Facial expression recognition with contextualized histograms

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.

面向船闸船舶的在线多目标跟踪技术研究

目的 满足船闸船舶在线跟踪要求,改善由于复杂背景、遮挡等因素导致轨迹不连续和身份变更的问题,提出一种增强上下文联系和上下文注意力的多目标跟踪方法。方法 基于设计的在线系统,采集连续帧图像,改进FairMOT多目标跟踪模型。首先,通过在骨干网络设计基于Bottleneck和Contextual Transformer的上下文建模模块,以加强上下文联系,增强场景理解的能力。其次,在迭代聚合后的特征图上应用全局上下文注意力,提高定位船舶目标的能力。结果 相对于原生的Fair MOT方法,设计上下文建模模块后,多目标跟踪准确度指标MOTA提高2.1%,继续添加全局上下文注意力MOTA,共计提高3.5%,同时在多项指标中取得了最佳表现。结论 改进的Fair MOT方法不仅拥有更强的轨迹保持能力,而且在身份维持方面更胜一筹。

Context-Based AI Translation From a Globalization Perspective:A Case Study of ChatGPT

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.

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography(CT).High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease,so it is of clinical importance to study super-resolution(SR)algorithms applied to CT images to improve the reso-lution of CT images.However,most of the existing SR algorithms are studied based on natural images,which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth,which is not suitable for machines with limited resources.To alleviate these issues,we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution(RFAFN).Specifically,we design a contextual feature extraction block(CFEB)that can extract CT image features more efficiently and accurately than ordinary residual blocks.In addition,we propose a feature-weighted cascading strategy(FWCS)based on attentional feature fusion blocks(AFFB)to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information.Finally,we suggest a global hierarchical feature fusion strategy(GHFFS),which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels.Numerous experiments show that our method performs better than most of the state-of-the-art(SOTA)methods on the COVID-19 chest CT dataset.In detail,the peak signal-to-noise ratio(PSNR)is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at×3 SR compared to the suboptimal method,but the number of parameters and multi-adds are reduced by 22K and 0.43G,respectively.Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.

LGNet:Local and global representation learning for fast biomedical image segmentation

Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems,yet still faces many challenges.Building on convolutional neural networks(CNNs),medical image segmentation has achieved tremendous progress.However,owing to the locality of convolution operations,CNNs have the inherent limitation in learning global context.To address the limitation in building global context relationship from CNNs,we propose LGNet,a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this paper.Specifically,we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global features.LGNet has two key insights:(1)We bridge two-branch to learn local and global features in an interactive way;(2)we present a novel multi-feature fusion model(MSFFM)to leverage the global contexture information from transformer and the local representational features from convolutions.Our method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse,ACDC and MOST.Specifically,LGNet achieves the state-of-the-art performance with Dice's indexes of 80.15%on Synapse,of 91.70%on ACDC,and of 95.56%on MOST.Meanwhile,the inference speed attains at 172 frames per second with 224-224 input resolution.The extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation.

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.

Enhanced Detection of Cerebral Atherosclerosis Using Hybrid Algorithm of Image Segmentation

In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques.Major objective of this work is to detect of cerebral atherosclerosis for image segmentation applica-tion.Detection of some abnormal structures in human body has become a difficult task to complete with some simple images.For expounding and distinguishing neural architecture of human brain in an effective manner,MRI(Magnetic Reso-nance Imaging)is one of the most suitable and significant technique.Here we work on detection of Cerebral Atherosclerosis from MRI images of patients.Cer-ebral Atherosclerosis is a cerebral vascular disease causes narrowing of the arteries due to buildup of fatty plaque inside the blood vessels of the brain.It leads to Ischemic stroke if not diagnosed early.Stroke affects majorly old age people and percentage of affected women is more compared to men.Results:Preproces-sing is done by using alpha trimmed meanfilter which is used to remove noise and also it enhances the image.Segmentation of cerebral atherosclerosis is done by using K-means clustering,Contextual clustering,and proposed Hybrid algo-rithm.Various parameters like Correlation,Pixel density,energy is determined and from the analysis of parameters it is determined that proposed Hybrid algo-rithm is efficient.

Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative,positive,or neutral while associating them with their identified aspects from the corresponding context.In this regard,prior methodologies widely utilize either word embedding or tree-based rep-resentations.Meanwhile,the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information loss.Generally,word embedding preserves the syntactic and semantic relations between a couple of terms lying in a sentence.Besides,the tree-based structure conserves the grammatical and logical dependencies of context.In addition,the sentence-oriented word position describes a critical factor that influences the contextual information of a targeted sentence.Therefore,knowledge of the position-oriented information of words in a sentence has been considered significant.In this study,we propose to use word embedding,tree-based representation,and contextual position information in combination to evaluate whether their combination will improve the result’s effectiveness or not.In the meantime,their joint utilization enhances the accurate identification and extraction of targeted aspect terms,which also influences their classification process.In this research paper,we propose a method named Attention Based Multi-Channel Convolutional Neural Net-work(Att-MC-CNN)that jointly utilizes these three deep features such as word embedding with tree-based structure and contextual position informa-tion.These three parameters deliver to Multi-Channel Convolutional Neural Network(MC-CNN)that identifies and extracts the potential terms and classifies their polarities.In addition,these terms have been further filtered with the attention mechanism,which determines the most significant words.The empirical analysis proves the proposed approach’s effectiveness compared to existing techniques when evaluated on standard datasets.The experimental results represent our approach outperforms in the F1 measure with an overall achievement of 94%in identifying aspects and 92%in the task of sentiment classification.

Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract

In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation,at the smart contract level of the blockchain,a customer churn prediction framework based on situational awareness and integrating customer attributes,the impact of project hotspots on customer interests,and customer satisfaction with the project has been built.This framework introduces the background factors in the financial customer environment,and further discusses the relationship between customers,the background of customers and the characteristics of pre-lost customers.The improved Singular Value Decomposition(SVD)algorithm and the time decay function are used to optimize the search and analysis of the characteristics of pre-lost customers,and the key index combination is screened to obtain the data of potential lost customers.The framework will change with time according to the customer’s interest,adding the time factor to the customer churn prediction,and improving the dimensionality reduction and prediction generalization ability in feature selection.Logistic regression,naive Bayes and decision tree are used to establish a prediction model in the experiment,and it is compared with the financial customer churn prediction framework under situational awareness.The prediction results of the framework are evaluated from four aspects:accuracy,accuracy,recall rate and F-measure.The experimental results show that the context-aware customer churn prediction framework can be effectively applied to predict customer churn trends,so as to obtain potential customer data with high churn probability,and then these data can be transmitted to the company’s customer service department in time,so as to improve customer churn rate and customer loyalty through accurate service.

Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore,it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However,the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus,the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification(MLC)problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods.

Widespread Debate over the Nature of Language Development and Language Teaching(Ⅱ)——Li Yongda with Noam Chomsky

Li Yongda(李永大,以下简称LI):I'd like to ask some questions about cognitive development in language versus linguistic development in cognition.Cognitive development refers to the growth of complex representationa1l and decontextualized thought as a human being grows from infancy to adulthood.In a similar vein,language appears to be an ability which may not be available at birth,but has to develop over a long time,given experience of the linguistic world.Can I perceive that a toddler grows,before reaching puberty,with both cognitive and linguistic development simultaneously?

A Personalized Adverse Drug Reaction Early Warning Method Based on Contextual Ontology and Rules Learning

Background: The fatality of adverse drug reactions (ADR) has become one of the major causes of the non-natural disease deaths globally, with the issue of drug safety emerging as a common topic of concern. Objective: The personalized ADR early warning method, based on contextual ontology and rule learning, proposed in this study aims to provide a reference method for personalized health and medical information services. Methods: First, the patient data is formalized, and the user contextual ontology is constructed, reflecting the characteristics of the patient population. The concept of ontology rule learning is then proposed, which is to mine the rules contained in the data set through machine learning to improve the efficiency and scientificity of ontology rule generation. Based on the contextual ontology of ADR, the high-level context information is identified and predicted by means of reasoning, so the occurrence of the specific adverse reaction in patients from different populations is extracted. Results: Finally, using diabetes drugs as an example, contextual information is identified and predicted through reasoning, to mine the occurrence of specific adverse reactions in different patient populations, and realize personalized medication decision-making and early warning of ADR.

