Low-Complexity Reconstruction of Covariance Matrix in Hybrid Uniform Circular Array

Spatial covariance matrix(SCM) is essential in many multi-antenna systems such as massive multiple-input multiple-output(MIMO). For multi-antenna systems operating at millimeter-wave bands, hybrid analog-digital structure has been widely adopted to reduce the cost of radio frequency chains.In this situation, signals received at the antennas are unavailable to the digital receiver, and as a consequence, traditional sample average approach cannot be used for SCM reconstruction in hybrid multi-antenna systems. To address this issue, beam sweeping algorithm(BSA) which can reconstruct the SCM effectively for a hybrid uniform linear array, has been proposed in our previous works. However, direct extension of BSA to a hybrid uniform circular array(UCA)will result in a huge computational burden. To this end, a low-complexity approach is proposed in this paper. By exploiting the symmetry features of SCM for the UCA, the number of unknowns can be reduced significantly and thus the complexity of reconstruction can be saved accordingly. Furthermore, an insightful analysis is also presented in this paper, showing that the reduction of the number of unknowns can also improve the accuracy of the reconstructed SCM. Simulation results are also shown to demonstrate the proposed approach.

A Unified Model Fusing Region of Interest Detection and Super Resolution for Video Compression

High-resolution video transmission requires a substantial amount of bandwidth.In this paper,we present a novel video processing methodology that innovatively integrates region of interest(ROI)identification and super-resolution enhancement.Our method commences with the accurate detection of ROIs within video sequences,followed by the application of advanced super-resolution techniques to these areas,thereby preserving visual quality while economizing on data transmission.To validate and benchmark our approach,we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical applications.The proposed model architecture leverages the transformer network framework,guided by a carefully designed multi-task loss function,which facilitates concurrent learning and execution of both ROI identification and resolution enhancement tasks.This unified deep learning model exhibits remarkable performance in achieving super-resolution on our custom dataset.The implications of this research extend to optimizing low-bitrate video streaming scenarios.By selectively enhancing the resolution of critical regions in videos,our solution enables high-quality video delivery under constrained bandwidth conditions.Empirical results demonstrate a 15%reduction in transmission bandwidth compared to traditional super-resolution based compression methods,without any perceivable decline in visual quality.This work thus contributes to the advancement of video compression and enhancement technologies,offering an effective strategy for improving digital media delivery efficiency and user experience,especially in bandwidth-limited environments.The innovative integration of ROI identification and super-resolution presents promising avenues for future research and development in adaptive and intelligent video communication systems.

A Study on the Internal Control and Countermeasures of Fixed Assets in Private Universities:A Case Study of N College

Fixed assets in universities occupy an important position in university management due to their wide coverage and large amount of money.Due to insufficient funding supply,private universities mainly focus on the allocation and utilization of fixed assets,which reflects the overall characteristics of cautious allocation,maximum utilization,and delayed elimination in the actual management of fixed assets.This article aims to conduct research and analysis on the entire lifecycle process of the allocation,use,and disposal of fixed assets in private universities,summarize the problems existing in the internal control of fixed assets in private universities,and propose corresponding countermeasures and suggestions in a targeted manner.

Measurement of public awareness of climate change in China:based on a national survey with 4,025 samples

Just before the new round of UN climate change conference in Bonn, a survey report, named as the Climate Change in the Chinese Mind 2017, was released in Beijing. The investigation was conducted in the form of a computer-assisted telephone survey with a sample size of 4,025 samples, covering 332 prefecture-level administrative units and four municipalities in China.Urban-rural proportion and sex proportion were specially taken into account, so as to demonstrate the Chinese public awareness objectively. The investigation measures the public awareness from six aspects, which includes climate change beliefs, impacts, response, policies, actions, and the assessment of the effectiveness of climate communication. This article presents the key findings of the survey and provides further insights behind the data.

