Social Radars: Finding Targets in Cyberspace for Cybersecurity

INSPIRED by the insight from American political scientist Lasswell, who summarized the environmental role in societal surveillance [1], Schramm coined the term “social radar” [2] as it resembles the activities of radar in collecting and processing information, playing a crucial role in helping humans perceive changes in the internal and external environment and promptly adjusting adaptive behaviors.

Intuitive Human-Robot-Environment Interaction With EMG Signals:A Review

A long history has passed since electromyography(EMG)signals have been explored in human-centered robots for intuitive interaction.However,it still has a gap between scientific research and real-life applications.Previous studies mainly focused on EMG decoding algorithms,leaving a dynamic relationship between the human,robot,and uncertain environment in real-life scenarios seldomly concerned.To fill this gap,this paper presents a comprehensive review of EMG-based techniques in human-robot-environment interaction(HREI)systems.The general processing framework is summarized,and three interaction paradigms,including direct control,sensory feedback,and partial autonomous control,are introduced.EMG-based intention decoding is treated as a module of the proposed paradigms.Five key issues involving precision,stability,user attention,compliance,and environmental awareness in this field are discussed.Several important directions,including EMG decomposition,robust algorithms,HREI dataset,proprioception feedback,reinforcement learning,and embodied intelligence,are proposed to pave the way for future research.To the best of what we know,this is the first time that a review of EMG-based methods in the HREI system is summarized.It provides a novel and broader perspective to improve the practicability of current myoelectric interaction systems,in which factors in human-robot interaction,robot-environment interaction,and state perception by human sensations are considered,which has never been done by previous studies.

Preliminary Reflections on the Establishment of a New Energy System for the Achievement of Dual Carbon Goals

China has pledged to the world to achieve carbon peak in 10 years and carbon neutrality in 30 years.This is an extremely arduous task,as it faces numerous challenges,including high energy consumption,heavy reliance on coal within its energy mix,and a large base of carbon emissions that must be controlled.To this end,it is necessary to advance the new energy security strategy of“Four Revolutions,One Cooperation”to a deeper level.According to interpretations from various parties,the new energy system is preliminarily summarized to have six features:new energy structure,new system form,new industrial system,new governance system,new system and mechanism,and new regulatory method.Considering building a new energy system comprehensively,“Ten Commitments”have been proposed to help achieve the dual-carbon goals.The specific measures include:ensuring the security and stability of energy supply,accelerating the transformation to green and low-carbon energy,giving priority to energy conservation and efficiency improvement,promoting multi-energy complementation and synergistic and integrated development,enhancing the digital intelligence level in the energy industry,developing centralized and distributed energy,advancing the rural energy revolution,developing critical and core technological equipment and the comprehensive energy service industry,and promoting high-quality development of the Belt and Road Initiative.

卫拉特方言语法研究综述

Oirat dialect is a unique Mongolian dialect,having its own writing system.This paper analyzed the grammatical research of the Oirat dialect,and summarized the achievements and existing problems of previous studies,and provided suggestions about strengthening comprehensive in-depth study of morphology,syntax and word formation in this Mongolian dialect.

EDSUCh:A robust ensemble data summarization method for effective medical diagnosis

Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data.Data summarization can create a concise version of the original data that can be used for effective diagnosis.In this paper,we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns.To the best of our knowledge,there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis.The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used.Therefore,the medical diagnosis becomes more effective,and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.

Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization.This method resulted in rapid exploration,indexing,and retrieval of massive video libraries.We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering algorithm.The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video sequences.The video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster center.Without applying any clustering parameters,the appropriate clusters number is determined using the silhouette coefficient.Experiments were carried out on a publicly available open video project(OVP)dataset that contained videos of different genres.The proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.

Adaptive Graph Convolutional Adjacency Matrix Network for Video Summarization

Video summarization aims to select key frames or key shots to create summaries for fast retrieval,compression,and efficient browsing of videos.Graph neural networks efficiently capture information about graph nodes and their neighbors,but ignore the dynamic dependencies between nodes.To address this challenge,we propose an innovative Adaptive Graph Convolutional Adjacency Matrix Network(TAMGCN),leveraging the attention mechanism to dynamically adjust dependencies between graph nodes.Specifically,we first segment shots and extract features of each frame,then compute the representative features of each shot.Subsequently,we utilize the attention mechanism to dynamically adjust the adjacency matrix of the graph convolutional network to better capture the dynamic dependencies between graph nodes.Finally,we fuse temporal features extracted by Bi-directional Long Short-Term Memory network with structural features extracted by the graph convolutional network to generate high-quality summaries.Extensive experiments are conducted on two benchmark datasets,TVSum and SumMe,yielding F1-scores of 60.8%and 53.2%,respectively.Experimental results demonstrate that our method outperforms most state-of-the-art video summarization techniques.

