319 research outputs found
Review on the Application of Information Technology in Physical Education Curriculum and Teaching
The importance of information technology guiding the development of physical education is reflected in every field of physical education curriculum and teaching. However, due to China\u27s late start in this field, the relevant literature is relatively confusion, lack of systematic arrangement and summary, which is not conducive to future research. The purpose of this study was to provide reference for further research in this field, and to lay a theoretical foundation for improving the level of physical education curriculum and teaching in our schools and the formation of students\u27 good physical and mental health. In this scoping review study, we used the following keywords: âinformation technologyâ, AND âphysical education curriculumâ AND âphysical education teachingâ AND/OR âapplicationsâ for the literature search via the CNKI. Through the method of literature and content analysis, this paper mainly analyzes the current situation and problems of the application of information technology in physical education curriculum and teaching. At present, especially in rural areas, the construction of extracurricular exercise information platform in schools in China still has great limitations and imperfections. In addition, part of the physical exercise APP is not perfect, people in the physical exercise is easy to be misled, resulting in sports injury. China\u27s physical education teaching management covers a lot of content, the lack of school talents, the implementation is relatively difficult. The integration of information technology and physical education teaching methods has been a hot topic since the 1980s today. However, schools in China have not shown good results in the implementation, and teachers\u27 teaching ideas, teaching creativity and professional knowledge are the main restraining factors. The mainly factors that affect the construction of online physical education curriculum and teaching resources in China include low utilization rate of the platform, weak computer ability of teachers, weak innovation ability, backward thinking, and unclear objectives of the platform construction. The development of information technology accelerates the reform of physical education. Therefore, it is necessary to clarify the nature and goal of physical education curriculum, and give full play to the role of information technology in curriculum construction and teaching under the guidance of advanced educational science theory. At the same time, the government, schools and society should coordinate efforts to address these problems
FederBoost: Private Federated Learning for GBDT
An emerging trend in machine learning and artificial intelligence is
federated learning (FL), which allows multiple participants to contribute
various training data to train a better model. It promises to keep the training
data local for each participant, leading to low communication complexity and
high privacy. However, there are still two problems in FL remain unsolved: (1)
unable to handle vertically partitioned data, and (2) unable to support
decision trees. Existing FL solutions for vertically partitioned data or
decision trees require heavy cryptographic operations. In this paper, we
propose a framework named FederBoost for private federated learning of gradient
boosting decision trees (GBDT). It supports running GBDT over both horizontally
and vertically partitioned data. The key observation for designing FederBoost
is that the whole training process of GBDT relies on the order of the data
instead of the values. Consequently, vertical FederBoost does not require any
cryptographic operation and horizontal FederBoost only requires lightweight
secure aggregation. We fully implement FederBoost and evaluate its utility and
efficiency through extensive experiments performed on three public datasets.
Our experimental results show that both vertical and horizontal FederBoost
achieve the same level of AUC with centralized training where all data are
collected in a central server; and both of them can finish training within half
an hour even in WAN.Comment: 15 pages, 8 figure
POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation
Low-resource languages (LRLs) face challenges in supervised neural machine
translation due to limited parallel data, prompting research into unsupervised
methods. Unsupervised neural machine translation (UNMT) methods, including
back-translation, transfer learning, and pivot-based translation, offer
practical solutions for LRL translation, but they are hindered by issues like
synthetic data noise, language bias, and error propagation, which can
potentially be mitigated by Large Language Models (LLMs). LLMs have advanced
NMT with in-context learning (ICL) and supervised fine-tuning methods, but
insufficient training data results in poor performance in LRLs. We argue that
LLMs can mitigate the linguistic noise with auxiliary languages to improve
translations in LRLs. In this paper, we propose Probability-driven Meta-graph
Prompter (POMP), a novel approach employing a dynamic, sampling-based graph of
multiple auxiliary languages to enhance LLMs' translation capabilities for
LRLs. POMP involves constructing a directed acyclic meta-graph for each source
language, from which we dynamically sample multiple paths to prompt LLMs to
mitigate the linguistic noise and improve translations during training. We use
the BLEURT metric to evaluate the translations and back-propagate rewards,
estimated by scores, to update the probabilities of auxiliary languages in the
paths. Our experiments show significant improvements in the translation quality
of three LRLs, demonstrating the effectiveness of our approach
GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence
Conditional story generation is significant in human-machine interaction,
particularly in producing stories with complex plots. While Large language
models (LLMs) perform well on multiple NLP tasks, including story generation,
it is challenging to generate stories with both complex and creative plots.
