A New Word Detection Method for Chinese Based on Local Context Information

Finding out out-of-vocabulary words is an urgent and difficult task in Chinese words segmentation. To avoid the defect causing by offline training in the traditional method, the paper proposes an improved prediction by partical match （PPM） segmenting algorithm for Chinese words based on extracting local context information, which adds the context information of the testing text into the local PPM statistical model so as to guide the detection of new words. The algorithm focuses on the process of online segmentatien and new word detection which achieves a good effect in the close or opening test, and outperforms some well-known Chinese segmentation system to a certain extent.

MSCANet: multiscale context information aggregation network for Tibetan Plateau lake extraction from remote sensing images

Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological environment,and regional water cycle.However,the differences in spatial-spectral characteristics of various types of plateau lakes,and the complex background information of plateau both influence the extraction effect of lakes.Therefore,it is a great challenge to completely and effectively extract plateau lakes.In this study,we proposed a multiscale contextual information aggregation network,termed MSCANet,to automatically extract Plateau lake regions.It consists of three main components:a multiscale lake feature encoder,a feature decoder,and a Multicore Pyramid Pooling Module(MPPM).The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes.The MPPM module aggregated the contextual information of various lakes globally.We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data;additionally,comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity.Finally,we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.

Arbitrary-oriented target detection in large scene sar images

Target detection in the field of synthetic aperture radar(SAR) has attracted considerable attention of researchers in national defense technology worldwide,owing to its unique advantages like high resolution and large scene image acquisition capabilities of SAR.However,due to strong speckle noise and low signal-to-noise ratio,it is difficult to extract representative features of target from SAR images,which greatly inhibits the effectiveness of traditional methods.In order to address the above problems,a framework called contextual rotation region-based convolutional neural network(RCNN) with multilayer fusion is proposed in this paper.Specifically,aimed to enable RCNN to perform target detection in large scene SAR images efficiently,maximum sliding strategy is applied to crop the large scene image into a series of sub-images before RCNN.Instead of using the highest-layer output for proposal generation and target detection,fusion feature maps with high resolution and rich semantic information are constructed by multilayer fusion strategy.Then,we put forwards rotation anchors to predict the minimum circumscribed rectangle of targets to reduce redundant detection region.Furthermore,shadow areas serve as contextual features to provide extraneous information for the detector identify and locate targets accurately.Experimental results on the simulated large scene SAR image dataset show that the proposed method achieves a satisfactory performance in large scene SAR target detection.

Graph-based method for human-object interactions detection

Human-object interaction(HOIs)detection is a new branch of visual relationship detection,which plays an important role in the field of image understanding.Because of the complexity and diversity of image content,the detection of HOIs is still an onerous challenge.Unlike most of the current works for HOIs detection which only rely on the pairwise information of a human and an object,we propose a graph-based HOIs detection method that models context and global structure information.Firstly,to better utilize the relations between humans and objects,the detected humans and objects are regarded as nodes to construct a fully connected undirected graph,and the graph is pruned to obtain an HOI graph that only preserving the edges connecting human and object nodes.Then,in order to obtain more robust features of human and object nodes,two different attention-based feature extraction networks are proposed,which model global and local contexts respectively.Finally,the graph attention network is introduced to pass messages between different nodes in the HOI graph iteratively,and detect the potential HOIs.Experiments on V-COCO and HICO-DET datasets verify the effectiveness of the proposed method,and show that it is superior to many existing methods.

Transmission Line Insulator Defect Detection Based on Swin Transformer and Context

Insulators are important components of power transmission lines.Once a failure occurs,it may cause a large-scale blackout and other hidden dangers.Due to the large image size and complex background,detecting small defect objects is a challenge.We make improvements based on the two-stage network Faster R-convolutional neural networks(CNN).First,we use a hierarchical Swin Transformer with shifted windows as the feature extraction network,instead of ResNet,to extract more discriminative features,and then design the deformable receptive field block to encode global and local context information,which is utilized to capture key clues for detecting objects in complex backgrounds.Finally,the filling data augmentation method is proposed for the problem of insufficient defects and more images of insulator defects under different backgrounds are added to the training set to improve the robustness of the model.As a result,the recall increases from 89.5%to 92.1%,and the average precision increases from 81.0%to 87.1%.To further prove the superiority of the proposed algorithm,we also tested the model on the public data set Pascal visual object classes(VOC),which also yields outstanding results.

