Keyword Information Research Articles

"Empowering the Modern Student”: The Impact of Digital Transformation on Education

In recent years, the rapid advancement of digital technology has significantly reshaped various facets of society, with education standing at the forefront of this transformation. This transformation offers students enhanced access to information, greater flexibility in learning, and the opportunity to develop skills essential for the modern workforce. The major objective of this study is to study the demographical impact on individual and overall factors of digital era transformations in the current society The study is purely descriptive. Random sampling method is used to collect the data from students who ae studying in schools, Arts and Science colleges and Engineering colleges in and around Vijayawada. Sample size of the study is 200. The questionnaire had 15 questions and the respondents were asked to rate their response on a 5point Likert’s scale, 1 being the least and 5 being top for pilot study. The analysis of variance is done by using R Programming, pivot table and charts by using Ms Excel. Findings of this study are the respondents whose age is below 30 and qualification is SSC, Degree leads to significantly influenced the factors of digital era transformations in the society. KEY WORDS INFORMATION AND COMMUNICATION TECHNOLOGY (ICT), DIGITAL ERA TRANSFORMATIONS, ANALYSIS OF VARIANCE, USING R PROGRAMMING, MS EXCEL.

Global Trends and Frontier in Research on Pancreatic Alpha Cells: A Bibliometric Analysis from 2013 to 2023.

Over the past 20 years, much of the research on diabetes has focused on pancreatic beta cells. In the last 10 years, interest in the important role of pancreatic alpha cells in the pathogenesis of diabetes, which had previously received little attention, has grown. We aimed to summarize and visualize the hotspot and development trends of pancreatic alpha cells through bibliometric analysis and to provide research direction and future ideas for the treatment of diabetes and other islet-related diseases. We used two scientometric software packages (CiteSpace 6.1.R6 and VOSviewer1.6.18) to visualize the information and connection of countries, institutions, authors, and keywords in this field. A total of 532 publications, published in 752 institutions in 46 countries and regions, were included in this analysis. The United States showed the highest output, accounting for 39.3% of the total number of published papers. The most active institution was Vanderbilt University, and the authors with highest productivity came from Ulster University. In recent years, research hotspots have concentrated on transdifferentiation, gene expression, and GLP-1 regulatory function. Visualization analysis shows that research hotspots mainly focus on clinical diseases as well as physiological and pathological mechanisms and related biochemical indicators. This study provides a review and summary of the literature on pancreatic alpha cells through bibliometric and visual methods and shows research hotspot and development trends, which can guide future directions for research.

Two Decades of Publications in Journals Dedicated to Autoimmunity: A Bibliometric Analysis of the Autoimmunity Field from 2004 to 2023

To carry out an in-depth analysis of the scientific research on autoimmunity, we performed the first bibliometric analysis focusing on publications in journals dedicated to autoimmunity (JDTA) indexed by science citation index during the period 2004–2023. Using bibliometric analysis, we quantitatively and qualitatively analyzed the country, institution, author, reference and keywords information of publications in JDTA, so as to understand the quantity, publication pattern and publication characteristics of these publications. The co-occurrence networks, clustering map and timeline map were created by CiteSpace and VOSviewer software to visualize the results. The CiteSpace was also used to analyze the strongest citation burst of keywords, which could describe the frequency, intensity and time period of high-frequency keywords, and indicate the research hotspots in the field. A total of 5 710 publications were analyzed, and their annual distribution number was basically stable from 2004 to 2023, fluctuating around 300. The United States and Italy led the way in terms of the number of publications, followed by France and China. For international cooperation, the developed countries represented by the United States cooperate more closely, but the cooperation was localized, reflecting that there was no unified model of autoimmunity among countries. UDICE-French Research Universities had the greatest number of publications. Subsequently, the number of publications decreased slowly with the ranking, and the gradient was not large. Eric Gershwin and Yehuda Shoenfeld stood out among the authors. They had an excellent academic reputation and great influence in the field of autoimmunity. The results of keyword analysis showed that JDTA publications mainly studied a variety of autoimmune diseases, especially SLE and RA. At the same time, JDTA publications also paid special attention to the research of cell function, autoantibody expression, animal experiments, disease activity, pathogenesis and treatment. This study is the first to analyze the publications in JDTA from multiple indicators by bibliometrics, thus providing new insights into the research hotspots and development trends in the field of autoimmunity.

