Clinical Knowledge-Based Hybrid Swin Transformer for Brain Tumor Segmentation

Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging(MRI)imaging is crucial in the pre-surgical planning of brain tumor malignancy.MRI images’heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging.Furthermore,recent studies have yet to fully employ MRI sequences’considerable and supplementary information,which offers critical a priori knowledge.This paper proposes a clinical knowledge-based hybrid Swin Transformermultimodal brain tumor segmentation algorithmbased on how experts identify malignancies from MRI images.During the encoder phase,a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a Convolutional Neural Network(CNN)-based backbone to represent local features have been constructed.Instead of directly connecting all the MRI sequences,the proposed method re-organizes them and splits them into two groups based on MRI principles and characteristics:T1 and T1ce,T2 and Flair.These aggregated images are received by the dual-stem Swin Transformer-based encoder branch,and the multimodal sequence-interacted cross-attention module(MScAM)captures the interactive information between two sets of linked modalities in each stage.In the CNN-based encoder branch,a triple down-sampling module(TDsM)has been proposed to balance the performance while downsampling.In the final stage of the encoder,the feature maps acquired from two branches are concatenated as input to the decoder,which is constrained by MScAM outputs.The proposed method has been evaluated on datasets from the MICCAI BraTS2021 Challenge.The results of the experiments demonstrate that the method algorithm can precisely segment brain tumors,especially the portions within tumors.

一种基于安全多方计算的快速Transformer安全推理方案

Transformer模型在自然语言处理、计算机视觉等众多领域得到了广泛应用,并且有着突出的表现.在Transformer的推理应用中用户的数据会被泄露给模型提供方.随着数据隐私问题愈发得到公众的关注,上述数据泄露问题引发了学者们对Transformer安全推理的研究,使用安全多方计算(secure multi-party computation,MPC)实现Transformer模型的安全推理是当前的一个研究热点.由于Transformer模型中存在大量非线性函数,因此使用MPC技术实现Transformer安全推理会造成巨大的计算和通信开销.针对Transformer安全推理过程中开销较大的Softmax注意力机制,提出了2种MPC友好的注意力机制Softmax freeDiv Attention和2Quad freeDiv Attention.通过将Transformer模型中的Softmax注意力机制替换为新的MPC友好的注意力机制,同时结合激活函数GeLU的替换以及知识蒸馏技术,提出了一个MPC友好的Transformer转换框架,通过将Transformer模型转化为MPC友好的Transformer模型,提高Transformer安全推理的效率.在局域网环境下使用安全处理器(secure processing unit,SPU)提供的隐私计算协议,基于所提出的MPC友好的Transformer转换框架,在SST-2上使用Bert-Base进行安全推理.测试结果表明,在保持推理准确率与无近似模型一致的情况下,安全推理计算效率提高2.26倍.

Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.

Research on Knowledge Creation in Software Requirement Development

After field survey and literature review, we found that software requirement development (SRD) is a knowledge creation process, and knowledge creation theory of Nonaka is appropriate for analyzing knowledge creating of SRD. The characteristics of knowledge in requirement elicitation process are analyzed, and dissymmetric knowledge of SRD is discussed. Experts on requirement are introduced into SRD process as a third knowledge entity. In addition, a knowledge creation model of SRD is put forward and the knowledge flow and the relationship of entities of this model are illustrated. Case study findings are illustrated in the following: 1) The necessary diversity of the project team can facilitate the implementation of the SRD. 2) The introduction of experts on requirement can achieve the transformation of knowledge effectively, thus helping to carry out the SRD. 3) Methodology and related technologies are important for carrying out the SRD.

The Exploratory Analysis on Knowledge Creation Effective Factors in Software Requirement Development

The knowledge creation effective factors were found in both necessary elements for stimulus of knowledge creation and the key influencing factors of software project success. The research was carried with the specific successful practices of Microsoft Corporation and William Johnson’s analysis of R & D project knowledge creation. The knowledge creation effective factors in requirement development project are clarified through deeply interviewing the software enterprises in Guangdong province as well as other corporate information departments. The effective factors are divided with R & D project knowledge creation model in the view of organizational, team, personal and technical four levels through literature research and interview in enterprises, and the empirical study was done with questionnaire and exploratory analysis.

Knowledge Creation Systems in Seven-Eleven Japan

Japanese convenience store chain Seven-Eleven Japan (hereinafter "SEJ") has been profitable for 30 years by constantly anticipating changing trends regardless of the severe business environment. In this paper, the knowledge creation process and its supportive systems in SEJ to examine how information system can support knowledge creation are described. Using case study method, the knowledge creation theory is applied to SEJ, and the results show that facilitating "dialogue" and reinforcing "creative routine" might be the most important functions of information system to support knowledge creation.