Existing methods often rely on detailed prompts to guide LLMs to meet target
conditions, which inadvertently restrict the creative potential of the
generated stories. We argue that leveraging information from exemplary
human-written stories facilitates generating more diverse plotlines. Delving
deeper into story details helps build complex and credible plots. In this
paper, we propose a retrieval-au\textbf{G}mented sto\textbf{R}y generation
framework with a f\textbf{O}rest of e\textbf{V}id\textbf{E}nce (GROVE) to
enhance stories' complexity. We build a retrieval repository for target
conditions to produce few-shot examples to prompt LLMs. Additionally, we design
an ``asking-why'' prompting scheme that extracts a forest of evidence,
providing compensation for the ambiguities that may occur in the generated
story. This iterative process uncovers underlying story backgrounds. Finally,
we select the most fitting chains of evidence from the evidence forest and
integrate them into the generated story, thereby enhancing the narrative's
complexity and credibility. Experimental results and numerous examples verify
the effectiveness of our method.Comment: Findings of EMNLP 202
Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain
Text-to-SQL aims at generating SQL queries for the given natural language
questions and thus helping users to query databases. Prompt learning with large
language models (LLMs) has emerged as a recent approach, which designs prompts
to lead LLMs to understand the input question and generate the corresponding
SQL. However, it faces challenges with strict SQL syntax requirements. Existing
work prompts the LLMs with a list of demonstration examples (i.e. question-SQL
pairs) to generate SQL, but the fixed prompts can hardly handle the scenario
where the semantic gap between the retrieved demonstration and the input
question is large. In this paper, we propose a retrieval-augmented prompting
method for a LLM-based Text-to-SQL framework, involving sample-aware prompting
and a dynamic revision chain. Our approach incorporates sample-aware
demonstrations, which include the composition of SQL operators and fine-grained
information related to the given question. To retrieve questions sharing
similar intents with input questions, we propose two strategies for assisting
retrieval. Firstly, we leverage LLMs to simplify the original questions,
unifying the syntax and thereby clarifying the users' intentions. To generate
executable and accurate SQLs without human intervention, we design a dynamic
revision chain which iteratively adapts fine-grained feedback from the
previously generated SQL. Experimental results on three Text-to-SQL benchmarks
demonstrate the superiority of our method over strong baseline models
A Case-Based Reasoning Framework for Adaptive Prompting in Cross-Domain Text-to-SQL
Recent advancements in Large Language Models (LLMs), such as Codex, ChatGPT
and GPT-4 have significantly impacted the AI community, including Text-to-SQL
tasks. Some evaluations and analyses on LLMs show their potential to generate
SQL queries but they point out poorly designed prompts (e.g. simplistic
construction or random sampling) limit LLMs' performance and may cause
unnecessary or irrelevant outputs. To address these issues, we propose
CBR-ApSQL, a Case-Based Reasoning (CBR)-based framework combined with GPT-3.5
for precise control over case-relevant and case-irrelevant knowledge in
Text-to-SQL tasks. We design adaptive prompts for flexibly adjusting inputs for
GPT-3.5, which involves (1) adaptively retrieving cases according to the
question intention by de-semantizing the input question, and (2) an adaptive
fallback mechanism to ensure the informativeness of the prompt, as well as the
relevance between cases and the prompt. In the de-semanticization phase, we
designed Semantic Domain Relevance Evaluator(SDRE), combined with Poincar\'e
detector(mining implicit semantics in hyperbolic space), TextAlign(discovering
explicit matches), and Positector (part-of-speech detector). SDRE semantically
and syntactically generates in-context exemplar annotations for the new case.
On the three cross-domain datasets, our framework outperforms the
state-of-the-art(SOTA) model in execution accuracy by 3.7\%, 2.5\%, and 8.2\%,
respectively
Knowledge-driven Meta-learning for CSI Feedback
Accurate and effective channel state information (CSI) feedback is a key
technology for massive multiple-input and multiple-output systems. Recently,
deep learning (DL) has been introduced for CSI feedback enhancement through
massive collected training data and lengthy training time, which is quite
costly and impractical for realistic deployment. In this article, a
knowledge-driven meta-learning approach is proposed, where the DL model
initialized by the meta model obtained from meta training phase is able to
achieve rapid convergence when facing a new scenario during target retraining
phase. Specifically, instead of training with massive data collected from
various scenarios, the meta task environment is constructed based on the
intrinsic knowledge of spatial-frequency characteristics of CSI for meta
training. Moreover, the target task dataset is also augmented by exploiting the
knowledge of statistical characteristics of wireless channel, so that the DL
model can achieve higher performance with small actually collected dataset and
short training time. In addition, we provide analyses of rationale for the
improvement yielded by the knowledge in both phases. Simulation results
demonstrate the superiority of the proposed approach from the perspective of
feedback performance and convergence speed.Comment: arXiv admin note: text overlap with arXiv:2301.1347
Ultrathin MOF nanosheet assembled highly oriented microporous membrane as an interlayer for lithium-sulfur batteries
Abstract(#br)Lithium sulfur (Li-S) batteries are attracting increasing attentions as promising next-generation rechargeable batteries. However, the rapid capacity fading of sulfur cathodes caused by the shuttling of polysulfide intermediates between the cathodes and anodes restricts the application of Li-S batteries. In this work, a facile wet-chemistry method is developed for the direct synthesis of few-molecular-layer thin metal-organic framework (MOF) nanosheets without using surfactant. By assembling these ultrathin MOF nanosheets with a facile vacuum filtration method, a highly oriented and flexible MOF membrane with favorable mechanical properties is achieved for the first time. The excellent features make the as-prepared MOF nanosheets ideal to fabricate lightweight interlayer modified separators for suppressing the polysulfide shuttling of Li-S batteries. When using the MOF membrane modified separator, the Li-S batteries made from commercial carbon materials exhibits the significantly enhanced cycling stabilities. This work brings new opportunities for the synthesis and application of MOF materials
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