A CNN-BiLSTM algorithm for Weibo emotion classification with attention mechanism

Weibo short text information contains a large amount of network language, emoticons, etc., and due to the long-time span of the content, the emotions of the posts posted by people often change due to time or the occurrence of certain special events. Therefore, traditional sentiment analysis methods are not suitable for this task. This article proposes a CNNs-Bi LSTM sentiment analysis method that integrates attention mechanism. It combines convolutional neural networks and bidirectional short-term memory networks to obtain keyword information in text through attention mechanism, efficiently and accurately realizing data temporal and semantic information mining. Through experimental verification using Weibo public opinion data, the results show that this method achieves higher accuracy compared to other benchmark models and can fully utilize multidimensional matrices to capture rich text features, with certain advantages.

A Novel Address-Matching Framework Based on Region Proposal

Geocoding is a fundamental component of geographic information science that plays a crucial role in various geographical studies and applications involving text data. Current mainstream geocoding methods fall into two categories: geodesic-grid prediction and address matching. However, the geodesic-grid-prediction method’s localization accuracy is hindered by the density of grid partitioning, struggling to strike a balance between prediction accuracy and grid density. Address-matching methods mainly focus on the semantics of query text. However, they tend to ignore keyword information that can be used to distinguish candidates and introduce potential interference, which reduces matching accuracy. Inspired by the human map-usage process, we propose a two-stage address-matching approach that integrates geodesic-grid prediction and text-matching models. Initially, a multi-level text-classification model is used to generate a retrieval region proposal for an input query text. Subsequently, we search for the most relevant point of interest (POI) within the region-proposal area using a semantics-based text-retrieval model. We evaluated the proposed method using POI data from the Beijing Chaoyang District. The experimental results indicate that the proposed method provides high address-matching accuracy, increasing Recall@1 by 0.55 to 1.56 percentage points and MRR@5 by 0.54 to 1.68 percentage points.

Multiple Keyword Search in Cloud Data

Cloud computing is often regarded as a great innovation due to the increasing demand for endless storage space and reliable retrieval services. Several works have been developed on ranked multi-keyword search over cloud data with numerous data owners concerned about privacy. However, most of these methods can be broken by a clever attacker using a combination of a keyword guess and an equivalency test. Also, when searching across data from many data owners, they can't reliably return the top k results to the users. The unreliability of search results and the potential for the exposure of critical keyword information are two obvious drawbacks. To address this issue, we first propose a novel efficient ranked multi-keyword retrieval scheme with keyword privacy for multiple data owners, allowing the cloud server to perform multikeyword search over the cloud data and then return the top-k ranked search results to data users without leaking any keyword and trapdoor information. Furthermore, we demonstrate through stringent security analysis that our method is safe from both internal and external threats. Finally, the results of the performance evaluation show that our scheme outperforms other ranked multi-keyword search systems. Keywords: cloud computing, multi-keyword retrieval, multiple data owner model, privacy sensitive, ranked.

Efficient public-key searchable encryption against inside keyword guessing attacks for cloud storage

Cloud storage offers data users relief from cumbersome management tasks and enhances overall efficiency. However, while it brings convenience, there is also the risk of privacy breaches. To address this, public-key encryption with keyword search (PEKE) presents a solution that balances efficiency, convenience, and security in the context of cloud storage. Nevertheless, PEKS is vulnerable to inside keyword guessing attacks and algorithm substitution attacks, posing a serious threat to its deployment. Cryptographic reverse firewall technique randomizes incoming messages to effectively defend against both types of attacks mentioned earlier through a gateway. However, this approach requires the gateway to store a random number for each keyword, increasing storage costs and potentially exposing keyword information. In response, we propose an improved scheme that inherits the remarkable properties of the method based on cryptographic reverse firewall. Additionally, the proposed scheme eliminates the need for gateways to store random numbers, reducing the management and storage burdens and supports multiple keywords for one document, a feature more aligned with real-world applications. Furthermore, we prove the security of the scheme, which achieves the same security goals as the existing scheme. Finally, we analyze the scheme s efficiency through theoretical analysis and performance evaluation, which demonstrates its efficiency.

Short Text Classification Model based on Pre-trained Language Model with Feature Fusion

In response to the low accuracy of Chinese short text classification in the current data mining field and the defects of existing deep learning models with more model parameters and higher time complexity, this paper proposes a new text classification model - short text classification model (ACBSM) based on pre-trained language model with feature expansion. In ACBSM, to address the problem of high dimensionality of text data without accurate text representation, the Bert model is used to train word vector representation to solve the problem of multiple meanings of a word. From the parallelization acceleration level, a parallel acceleration strategy of two-channel neural network is designed to improve the efficiency of the algorithm in processing massive data. To address the sparsity of text data and the more complex semantics, an attention mechanism is introduced and a CNN model is used to enhance the extraction of keyword information; secondly, BiSRU is used to capture the contextual features of the text, and finally, experimental validation is conducted on a news dataset. The experimental results show that ACBSM improves the accuracy of text classification to 95.83% under the same environment and dataset, and its classification performance is better than other text classification methods.

