深度学习导向下小学数学交流策略探究

随着教育的不断深入革新,人们对小学数学教育提出了更高的要求。小学生在学校除了要接受教育,掌握必要的数学知识以外,还要进行深度学习,并在深度学习的过程中进行良好的数学交流,以此让学生掌握更多的数学知识以及与数学有关的学习方法、学习思维,通过这样的方式全方面提高小学生的数学综合素养。     

“深度学习”理念下小学音乐课堂的团队合作

在新一轮课改的推动下,小学音乐课堂教学的方式方法也发生了相应的变化。在"深度学习"理念支持下,有效地发挥团队合作在课堂中的作用,可以更好地提升课堂效率,提高学生的音乐素养。而探究在小学音乐课堂中组织团队合作学习的策略,成为当下教师思考的问题之一。     

Study on determination of the stable slope configuration for deep open pit mine

The space effects of deep pit slope are analyzed by an elastic mechanics principle. The interaction among the critical slide angle, the friction coefficient, the cohesion, and the horizontal radius of the deep pits is derived in this paper. It indicates that the deeper the pit is excavated, the greater the critical slide angle is. Both the theory for reducing stripping waste rock in deep pit and the approach to determining the configuration of the stable slope are developed from the interaction. The theory in this paper comprises the preceding principles of stability analysis of slopes and is suitable for analyzing that of deep pit.     

Invisible Web研究综述

本文对有关Invisible Web的内容进行了全面、详细地介绍。首先论述了什么是InvisIble Web以及为什么要研究Invisible Web，同时介绍了两个相关概念；其次对Invisible Web不可见的原因加以分析；对Invisible Web的类型进行了划分；介绍了Invisible Web的检索工具；最后总结了Invisible Web研究的方向。     

Deep transitions: Emergence,acceleration, stabilization and directionality

Industrial society has not only led to high levels of wealth and welfare in the Western world, but also to increasing global ecological degradation and social inequality. The socio-technical systems that underlay contemporary societies have substantially contributed to these outcomes. This paper proposes that these socio-technical systems are an expression of a limited number of meta-rules that, for the past 250 years, have driven innovation and hence system evolution in a particular direction, thereby constituting the First Deep Transition. Meeting the cumulative social and ecological consequences of the overall direction of the First Deep Transition would require a radical change, not only in socio-technical systems but also in the meta-rules driving their evolution – the Second Deep Transition. This paper develops a new theoretical framework that aims to explain the emergence, acceleration, stabilization and directionality of Deep Transitions. It does so through the synthesis of two literatures that have attempted to explain large-scale and long-term socio-technical change: the Multi-level Perspective (MLP) on socio-technical transitions, and Techno-economic Paradigm (TEP) framework.     

论图书馆信息资源的深层开发

现代信息社会里图书馆对信息资源的深层次开发 ,应把重点放在开发信息资源中的智力性资源、预测性资源、网络资源以及用户所表现出来的能力性资源上。图 2。参考文献 4。     

Building for tomorrow: Assessing the temporal persistence of text classifiers

Performance of text classification models tends to drop over time due to changes in data, which limits the lifetime of a pretrained model. Therefore an ability to predict a model’s ability to persist over time can help design models that can be effectively used over a longer period of time. In this paper, we provide a thorough discussion into the problem, establish an evaluation setup for the task. We look at this problem from a practical perspective by assessing the ability of a wide range of language models and classification algorithms to persist over time, as well as how dataset characteristics can help predict the temporal stability of different models. We perform longitudinal classification experiments on three datasets spanning between 6 and 19 years, and involving diverse tasks and types of data. By splitting the longitudinal datasets into years, we perform a comprehensive set of experiments by training and testing across data that are different numbers of years apart from each other, both in the past and in the future. This enables a gradual investigation into the impact of the temporal gap between training and test sets on the classification performance, as well as measuring the extent of the persistence over time. Through experimenting with a range of language models and algorithms, we observe a consistent trend of performance drop over time, which however differs significantly across datasets; indeed, datasets whose domain is more closed and language is more stable, such as with book reviews, exhibit a less pronounced performance drop than open-domain social media datasets where language varies significantly more. We find that one can estimate how a model will retain its performance over time based on (i) how well the model performs over a restricted time period and its extrapolation to a longer time period, and (ii) the linguistic characteristics of the dataset, such as the familiarity score between subsets from different years. Findings from these experiments have important implications for the design of text classification models with the aim of preserving performance over time.     

