This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabu...This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabulary teaching and learning. The authors find that in a short duration there is a significant difference between the effect of bilingual （English ＆ Chinese） explanation and that of monolingual （Chinese） explanation on the students＇ recognition of English new words.展开更多
A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely...A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.展开更多
For event analysis,the information from both before and after the event can be crucial in certain scenarios.By incorporating a contextualized perspective in event analysis,analysts can gain deeper insights from the ev...For event analysis,the information from both before and after the event can be crucial in certain scenarios.By incorporating a contextualized perspective in event analysis,analysts can gain deeper insights from the events.We propose a contextualized visual analysis framework which enables the identification and interpretation of temporal patterns within and across multivariate events.The framework consists of a design of visual representation for multivariate event contexts,a data processing workflow to support the visualization,and a context-centered visual analysis system to facilitate the interactive exploration of temporal patterns.To demonstrate the applicability and effectiveness of our framework,we present case studies using real-world datasets from two different domains and an expert study conducted with experienced data analysts.展开更多
1 Introduction Document-level Role Filler Extraction aims to identify those spans of text that denote the role fillers for each event described in the document[1].Despite achieving certain accomplishments,existing met...1 Introduction Document-level Role Filler Extraction aims to identify those spans of text that denote the role fillers for each event described in the document[1].Despite achieving certain accomplishments,existing methods are still not effective due to the following two issues:(1)there are difficulties in contextual modeling of long text,which requires modeling and understanding coherence and connections across sentences and paragraphs;(2)there usually ignore the explicit dependency relationships between event elements displayed in long text.To this end,we propose a novel graph-augmented approach for document-level event role filler extraction,named element relational graph-augmented multi-granularity contextualized encoder(ERGM),whose main idea is to effectively enhance the model's capabilities in capturing deep semantic information of events in long texts and modeling dependency relationships among event elements by incorporating the Event elements relational graph.Specifically,this method first constructs the structural graph by extracting elements from the source document.展开更多
The urban evolution of Gulangyu,a historic international settlement in the city of Xiamen on China’s southeast coast,has long attracted the attention of scholars working in the field of Chinese urban history.Until re...The urban evolution of Gulangyu,a historic international settlement in the city of Xiamen on China’s southeast coast,has long attracted the attention of scholars working in the field of Chinese urban history.Until recently,however,few efforts have been made to theorize the spatial formation of Gulangyu.Drawing upon existing research on the island’s development,particularly since the early 1840s,this study aims to offer a critical reading of the modern transformation of Gulangyu.By retracing the urban evolution of Gulangyu on the basis of historical maps and fieldtrip data,its spatial restructuring up to the early 1940s has been reviewed and analyzed.With GIS and CAD-based tools,conflated results are presented to explain the island’s spatial mixity.The results show that from military fortification to village settlements,through the competition between local inhabitants and earlier imperialists,and then the coopetition of foreign residents and returned overseas Chinese,the making of Gulangyu as a historic international settlement was hardly a Western-dominant process.Instead,it should provide an entry point for a contextualized critique of current research on similar urban space in China’s modern history.展开更多
Purpose:The paper aims to enhance Arabic machine translation(MT)by proposing novel approaches:(1)a dimensionality reduction technique for word embeddings tailored for Arabic text,optimizing efficiency while retaining ...Purpose:The paper aims to enhance Arabic machine translation(MT)by proposing novel approaches:(1)a dimensionality reduction technique for word embeddings tailored for Arabic text,optimizing efficiency while retaining semantic information;(2)a comprehensive comparison of meta-embedding techniques to improve translation quality;and(3)a method leveraging self-attention and Gated CNNs to capture token dependencies,including temporal and hierarchical features within sentences,and interactions between different embedding types.These approaches collectively aim to enhance translation quality by combining different embedding schemes and leveraging advanced modeling techniques.Design/methodology/approach:Recent works on MT in general and Arabic MT in particular often pick