Program med microcontroller for information security devices

A programmed microcontroller for an electronic information security device is developed. Programming of the microcontroller is carried out by using the Atmega 32a controller and the SI programming language. The program runs inside the microcontroller and interacts with circuit components using PINs, thanks to which information can be read and entered. When implementing encryption and decryption algorithms and the Magma encryption algorithm, four types of transformations are used. The controller has small dimensions, so it is convenient to use in devices for fast encryption and short messages that need to be transmitted urgently. To quickly encrypt large amounts of data, the microcontroller can be connected to a given electronic circuit and the input/ output interaction can be adjusted, which expands its functionality. Keywords information security, microcontroller, encryption, decryption, programming, SI programming language, information

StopGuess: A framework for public-key authenticated encryption with keyword search

Public key encryption with keyword search (PEKS) allows users to search on encrypted data without leaking the keyword information from the ciphertexts. But it does not preserve keyword privacy within the trapdoors, because an adversary (e.g., untrusted server) might launch inside keyword-guessing attacks (IKGA) to guess keywords from the trapdoors. In recent years, public key authenticated encryption with keyword search (PAEKS) has become a promising primitive to counter the IKGA. However, existing PAEKS schemes focus on the concrete construction of PAEKS, making them unable to support modular construction, intuitive proof, or flexible extension. In this paper, our proposal called “StopGuess” is the first elegant framework to achieve the above-mentioned features. StopGuess provides a general solution to eliminate IKGA, and we can construct a bundle of PAEKS schemes from different cryptographic assumptions under the framework. To show its feasibility, we present two generic constructions of PAEKS and their (pairing-based and lattice-based) instantiations in a significantly simpler and more modular manner. Besides, without additional costs, we extend PAEKS to achieve anonymity which preserves the identity of users; we integrate it with symmetric encryption to support data retrieval functionality which makes it practical in resource-constrained applications.

Chinese legal judgment prediction via knowledgeable prompt learning

In recent years, applying AI techniques in the legal field has attracted researchers’ attention. In particular, Legal Judgment Prediction (LJP), which aims to predict accusations based on given case description texts, has attracted much attention from the natural language processing community. However, most of the existing LJP methods are data-intensive. As we know, data annotation in the legal field is expensive. Prompt learning is a recently prevalent methodology, which often achieves surprising results in few-shot or even zero-shot scenarios. We propose a novel method for Chinese LJP based on prompt learning called KnowPrompt4LJP. The method aligns the Chinese LJP task with the pre-training task of a Pre-trained Language Model (PLM) via a prompt template to stimulate the PLM’s recall of learned knowledge. In addition, the well-designed prompt template can enhance the PLM’s understanding of the Chinese LJP task. We also use an external knowledge base to extract keyword information from the Chinese case description texts and incorporate it into the prompt template, thus enhancing the guidance of the prompt template to the PLM. Experimental results on CAIL2018, a high-quality Chinese LJP competition dataset, show that KnowPrompt4LJP achieves far better results than the baselines in zero-shot, few-shot, and full-size training data scenarios. KnowPrompt4LJP can achieve a macro F1 value of 0.70 in the low-resource scenario, which is comparable to the baselines’ results in the data-rich scenario. In the scenario of using full-size training data, KnowPrompt4LJP can achieve a macro F1 value of 0.81.

Improving Abstractive Dialogue Summarization Using Keyword Extraction

Abstractive dialogue summarization aims to generate a short passage that contains important content for a particular dialogue spoken by multiple speakers. In abstractive dialogue summarization systems, capturing the subject in the dialogue is challenging owing to the properties of colloquial texts. Moreover, the system often generates uninformative summaries. In this paper, we propose a novel keyword-aware dialogue summarization system (KADS) that easily captures the subject in the dialogue to alleviate the problem mentioned above through the efficient usage of keywords. Specifically, we first extract the keywords from the input dialogue using a pre-trained keyword extractor. Subsequently, KADS efficiently leverages the keywords information of the dialogue to the transformer-based dialogue system by using the pre-trained keyword extractor. Extensive experiments performed on three benchmark datasets show that the proposed method outperforms the baseline system. Additionally, we demonstrate that the proposed keyword-aware dialogue summarization system exhibits a high-performance gain in low-resource conditions where the number of training examples is highly limited.