KDTGAN:基于Transformer-GAN和知识蒸馏的高光谱目标检测

高光谱目标检测在地球观测中至关重要,被广泛应用于军事和民用领域。然而,由于高光谱图像的背景复杂性和目标样本的有限性,该任务面临较大的挑战。本文首先采用CEM (约束能量最小化)粗检测方法提取背景数据。随之,引入了一种新的知识蒸馏模型,即KDTGAN (通过Transformer-GAN实现)。教师模型的生成器采用了Transformer编码器的结构,并结合多尺度数据融合的方法,能够准确地学习背景分布,进而通过重构背景信息实现目标检测。为了克服GAN(生成对抗网络)训练不稳定的挑战,特别是纯背景数据的稀缺性,本文提出了一种新的损失算法,以减小可疑目标样本对模型性能的负面影响。为了降低模型的计算负担,本文引入知识蒸馏,并设计新的蒸馏损失对学生模型加以约束,使模型轻量化的同时提高学生模型检测精度。实验结果表明:KDTGAN相较于当前检测方法表现更优,具有更高的检测精度和鲁棒性。

Talent Value Creation during Digital Transformation:A Inductive Model of HR Reengineering

In this study,we are to explore(1)features of HR reengineering,(2)the impact of business digitalization strategies on digital transformation and HR engineering,(3)the impact of business digitalization strategies and HR reengineering on talent value creation,and present the results of a qualitative study that offers insight into 42“thought units”,which were“categorizing”into four dimensions corresponding to our research questions:(1)plan,(2)do,(3)check,and(4)action.The“check”dimension corresponds to the four key features of HR reengineering related to business digitalization strategy,and how to create talent value when a company successfully implements business-led digital transformation,HR reengineering,and talent value creation,including(1)talent planning,(2)talent introduction,(3)talent adjustment,and(4)talent development.

Transformer优化及其在苹果病虫命名实体识别中的应用

为提高苹果生产领域实体识别的准确性,提出一种新的Transformer优化模型。首先,为解决苹果生产数据集的缺失,基于苹果栽培领域园艺专家的知识经验,创建以苹果病虫害为主的产业数据集。通过字向量与词向量的拼接,提高文本语义表征的准确性;随后,为防止位置信息缺失,引入具有方向和距离感知的注意力机制,平均集成BiLSTM的上下文长距离依赖特征;最后,结合条件随机场(Conditional random fields, CRF)约束上下文标注结果,最终得到Transformer优化模型。实验结果表明,所提方法在苹果病虫命名实体识别中的F1值可达92.66%,可为农业命名实体的准确智能识别提供技术手段。

Knowledge Graph Enhanced Transformers for Diagnosis Generation of Chinese Medicine

Chinese medicine(CM)diagnosis intellectualization is one of the hotspots in the research of CM modernization.The traditional CM intelligent diagnosis models transform the CM diagnosis issues into classification issues,however,it is difficult to solve the problems such as excessive or similar categories.With the development of natural language processing techniques,text generation technique has become increasingly mature.In this study,we aimed to establish the CM diagnosis generation model by transforming the CM diagnosis issues into text generation issues.The semantic context characteristic learning capacity was enhanced referring to Bidirectional Long Short-Term Memory(BILSTM)with Transformer as the backbone network.Meanwhile,the CM diagnosis generation model Knowledge Graph Enhanced Transformer(KGET)was established by introducing the knowledge in medical field to enhance the inferential capability.The KGET model was established based on 566 CM case texts,and was compared with the classic text generation models including Long Short-Term Memory sequence-to-sequence(LSTM-seq2seq),Bidirectional and Auto-Regression Transformer(BART),and Chinese Pre-trained Unbalanced Transformer(CPT),so as to analyze the model manifestations.Finally,the ablation experiments were performed to explore the influence of the optimized part on the KGET model.The results of Bilingual Evaluation Understudy(BLEU),Recall-Oriented Understudy for Gisting Evaluation 1(ROUGE1),ROUGE2 and Edit distance of KGET model were 45.85,73.93,54.59 and 7.12,respectively in this study.Compared with LSTM-seq2seq,BART and CPT models,the KGET model was higher in BLEU,ROUGE1 and ROUGE2 by 6.00–17.09,1.65–9.39 and 0.51–17.62,respectively,and lower in Edit distance by 0.47–3.21.The ablation experiment results revealed that introduction of BILSTM model and prior knowledge could significantly increase the model performance.Additionally,the manual assessment indicated that the CM diagnosis results of the KGET model used in this study were highly consistent with the practical diagnosis results.In conclusion,text generation technology can be effectively applied to CM diagnostic modeling.It can effectively avoid the problem of poor diagnostic performance caused by excessive and similar categories in traditional CM diagnostic classification models.CM diagnostic text generation technology has broad application prospects in the future.

