Vector Space Model Research Articles

AR Search Engine: Semantic Information Retrieval for Augmented Reality Domain

With the emergence of the metaverse, the popularity of augmented reality (AR) is increasing; accessing concise, accurate, and precise information in this field is becoming challenging on the world wide web. In regard to accessing the right information through search engines, semantic information retrieval via a semantic analysis delivers more relevant information pertaining to the user’s query. However, there is insufficient research on developing semantic information retrieval methods in the AR domain that ranks and clusters AR-based search results in a fair fashion. This paper develops an AR search engine that automatically organizes, understands, searches, and summarizes web documents to enhance the relevancy scores in AR domains. The engine enables users to organize and manage relevant AR documents in various AR concepts and efficiently discover more accurate results in terms of relevancy in the AR field. First, we propose an AR ontology for clustering AR documents into AR topics and concepts. Second, we developed an ontology-based clustering method using the k-means clustering algorithm, vector space model, and term frequency-inverse document frequency (TF-IDF) weighting model with ontology to explore and cluster the AR documents. Third, an experiment was designed to evaluate the proposed AR search engine and compare it with the custom search engine in the AR domains. The results showed that the AR search engine accessed the right information about 42.33% faster and with a 34% better ranking.

A knowledge recommendation approach in design for multi-material 4D printing based on semantic similarity vector space model and case-based reasoning

4D printing refers to a breakthrough technology combining additive manufacturing (AM) and smart materials (SMs) under the effect of an energy stimulation. From the numerous experiments proposed in the literature, which highlight the broad spectrum of shape- and/or property-changing capacities, an emerging research interest is to tackle 4D printing from a design perspective. The so-called design for 4D printing requires rethinking well-established models, methods, and tools to develop innovative devices with changing capacities. It is particularly suitable to focus on the computational design synthesis where decisions on AM techniques and SMs selection have an important influence on the part geometry and materials distribution. Previous research work has developed an ontology for capturing multiple perspective knowledge related to SMs, stimuli, AM, and product design. However, the construction of a complex and interdisciplinary knowledge base is not sufficient and requires knowledge reuse/recommendation mechanisms for guiding designers and engineers. Therefore, the article aims to propose a knowledge recommendation approach in computational design for 4D printing with a semantic similarity vector space model. A specific emphasis is made on multi-material 4D printing where recommendations are needed to determine active and passive materials’ distributions for achieving the desired shape change. An implementation of the approach in a computer-aided design plugin is described and illustrative cases from the literature are introduced to demonstrate its applicability.

How vector space models disambiguate adjectives: A perilous but valid enterprise

AbstractThe present study is part of a larger research project developing computational tools for large-scale corpus-based semantic analyses. One such tool represents semantic structure with vector space models (VSMs). The paper shows that this tool and the models built require a deeper understanding, especially with a view to how its results relate to cognitive theories of meaning. Although token-based VSMs are increasingly used in corpus-based cognitive semantics, we believe it is insufficiently appreciated how alternative parameter settings deal with a range of semantic issues, such as granularity of meaning, prototypicality of the domain of application and interaction with syntactic patterns. For the purpose of this paper, we will focus on only one of those issues, viz. the prototypicality of the domain of application, presenting the results of three of our case studies on the Dutch adjectivesheilzaam,hoekig,hachelijkandgeldig. The models presented are built from a 520MW corpus of contemporary Dutch and Flemish newspapers and by varying parameters such as window size, part-of-speech and frequency thresholds in the selection of features. The resulting VSMs are evaluated through visual analytics: although multidimensional, they can be reduced to 2D and represented in scatterplots where more similar tokens appear closer to each other. The color-coding with manual sense tags employed here makes it possible to compare the groupings provided by human annotators with those of the computational models in a way consistent with the cognitive approach to meaning and categorization.