Long-term analysis of clear nights using satellite data considering astronomical sites in western China

A large ground-based optical/infrared telescope is being planned for a world-class astronomical site in China.The cloud-free night percentage is the primary meteorological consideration for evaluating candidate sites.The data from GMS and NOAA satellites and the MODIS instrument were utilized in this research,covering the period from 1996 to 2015.Our data analysis benefits from overlapping results from different independent teams as well as a uniform analysis of selected sites using GMS+NOAA data.Although significant ground-based monitoring is needed to validate these findings,we identify three different geographical regions with a high percentage of cloud-free conditions(~83%on average),which is slightly lower than at Mauna Kea and Cerro Armazones(~85%on average)and were chosen for the large international projects TMT and ELT respectively.Our study finds evidence that cloud distributions and the seasonal changes affected by the prevailing westerly winds and summer monsoons reduce the cloud cover in areas influenced by the westerlies.This is consistent with the expectations from climate change models and is suggestive that most of the identified sites will have reduced cloud cover in the future.

Research on the Public Opinions of the USA by the Scholars in China

When researching the public opinions abroad,scholars in China tend to choose the USA as the research object. Well- known for democracy,the USA attaches great importance to the public opinions,the influence of which has already spread to the foreign policies of the USA. As the hot spot of the international politics,there is lots of research upon the relationship between the public opinions and the foreign policies. This paper attempts to analyze the research on the public opinions of the USA by the scholars in China in order to make clear the direction of the related research in the future.

Thinking on the Theory of "China's Century"

As China's economy and global influence increase,its international relations are quickly changing.As more credence is given to predictions of a'China Century'to follow the'American Century,'interest is also increasing in the adjustments China is making to its strategic diplomacy as it prepares to take the first chair from the United States.The conceptual innovation in China's diplomacy in 2014 is a significant harbinger in the eyes of many as to how this China Century will shape up.While China's new diplomacy is praised,opinion differs on its future intentions.After a series of proposals were put forward at summit meetings of CICA(Conference on Interaction and Confidence-Building Measures

Yang Weiguang, Trailblazer of China’s TV Industry

YANG Wei guang,former president of CCTV,died on September 20 at the age of 79.Although Yang asked that mourning and memorial services be abbreviated,voluntary celebrations of his life continued for almost a month.

China and the World in the Perspective of Global New Public Diplomacy

Since the 1990s,the development of globalization and information technology has been changing the political ecology of traditional international relations.The all-round and close communication and competition in terms of communication competence among nation-states promoted the constant change of international power

Theoretical Reflection on Meaning Production of Media Discourse in Intercultural Communications

Media discourse in the context of intercultural communications is an important channel that countries and cultures use to communicate. It is also a process of meaning interpretation and knowledge production, which exerts a great impact on the establishment of the world's cultural order. This paper discusses media discourse in intercultural communications theoretically from the perspective of knowledge production, media dialogue and meaning construction. It is suggested that an effective ideographic mechanism be developed and improved, and the essential meaning of Chinese culture be initiatively exported and integrated into a knowledge system of cognition and understanding about the world to promote the understanding and exchange between China and other countries and to help create an equal and reasonable world cultural order.

Predicting the Popularity of Online News Based on the Dynamic Fusion of Multiple Features

Predicting the popularity of online news is essential for news providers and recommendation systems.Time series,content and meta-feature are important features in news popularity prediction.However,there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model’s performance.This work proposes a novel deep learning model named Multiple Features Dynamic Fusion(MFDF)for news popularity prediction.For modeling time series,long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations of online news popularity.The typical convolution neural network gets headline semantic representation for modeling news headlines.In addition,a hierarchical attention network is exploited to extract news content semantic representation while using the latent Dirichlet allocation model to get the subject distribution of news as a semantic supplement.A factorization machine is employed to model the interaction relationship between metafeatures.Considering the role of these features at different stages,the proposed model exploits a time-based attention fusion layer to fuse multiple features dynamically.During the training phase,thiswork designs a loss function based on Newton’s cooling law to train the model better.Extensive experiments on the real-world dataset from Toutiao confirm the effectiveness of the dynamic fusion of multiple features and demonstrate significant performance improvements over state-of-the-art news prediction techniques.