one type of word embedding model.In this paper,we present a novel approach to enhance Arabic MT by addressing three key aspects.Firstly,we propose a new dimensionality reduction technique for word embeddings,specifically tailored for Arabic text.This technique optimizes the efficiency of embeddings while retaining their semantic information.Secondly,we conduct an extensive comparison of different meta-embedding techniques,exploring the combination of static and contextual embeddings.Through this analysis,we identify the most effective approach to improve translation quality.Lastly,we introduce a novel method that leverages self-attention and Gated convolutional neural networks(CNNs)to capture token dependencies,including temporal and hierarchical features within sentences,as well as interactions between different types of embeddings.Our experimental results demonstrate the effectiveness of our proposed approach in significantly enhancing Arabic MT performance.It outperforms baseline models with a BLEU score increase of 2 points and achieves superior results compared to state-of-the-art approaches,with an average improvement of 4.6 points across all evaluation metrics.Findings:The proposed approaches significantly enhance Arabic MT performance.The dimensionality reduction technique improves the efficiency of word embeddings while preserving semantic information.Comprehensive comparison identifies effective meta-embedding techniques,with the contextualized dynamic meta-embeddings(CDME)model showcasing competitive results.Integration of Gated CNNs with the transformer model surpasses baseline performance,leveraging both architectures’strengths.Overall,these findings demonstrate substantial improvements in translation quality,with a BLEU score increase of 2 points and an average improvement of 4.6 points across all evaluation metrics,outperforming state-of-the-art approaches.Originality/value:The paper’s originality lies in its departure from simply fine-tuning the transformer model for a specific task.Instead,it introduces modifications to the internal architecture of the transformer,integrating Gated CNNs to enhance translation performance.This departure from traditional fine-tuning approaches demonstrates a novel perspective on model enhancement,offering unique insights into improving translation quality without solely relying on pre-existing architectures.The originality in dimensionality reduction lies in the tailored approach for Arabic text.While dimensionality reduction techniques are not new,the paper introduces a specific method optimized for Arabic word embeddings.By employing independent component analysis(ICA)and a post-processing method,the paper effectively reduces the dimensionality of word embeddings while preserving semantic information which has not been investigated before especially for MT task.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performa...Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performance is affected by a combination of factors such as the mobility of user devices,limited communication and computational resources,thus making the user scheduling problem crucial.To tackle this problem,we jointly consider the user mobility,communication and computational capacities,and develop a stochastic optimization problem to minimize the convergence time.Specifically,we first establish a convergence bound on the training performance based on the heterogeneity of users’data,and then leverage this bound to derive the participation rate for each user.After deriving the user-specific participation rate,we aim to minimize the training latency by optimizing user scheduling under the constraints of the energy consumption and participation rate.Afterward,we transform this optimization problem to the contextual multi-armed bandit framework based on the Lyapunov method and solve it with the submodular reward enhanced linear upper confidence bound(SR-linUCB)algorithm.Experimental results demonstrate the superiority of our proposed algorithm on the training performance and time consumption compared with stateof-the-art algorithms for both independent and identically distributed(IID)and non-IID settings.展开更多
Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells an...Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.展开更多
Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently...Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently,in music appreciation activities for senior kindergarten classes,there is a widespread phenomenon of homogenized teaching content and mechanized teaching methods,which results in insufficient enthusiasm for participation among young children and a superficial understanding of music.The situational teaching method,by constructing concrete and immersive learning scenarios,can effectively activate young children’s multi-dimensional sensory experiences.Its characteristics of intuitiveness and interactivity are highly consistent with the traits of young children’s concrete thinking,thus providing a new approach to resolving the current predicament.The research focuses on the practical pain points in music appreciation activities for senior kindergarten classes and proposes targeted solutions from four dimensions:content design,method innovation,resource integration,and teacher training,aiming to reconstruct a child-centered,in-depth music learning model.Practice has shown that the situational teaching method can not only enhance young children’s perceptual sensitivity to musical elements but also guide them to achieve emotional resonance through role-playing and life-related associations,laying a foundation for the sustainable development of young children’s musical literacy.展开更多
