Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network for Visible-Infrared Person Re-Identification

Visible-infrared Cross-modality Person Re-identification(VI-ReID)is a critical technology in smart public facilities such as cities,campuses and libraries.It aims to match pedestrians in visible light and infrared images for video surveillance,which poses a challenge in exploring cross-modal shared information accurately and efficiently.Therefore,multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes.However,existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks,the fusion module.This paper introduces a novel network called the Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network(ADMPFF-Net),incorporating the Multi-Granularity Pose-Aware Feature Fusion(MPFF)module to generate discriminative representations.MPFF efficiently explores and learns global and local features with multi-level semantic information by inserting disentangling and duplicating blocks into the fusion module of the backbone network.ADMPFF-Net also provides a new perspective for designing multi-granularity learning networks.By incorporating the multi-granularity feature disentanglement(mGFD)and posture information segmentation(pIS)strategies,it extracts more representative features concerning body structure information.The Local Information Enhancement(LIE)module augments high-performance features in VI-ReID,and the multi-granularity joint loss supervises model training for objective feature learning.Experimental results on two public datasets show that ADMPFF-Net efficiently constructs pedestrian feature representations and enhances the accuracy of VI-ReID.

Application of Feature, Event, and Process Methods to Leakage Scenario Development for Offshore CO_(2) Geological Storage

Offshore carbon dioxide(CO_(2)) geological storage(OCGS) represents a significant strategy for addressing climate change by curtailing greenhouse gas emissions. Nonetheless, the risk of CO_(2) leakage poses a substantial concern associated with this technology. This study introduces an innovative approach for establishing OCGS leakage scenarios, involving four pivotal stages, namely, interactive matrix establishment, risk matrix evaluation, cause–effect analysis, and scenario development, which has been implemented in the Pearl River Estuary Basin in China. The initial phase encompassed the establishment of an interaction matrix for OCGS systems based on features, events, and processes. Subsequent risk matrix evaluation and cause–effect analysis identified key system components, specifically CO_(2) injection and faults/features. Building upon this analysis, two leakage risk scenarios were successfully developed, accompanied by the corresponding mitigation measures. In addition, this study introduces the application of scenario development to risk assessment, including scenario numerical simulation and quantitative assessment. Overall, this research positively contributes to the sustainable development and safe operation of OCGS projects and holds potential for further refinement and broader application to diverse geographical environments and project requirements. This comprehensive study provides valuable insights into the establishment of OCGS leakage scenarios and demonstrates their practical application to risk assessment, laying the foundation for promoting the sustainable development and safe operation of ocean CO_(2) geological storage projects while proposing possibilities for future improvements and broader applications to different contexts.

Simulation on Dynamic Bending Features of Fabric Based on Fluid-Solid Interaction Technique

This paper is devoted to the two-dimensional nonlinear modeling of the fluid-solid interaction (FSI) between fabric and air flow, which is based on the Automatic Incremental Dynamic Nonlinear Analysis (AIDNA)-FSI program in order to study the dynamic bending features of fabrics in a specific air flow filed. The computational fluid dynamics (CFD) model for flow and the finite element model (FEM) for fabric was set up to constitute an FSI model in which the geometric nonlinear behavior and the dynamic stress-strain variation of the relatively soft fabric material were taken into account. Several FSI cases with different time-dependent wind load and the model frequency analysis for fabric were carried out. The dynamic response of fabric and the distribution of fluid variables were investigated. The results of numerical simulation and experiments fit quite well. Hence, this work contributes to the research of modeling the dynamic bending behavior of fabrics in air field.

Click-Through Rate Prediction Network Based on User Behavior Sequences and Feature Interactions

In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.

Incorporation ofκ-carrageenan improves the practical features of agar/konjac glucomannan/κ-carrageenan ternary system

Three materials(agar,konjac glucomannan(KGM)andκ-carrageenan)were used to prepare ternary systems,i.e.,sol-gels and their dried composites conditioned at varied relative humidity(RH)(33%,54%and 75%).Combined methods,e.g.,scanning electron microscopy,small-angle X-ray scattering,infrared spectroscopy(IR)and X-ray diffraction(XRD),were used to disclose howκ-carrageenan addition tailors the features of agar/KGM/κ-carrageenan ternary system.As affirmed by IR and XRD,the ternary systems withκ-carrageenan below 25%(agar/KGM/carrageenan,50:25:25,m/m)displayed proper component interactions,which increased the sol-gel transition temperature and the hardness of obtained gels.For instance,the ternary composites could show hardness about 3 to 4 times higher than that for binary counterpart.These gels were dehydrated to acquire ternary composites.Compared to agar/KGM composite,the ternary composites showed fewer crystallites and nanoscale orders,and newly-formed nanoscale structures from chain assembly.Such multi-scale structures,for composites withκ-carrageenan below 25%,showed weaker changes with RH,as revealed by especially morphologic and crystalline features.Consequently,the ternary composites with lessκ-carrageenan(below 25%)exhibited stabilized elongation at break and hydrophilicity at different RHs.This hints to us that agar/KGM/κ-carrageenan composite systems can display series applications with improved features,e.g.,increased sol-gel transition point.