Research on Personalized Recommendation Method of Educational Resources Based on Learner Behavior Analysis

The explosion of online learning resources makes the research on personalized recommendation of educational resources increasingly prominent. Based on the theory of learner behavior analysis, this paper analyzes the learning behavior logs of the online learning platform, and constructs a personalized recommendation method for educational resources. It analyzes the learner behavior from the three dimensions of basic attributes, behavior characteristics and result characteristics, and solves the problem of resource analysis. In the simulation process, the method uses the vector space model to complete the modeling of learner behavior, and realizes the division of learner groups based on learner behavior, and further evaluates and optimizes the division of learner groups. In order to further verify the clustering effect in combination with the actual data, the classification results are used for example analysis: the experimental results show that the sample points in the educational resource space are divided into 23 categories according to the distance relationship, and [Formula: see text] is the best. The silhouette coefficient value is 0.54436, and the Calinski–Harabasz score value is 1464.9, which effectively improves the clustering effect of the personalized recommendation method.

Retrieval methodology for similar NPP LCO cases based on domain specific NLP

Nuclear power plants (NPPs) have technical specifications (Tech Specs) to ensure that the equipment and key operating parameters necessary for the safe operation of the power plant are maintained within limiting conditions for operation (LCO) determined by a safety analysis. The LCO of Tech Specs that identify the lowest functional capability of equipment required for safe operation for a facility must be complied for the safe operation of NPP.There have been previous studies to aid in compliance with LCO relevant to rule-based expert systems; however, there is an obvious limit to expert systems for implementing the rules for many situations related to LCO. Therefore, in this study, we present a retrieval methodology for similar LCO cases in determining whether LCO is met or not met. To reflect the natural language processing of NPP features, a domain dictionary was built, and the optimal term frequency-inverse document frequency variant was selected. The retrieval performance was improved by adding a Boolean retrieval model based on terms related to the LCO in addition to the vector space model. The developed domain dictionary and retrieval methodology are expected to be exceedingly useful in determining whether LCO is met.

Approach to Predict Software Vulnerability Based on Multiple-Level N-gram Feature Extraction and Heterogeneous Ensemble Learning

Software vulnerabilities are one of the roots of computer security problems. The traditional static analysis and dynamic analysis methods based on software source code mainly have some deficiencies, such as high false positive rate, high false negative rate and insufficient semantic information captured. Nevertheless, the application of machine learning, Natural Language Processing and other technologies in software vulnerability prediction can effectively mitigate such issues. This paper proposed a vulnerability prediction method based on multiple-level N-gram feature extraction and heterogeneous ensemble learning. First, by code intermediate representation and constructing a multiple-level N-gram feature generation model, two kinds of N-gram semantic features with different window size and different granularity at word and char level were extracted to retain the semantic and structural information of code. Second, TF–IDF was used to construct the vector space model as the input of prediction model. As a single classifier was prone to overfitting and poor generalization, this paper conducted benchmark testing on five classical machine learning algorithms (NB, SVM, DT, LR, RF), and then combined four (SVM, DT, LR, RF) among them, which had better performance as the base classifiers to form the stacking heterogeneous ensemble method to build the vulnerability prediction model. Finally, the proposed method was verified on buffer overflow vulnerability and resource management vulnerability datasets, with a lowest false positive rate and false negative rate which can reach 1.58% and 4.06%, respectively.

Trigonometric words ranking model for spam message classification

AbstractThe significant increase in the volume of fake (spam) messages has led to an urgent need to develop and implement a robust anti‐spam method. Several of the current anti‐spam systems depend mainly on the word order of the message in determining the spam message, which results in the system's inability to predict the correct type of message when the word order changes. In this paper, a new framework is proposed for anti‐spam filtering that does not depend on the word's position in the message, called the Trigonometric Words Ranking Model (TWRM). The proposed TWRM is based on restricting spammers over the network by measuring a theta angle, which is a relationship between message weight and spam. TWRM classifies messages by calculating the rank of each word that places the corresponding message in the correct class. The rank of words is derived from their frequency in the entire data category. The proposed method is applied to three datasets of spam messages: UCI spam email, Enron spam, and TREC spam data. The proposed model is proven as more efficient than the Minhash and vector space models. Moreover, the TWRM performance provided better retrieval time and defence, which is reflected in the accuracy of (99.64%), which is higher than that of Minhash (88.79%) and vector space (92.59%).