This paper proposes Flex-QUIC,an AIempowered quick UDP Internet connections(QUIC)enhancement framework that addresses the challenge of degraded transmission efficiency caused by the static parameterization of acknowle...This paper proposes Flex-QUIC,an AIempowered quick UDP Internet connections(QUIC)enhancement framework that addresses the challenge of degraded transmission efficiency caused by the static parameterization of acknowledgment(ACK)mechanisms,loss detection,and forward error correction(FEC)in dynamic wireless networks.Unlike the standard QUIC protocol,Flex-QUIC systematically integrates machine learning across three critical modules to achieve high-efficiency operation.First,a contextual multi-armed bandit-based ACK adaptation mechanism optimizes the ACK ratio to reduce wireless channel contention.Second,the adaptive loss detection module utilizes a long short-term memory(LSTM)model to predict the reordering displacement for optimizing the packet reordering tolerance.Third,the FEC transmission scheme jointly adjusts the redundancy level based on the LSTM-predicted loss rate and congestion window state.Extensive evaluations across Wi-Fi,5G,and satellite network scenarios demonstrate that Flex-QUIC significantly improves throughput and latency reduction compared to the standard QUIC and other enhanced QUIC variants,highlighting its adaptability to diverse and dynamic network conditions.Finally,we further discuss open issues in deploying AI-native transport protocols.展开更多
Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One f...Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication.展开更多
The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gaine...The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gained significant attention for improving training efficiency.Most current algorithms rely on Convolutional Neural Networks(CNNs)for feature extraction.Although CNNs are proficient at capturing local features,they often struggle with global context,leading to incomplete and false Class Activation Mapping(CAM).To address these limitations,this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation(CPEWS)model,which improves feature extraction by utilizing the Vision Transformer(ViT).By incorporating its intermediate feature layers to preserve semantic information,this work introduces the Intermediate Supervised Module(ISM)to supervise the final layer’s output,reducing boundary ambiguity and mitigating issues related to incomplete activation.Additionally,the Contextual Prototype Module(CPM)generates class-specific prototypes,while the proposed Prototype Discrimination Loss and Superclass Suppression Loss guide the network’s training,(LPDL)(LSSL)effectively addressing false activation without the need for extra supervision.The CPEWS model proposed in this paper achieves state-of-the-art performance in end-to-end weakly supervised semantic segmentation without additional supervision.The validation set and test set Mean Intersection over Union(MIoU)of PASCAL VOC 2012 dataset achieved 69.8%and 72.6%,respectively.Compared with ToCo(pre trained weight ImageNet-1k),MIoU on the test set is 2.1%higher.In addition,MIoU reached 41.4%on the validation set of the MS COCO 2014 dataset.展开更多
The current study attempted to formulate a conceptualization of Muslim fundamentalism as well as its counter-narratives as grounded in religious experience of Pakistani Muslims.Open ended interviews were conducted wit...The current study attempted to formulate a conceptualization of Muslim fundamentalism as well as its counter-narratives as grounded in religious experience of Pakistani Muslims.Open ended interviews were conducted with 133 Pakistani Muslim men and women of prominent local religious affiliations.Analysis revealed a grounded theory model of Muslim fundamentalism highlighting cognitive,and social psychological processes involved.Participants saw their religion as a complete code of conduct and inferred various meanings from completeness of Islam as finalized,closed to inquiry as well as rejecting of other cultures and religious traditions.The major inter-related themes of the model were totalitarianism,closed mindedness,binary thinking,hyper-exotericism,ambiguity intolerance,authoritarianism,punitive approach,violent tendencies,labelling,diversity intolerance and paranoia or threat perceiving attitude.The interplay of these factors is discussed in the light of earlier research on fundamentalism.The research also revealed strong counter narratives to fundamentalist stance which formulated the major themes of esoteric religiosity,open mindedness,pluralism,and Islam and civil society.The study carries implications for religious education of Muslims and their socialization with believers of other religious traditions.展开更多
