Multi-Label Text Classification (MLTC) is an important but challenging task in the field of natural language processing. In this paper, we propose a novel method, Semantic-sensitive Graph Convolutional Network (S-GCN), by simultaneously considering semantic and word-global associations. More specifically, we first leverage texts, words, and labels to construct a global graph, which helps mine the relevance between similar documents. Then we design and pre-train an encoder to initialize text nodes in the graph, from which the semantic features of documents are extracted. Next, we employ a graph convolutional network to classify text nodes, which can well fuse node information. Finally, we normalize the adjacency matrix and store hidden layer representations of word nodes, tackling the issue that conventional graph-based methods cannot predict texts that did not appear during training. We conduct experiments on three public datasets, AAPD, RMSC-V2, and Reuters-21578, and demonstrate the superiority of our model over the baselines on the MLTC task. Source code is available at https://github.com/sysu18364004/SGCN. 

Multi-label text classification based on semantic-sensitive graph convolutional network

Modern image and video compression technologies include both lossless compression methods, such as entropy coding, Inter-frame and Intra-frame coding, and lossy compression methods, such as discrete orthogonal transforms with quantization. All these techniques are actively applied in video codecs based on the H.264 and H.265 standards.

The Use of Intra Prediction Method in Wavelet-Based Video Coding Systems

Summary Objective The use of standardized structured reports (SSR) and suitable terminologies like SNOMED-CT can enhance data retrieval and analysis, fostering large-scale studies and collaboration. However, the still large prevalence of narrative reports in our laboratories warrants alternative and automated labeling approaches. In this project, natural language processing (NLP) methods were used to associate SNOMED-CT codes to structured and unstructured reports from an Italian Digital Pathology Department. Methods Two NLP-based automatic coding systems (support vector machine, SVM, and long-short term memory, LSTM) were trained and applied to a series of narrative reports. Results The 1163 cases were tested with both algorithms, showing good performances in terms of accuracy, precision, recall, and F1 score, with SVM showing slightly better performances as compared to LSTM (0.84, 0.87, 0.83, 0.82 vs 0.83, 0.85, 0.83, 0.82, respectively). The integration of an explainability allowed identification of terms and groups of words of importance, enabling fine-tuning, balancing semantic meaning and model performance. Conclusions AI tools allow the automatic SNOMED-CT labeling of the pathology archives, providing a retrospective fix to the large lack of organization of narrative reports.

Natural Language Processing to extract SNOMED-CT codes from pathological reports

High-resolution image compression remains a hot research topic today due to the widespread use of high-definition displays in computers and mobile devices Conventionally, there are two distinct categories in image compression: 1) Subband coding based on multi-level discrete wavelet transform (DWT); 2) Hybrid coding based on block prediction and discrete cosine transform (DCT) Wavelet image compression has delivered a much higher coding gain over the classical DCT-based JPEG More recently, through using a delicate yet innovative block prediction scheme before DCT, HEVC Intra coding has delivered an impressive coding gain over JPEG 2000, where the coding gain is measured by the peak signal-to-noise ratio (PSNR) gain at the same bit rate However, it is hard to integrate a block prediction scheme with wavelet transform A block prediction error image could be statistically quite different from an ordinary image In the paper, we focus on developing a wavelet image coder SLCCA Plus, which is a much-enhanced version of SLCCA The enhancement involves quantization, cluster filtering, shared-boundary-zero handling and context model design for adaptive arithmetic coding The experiments on standard images of $512\times 512$ are conducted to compare the proposed SLCCA Plus to JPEG 2000 and SLCCA On average, SLCCA Plus achieves 7% bit saving over JPEG 2000 and 4% over SLCCA

An Enhanced Wavelet Image Codec: SLCCA PLUS

With the rapid development of information technology, the amount of data in various digital archives has exploded. How to reasonably mine and analyze archive data and improve the effect of intelligent management of newly included archives has become an urgent problem to be solved. The existing archival data classification method is manual classification oriented to management needs. This manual classification method is inefficient and ignores the inherent content information of the archives. In addition, for the discovery and utilization of archive information, it is necessary to further explore and analyze the correlation between the contents of the archive data. Facing the needs of intelligent archive management, from the perspective of the text content of archive data, further analysis of manually classified archives is carried out. Therefore, this paper proposes an intelligent classification method for archive data based on multigranular semantics. First, it constructs a semantic-label multigranular attention model; that is, the output of the stacked expanded convolutional coding module and the label graph attention module are jointly connected to the multigranular attention Mechanism network, the weighted label output by the multigranularity attention mechanism network is used as the input of the fully connected layer, and the output value of the fully connected layer used to map the predicted label is input into a Sigmoid layer to obtain the predicted probability of each label; then, the model for training: use the multilabel data set to train the constructed semantic-label multigranularity attention model, adjust the parameters until the semantic-label multigranularity attention model converges, and obtain the trained semantic-label multigranularity attention model. Taking the multilabel data set to be classified as input, the semantic-label multigranularity attention model after training outputs the classification result.