Expert Perception of ChatGPT in Computer System Architecture

Question 1:what do you think are the most impressive features of ChatGPT as a language model?Answer:Well,ChatGPT has several impressive features.Firstly,its ability to generate contextually relevant and coherent responses to natural language prompts is truly remarkable.This is achieved through the model's architecture,which includes multiple layers of artificial neural networks,and the vast amount of text data it has been trained on.The model can generate responses that are not only grammatically correct but also contextually appropriate and engaging,making it a powerful tool for natural language processing.

Muslim Fundamentalism: Psychological Orientations and Counter-Narratives

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.

Context and Teaching of EFL Reading

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.

Insouciance学习任务答案与篇章讲解赏析

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.

A Research on the Improvement of Pragmatic Competence in College English Teaching

A core issue in foreign language teaching is to foster actual competence of language learners to practically utilize language, which is called pragmatic competence. But compared with traditional linguistic competence, the levels of their pragmatic competence need to be further understood. Thus it is essential to make an investigation into this. From the investigation results it is expected to reveal from which aspects teachers and students should carry on in order to improve pragmatic competence and meet foreign language learning need in new age.

A Study on Translation of Chinese Culture-loaded Words from the Per-spective of Relevance Theory

The essay tends to analyze the translation of Chinese Culture-loaded words from the perspective of Relevance Theory.The theory gains its prominence by studying the translation process and transcending the conflicts between literal and free translation. It incorporates recent work in cognitive linguistics, with ostensive-inference as its key model. Under the influence of Relevance theory, the translation of culture-loaded words is reader-oriented. Translators are obliged to help target readers to establish new assumptions to achieve equivalent response.

GLYX-13 pretreatment ameliorates long-term isoflurane exposure-induced cognitive impairment in mice

Accumulating evidence indicates that inhalation anesthetics induce or increase the risk of cognitive impairment.GLYX-13(rapastinel)acts on the glycine site of N-methyl-D-aspartate receptors(NMDARs)and has been shown to enhance hippocampus-dependent learning and memory function.However,the mechanisms by which GLYX-13 affects learning and memory function are still unclear.In this study,we investigated these mechanisms in a mouse model of long-term anesthesia exposure.Mice were intravenously administered 1 mg/kg GLYX-13 at 2 hours before isoflurane exposure(1.5%for 6 hours).Cognitive function was assessed using the contextual fear conditioning test and the novel object recognition test.The mRNA expression and phosphorylated protein levels of NMDAR pathway components,N-methyl-D-aspartate receptor subunit 2B(NR2B)-Ca^2+/calmodulin dependent protein kinaseⅡ(CaMKII)-cyclic adenosine monophosphate response element binding protein(CREB),in the hippocampus were evaluated by quantitative RT-PCR and western blot assay.Pretreatment with GLYX-13 ameliorated isoflurane exposure-induced cognitive impairment and restored NR2B,CaMKII and CREB mRNA and phosphorylated protein levels.Intracerebroventricular injection of KN93,a selective CaMKII inhibitor,significantly diminished the effect of GLYX-13 on cognitive function and NR2B,CaMKII and CREB levels in the hippocampus.Taken together,our findings suggest that GLYX-13 pretreatment alleviates isoflurane-induced cognitive dysfunction by protecting against perturbation of the NR2B/CaMKII/CREB signaling pathway in the hippocampus.Therefore,GLYX-13 may have therapeutic potential for the treatment of anesthesia-induced cognitive dysfunction.This study was approved by the Experimental Animal Ethics Committee of Drum Tower Hospital affiliated to the Medical College of Nanjing University,China(approval No.20171102)on November 20,2017.