The trend of globalization has brought new requirements to existing translation industry.Need for flexibility calls for contextualized translation that can be applied to various scenarios.This is an exploratory case s...The trend of globalization has brought new requirements to existing translation industry.Need for flexibility calls for contextualized translation that can be applied to various scenarios.This is an exploratory case study of ChatGPT,aiming at discovering potential of artificial intelligence(AI)translation tools.ChatGPT is compared with machine-aided translation tools like Google Translate,Microsoft Translate,Youdao Translate,and Baidu Translate.Data were collected based on accuracy of terminologies in the fields of economy,politics,and arts.This study found that ChatGPT translation has more contextual understanding that makes the generative translation more accurate and more relevant to specific fields.This study also analyzes its economic and cultural benefits to the process of globalization.展开更多
Both linguistics and language teaching take language as their subject. Linguistics can provide language teaching with theories and language teaching can further prove the validity of linguistics. To some extent, lingu...Both linguistics and language teaching take language as their subject. Linguistics can provide language teaching with theories and language teaching can further prove the validity of linguistics. To some extent, linguistics is more theoretical while language teaching is more practical. Since linguistics is defined as the scientific study of language, it seems obvious that such a study would have direct relations to language teaching. This paper attempts to make an analysis of difference between Contextual theory of meaning and the teaching of EFL reading.展开更多
三期答案1.Explain the contextual meaning of the following words and expressions(highlighted in blue)in English.(1)insouciance(title)insouciance:Insouciance is lack of concern shown by someone about something which the...三期答案1.Explain the contextual meaning of the following words and expressions(highlighted in blue)in English.(1)insouciance(title)insouciance:Insouciance is lack of concern shown by someone about something which they might be expected to take more seriously.(2)peep,bridge(Para.2)a)peep:If you peep,or peep at something,you have a quick look at it,often secretly and quietly.展开更多
文摘This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabulary teaching and learning. The authors find that in a short duration there is a significant difference between the effect of bilingual （English ＆ Chinese） explanation and that of monolingual （Chinese） explanation on the students＇ recognition of English new words.
基金Supported by the National Natural Science Foundation of China(60772066)
文摘A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.
基金supported by Natural Science Foundation of China(NSFC No.62472099 and No.62202105)Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr Institute for Machine Learning and Artificial Intelligence(Lamarr22B)by EU in project CrexData(grant agreement No.101092749).
文摘For event analysis,the information from both before and after the event can be crucial in certain scenarios.By incorporating a contextualized perspective in event analysis,analysts can gain deeper insights from the events.We propose a contextualized visual analysis framework which enables the identification and interpretation of temporal patterns within and across multivariate events.The framework consists of a design of visual representation for multivariate event contexts,a data processing workflow to support the visualization,and a context-centered visual analysis system to facilitate the interactive exploration of temporal patterns.To demonstrate the applicability and effectiveness of our framework,we present case studies using real-world datasets from two different domains and an expert study conducted with experienced data analysts.
基金supported by the National Natural Science Foundation of China(Grant Nos.U21B2027,U23A20388,62266028)the Yunnan Provincial Major Science and Technology Special Plan Projects(202302AD080003,202202AD080003,202303AP140008)+1 种基金the Yunnan Fundamental Research Projects(202301AS070047)the Kunming University of Science and Technology’s”Double First-rate”Construction Joint Project(202201BE070001-021).
文摘1 Introduction Document-level Role Filler Extraction aims to identify those spans of text that denote the role fillers for each event described in the document[1].Despite achieving certain accomplishments,existing methods are still not effective due to the following two issues:(1)there are difficulties in contextual modeling of long text,which requires modeling and understanding coherence and connections across sentences and paragraphs;(2)there usually ignore the explicit dependency relationships between event elements displayed in long text.To this end,we propose a novel graph-augmented approach for document-level event role filler extraction,named element relational graph-augmented multi-granularity contextualized encoder(ERGM),whose main idea is to effectively enhance the model's capabilities in capturing deep semantic information of events in long texts and modeling dependency relationships among event elements by incorporating the Event elements relational graph.Specifically,this method first constructs the structural graph by extracting elements from the source document.
