积德蓄能 情注教育

自1988年踏上三尺讲台,风雨兼程三十年,尝遍为人师者种种味,深感教师不好当,做好老师不容易,做现代的优秀老师更难。"今天,我们怎样做老师",笔者试图通过"积德""蓄能"两方面进行论述。

情注洮儿河

记忆是一种留恋，记忆是一种情愫。 我的家住在洮儿河畔，我是喝着洮儿河的水长大的。

情注芦山

芦山挺住 你这一震牵动了亿万人民的心 连日来亿万人民与你同呼吸共命运 芦山加油 你这一痛连接着无数根神经

注情入心,抒情于声——声乐演唱初探

声乐演唱是一种能够让人获得美的感受的艺术形式。它就如同一块宝石,在没有经过正确的装饰处理之前,是不会闪射出耀眼的光芒。要想完美地表现声乐艺术,做到声情并茂地演唱,就必须认识到声乐艺术中情感表现的灵魂和核心作用,做到注情入心,抒情于声。本文从声音和情感的关系出发,分析情感表达所应具备的素质修养,进而把握情感的要素,探索如何注情入心,抒情于声。

Auditory attention model based on Chirplet for cross-corpus speech emotion recognition

To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for feature extraction.First, in order to extract the spectra features, the auditory attention model is employed for variational emotion features detection. Then, the selective attention mechanism model is proposed to extract the salient gist features which showtheir relation to the expected performance in cross-corpus testing.Furthermore, the Chirplet time-frequency atoms are introduced to the model. By forming a complete atom database, the Chirplet can improve the spectrum feature extraction including the amount of information. Samples from multiple databases have the characteristics of multiple components. Hereby, the Chirplet expands the scale of the feature vector in the timefrequency domain. Experimental results show that, compared to the traditional feature model, the proposed feature extraction approach with the prototypical classifier has significant improvement in cross-corpus speech recognition. In addition, the proposed method has better robustness to the inconsistent sources of the training set and the testing set.

情感诠释:儒家古典诠释学的一个传统

儒家古典诠释实践蕴含着一种“情感诠释”传统。这个传统不仅是指儒家对于情感现象的诠释,更是指儒家诠释实践本身乃是情感诠释。无论情境性的日常对话、阅读讲经,还是对象性的经典注释,其本身都具有情感性,情感对于这些诠释活动的展开具有奠基性作用。在中国经典诠释传统之现代转型的当下,这种“情感诠释”传统理应为“中国诠释学”的现代建构提供有益的思想资源。

论陆游诗歌中“矛盾”的自我形象

陆游诗中的自我形象存在多方面的矛盾,极短时间内个人形象呈现出较大的差异。陆游创作中的这一现象十分突出,在文学史上可谓仅见。作为创作主体的陆游,思想复杂而难以自洽,性格直露而情绪化。创作过程中,陆游"书触目"和"日课一诗"的创作方式导致诗材的匮乏和诗歌的平淡,促使他在诗歌中注入丰富而多变的感情,使用夸张的表现手法,以使诗歌产生变化,避免重复和呆板。一方面,陆游诗歌中个人形象的突出矛盾在某种程度上消解了其诗歌的信服力。另一方面,矛盾而立体的陆游形象也直接体现了陆游丰富的情感,使陆游的诗更能以情动人。认识陆游诗歌的这一特质,对于解读陆游的作品有着重要的意义。

Transformer-like model with linear attention for speech emotion recognition

Because of the excellent performance of Transformer in sequence learning tasks,such as natural language processing,an improved Transformer-like model is proposed that is suitable for speech emotion recognition tasks.To alleviate the prohibitive time consumption and memory footprint caused by softmax inside the multihead attention unit in Transformer,a new linear self-attention algorithm is proposed.The original exponential function is replaced by a Taylor series expansion formula.On the basis of the associative property of matrix products,the time and space complexity of softmax operation regarding the input's length is reduced from O(N2)to O(N),where N is the sequence length.Experimental results on the emotional corpora of two languages show that the proposed linear attention algorithm can achieve similar performance to the original scaled dot product attention,while the training time and memory cost are reduced by half.Furthermore,the improved model obtains more robust performance on speech emotion recognition compared with the original Transformer.

Sentiment classification model for bullet screen based on self-attention mechanism

With the development of short video industry,video and bullet screen have become important ways to spread public opinions.Public attitudes can be timely obtained through emotional analysis on bullet screen,which can also reduce difficulties in management of online public opinions.A convolutional neural network model based on multi-head attention is proposed to solve the problem of how to effectively model relations among words and identify key words in emotion classification tasks with short text contents and lack of complete context information.Firstly,encode word positions so that order information of input sequences can be used by the model.Secondly,use a multi-head attention mechanism to obtain semantic expressions in different subspaces,effectively capture internal relevance and enhance dependent relationships among words,as well as highlight emotional weights of key emotional words.Then a dilated convolution is used to increase the receptive field and extract more features.On this basis,the above multi-attention mechanism is combined with a convolutional neural network to model and analyze the seven emotional categories of bullet screens.Testing from perspectives of model and dataset,experimental results can validate effectiveness of our approach.Finally,emotions of bullet screens are visualized to provide data supports for hot event controls and other fields.

Discussion on the Application of Sentiment Elements to Advertising Design

With the continuous increase of social and economic competitiveness, the position of advertising becomes more and more important. For attracting people＇s attention to the advertising, merchants should not only rely on novel advertising creativity and also add some sentiment elements in advertising design under the background of today＇s advertising all over the world. Sentiments are the most capable of touching human nerves so as to leave a deep impression in the minds of people, so the quality of advertising can be well improved if appropriate sentiment elements are added in advertising design. In this paper, the application of emotional elements to advertising design is specifically discussed from emotional elements classification, main application methods, and matters needing attention.

Multi-head attention-based long short-term memory model for speech emotion recognition

To fully make use of information from different representation subspaces,a multi-head attention-based long short-term memory(LSTM)model is proposed in this study for speech emotion recognition(SER).The proposed model uses frame-level features and takes the temporal information of emotion speech as the input of the LSTM layer.Here,a multi-head time-dimension attention(MHTA)layer was employed to linearly project the output of the LSTM layer into different subspaces for the reduced-dimension context vectors.To provide relative vital information from other dimensions,the output of MHTA,the output of feature-dimension attention,and the last time-step output of LSTM were utilized to form multiple context vectors as the input of the fully connected layer.To improve the performance of multiple vectors,feature-dimension attention was employed for the all-time output of the first LSTM layer.The proposed model was evaluated on the eNTERFACE and GEMEP corpora,respectively.The results indicate that the proposed model outperforms LSTM by 14.6%and 10.5%for eNTERFACE and GEMEP,respectively,proving the effectiveness of the proposed model in SER tasks.

基于感知模式纹理的人脸识别

稀疏表示分类法和局部二值模式是目前广泛运用的人脸表示方法.虽然稀疏表示分类法通过重构算法能很好的稀疏表示人脸,但在人脸的局部特征表示上不够理想.局部二值模式是基于局部纹理特征表示人脸,其提取的局部纹理简单、不连续、不光滑,造成纹理损失严重.提出局部纹理特征的感知模式纹理的概念,克服了稀疏表示分类法在局部特征表现不佳的缺点,同时又比局部二值模式纹理光滑、鲁棒.利用感知模式纹理特征在Yale和ORL数据库取得了较好的识别效果.