Spotted Hyena Optimizer Driven Deep Learning-Based Drug-Drug Interaction Prediction in Big Data Environment

Nowadays,smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature,computational approaches,and discoveries,owing to which a massive quantity of experimental datasets was published and generated(Big Data)for describing and validating such novelties.Drug-drug interaction(DDI)significantly contributed to drug administration and development.It continues as the main obstacle in offering inexpensive and safe healthcare.It normally happens for patients with extensive medication,leading them to take many drugs simultaneously.DDI may cause side effects,either mild or severe health problems.This reduced victims’quality of life and increased hospital healthcare expenses by increasing their recovery time.Several efforts were made to formulate new methods for DDI prediction to overcome this issue.In this aspect,this study designs a new Spotted Hyena Optimizer Driven Deep Learning based Drug-Drug Interaction Prediction(SHODL-DDIP)model in a big data environment.In the presented SHODL-DDIP technique,the relativity and characteristics of the drugs can be identified from different sources for prediction.The input data is preprocessed at the primary level to improve its quality.Next,the salp swarm optimization algorithm(SSO)is used to select features.In this study,the deep belief network(DBN)model is exploited to predict the DDI accurately.The SHO algorithm is involved in improvising the DBN model’s predictive outcomes,showing the novelty of the work.The experimental result analysis of the SHODL-DDIP technique is tested using drug databases,and the results signified the improvements of the SHODLDDIP technique over other recent models in terms of different performance measures.

Exploiting Human Pose and Scene Information for Interaction Detection

Identifying human actions and interactions finds its use in manyareas, such as security, surveillance, assisted living, patient monitoring, rehabilitation,sports, and e-learning. This wide range of applications has attractedmany researchers to this field. Inspired by the existing recognition systems,this paper proposes a new and efficient human-object interaction recognition(HOIR) model which is based on modeling human pose and scene featureinformation. There are different aspects involved in an interaction, includingthe humans, the objects, the various body parts of the human, and the backgroundscene. Themain objectives of this research include critically examiningthe importance of all these elements in determining the interaction, estimatinghuman pose through image foresting transform (IFT), and detecting the performedinteractions based on an optimizedmulti-feature vector. The proposedmethodology has six main phases. The first phase involves preprocessing theimages. During preprocessing stages, the videos are converted into imageframes. Then their contrast is adjusted, and noise is removed. In the secondphase, the human-object pair is detected and extracted from each image frame.The third phase involves the identification of key body parts of the detectedhumans using IFT. The fourth phase relates to three different kinds of featureextraction techniques. Then these features are combined and optimized duringthe fifth phase. The optimized vector is used to classify the interactions in thelast phase. TheMSRDaily Activity 3D dataset has been used to test this modeland to prove its efficiency. The proposed system obtains an average accuracyof 91.7% on this dataset.

Gate Feature Interaction Network for Relation Prediction in Knowledge Graph

Recently,many knowledge graph embedding models for knowledge graph completion have been proposed,ranging from the initial translation-based model such as TransE to recent CNN-based models such as ConvE.These models fill in the missing relations between entities by focusing on capturing the representation features to further complete the existing knowledge graph(KG).However,the above KG-based relation prediction research ignores the interaction information among entities in KG.To solve this problem,this work proposes a novel model called Gate Feature Interaction Network(GFINet)with a weighted loss function that takes the benefit of interaction information and deep expressive features together.Specifically,the proposed GFINet consists of a gate convolution block and an interaction attention module,corresponding to catching deep expressive features and interaction information based on these valid features respectively.Our method establishes state-of-the-art experimental results on the standard datasets for knowledge graph completion.In addition,we make ablation experiments to verify the effectiveness of the gate convolution block and the interaction attention module.