基金funded by the National Natural Science Foundation of ChinaGrant Numbers 41030479,41801161,41801163the National Social Science Foundation of China(21AZD034)。
文摘The urban evolution of Gulangyu,a historic international settlement in the city of Xiamen on China’s southeast coast,has long attracted the attention of scholars working in the field of Chinese urban history.Until recently,however,few efforts have been made to theorize the spatial formation of Gulangyu.Drawing upon existing research on the island’s development,particularly since the early 1840s,this study aims to offer a critical reading of the modern transformation of Gulangyu.By retracing the urban evolution of Gulangyu on the basis of historical maps and fieldtrip data,its spatial restructuring up to the early 1940s has been reviewed and analyzed.With GIS and CAD-based tools,conflated results are presented to explain the island’s spatial mixity.The results show that from military fortification to village settlements,through the competition between local inhabitants and earlier imperialists,and then the coopetition of foreign residents and returned overseas Chinese,the making of Gulangyu as a historic international settlement was hardly a Western-dominant process.Instead,it should provide an entry point for a contextualized critique of current research on similar urban space in China’s modern history.
文摘Purpose:The paper aims to enhance Arabic machine translation(MT)by proposing novel approaches:(1)a dimensionality reduction technique for word embeddings tailored for Arabic text,optimizing efficiency while retaining semantic information;(2)a comprehensive comparison of meta-embedding techniques to improve translation quality;and(3)a method leveraging self-attention and Gated CNNs to capture token dependencies,including temporal and hierarchical features within sentences,and interactions between different embedding types.These approaches collectively aim to enhance translation quality by combining different embedding schemes and leveraging advanced modeling techniques.Design/methodology/approach:Recent works on MT in general and Arabic MT in particular often pick one type of word embedding model.In this paper,we present a novel approach to enhance Arabic MT by addressing three key aspects.Firstly,we propose a new dimensionality reduction technique for word embeddings,specifically tailored for Arabic text.This technique optimizes the efficiency of embeddings while retaining their semantic information.Secondly,we conduct an extensive comparison of different meta-embedding techniques,exploring the combination of static and contextual embeddings.Through this analysis,we identify the most effective approach to improve translation quality.Lastly,we introduce a novel method that leverages self-attention and Gated convolutional neural networks(CNNs)to capture token dependencies,including temporal and hierarchical features within sentences,as well as interactions between different types of embeddings.Our experimental results demonstrate the effectiveness of our proposed approach in significantly enhancing Arabic MT performance.It outperforms baseline models with a BLEU score increase of 2 points and achieves superior results compared to state-of-the-art approaches,with an average improvement of 4.6 points across all evaluation metrics.Findings:The proposed approaches significantly enhance Arabic MT performance.The dimensionality reduction technique improves the efficiency of word embeddings while preserving semantic information.Comprehensive comparison identifies effective meta-embedding techniques,with the contextualized dynamic meta-embeddings(CDME)model showcasing competitive results.Integration of Gated CNNs with the transformer model surpasses baseline performance,leveraging both architectures’strengths.Overall,these findings demonstrate substantial improvements in translation quality,with a BLEU score increase of 2 points and an average improvement of 4.6 points across all evaluation metrics,outperforming state-of-the-art approaches.Originality/value:The paper’s originality lies in its departure from simply fine-tuning the transformer model for a specific task.Instead,it introduces modifications to the internal architecture of the transformer,integrating Gated CNNs to enhance translation performance.This departure from traditional fine-tuning approaches demonstrates a novel perspective on model enhancement,offering unique insights into improving translation quality without solely relying on pre-existing architectures.The originality in dimensionality reduction lies in the tailored approach for Arabic text.While dimensionality reduction techniques are not new,the paper introduces a specific method optimized for Arabic word embeddings.By employing independent component analysis(ICA)and a post-processing method,the paper effectively reduces the dimensionality of word embeddings while preserving semantic information which has not been investigated before especially for MT task.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
基金supported in part by the Key Technologies R&D Program of Jiangsu under Grants BE2023022 and BE2023022-2National Natural Science Foundation of China under Grants 62471204, 62531015+2 种基金Major Natural Science Foundation of the Higher Education Institutions of Jiangsu Province under Grant 24KJA510003Shanghai Kewei 24DP1500500the Fundamental Research Funds for the Central Universities under Grant 2242025K30025
文摘Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performance is affected by a combination of factors such as the mobility of user devices,limited communication and computational resources,thus making the user scheduling problem crucial.To tackle this problem,we jointly consider the user mobility,communication and computational capacities,and develop a stochastic optimization problem to minimize the convergence time.Specifically,we first establish a convergence bound on the training performance based on the heterogeneity of users’data,and then leverage this bound to derive the participation rate for each user.After deriving the user-specific participation rate,we aim to minimize the training latency by optimizing user scheduling under the constraints of the energy consumption and participation rate.Afterward,we transform this optimization problem to the contextual multi-armed bandit framework based on the Lyapunov method and solve it with the submodular reward enhanced linear upper confidence bound(SR-linUCB)algorithm.Experimental results demonstrate the superiority of our proposed algorithm on the training performance and time consumption compared with stateof-the-art algorithms for both independent and identically distributed(IID)and non-IID settings.