Research progress on the treatment of epilepsy with traditional Chinese medicine

BackgroundTraditional Chinese Medicine (TCM) system is a medical system that has been expanding for thousands of years that was formed by the extensive clinical practice experience of many physicians and the accumulation of personal medication habits in China. In TCM, there is a history of long-term medication for epilepsy, the main treatment for epilepsy is TCM drugs and its prescription, supplemented by TCM modalities such as acupuncture therapy, moxibustion therapy, tuina, emotion adjustment therapy, etc. PurposeWith the modernization of TCM, the active ingredients and molecular mechanisms of TCM for epilepsy treatment have been gradually revealed. This review aimed to comprehensively summarize the TCM treatment of epilepsy, focusing on the current TCM drugs and some TCM formulae for the treatment of epilepsy, and to discuss the research progress of TCM for the treatment of epilepsy, and to provide a reference to develop future related studies in this field. Materials and methodsThe mechanism of action of antiepileptic drugs (AEDs) was interpreted from different perspectives by searching online databases and querying various materials identify drugs used in both modern medicine and TCM systems for the treatment of epilepsy. We collected all relevant TCM for epilepsy literature published in the last 30 years up to December 2022 from electronic databases such as PubMed, CNKI and Web of Science, and statistically analyzed the literature for the following keyword information. The search terms comprise the keywords “TCM”, “phytochemistry”, “pharmacological activity”, “epilepsy” and “traditional application” as a combination. Scientific plant names were provided by “The Plant List” (www.theplantlist.org). ResultsEpilepsy is a complex and serious disease of the brain and nervous system. At present, the treatment of epilepsy in modern medicine is mainly surgery and chemotherapy, but there are many serious side effects. By summarizing the treatment of epilepsy in TCM, it is found that there are various methods to treat epilepsy in TCM, mainly TCM drugs and its formulae. Many TCM drugs have antiepileptic effects. Now found that the main effective TCM drugs for the treatment of epilepsy are Curcumae Longae Rhizoma, Scorpio, Acori Tatarinowii Rhizoma, Uncariae Ramulus Cum Uncis and Ganoderma, etc. And the main compounds that play a role in the treatment of epilepsy are curcumin, gastrodin, ligustrazine, baicalin and rhynchophylline, etc. These TCM drugs have played an important role in the treatment of epilepsy in TCM clinic. However, the chemically active components of these TCM drugs are diverse and their mechanisms of action are complex, which are not fully understood and need to be further explored. ConclusionsTCM treats epilepsy in a variety of ways, and with the discovery of a variety of potential bioactive substances for treatment of epilepsy. With the new progress in the research of other TCM treatment methods for epilepsy, TCM will have greater potential in the clinical application of epilepsy.

The impact of digital finance on SMEs financialization: Evidence from thirty million Chinese enterprise registrations

Based on the registration information of 30 million Chinese enterprises, this study innovatively constructs a financialization index based on the text information of enterprise business scope. Then, the impact of digital finance on small and medium-sized enterprise (SME) financialization is examined. Specifically, this study screens out SMEs involved in financial transactions by counting the keyword information in their business scope. The level of SME financialization is measured at the provincial level, based on a large number of registration samples. Empirical results based on panel fixed effects show that digital finance significantly inhibits SME financialization. On average, for each standard deviation increase in digital finance, SME financialization decreases by 0.087 standard deviations. This conclusion remains valid after a series of robustness analyses. A mechanism analysis shows that digital finance inhibits SME financialization by alleviating financing constraints, especially by providing liquidity to SMEs with relatively high financing constraints. In addition, the risk consequences of SME financialization are further examined, and SME financialization is found to significantly increase bankruptcy risk, while digital finance alleviates financing constraints and thus reduces bankruptcy risk. This study provides a new perspective for the governance of SME financialization and the optimization of the survival environment for SMEs in the context of the digital economy.

Public key authenticated searchable encryption against frequency analysis attacks

To retrieve data in form of encryption, Boneh et al. proposed a primitive called the public key encryption with keyword search (PEKS). Unfortunately, PEKS is inherently vulnerable to the keyword guessing attack (KGA). To resist such an attack, Huang et al. introduced an authenticated version of PEKS (i.e., PAEKS), in which the sender not only encrypts the keyword, but also uses his/her secret key to authenticate it. Since then, many PAEKS schemes have emerged to improve the security model of the PAEKS system.However, none of these PAEKS schemes consider frequency analysis attacks (FAA). This attack works by tabulating the frequency distribution of trapdoors generated by user when searching, i.e., placing trapdoors embedded with the same keyword in the same category and recording the number of trapdoors in each category. These frequencies are then compared with the priori knowledge of the frequency of keyword searched by user to extract the keyword embedded in the trapdoor, thereby corrupting the user's search privacy.This work formally analyzes how frequency analysis attacks lead to the leakage of keyword information of the trapdoor in the PAEKS system. Then, to resist against this attack, we propose the first PAEKS scheme with frequency analysis attack resistance to protect the user's search privacy. In order to simplify the management of the sender/receiver's public keys, we further propose our second PAEKS scheme based on the first scheme. Finally, we show that both of these two schemes are proven secure under static assumptions.

