Construction of an Automatic Bengali Text Summarizer Using Machine Learning Approaches

In our study, we chose python as the programming platform for finding an Automatic Bengali Document Summarizer. English has sufficient tools to process and receive summarized records. However, there is no specifically applicable to Bengali since Bengali has a lot of ambiguity, it differs from English in terms of grammar. Afterward, this language holds an important place because this language is spoken by 26 core people all over the world. As a result, it has taken a new method to summarize Bengali documents. The proposed system has been designed by using the following stages: pre-processing the sample doc/input doc, word tagging, pronoun replacement, sentence ranking, as well as summary. Pronoun replacement has been used to reduce the incidence of swinging pronouns in the performance review. We ranked sentences based on sentence frequency, numerical figures, and pronoun replacement. Checking the similarity between two sentences in order to exclude one since it has less duplication. Hereby, we’ve taken 3000 data as input from newspaper and book documents and learned the words to be appropriate with syntax. In addition, to evaluate the performance of the designed summarizer, the design system looked at the different documents. According to the assessment method, the recall, precision, and F-score were 0.70, 0.82 and 0.74, respectively, representing 70%, 82% and 74% recall, precision, and F-score. It has been found that the proper pronoun replacement was 72%.

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.

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.

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.

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.

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.

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.

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.

Weakly Supervised Abstractive Summarization with Enhancing Factual Consistency for Chinese Complaint Reports

A large variety of complaint reports reflect subjective information expressed by citizens.A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary.Therefore,in this paper,a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words.Furthermore,it considers the importance of entity in complaint reports to ensure factual consistency of summary.Experimental results on the customer review datasets(Yelp and Amazon)and complaint report dataset(complaint reports of Shenyang in China)show that the proposed framework outperforms state-of-the-art approaches in ROUGE scores and human evaluation.It unveils the effectiveness of our approach to helping in dealing with complaint reports.

An Efficient Long Short-Term Memory Model for Digital Cross-Language Summarization

The rise of social networking enables the development of multilingual Internet-accessible digital documents in several languages.The digital document needs to be evaluated physically through the Cross-Language Text Summarization(CLTS)involved in the disparate and generation of the source documents.Cross-language document processing is involved in the generation of documents from disparate language sources toward targeted documents.The digital documents need to be processed with the contextual semantic data with the decoding scheme.This paper presented a multilingual crosslanguage processing of the documents with the abstractive and summarising of the documents.The proposed model is represented as the Hidden Markov Model LSTM Reinforcement Learning(HMMlstmRL).First,the developed model uses the Hidden Markov model for the computation of keywords in the cross-language words for the clustering.In the second stage,bi-directional long-short-term memory networks are used for key word extraction in the cross-language process.Finally,the proposed HMMlstmRL uses the voting concept in reinforcement learning for the identification and extraction of the keywords.The performance of the proposed HMMlstmRL is 2%better than that of the conventional bi-direction LSTM model.

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).

ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with Predictive Machine Learning Approach

Virtual Machines are the core of cloud computing and are utilized toget the benefits of cloud computing. Other essential features include portability,recovery after failure, and, most importantly, creating the core mechanismfor load balancing. Several study results have been reported in enhancing loadbalancingsystems employing stochastic or biogenetic optimization methods.It examines the underlying issues with load balancing and the limitationsof present load balance genetic optimization approaches. They are criticizedfor using higher-order probability distributions, more complicated solutionsearch spaces, and adding factors to improve decision-making skills. Thus, thispaper explores the possibility of summarizing load characteristics. Second,this study offers an improved prediction technique for pheromone level predictionover other typical genetic optimization methods during load balancing.It also uses web-based third-party cloud service providers to test and validatethe principles provided in this study. It also reduces VM migrations, timecomplexity, and service level agreements compared to other parallel standardapproaches.

Topic-Aware Abstractive Summarization Based on Heterogeneous Graph Attention Networks for Chinese Complaint Reports

Automatic text summarization(ATS)plays a significant role in Natural Language Processing(NLP).Abstractive summarization produces summaries by identifying and compressing the most important information in a document.However,there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics.In particular,Chinese complaint reports,generated by urban complainers and collected by government employees,describe existing resident problems in daily life.Meanwhile,the reflected problems are required to respond speedily.Therefore,automatic summarization tasks for these reports have been developed.However,similar to traditional summarization models,the generated summaries still exist problems of informativeness and conciseness.To address these issues and generate suitably informative and less redundant summaries,a topic-based abstractive summarization method is proposed to obtain global and local features.Additionally,a heterogeneous graph of the original document is constructed using word-level and topic-level features.Experiments and analyses on public review datasets(Yelp and Amazon)and our constructed dataset(Chinese complaint reports)show that the proposed framework effectively improves the performance of the abstractive summarization model for Chinese complaint reports.

A Dual Attention Encoder-Decoder Text Summarization Model

A worthy text summarization should represent the fundamental content of the document.Recent studies on computerized text summarization tried to present solutions to this challenging problem.Attention models are employed extensively in text summarization process.Classical attention techniques are utilized to acquire the context data in the decoding phase.Nevertheless,without real and efficient feature extraction,the produced summary may diverge from the core topic.In this article,we present an encoder-decoder attention system employing dual attention mechanism.In the dual attention mechanism,the attention algorithm gathers main data from the encoder side.In the dual attentionmodel,the system can capture and producemore rational main content.The merging of the two attention phases produces precise and rational text summaries.The enhanced attention mechanism gives high score to text repetition to increase phrase score.It also captures the relationship between phrases and the title giving them higher score.We assessed our proposed model with or without significance optimization using ablation procedure.Our model with significance optimization achieved the highest performance of 96.7%precision and the least CPU time among other models in both training and sentence extraction.