基金funded by the China Chongqing Municipal Science and Technology Bureau,grant numbers 2024TIAD-CYKJCXX0121,2024NSCQ-LZX0135Chongqing Municipal Commission of Housing and Urban-Rural Development,grant number CKZ2024-87+3 种基金the Chongqing University of Technology graduate education high-quality development project,grant number gzlsz202401the Chongqing University of Technology-Chongqing LINGLUE Technology Co.,Ltd.,Electronic Information(Artificial Intelligence)graduate joint training basethe Postgraduate Education and Teaching Reform Research Project in Chongqing,grant number yjg213116the Chongqing University of Technology-CISDI Chongqing Information Technology Co.,Ltd.,Computer Technology graduate joint training base.
文摘Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.
文摘Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently,in music appreciation activities for senior kindergarten classes,there is a widespread phenomenon of homogenized teaching content and mechanized teaching methods,which results in insufficient enthusiasm for participation among young children and a superficial understanding of music.The situational teaching method,by constructing concrete and immersive learning scenarios,can effectively activate young children’s multi-dimensional sensory experiences.Its characteristics of intuitiveness and interactivity are highly consistent with the traits of young children’s concrete thinking,thus providing a new approach to resolving the current predicament.The research focuses on the practical pain points in music appreciation activities for senior kindergarten classes and proposes targeted solutions from four dimensions:content design,method innovation,resource integration,and teacher training,aiming to reconstruct a child-centered,in-depth music learning model.Practice has shown that the situational teaching method can not only enhance young children’s perceptual sensitivity to musical elements but also guide them to achieve emotional resonance through role-playing and life-related associations,laying a foundation for the sustainable development of young children’s musical literacy.
基金supported in part by the National Key R&D Program of China with Grant number 2019YFB1803400.
文摘This paper proposes Flex-QUIC,an AIempowered quick UDP Internet connections(QUIC)enhancement framework that addresses the challenge of degraded transmission efficiency caused by the static parameterization of acknowledgment(ACK)mechanisms,loss detection,and forward error correction(FEC)in dynamic wireless networks.Unlike the standard QUIC protocol,Flex-QUIC systematically integrates machine learning across three critical modules to achieve high-efficiency operation.First,a contextual multi-armed bandit-based ACK adaptation mechanism optimizes the ACK ratio to reduce wireless channel contention.Second,the adaptive loss detection module utilizes a long short-term memory(LSTM)model to predict the reordering displacement for optimizing the packet reordering tolerance.Third,the FEC transmission scheme jointly adjusts the redundancy level based on the LSTM-predicted loss rate and congestion window state.Extensive evaluations across Wi-Fi,5G,and satellite network scenarios demonstrate that Flex-QUIC significantly improves throughput and latency reduction compared to the standard QUIC and other enhanced QUIC variants,highlighting its adaptability to diverse and dynamic network conditions.Finally,we further discuss open issues in deploying AI-native transport protocols.
文摘Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication.