Sparsity-based efficient simulation of cluster targets electromagnetic scattering

An efficient and real-time simulation method is proposed for the dynamic electromagnetic characteristics of cluster targets to meet the requirements of engineering practical applications.First,the coordinate transformation method is used to establish a geometric model of the observation scene,which is described by the azimuth angles and elevation angles of the radar in the target reference frame and the attitude angles of the target in the radar reference frame.Then,an approach for dynamic electromagnetic scattering simulation is proposed.Finally,a fast-computing method based on sparsity in the time domain,space domain,and frequency domain is proposed.The method analyzes the sparsity-based dynamic scattering characteristic of the typical cluster targets.The error between the sparsity-based method and the benchmark is small,proving the effectiveness of the proposed method.

From Spiritual Communication to Digital Communication:Rethinking Marx’s Communication Theory in the Information Age

Karl Marx and Friedrich Engels introduced the theory of spiritual communication in their influential work,The German Ideology,thereby establishing the Marxist perspective on communication.This theory has a distinct historical context,meaning,and significance.As society advances,driven by the proliferation of commodity-based economies and advancements in science and technology,humanity has entered the digital information age.The prevailing mode of communication in this era,centered around the Internet and big data,brings great convenience to society but also raises concerns about the potential alienation of both subjective and objective aspects of communication,as well as social relationships.Against this backdrop,the rethinking of Marx’s communication theory in the 21st century provides a logical framework and theoretical basis for critiquing the contemporary form of spiritual communication through the digital communication of the information age.

Improve Chinese Aspect Sentiment Quadruplet Prediction via Instruction Learning Based on Large Generate Models

Aspect-Based Sentiment Analysis(ABSA)is a fundamental area of research in Natural Language Processing(NLP).Within ABSA,Aspect Sentiment Quad Prediction(ASQP)aims to accurately identify sentiment quadruplets in target sentences,including aspect terms,aspect categories,corresponding opinion terms,and sentiment polarity.However,most existing research has focused on English datasets.Consequently,while ASQP has seen significant progress in English,the Chinese ASQP task has remained relatively stagnant.Drawing inspiration from methods applied to English ASQP,we propose Chinese generation templates and employ prompt-based instruction learning to enhance the model’s understanding of the task,ultimately improving ASQP performance in the Chinese context.Ultimately,under the same pre-training model configuration,our approach achieved a 5.79%improvement in the F1 score compared to the previously leading method.Furthermore,when utilizing a larger model with reduced training parameters,the F1 score demonstrated an 8.14%enhancement.Additionally,we suggest a novel evaluation metric based on the characteristics of generative models,better-reflecting model generalization.Experimental results validate the effectiveness of our approach.

Improving Diversity with Multi-Loss Adversarial Training in Personalized News Recommendation

Users’interests are often diverse and multi-grained,with their underlying intents even more so.Effectively captur-ing users’interests and uncovering the relationships between diverse interests are key to news recommendation.Meanwhile,diversity is an important metric for evaluating news recommendation algorithms,as users tend to reject excessive homogeneous information in their recommendation lists.However,recommendation models themselves lack diversity awareness,making it challenging to achieve a good balance between the accuracy and diversity of news recommendations.In this paper,we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity.Unlike most existing works that solely optimize accuracy or employ more features to meet diversity,the proposed algorithm leverages the diversity-aware capability of the model.First,we introduce an augmented user model to fully capture user intent and the behavioral guidance they might undergo as a result.Specifically,we focus on the relationship between the original clicked news and the augmented clicked news.Moreover,we propose an effective adversarial training method for diversity(AT4D),which is a pluggable component that can enhance both the accuracy and diversity of news recommendation results.Extensive experiments on real-world datasets confirm the efficacy of the proposed algorithm in improving both the accuracy and diversity of news recommendations.