https://downloads.hindawi.com/journals/cin/2022/7559523.pdf

Intelligent Classification Method of Archive Data Based on Multigranular Semantics

• A knowledge graph for open-world link prediction in the AI chip domain is created. • A triple type constraint encoding is proposed to improve the training and inference. • A hybrid attention method is proposed to learn deep inner associations of knowledge. • A TC-THA model to realize the automated AI chip design knowledge reuse is proposed. AI chips play a vital role in real-time AI applications. AI chip design requires vast knowledge reuse in integrated circuits and neural networks, and this knowledge continues to grow at a rapid rate, leading to a heavy burden on designers. Automated methods are urgently needed to handle the massive and rapidly growing literature. In the realm of traditional EDA, deep learning can produce excellent designs by learning from a large number of design examples. However, in front-end design such as algorithm and architecture design, a considerable amount of knowledge exists in the fast-growing literature. The use of this knowledge is still in a primitive state of manual processing. Therefore, this paper leverages the knowledge graph and the link prediction to organize and reuse AI chip design knowledge (ACDK). First, a knowledge graph is built to organize ACDK in triple format. Second, an open-world link prediction model named TC-THA is proposed, which aims at the closed-world limitation, the lack of type constraint, and the deep inner-association learning weakness of the related methods. TC-THA converts the type constraint of ACDK triples into vectors for better training and inference. It also uses a hybrid attention mechanism for the sufficient semantic interaction among inner-triple information, which is used to capture the deep internal associations of knowledge for the superior ACDK reuse. Experiments show that the TC-THA outperforms other methods and achieves the best MRR and H@10 score of 0.34 and 0.51 in ACDK link prediction. A case study of ACDK reuse shows that, for the given problems, the TC-THA predicts highly feasible solutions, which can be confirmed in the latest AI chip studies. 

Open-world link prediction via type-constraint embedding and hybrid attention for knowledge reuse of AI chip design

The third-generation semiconductor materials (TGSMs) is a frontier scientific domain, where researchers need to consult extensive literature for the entity information on materials, devices, preparation methods, and experimental performances, and sort out the complex relations between them. However, the explosion of relevant papers has far exceeded researchers’ reading ability. In this article, the TGSM-field automatic information extraction is conducted based on entity recognition (ER) and relation extraction (RE) techniques. First, the corpora used for ER and RE in this field are created. Second, aiming at the complexity of the entities, a neural network using domain knowledge (DKNet) is proposed to improve ER performance. It uses the keyword sequence of each entity type as prior knowledge, adds a dedicated embedding to encode entity categories, then combines prior knowledge and encoded vectors with the context through a gated information fusion module to assist recognition. As for the indicative word dependence problem of entity relations, a multi-aspect attention-based network model (MANet) is proposed to enhance the attention to relation-indicative words, thereby improving the RE performance. Finally, F1 scores of 74.5 and 85.9 were achieved on the created ER and RE test sets, outperforming other advanced models by $3.4~\sim ~10.1$ , which is the best performance of the TGSM-field automatic information extraction.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09733892.pdf

Automatic Information Extraction in the Third-Generation Semiconductor Materials Domain Based on DKNet and MANet

Growing popularity of social networks demands a highly efficient Personalized PageRank (PPR) updating due to the fast-evolving web graphs of enormous size. While current researches are focusing on PPR updating under link structure modification, efficiently updating PPR when node insertion/ deletion involved remains a challenge. In the previous work called Virtual Web (VW), a few VW architectures are designed, which results in some highly effective initializations to significantly accelerate PageRank updating under both link modification and page insertion/deletion. In the paper, under the general scenario of link modification and node insertion/deletion we tackle the fast PPR updating problem. Specifically, we combine VW with the TrackingPPR method to generate initials, which are then used by the Gauss-Southwell method for fast PPR updating. The algorithm is named VWPPR method. In extensive experiments, three real-world datasets are used that contain 1~5.6M nodes and 6.7M~129M links, while a node perturbation of 40k and link perturbation of 1% are applied. Comparing to the more recent LazyForwardUpdate method, which handles the general PPR updating problem, the VWPPR method is 3~6 times faster in terms of running time, or 4.4~10 times faster in terms of iteration numbers.

Virtual Web Based Personalized PageRank Updating.

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