基金funding from the following sources:National Natural Science Foundation of China(U1904119)Research Programs of Henan Science and Technology Department(232102210054)+3 种基金Chongqing Natural Science Foundation(CSTB2023NSCQ-MSX0070)Henan Province Key Research and Development Project(231111212000)Aviation Science Foundation(20230001055002)supported by Henan Center for Outstanding Overseas Scientists(GZS2022011).
文摘The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gained significant attention for improving training efficiency.Most current algorithms rely on Convolutional Neural Networks(CNNs)for feature extraction.Although CNNs are proficient at capturing local features,they often struggle with global context,leading to incomplete and false Class Activation Mapping(CAM).To address these limitations,this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation(CPEWS)model,which improves feature extraction by utilizing the Vision Transformer(ViT).By incorporating its intermediate feature layers to preserve semantic information,this work introduces the Intermediate Supervised Module(ISM)to supervise the final layer’s output,reducing boundary ambiguity and mitigating issues related to incomplete activation.Additionally,the Contextual Prototype Module(CPM)generates class-specific prototypes,while the proposed Prototype Discrimination Loss and Superclass Suppression Loss guide the network’s training,(LPDL)(LSSL)effectively addressing false activation without the need for extra supervision.The CPEWS model proposed in this paper achieves state-of-the-art performance in end-to-end weakly supervised semantic segmentation without additional supervision.The validation set and test set Mean Intersection over Union(MIoU)of PASCAL VOC 2012 dataset achieved 69.8%and 72.6%,respectively.Compared with ToCo(pre trained weight ImageNet-1k),MIoU on the test set is 2.1%higher.In addition,MIoU reached 41.4%on the validation set of the MS COCO 2014 dataset.
文摘The current study attempted to formulate a conceptualization of Muslim fundamentalism as well as its counter-narratives as grounded in religious experience of Pakistani Muslims.Open ended interviews were conducted with 133 Pakistani Muslim men and women of prominent local religious affiliations.Analysis revealed a grounded theory model of Muslim fundamentalism highlighting cognitive,and social psychological processes involved.Participants saw their religion as a complete code of conduct and inferred various meanings from completeness of Islam as finalized,closed to inquiry as well as rejecting of other cultures and religious traditions.The major inter-related themes of the model were totalitarianism,closed mindedness,binary thinking,hyper-exotericism,ambiguity intolerance,authoritarianism,punitive approach,violent tendencies,labelling,diversity intolerance and paranoia or threat perceiving attitude.The interplay of these factors is discussed in the light of earlier research on fundamentalism.The research also revealed strong counter narratives to fundamentalist stance which formulated the major themes of esoteric religiosity,open mindedness,pluralism,and Islam and civil society.The study carries implications for religious education of Muslims and their socialization with believers of other religious traditions.
文摘The trend of globalization has brought new requirements to existing translation industry.Need for flexibility calls for contextualized translation that can be applied to various scenarios.This is an exploratory case study of ChatGPT,aiming at discovering potential of artificial intelligence(AI)translation tools.ChatGPT is compared with machine-aided translation tools like Google Translate,Microsoft Translate,Youdao Translate,and Baidu Translate.Data were collected based on accuracy of terminologies in the fields of economy,politics,and arts.This study found that ChatGPT translation has more contextual understanding that makes the generative translation more accurate and more relevant to specific fields.This study also analyzes its economic and cultural benefits to the process of globalization.
文摘Both linguistics and language teaching take language as their subject. Linguistics can provide language teaching with theories and language teaching can further prove the validity of linguistics. To some extent, linguistics is more theoretical while language teaching is more practical. Since linguistics is defined as the scientific study of language, it seems obvious that such a study would have direct relations to language teaching. This paper attempts to make an analysis of difference between Contextual theory of meaning and the teaching of EFL reading.
文摘三期答案1.Explain the contextual meaning of the following words and expressions(highlighted in blue)in English.(1)insouciance(title)insouciance:Insouciance is lack of concern shown by someone about something which they might be expected to take more seriously.(2)peep,bridge(Para.2)a)peep:If you peep,or peep at something,you have a quick look at it,often secretly and quietly.