Enhanced Topic-Aware Summarization Using Statistical Graph Neural Networks

The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Current approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent summaries.This study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical information.Our approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text structures.This model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and Multi-News.The results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document contexts.This research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.

SHEL:a semantically enhanced hardware-friendly entity linking method

With the help of pre-trained language models,the accuracy of the entity linking task has made great strides in recent years.However,most models with excellent performance require fine-tuning on a large amount of training data using large pre-trained language models,which is a hardware threshold to accomplish this task.Some researchers have achieved competitive results with less training data through ingenious methods,such as utilizing information provided by the named entity recognition model.This paper presents a novel semantic-enhancement-based entity linking approach,named semantically enhanced hardware-friendly entity linking(SHEL),which is designed to be hardware friendly and efficient while maintaining good performance.Specifically,SHEL's semantic enhancement approach consists of three aspects:(1)semantic compression of entity descriptions using a text summarization model;(2)maximizing the capture of mention contexts using asymmetric heuristics;(3)calculating a fixed size mention representation through pooling operations.These series of semantic enhancement methods effectively improve the model's ability to capture semantic information while taking into account the hardware constraints,and significantly improve the model's convergence speed by more than 50%compared with the strong baseline model proposed in this paper.In terms of performance,SHEL is comparable to the previous method,with superior performance on six well-established datasets,even though SHEL is trained using a smaller pre-trained language model as the encoder.

Current Research Status of Traditional Chinese Medicine External Treatment for Diarrhea Type Irritable Bowel Syndrome

Irritable bowel syndrome(IBS-D)with diarrhea is a common gastrointestinal functional disease in clinical practice,which seriously affects the quality of life of patients.Cur‐rently,Western medicine has poor therapeutic effects,while traditional Chinese medi‐cine has unique advantages in relieving IBS-D symptoms and preventing recurrence.In recent years,especially with external treatment of traditional Chinese medicine,it has become a new treatment direction in clinical practice and has achieved good therapeutic effects.This article will provide a review of recent research on the treatment of IBS-D using traditional Chinese medicine external treatment methods.

Development of Intrinsic Room-Temperature 2D Ferromagnetic Crystals for 2D Spintronics

Two-dimensional(2D)ferromagnetic crystals with fascinating optical and electrical properties are crucial for nanotechnology and have a wide variety of applications in spintronics.However,low Curie temperatures of most 2D ferromagnetic crystals seriously hinder their practical applications,thus searching for intrinsic roomtemperature 2D ferromagnetic crystals is of great importance for development of information technology.Fortunately,progresses have been achieved in the last few years.Here we review recent advances in the field of intrinsic room-temperature 2D ferromagnetic crystals and introduce their applications in spintronic devices based on van der Waals heterostructures.Finally,the remaining challenge and future perspective on the development direction of intrinsic room-temperature 2D ferromagnetic crystals for 2D spintronics and van der Waals spintronics are briefly summarized.

The Versatility and Diagnostic Potential of VOC Profiling for Noninfectious Diseases

variety of volatile organic compounds(VOCs)are produced and emitted by the human body every day.The identity and concentration of these VOCs reflect an individual’s metabolic condition.Information regarding the production and origin of VOCs,however,has yet to be congruent among the scientific community.This review article focuses on the recent investigations of the source and detection of biological VOCs as a potential for noninvasive discrimination between healthy and diseased individuals.Analyzing the changes in the components of VOC profiles could provide information regarding the molecular mechanisms behind disease as well as presenting new approaches for personalized screening and diagnosis.VOC research has prioritized the study of cancer,resulting in many research articles and reviews being written on the topic.This review summarizes the information gained about VOC cancer studies over the past 10 years and looks at how this knowledge correlates with and can be expanded to new and upcoming fields of VOC research,including neurodegenerative and other noninfectious diseases.Recent advances in analytical techniques have allowed for the analysis of VOCs measured in breath,urine,blood,feces,and skin.New diagnostic approaches founded on sensor-based techniques allow for cheaper and quicker results,and we compare their diagnostic dependability with gas chromatography-and mass spectrometry-based techniques.The future of VOC analysis as a clinical practice and the challenges associated with this transition are also discussed and future research priorities are summarized.