Interactivity Features of Online Newspapers:Use and Effect on Gratification Among Zambian Readers

Interactivity in online newspapers is the focus of this chapter in eliciting readers’evaluation of Zambian online newspapers.This aspect of the study investigates and characterises the motivations(gratification sought)for use of interactivity features(“process motivation”)and how widely they are used.It also attempts to ascertain the gratification obtained from their use among readers.The probable relationships between use of the interactivity features(“audience interactivity”)and gratification obtained from them(“process gratification”)and the impact of the perceived credibility of the online newspapers on gratification are also examined.Past studies present mixed results on use of interactivity and gratification obtained from it.This study finds that use of interactivity in Zambian online newspapers is at a low level,although among the three broad categorisations of features of online newspapers,interactivity attracts greater use than hyper-textuality and multi-mediality.Human interactivity features-“knowing what others think about an issue”,“chat on the Facebook page of the newspaper”,“ability to navigate on the Facebook page of the newspaper”,and“posting own comments on stories”-are the main motivations for use of online newspapers,the most frequently used,and the most gratifying to the readers.While readers express an interest in interacting with other readers via online newspapers,they seem less interested in posting their own stories as“citizen journalists”and linking up with the publishers and editors.This finding challenges the notion that all new media are catalysts of participatory and cyclic communication.

A study on dynamical features of air-sea coupling waves in the tropics

The dynamical features of air-sea coupling waves and their stabilities in a simple coupled air-sea model in the tropics have been studied with respect to interaction occurring among different types of the free waves in the o-cean and in the atmosphere. It is pointed out that there exist a stable and an unstable air-sea interaction modes in the tropical coupled system , respectively. The propagation of the unstable mode relies greatly on the zonal space scale, i. e. only for wave length ranging from 5 000 km to 10 000 km can the disturbance unstably move slowly eastward. The waves that slowly propagate unstably eastward agree well with the observational facts. Finally,it is also proposed that the interaction between Kelvin wave in one medium and Rossby wave in another medium is a necessary condition for the occurrence of destabilization of the coupled air-sea system in the tropics.

ST-SIGMA:Spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting

Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving(AD)system.However,most proposed methods aim at addressing one of the two challenges mentioned above with a single model.To tackle this dilemma,this paper proposes spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting(STSIGMA),an efficient end-to-end method to jointly and accurately perceive the AD environment and forecast the trajectories of the surrounding traffic agents within a unified framework.ST-SIGMA adopts a trident encoder-decoder architecture to learn scene semantics and agent interaction information on bird’s-eye view(BEV)maps simultaneously.Specifically,an iterative aggregation network is first employed as the scene semantic encoder(SSE)to learn diverse scene information.To preserve dynamic interactions of traffic agents,ST-SIGMA further exploits a spatio-temporal graph network as the graph interaction encoder.Meanwhile,a simple yet efficient feature fusion method to fuse semantic and interaction features into a unified feature space as the input to a novel hierarchical aggregation decoder for downstream prediction tasks is designed.Extensive experiments on the nuScenes data set have demonstrated that the proposed ST-SIGMA achieves significant improvements compared to the state-of-theart(SOTA)methods in terms of scene perception and trajectory forecasting,respectively.Therefore,the proposed approach outperforms SOTA in terms of model generalisation and robustness and is therefore more feasible for deployment in realworld AD scenarios.

Interaction behavior recognition from multiple views

This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature representation which encodes both the spatial distribution of local motion of interest points and their contexts. Furthermore, local self-similarity descriptor represented by temporal-pyramid bag of words(BOW) was applied to decreasing the influence of observation angle change on recognition and retaining the temporal information. For the purpose of exploring latent correlation between different interactive behaviors from different views and retaining specific information of each behaviors, graph shared multi-task learning was used to learn the corresponding interactive behavior recognition model. Experiment results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA, i3Dpose dataset and self-built database for interactive behavior recognition.

Improved AAG based recognization of machining feature

The lost information caused by feature interaction is restored by using auxiliary faces (AF) and virtual links (VL). The delta volume of the interacted features represented by concave attachable connected graph (CACG) can be decomposed into several isolated features represented by complete concave adjacency graph (CCAG). We can recognize the feature’s sketchy type by using CCAG as a hint; the exact type of the feature can be attained by deleting the auxiliary faces from the isolated feature. United machining feature (UMF) is used to represent the features that can be machined in the same machining process. It is important to the rationalizing of the process plans and reduce the time costing in machining. An example is given to demonstrate the effectiveness of this method.

IMM/MHT FUSING FEATURE INFORMATION IN VISUAL TRACKING

In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.

