model to robotic imitation learning. However, extensive expert demonstration data is still needed during training, requiring considerable human effort to collect. Natural human action data is cheaper and easier to obtain than expert demonstration data, but its use in direct robot policy training is challenging due to the distribution gap. In this study, we propose the double diffusion policy learning paradigm, which incorporates low-cost human action data to diminish the reliance of diffusion policy on extensive expert demonstration data. Specifically, we extract human intentions from the diffusion policy modeling human actions, then integrate these intentions into training the diffusion policy for generating robot actions. This processing guides the policy model to train better on small expert demonstration datasets. Experiments show that our double diffusion policy outperforms the vanilla diffusion policy and other state-of-the-art imitation learning algorithms with limited expert demonstration data.

MongoDB is a NoSQL type of database management system which does not adhere to the commonly used relational database management model. MongoDB is used for horizontal scaling across a large number of servers which may have tens, hundreds or even thousands of servers. This horizontal scaling is performed using sharding. Sharding is a database partitioning technique which partitions large database into smaller parts which are easy to manage and faster to access. There are hundreds of NoSQL databases available in the market. But each NoSQL product is different in terms of features, implementations and behavior. NoSQL and RDBMS solve different set of problems and have different requirements. MongoDB has a powerful query language which extends SQL to JSON enabling developers to take benefit of power of SQL and flexibility of JSON. Along with support for select/from/where type of queries, MongoDB supports aggregation, sorting, joins as well as nested array and collections. To improve query performance, indexes and many more features are also available.

This article intends to review the underlying concepts and technologies of the Semantic Web and the potential they provide for metadata management covering bibliographic resources. To get closer to a semantic web data space, different libraries are adhering to the initiatives making their traditional Knowledge Organization Systems (KOS) operational on the web through SKOS techniques, as well as releasing bibliographic data under open licenses (open bibliographic data) and publishing it with Linked Data (LD) mechanisms. LD meaningful semantic connections create the Web of Data, a global database representing the first practical step to the Semantic Web. Here interoperable data can be processed independently of application, platform or domain, providing rich retrieval results produced by powerful query languages. From a library perspective, a problem statement is a global promotion within the Library community of understanding and of adoption of Linked Open Data (LOD), of LODe-BD recommendations, as well as releasing bibliographic data as Linked Library Data (LLD). In this way, different bibliographic datasets could become full members of the Semantic Web making interoperable different knowledge datasets of heterogeneous web communities.

We present an effective optimization framework for general SQL-like map-reduce queries, which is based on a novel query algebra and uses a small number of higher-order physical operators that are directly implementable on existing map-reduce systems, such as Hadoop. Although our framework is applicable to any SQL-like map-reduce query language, we focus on a powerful query language, called MRQL. Current map-reduce query languages, such as HiveQL and PigLatin, enable users to plug-in custom map-reduce scripts into queries for those jobs that cannot be declaratively coded in the query language, which may result to suboptimal, error-prone, and hard-to-maintain code. In contrast to these languages, MRQL is expressive enough to capture most of these computations in declarative form and at the same time is amenable to optimization. We describe an optimization framework that maps the algebraic forms derived from the MRQL queries to efficient workflows of map-reduce operations that consist of our physical plan operators. We also describe many algebraic optimizations, such as fusing cascading map-reduce jobs into one job and synthesizing a combine function from the reduce function of a map-reduce job. Finally, we report on a prototype system implementation and we show some performance results of evaluating MRQL queries on a small cluster of computers.

Recent years have seen an increased interest in and availability of parallel corpora. Large corpora from international organizations (e.g. European Union, United Nations, European Patent Office), or from multilingual Internet sites (e.g. OpenSubtitles) are now easily available and are used for statistical machine translation but also for online search by different user groups. This paper gives an overview of different usages and different types of search systems. In the past, parallel corpus search systems were based on sentence-aligned corpora. We argue that automatic word alignment allows for major innovations in searching parallel corpora. Some online query systems already employ word alignment for sorting translation variants, but none supports the full query functionality that has been developed for parallel treebanks. We propose to develop such a system for efficiently searching large parallel corpora with a powerful query language.

Anomaly detection is very important for modern network service. Yet it is still a big challenge to conduct effective anomaly detection due to the high rate of service data and the complex correlations among them. Owing to the powerful query language and performance potential, complex event processing (CEP) is very suitable for this situation. In this paper, we present NEPnet, a high-performance and scalable monitoring system, which can process events for anomaly detection of network service in real time. NEPnet is based on CEP and provides a SQL-like language supporting various event correlations. On accepting user-defined queries as input, NEPnet builds a tree-based monitoring net for detailed anomaly detection. Considering the anomaly features of network service, the monitoring net utilizes limit trigger, predicate index and route table for different types of processing nodes in it. Our preliminary experiment results show that NEPnet can effectively detect anomaly of network service, with a high-speed of 100,000 events per second and 3~6 times faster than Esper, a general CEP engine.

We propose a new algorithm for the parallel generation of suffix arrays for large text databases on high-bandwidth computer networks. Suffix arrays are structures used in full text indexing which support very powerful query languages. Our algorithm is based on a parallel indirect mergesort (it is not a simple mergesort procedure) and is compared with a well known sequential algorithm (which is very efficient running on a single machine). Although network-bounded, the parallel version is theoretically and experimentally a much better alternative when compared to the sequential version (which is I/O-bounded in disk).

Because of societal changes, human activity recognition, part of home care systems, has become increasingly important. Camera-based recognition is mainstream but has privacy concerns and is less accurate under dim lighting. In contrast, radar sensors do not record sensitive information, avoid the invasion of privacy, and work in poor lighting. However, the collected data are often sparse. To address this issue, we propose a novel Multimodal Two-stream GNN Framework for Efficient Point Cloud and Skeleton Data Alignment (MTGEA), which improves recognition accuracy through accurate skeletal features from Kinect models. We first collected two datasets using the mmWave radar and Kinect v4 sensors. Then, we used zero-padding, Gaussian Noise (GN), and Agglomerative Hierarchical Clustering (AHC) to increase the number of collected point clouds to 25 per frame to match the skeleton data. Second, we used Spatial Temporal Graph Convolutional Network (ST-GCN) architecture to acquire multimodal representations in the spatio-temporal domain focusing on skeletal features. Finally, we implemented an attention mechanism aligning the two multimodal features to capture the correlation between point clouds and skeleton data. The resulting model was evaluated empirically on human activity data and shown to improve human activity recognition with radar data only. All datasets and codes are available in our GitHub.

ABSTRACTInvasive species like Ageratina adenophora significantly threaten biodiversity, ecosystem stability, and economic resources in China. This study assesses the current and future potential invasion risk of A. adenophora across China, focusing on the provinces of Yunnan, Sichuan, Guizhou, and Guangxi—areas identified as highly susceptible to invasion. Utilizing both climate and human activity data, our results reveal that population density and temperature seasonality are dominant national‐scale factors that affect the invasion risk of A. adenophora, while regional drivers exhibit significant variation. Projections show a northward and altitudinal shift in invasion risk by the 2060s, highlighting new potential invasion hotspots driven by future changes in climate and human activities. These findings underscore the importance of region‐specific management strategies and establish a foundation for adaptive measures to mitigate the ecological and economic impacts of A. adenophora.