Enhancing Deep Learning Semantics:The Diffusion Sampling and Label-Driven Co-Attention Approach

The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms,yielding outstanding achievements across diverse domains.Nonetheless,self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures.In response,this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network(DSLD),which adopts a diffusion sampling method to capture more comprehensive semantic information of the data.Additionally,themodel leverages the joint correlation information of labels and data to introduce the computation of text representation,correcting semantic representationbiases in thedata,andincreasing the accuracyof semantic representation.Ultimately,the model computes the corresponding classification results by synthesizing these rich data semantic representations.Experiments on seven benchmark datasets show that our proposed model achieves competitive results compared to state-of-the-art methods.

Research on Sarcasm Detection Technology Based on Image-Text Fusion

The emergence of new media in various fields has continuously strengthened the social aspect of social media.Netizens tend to express emotions in social interactions,and many people even use satire,metaphors,and other techniques to express some negative emotions,it is necessary to detect sarcasm in social comment data.For sarcasm,the more reference data modalities used,the better the experimental effect.This paper conducts research on sarcasm detection technology based on image-text fusion data.To effectively utilize the features of each modality,a feature reconstruction output algorithm is proposed.This algorithm is based on the attention mechanism,learns the low-rank features of another modality through cross-modality,the eigenvectors are reconstructed for the corresponding modality through weighted averaging.When only the image modality in the dataset is used,the preprocessed data has outstanding performance in reconstructing the output model,with an accuracy rate of 87.6%.When using only the text modality data in the dataset,the reconstructed output model is optimal,with an accuracy rate of 85.2%.To improve feature fusion between modalities for effective classification,a weight adaptive learning algorithm is used.This algorithm uses a neural network combined with an attention mechanism to calculate the attention weight of each modality to achieve weight adaptive learning purposes,with an accuracy rate of 87.9%.Extensive experiments on a benchmark dataset demonstrate the superiority of our proposed model.

Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to multimodalinformation exchange and fusion, with many methods attempting to integrate unimodal features to generatemultimodal news representations. However, they still need to fully explore the hierarchical and complex semanticcorrelations between different modal contents, severely limiting their performance detecting multimodal falseinformation. This work proposes a two-stage detection framework for multimodal false information detection,called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency andinconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training(CLIP) model to learn the relationship between text and images through label awareness and train an imageaesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity betweenthe image and related images and use this similarity as a threshold to divide the multimodal correlation matrixinto consistency and inconsistencymatrices. Finally, the fusionmodule is designed to identify essential features fordetectingmultimodal false information. In extensive experiments on four datasets, the performance of the ASMFDis superior to state-of-the-art baseline methods.

Aspect-Level Sentiment Analysis Based on Deep Learning

In recent years,deep learning methods have developed rapidly and found application in many fields,including natural language processing.In the field of aspect-level sentiment analysis,deep learning methods can also greatly improve the performance of models.However,previous studies did not take into account the relationship between user feature extraction and contextual terms.To address this issue,we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method.To be specific,we design user comment feature extraction(UCFE)to distill salient features from users’historical comments and transform them into representative user feature vectors.Then,the aspect-sentence graph convolutional neural network(ASGCN)is used to incorporate innovative techniques for calculating adjacency matrices;meanwhile,ASGCN emphasizes capturing nuanced semantics within relationships among aspect words and syntactic dependency types.Afterward,three embedding methods are devised to embed the user feature vector into the ASGCN model.The empirical validations verify the effectiveness of these models,consistently surpassing conventional benchmarks and reaffirming the indispensable role of deep learning in advancing sentiment analysis methodologies.

A privacy-preserving method for publishing data with multiple sensitive attributes

The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes(SAs)have not considered the personalised privacy requirement.Furthermore,sensitive information disclosure may also be caused by these personalised requirements.To address the matter,this article develops a personalised data publishing method for multiple SAs.According to the requirements of individuals,the new method partitions SAs values into two categories:private values and public values,and breaks the association between them for privacy guarantees.For the private values,this paper takes the process of anonymisation,while the public values are released without this process.An algorithm is designed to achieve the privacy mode,where the selectivity is determined by the sensitive value frequency and undesirable objects.The experimental results show that the proposed method can provide more information utility when compared with previous methods.The theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an adversary.The overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.