ELF Interactions Among Chinese, Greek, and Swiss Speakers of English

The paper concerns the issue of ELF （English as a lingua franca） in the European and Asian context. The authors start from a brief conceptual perspective to shed light on salient aspects related to ELF. Then, this paper discusses the study investigating the interactions among NNS （non-native speakers） of English in the naturalistic settings, namely in Zhangjiajie （China）, Masouri （Kalymnos/Greece）, and Unterwasser （Switzerland）. The main objective of the research based on the qualitative methodology was to analyze the ELF interactions from the linguistic point of view focusing on lexicogrammar and pragmatic features. The secondary objective was to establish whether the identified ELF features contributed to communication intelligibility. The obtained results indicated a few significant similarities with the Seidlhofer＇s list of the ELT characteristics. Furthermore, it was established in the study that the ELF features did not interfere with effective communication between interlocutors

FeatureCAM在车铣复合机床上同步加工的应用案例

本文通过一个典型零件在车铣复合机床上的编程过程，简要介绍了英国Delcam公司FeatureCAM软件在产品加工领域的智能化“特征”识别技术（AFR）的应用，车铣复合同步操作编程的便捷性，以及通过该软件比较完美地解决制造部门工艺知识库的标准化思路。

Feature Setup Determination in Integrated CAD/CAM System For Concurrent Engineering

This paper presents a feature-based method for machining process planning in integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The feature setup generation and machining sequence can be determined automatically in this system. The set of knowledge-based rules for process planning and manufacturability evaluation is provided and can be shared by all stages of full product life-cycle. An approach for MTAD (Multiple Tool Axis Direction) feature setup generation is presented and the appropriate Tool Axis Direction(TAD) is chosen to minimize the total setup numbers of a part. The classification and process planning of interacting feature are discussed and the knowledge-based rules are used to solve the feature interaction problem.

改进YOLOv4的遥感图像目标检测算法

为有效解决遥感图像目标检测算法在复杂背景下的检测效果不佳的问题,提出一种改进YOLOv4的目标检测算法。设计一种跨阶段残差结构,替换原主干网络的简单残差结构,降低模型参数量和计算负担;引入CBAM注意力机制,加强CSP模块间有效特征交互;使用跨阶段分层卷积模块重构特征融合阶段对深层特征图的处理方式,防止网络退化和梯度消失;采用Mish激活函数,增强融合网络对非线性特征的提取能力。在RSOD、DIOR数据集上的实验结果表明,改进YOLOv4算法的测试mAP相比原YOLOv4算法分别高出4.5%、7.3%,其检测速度分别达到48 fps、45 fps,在保证实时性的同时检测精度有较大提升。

基于复合跨模态交互网络的时序多模态情感分析

针对多模态情感分析中存在的不同模态间语义特征差异性导致模态融合不充分、交互性弱等问题,通过研究分析不同模态之间存在的潜在关联性,搭建一种基于复合跨模态交互网络的时序多模态情感分析(CCIN-SA)模型。该模型首先使用双向门控循环单元和多头注意力机制提取具有上下文语义信息的文本、视觉和语音模态时序特征;然后,设计跨模态注意力交互层,利用辅助模态的低阶信号不断强化目标模态,使得目标模态学习到辅助模态的信息,捕获模态间的潜在适应性;再将增强后的特征输入到复合特征融合层,通过条件向量进一步捕获不同模态间的相似性,增强重要特征的关联程度,挖掘模态间更深层次的交互性;最后,利用多头注意力机制将复合跨模态强化后的特征与低阶信号做拼接融合,提高模态内部重要特征的权重,保留初始模态独有的特征信息,将得到的多模态融合特征进行最终的情感分类任务。在CMU-MOSI和CMUMOSEI数据集上进行模型评估,结果表明,CCIN-SA模型相比其他现有模型在准确率和F1指标上均有提高,能够有效挖掘不同模态间的关联性,做出更加准确的情感判断。

局部注意力引导下的全局池化残差分类网络

大部分注意力机制虽然能增强图像特征,但没有考虑局部特征的关联性影响特征整体的问题。针对以上问题,本文提出局部注意力引导下的全局池化残差分类网络(MSLENet)。MSLENet的基线网络为ResNet34,首先改变首层结构,保留图像重要信息;其次提出多分割局部增强注意力机制(MSLE)模块,MSLE模块将图像整体分割成多个小图像,增强每个小图像的局部特征,通过特征组交互的方式将局部重要特征引导到全局特征中;最后提出池化残差(PR)模块来处理ResNet残差结构丢失信息的问题,提高各层之间的信息利用率。实验结果表明,MSLENet通过增强局部特征的关联性,在多个数据集上均有良好的效果,有效地提高了网络的表达能力。