Progress and Knowledge Transfer from Science to Technology in the Research Frontier of CRISPR Based on the LDA Model

Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/methodology/approach:We collected publications on CRISPR between 2011 and2020 from the Web of Science,and traced all the patents citing them from lens.org.15,904 articles and 18,985 patents in total are downloaded and analyzed.The LDA model was applied to identify underlying research topics in related research.In addition,some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents.Findings:The emerging research topics on CRISPR were identified and their evolution over time displayed.Furthermore,a big picture of knowledge transition from research topics to technological classes of patents was presented.We found that for all topics on CRISPR,the average first transition year,the ratio of articles cited by patents,the NPR transition rate are respectively 1.08,15.57%,and 1.19,extremely shorter and more intensive than those of general fields.Moreover,the transition patterns are different among research topics.Research limitations:Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org.A limitation inherent with LDA analysis is in the manual interpretation and labeling of"topics".Practical implications:Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR.Originality/value:The LDA model here is applied to topic identification in the area of transformative researches for the first time,as exemplified on CRISPR.Additionally,the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T.

Influence of Network Embedding on the Transformation Performance of Original Equipment Manufactures

Based on the embeddedness theory,this paper analyzes the impact of network embedding features and types on the transformation performance of foundry enterprises.Through the investigation and analysis of more than 200 foundry enterprises in Zhejiang Province,it is found that network relationship embedding and structure embedding have positive impacts on transformation of foundry enterprises.Professional embedding and technical embedding have a positive effect in the transformation of foundry enterprises,and knowledge absorption ability has a positive adjustment role in the transformation performance of network embedding foundry enterprises.

Fuzzy Methodology for Taxonomy and Knowledge Base Design

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Research on Explicit and Tacit Knowledge Interaction in Software Process Improvement Project

This research develops a knowledge model for Software Process Improvement (SPI) project based on knowledge creation theory and its twenty-four measurement items, and proposes two hypothesizes about the interaction of explicit knowledge and tacit knowledge in SPI. Eleven factors are extracted through statistical analysis. Three knowledge-creation practices for capturing tacit knowledge contribute greatly to SPI, which are communication among members, crossover collaboration in practical work and pair programming. Two knowledge-creation practices for capturing explicit knowledge have significant positive impact on SPI, which are integrating project document and on-the-job training. Ultimately, suggestions for improvement are put forward, that is, encouraging communication among staff and integrating documents in real time, and future research is also illustrated.

Leadership,Knowledge Management,and Human Capital Development

This research highlights the need to develop a framework for leadership,human capital development,and knowledge management by reviewing existing literature in the field of research.The main aim of this research is to propose a model which supports the relationship between leadership(servant leadership,transformational leadership)and human capital development.The study also proposes that knowledge management(knowledge sharing,knowledge acquisition)will moderate the relationship between leadership(servant leadership,transformational leadership)and human capital development.A set of propositions that represent an empirically-driven research agenda,and also describe the relationships between the focal variables are presented to enhance audience’s understanding within a business context.

Basic Elements Knowledge Acquisition Study in the Chinese Character Intelligent Formation System

In the Chinese character intelligent formation system without Chinese character library, it is possible that the same basic element in different Chinese characters is different in position, size and shape. The geometry transformation from basic elements to the components of Chinese characters can be realized by affine transformation, the transformation knowledge acquisition is the premise of Chinese character intelligent formation. A novel algorithm is proposed to ac-quire the affine transformation knowledge of basic elements automatically in this paper. The interested region of Chi-nese character image is determined by the structure of the Chinese character. Scale invariant and location invariant of basic element and Chinese character image are extracted with SIFT features, the matching points of the two images are determined according to the principle of Minimum Euclidean distance of eigenvectors. Using corner points as identifi-cation features, calculating the one-way Hausdorff distance between corner points as the similarity measurement from the affine image to the Chinese character sub-image, affine coefficients are determined by optimal similarity. 70244 Chinese characters in National Standards GB18030-2005 character set are taken as the experimental object, all the characters are performed and the experimental courses and results are presented in this paper.

Successful Digital Transformation and HRM Humanization

This paper investigates a core role of Human Resource Management(HRM)humanization for successful digital transformation in digital economy.The term“humanization”is applied to an iterative method of human relation development for human resources satisfaction and high results of organizational performance.The author summarized the peculiarities of digitalization in Russian companies in the context of the Russian labor market trends.The paper focuses on factors that determine human potential utilization and development in modern condition.The author grounded linkage between HRM humanization and digital transformation projects effect by three examples of Russian companies.The results let us conclude importance of HRM humanization and define core problems and directions in Russian context.