Self-Assembled Junctions of 2D Single-Layer Semiconductors:A Potential Way toward Advanced Photocatalysis

Recently,Zhao et al.have reported achieving impressive solar water splitting efficiency,using self-assembled heterojunctions between two-dimensional(2D)near-single-layer carbon nitride nanosheets.^([1])What the authors did not realize was that their optical absorbance and X-ray photoelectron spectroscopy(XPS),as summarized in Zhao et al.:^([1])figure 1,indicated that their 2D nanosheets were free of Fermi level pinning(FLP).

Business and IndustryCooperation Between China and Central Asia Marches Forward

The China-Central Asia Summit was held in Xi’an,Shaanxi during May 18-19.This was China’s first major home diplomatic event this year and the first on-site summit held between China and the five Central Asian countries in the 31 years since the establishment of diplomatic relations.This meeting not only summarized the long-term cooperation achievements between China and Central Asian countries,but also drew up a new blueprint for future cooperation and development.

国家电网标准创新发展之路——访国家电网有限公司总工程师 孟庆强

Recently, State Grid Corporation of China won the Organization Award of China Standards Innovation and Contribution Award, the top national award on standardization. In an interview with China Standardization, Meng Qingqiang, Chief Engineer of State Grid, shared the company’s exploration in standardization area, and summarized its experience in the innovative development of standards, providing valuable reference for the development of standardization undertaking in China.

Spring, the loely time of making and implementing plans

One of the most important standardization events in China—the annual National Standardization Work Conference was held on February 28 in Beijing,which summarized the standardization work in 2022 and arranged the key tasks this year.

全国标准化工作会议在京召开

The annual National Standardization Work Conference was held in Beijing on February 28,2023.By fully implementing the guiding principles of the 20th National Congress of the Communist Party of China(CPC)and the National Standardization Development Outline,the conference summarized the standardization work in 2022 and deployed the key tasks this year.

Editorial for Advances in Geological and Geotechnical Engineering Research:Vol.5 Issue 4(2023)

1.Introduction The scope of Advances in Geological and Geotechnical Engineering Research journal covers a variety of geology and environmental earth sciences.This journal is preparing to rivet a diversity of research and support the Earth in the imminent future.The achievements of articles presented in this volume are summarized in the following section.

Research progress in development and application of Calotropis gigantea fiber

Calotropis gigantea fiber(CGF)is to a type of seed fiber collected from the fruits of the Calotropis gigantea(CG).From a sustainable biomass resource,CGF is a naturally abundant cellulose fiber with good economic value,excellent biocompatibility and biodegradability.CGF has a high hollow structure and is lightweight,which means CGF has received increasing attention in recent years as the starting material for fabrication of different types of functional materials.In this paper,we have reviewed the morphology and characteristics of CGF,including its biological properties,chemical composition,and physical properties.Additionally,we provide an overview of the recent advancements in the extraction process,toughening treatment process,spinning process,and weaving process of CGF,which have garnered significant attention from researchers in the field.The applications of CGF in the fields of environment processing,antibacterial,and smart materials are also summarized.Finally,the challenges and development prospects of CGF are introduced with the ultimate goal of implementing its potential in real-world applications.

TG-SMR:AText Summarization Algorithm Based on Topic and Graph Models

Recently,automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization.However,most of the computing methods that are used in real systems are based on graph models,which are characterized by their simplicity and stability.Thus,this paper proposes an improved extractive text summarization algorithm based on both topic and graph models.The methodology of this work consists of two stages.First,the well-known TextRank algorithm is analyzed and its shortcomings are investigated.Then,an improved method is proposed with a new computational model of sentence weights.The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods.Finally,through experiments on the DUC2004 and DUC2006 datasets,our proposed improved graph model algorithm TG-SMR(Topic Graph-Summarizer)is compared to other text summarization systems.The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores.It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.