Evaluating the generalisability of neural rumour verification models

Research on automated social media rumour verification, the task of identifying the veracity of questionable information circulating on social media, has yielded neural models achieving high performance, with accuracy scores that often exceed 90%. However, none of these studies focus on the real-world generalisability of the proposed approaches, that is whether the models perform well on datasets other than those on which they were initially trained and tested. In this work we aim to fill this gap by assessing the generalisability of top performing neural rumour verification models covering a range of different architectures from the perspectives of both topic and temporal robustness. For a more complete evaluation of generalisability, we collect and release COVID-RV, a novel dataset of Twitter conversations revolving around COVID-19 rumours. Unlike other existing COVID-19 datasets, our COVID-RV contains conversations around rumours that follow the format of prominent rumour verification benchmarks, while being different from them in terms of topic and time scale, thus allowing better assessment of the temporal robustness of the models. We evaluate model performance on COVID-RV and three popular rumour verification datasets to understand limitations and advantages of different model architectures, training datasets and evaluation scenarios. We find a dramatic drop in performance when testing models on a different dataset from that used for training. Further, we evaluate the ability of models to generalise in a few-shot learning setup, as well as when word embeddings are updated with the vocabulary of a new, unseen rumour. Drawing upon our experiments we discuss challenges and make recommendations for future research directions in addressing this important problem.     

Joint contrastive triple-learning for deep multi-view clustering

Deep multi-view clustering (MVC) is to mine and employ the complex relationships among views to learn the compact data clusters with deep neural networks in an unsupervised manner. The more recent deep contrastive learning (CL) methods have shown promising performance in MVC by learning cluster-oriented deep feature representations, which is realized by contrasting the positive and negative sample pairs. However, most existing deep contrastive MVC methods only focus on the one-side contrastive learning, such as feature-level or cluster-level contrast, failing to integrating the two sides together or bringing in more important aspects of contrast. Additionally, most of them work in a separate two-stage manner, i.e., first feature learning and then data clustering, failing to mutually benefit each other. To fix the above challenges, in this paper we propose a novel joint contrastive triple-learning framework to learn multi-view discriminative feature representation for deep clustering, which is threefold, i.e., feature-level alignment-oriented and commonality-oriented CL, and cluster-level consistency-oriented CL. The former two submodules aim to contrast the encoded feature representations of data samples in different feature levels, while the last contrasts the data samples in the cluster-level representations. Benefiting from the triple contrast, the more discriminative representations of views can be obtained. Meanwhile, a view weight learning module is designed to learn and exploit the quantitative complementary information across the learned discriminative features of each view. Thus, the contrastive triple-learning module, the view weight learning module and the data clustering module with these fused features are jointly performed, so that these modules are mutually beneficial. The extensive experiments on several challenging multi-view datasets show the superiority of the proposed method over many state-of-the-art methods, especially the large improvement of 15.5% and 8.1% on Caltech-4V and CCV in terms of accuracy. Due to the promising performance on visual datasets, the proposed method can be applied into many practical visual applications such as visual recognition and analysis. The source code of the proposed method is provided at https://github.com/ShizheHu/Joint-Contrastive-Triple-learning.     

PS-Mixer: A Polar-Vector and Strength-Vector Mixer Model for Multimodal Sentiment Analysis

Multimodal sentiment analysis aims to judge the sentiment of multimodal data uploaded by the Internet users on various social media platforms. On one hand, existing studies focus on the fusion mechanism of multimodal data such as text, audio and visual, but ignore the similarity of text and audio, text and visual, and the heterogeneity of audio and visual, resulting in deviation of sentiment analysis. On the other hand, multimodal data brings noise irrelevant to sentiment analysis, which affects the effectness of fusion. In this paper, we propose a Polar-Vector and Strength-Vector mixer model called PS-Mixer, which is based on MLP-Mixer, to achieve better communication between different modal data for multimodal sentiment analysis. Specifically, we design a Polar-Vector (PV) and a Strength-Vector (SV) for judging the polar and strength of sentiment separately. PV is obtained from the communication of text and visual features to decide the sentiment that is positive, negative, or neutral sentiment. SV is gained from the communication between the text and audio features to analyze the sentiment strength in the range of 0 to 3. Furthermore, we devise an MLP-Communication module (MLP-C) composed of several fully connected layers and activation functions to make the different modal features fully interact in both the horizontal and the vertical directions, which is a novel attempt to use MLP for multimodal information communication. Finally, we mix PV and SV to obtain a fusion vector to judge the sentiment state. The proposed PS-Mixer is tested on two publicly available datasets, CMU-MOSEI and CMU-MOSI, which achieves the state-of-the-art (SOTA) performance on CMU-MOSEI compared with baseline methods. The codes are available at: https://github.com/metaphysicser/PS-Mixer.