An Adaptive Padding Correlation Filter With Group Feature Fusion for Robust Visual Tracking

In recent visual tracking research,correlation filter(CF)based trackers become popular because of their high speed and considerable accuracy.Previous methods mainly work on the extension of features and the solution of the boundary effect to learn a better correlation filter.However,the related studies are insufficient.By exploring the potential of trackers in these two aspects,a novel adaptive padding correlation filter(APCF)with feature group fusion is proposed for robust visual tracking in this paper based on the popular context-aware tracking framework.In the tracker,three feature groups are fused by use of the weighted sum of the normalized response maps,to alleviate the risk of drift caused by the extreme change of single feature.Moreover,to improve the adaptive ability of padding for the filter training of different object shapes,the best padding is selected from the preset pool according to tracking precision over the whole video,where tracking precision is predicted according to the prediction model trained by use of the sequence features of the first several frames.The sequence features include three traditional features and eight newly constructed features.Extensive experiments demonstrate that the proposed tracker is superior to most state-of-the-art correlation filter based trackers and has a stable improvement compared to the basic trackers.

Towards consistent machine translation of abbreviated terms in scientific literature

Scientific literature often contains abbreviated terms in English for brief.Machine translation(MT)systems can help to share knowledge in different languages among researchers.Current MT systems may translate the same abbreviated term in different sentences into different target terms.MT systems translate the abbreviated term in two ways:one is to use translation of the full name,the other is to use the abbreviated term directly.Abbreviated terms may be ambiguous and polysemous,and MT systems do not have an explicit strategy to decide which way to use without context information.To get the consistent translation for abbreviated terms in scientific literature,this paper proposes a translation model for abbreviated terms that integrates context information to get consistent translation of abbreviated terms.The context information includes the positions of abbreviated term and domain attributes of scientific literature.The first abbreviated term is translated in full name while the latter ones of the same abbreviated term will show the abbreviated form in the translation text.Experiments of translation from Chinese to English show the effectiveness of the proposed translation model.

意义维度的信息与信息化

从诠释学视角来看,信息的功能是促进人与人之间的交往,意义是交往的内容。尝试给出信息的哲学定义:交往过程中的信号及其所负载意义的总和。信息的三个重要性质:1)意义的解释生成性;2)意义解释的语境依赖性;3)意义解释的多元性、意见一致的获得性。信息的上述性质否定了本真信息的存在,信息意义取决于具体的历史和社会语境。所谓的信息化是基于计算机技术、网络技术,信息生成、传播、接收的高速化、简单化。信息化时代的特点是,信息来源的多元化及信息解释权威的弱化。社会所需主流信息的生成及维护变得更加困难。本真信息的非存在性和各异性,决定了信息失真现象的合理性和必然性。

网络环境下大学汉语言文学的发展趋势及提升策略研究

大学汉语言文学作为国内高校发展较早的一门学科,在发展上有着较为成熟的教学体系。但是,随着新时期信息化时代的到来,在网络环境不断发展的背景下,大学汉语言文学呈现出与信息化相结合的发展趋势。如何使大学汉语言文学适应网络环境的发展,也是当前培养全能型大学汉语言文学人才的重要发展课题。网络环境下大学汉语言文学的信息化发展策略是:加大大学汉语言文学教学网络技术的投入;建立完善的汉语言文学和信息化结合的教学体系;强化大学汉语言文学师资力量的发展。