Ernie-Gram BiGRU Attention: An Improved Multi-Intention Recognition Model for Air Traffic Control

In recent years, the emergence of large-scale pre-trained language models has made transfer learning possible in natural language processing, which overturns the traditional model architecture based on recurrent neural networks (RNN). In this study, we constructed a multi-intention recognition model, Ernie-Gram_Bidirectional Gate Recurrent Unit (BiGRU)_Attention (EBA), for air traffic control (ATC). Firstly, the Ernie-Gram pre-training model is used as the bottom layer of the overall architecture to implement the encoding of text information. The BiGRU module that follows is used for further feature extraction of the encoded information. Secondly, as keyword information is very important in Chinese radiotelephony communications, the attention layer after the BiGRU module is added to realize the extraction of keyword information. Finally, two fully connected layers (FC) are used for feature vector fusion and outputting intention classification vector, respectively. We experimentally compare the effects of two different tokenizer tools, the BERT tokenizer tool and Jieba tokenizer tool, on the final performance of the Bert model. The experimental results reveal that although the Jieba tokenizer tool has considered word information, the effect of the Jieba tokenizer tool is not as good as that of the BERT tokenizer tool. The final model’s accuracy is 98.2% in the intention recognition dataset of the ATC instructions, which is 2.7% higher than the Bert benchmark model and 0.7–3.1% higher than other improved models based on BERT.

Keyword-Aware Transformers Network for Chinese Open-Domain Conversation Generation

The open-domain conversation generation task aims to generate contextually relevant and informative responses based on a given conversation history. A critical challenge in open-domain dialogs is the tendency of models to generate safe responses. Existing work has often incorporated keyword information in the conversation history for response generation to relieve this problem. However, these approaches interact weakly between responses and keywords or ignore the association between keyword extraction and conversation generation. In this paper, we propose a method based on a Keyword-Aware Transformers Network (KAT) that can fuse contextual keywords. Specifically, the model enables keywords and contexts to fully interact with responses for keyword semantic enhancement. We jointly model the keyword extraction task and the dialog generation task in a multi-task learning fashion. Experimental results of two Chinese open-domain dialogue datasets showed that our proposed model outperformed the methods in both semantic and non-semantic evaluation metrics, improving Coherence, Fluency, and Informativeness in manual evaluation.

THE TECHNOLOGY INDUSTRY ANALYSIS

ABSTRACT The Information and Communication Technology (ICT) industry are rapidly growing and highly competitive, with new technologies and innovations constantly being developed. Companies in the ICT industry are constantly seeking ways to improve the speed, efficiency, and functionality of their products and services and to stay ahead of their competitors in terms of technology and customer experience. This paper aims to provide a comprehensive and in-depth Technology industry analysis to assist companies in achieving tremendous success and maintaining their competence. The analysis will cover various aspects, such as the industry life cycle, competitive landscape, market segments, and untapped Markets in the industry. Conditions that can disrupt the ICT industry, current problems in the ICT industry and the future of technology will be discussed in this paper to form a general perception of the technology industry. A quick summary of the technology sector’s history is also provided to aid in a deeper and better understanding of the industry. Keywords Information and Communication Technology (ICT), the technology industry, industry life cycle, competitive landscape

A remote sensing encrypted data search method based on a novel double-chain

Remote sensing is considered to be a key technology for the development of smart cities. By analysing the data obtained by remote sensing, it is effective to manage and develop smart cities. However, with the rapid urbanisation and the increase in data capacity, how to store and retrieve remote sensing data which have more potential value and privacy with a more effective and safe method has become an imminent issue. In this paper, we propose an efficient verifiable and dynamic multi-keyword searchable encryption scheme for remote sensing data in smart cities. Moreover, we construct a double-chain index to support a multi-keyword search, where the Verification Index Chain is used to identify the users and the Keyword Index Chain is used to store the information of keywords. In addition, we adopt extended bitmap technology to realise update operations and verify the correctness and integrity of the search results. Experiments show that compared to the related existing schemes, the search time of the proposed scheme is reduced by 35% at most, the verification time is reduced by 14% at most and the update time is reduced by 57% at most.

Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.