信息化背景下高校思政教育协同管理的必要性研究

信息时代的到来改变了现代人的生存理念,也对高校的思想政治教育工作提出了更高的要求。文中分析了信息化背景下高校思政教育协同管理的必要性,提出了高校思政教育协同管理的有效策略,希望能够为高校思政教育工作的顺利开展提供帮助。

Dual context prior and refined prediction for semantic segmentation

Recently,the focus of semantic segmentation research has shifted to the aggregation of context prior and refined boundary.A typical network adopts context aggregation modules to extract rich semantic features.It also utilizes top-down connection and skips connections for refining boundary details.But it still remains disadvantage,an obvious fact is that the problem of false segmentation occurs as the object has very different textures.The fusion of weak semantic and low-level features leads to context prior degradation.To tackle the issue,we propose a simple yet effective network,which integrates dual context prior and spatial propagation-dubbed DSPNet.It extends two mainstreams of current segmentation researches:(1)Designing a dual context prior module,which pays attention to context prior again with a shortcut connection.(2)The network can inherently learn semantic aware affinity values for each pixel and refine the segmentation.We will present detailed comparisons,which perform on PASCAL VOC 2012 and Cityscapes.The result demonstrates the validation of our approach.

Space-air-ground integrated vehicular network for connected and automated vehicles:Challenges and solutions

Unlimited and seamless coverage as well as ultra-reliable and low-latency communications are vital for connected vehicles,in particular for new use cases like autonomous driving and vehicle platooning.In this paper,we propose a novel Space-Air-Ground integrated vehicular network(SAGiven)architecture to gracefully integrate the multi-dimensional and multi-scale context-information and network resources from satellites,High-Altitude Platform stations(HAPs),low-altitude Unmanned Aerial Vehicles(UAVs),and terrestrial cellular communication systems.One of the key features of the SAGiven is the reconfigurability of heterogeneous network functions as well as network resources.We first give a comprehensive review of the key challenges of this new architecture and then provide some up-to-date solutions on those challenges.Specifically,the solutions will cover the following topics:(1)space-air-ground integrated network reconfiguration under dynamic space resources constraints;(2)multi-dimensional sensing and efficient integration of multi-dimensional context information;(3)real-time,reliable,and secure communications among vehicles and between vehicles and the SAGiven platform;and(4)a holistic integration and demonstration of the SAGiven.Finally,it is concluded that the SAGiven can play a key role in future autonomous driving and Internet-of-Vehicles applications.

Validation of Pervasive Cloud Task Migration with Colored Petri Net

Mobile devices are resource-limited, and task migration has become an important and attractive feature of mobile clouds. To validate task migration, we propose a novel approach to the simulation of task migration in a pervasive cloud environment. Our approach is based on Colored Petri Net（CPN）. In this research, we expanded the semantics of a CPN and created two task migration models with different task migration policies： one that took account of context information and one that did not. We evaluated the two models using CPN-based simulation and analyzed their task migration accessibility, integrity during the migration process, reliability, and the stability of the pervasive cloud system after task migration. The energy consumption and costs of the two models were also investigated. Our results suggest that CPN with context sensing task migration can minimize energy consumption while preserving good overall performance.

Topic-aware pivot language approach for statistical machine translation

The pivot language approach for statistical machine translation(SMT) is a good method to break the resource bottleneck for certain language pairs. However, in the implementation of conventional approaches, pivotside context information is far from fully utilized, resulting in erroneous estimations of translation probabilities. In this study, we propose two topic-aware pivot language approaches to use different levels of pivot-side context. The first method takes advantage of document-level context by assuming that the bridged phrase pairs should be similar in the document-level topic distributions. The second method focuses on the effect of local context. Central to this approach are that the phrase sense can be reflected by local context in the form of probabilistic topics, and that bridged phrase pairs should be compatible in the latent sense distributions. Then, we build an interpolated model bringing the above methods together to further enhance the system performance. Experimental results on French-Spanish and French-German translations using English as the pivot language demonstrate the effectiveness of topic-based context in pivot-based SMT.