Privacy-Preserving Threshold Spatial Keyword Search in Cloud-Assisted IIoT

Cloud-assisted Industrial Internet of Things (IIoT) systems are increasingly deployed in various applications such as location-based services. Outsourcing data to cloud servers can help minimize local data storage and computation overheads, but it may introduce security and privacy concerns. Therefore, privacy-preserving spatial keyword search has been extensively explored in the literature. However, existing solutions still reveal the order of the spatio-textual similarity values between the query point and all data objects, and do not support searching for arbitrary geometric regions. To solve these issues, in this article we propose a privacy-preserving threshold spatial keyword search (TSKS) scheme. Specifically, we use the polynomial fitting technology, vector space model, and randomizable matrix multiplication technology to allow the cloud server to find relevant objects that are within some arbitrary geometric range and contain all query keywords. Finally, formal security analysis proves that our scheme can protect the privacy of data sets and queries, and extensive experiments demonstrate that our scheme is efficient and practical.

A Personalized Recommendation Method for Ethnic Music Teaching Resources Based on Video Tags

In order to improve the effect of ethnic music teaching and improve the recommendation accuracy of teaching resources, this paper proposes a personalized recommendation method for ethnic music teaching resources based on video tags. A national music teaching resource library is constructed as the resource source, and the K-nearest neighbor (KNN) algorithm is used to classify the teaching resources in the resource library. According to the results of resource classification, describe the characteristics of teaching resources, so as to recommend suitable teaching resources to corresponding students to meet individual needs. According to the main characteristics of tags, a tagging system is established, and at the same time, the decay of user interest over time is considered, and weights are assigned to user video interest tags in descending order of time, and the total user interest tag vector is calculated. In this paper, the obtained user total interest label vector is normalized. The vector space model is used to represent teaching resources and user interests, so as to realize personalized recommendation of teaching resources. The experimental results show that the accuracy rate of the proposed method is higher than 90%, the recall rate is more than 97%, and the F1 value is larger, and the convergence is better, reflecting the better recommendation effect of the method.

Security breaches and modifications on cybersecurity disclosures

Research Question: How do firms approach their cybersecurity disclosure obligations, especially for those who experienced a cyber-attack? Prior research has found that year-after-year modification on textual disclosures adds more appreciable information that makes it more relevant. But do firms provide meaningful disclosures to promote market transparency? Motivation: Because of growing cybersecurity threats in recent years, the U.S. Securities and Exchange Commission (SEC) has issued several regulations and guidance that emphasized on the disclosure of material information on cybersecurity. Given that the mandatory risk factor disclosures in SEC Form 10-K is the first place firms are encouraged to disclose cybersecurity-related assessment, it is important to examine how firms approach their disclosure expectations. Idea: To examine whether firms respond to cyber-attacks with meaningful disclosures, we use the Vector Space Model (VSM) to calculate disclosure modifications before and after major cyber-attack incident. Data: We extracted cybersecurity breach incidents from the Data Breach Database, a centralized and global database of data breaches maintained by a leading security company. In addition, we use the SEC data depository to find firms’ 10-K disclosures. Findings: We find that firms modify their cybersecurity disclosures by increasing the quantity of disclosures, but not necessarily the quality of disclosures as measured by document similarity. Furthermore, we find partial evidence that the degree of modification is positively associated with the severity of cyber-attacks. Contribution: Our evidence suggests that firms tend to use boilerplate language to disclose cybersecurity-related issues. This finding is consistent with prior research. That is, consistent with prior literature, the information content in public company 10-Ks is limited. We find that this seems to be the case as well when it comes to cybersecurity disclosures.

Text Clustering and Economic Analysis of Free Trade Zone Governance Strategies Based on Random Matrix and Subject Analysis

The steps of generating basic data by the LDA model and calculating text by the weighted algorithm have a good effect on text clustering. In this paper, the LDA topic model is used to effectively improve the accuracy of strategy text clustering. FTZ economics text clustering simulates FTA economics text data and economic data, imports economics and economic figures and word lists, and uses the traditional vector space model for factor representation. After that, the text vectors are independent of each other, ignoring the semantic relationship, which affects the clustering analysis results. A Chinese text clustering algorithm based on semantic clustering is proposed. Based on the principle of cooccurrence and semantic relevance of words, the algorithm uses the collocation vector of feature words to construct semantic clustering; find the document vector with embedded semantic information. Finally, document vectors with embedded semantic information are used. Finally, K vector is used for cluster analysis. The simulation analysis in this paper shows that the economic growth of the free trade zone is the largest under the economics guidance, which can reach 15%.

ExpFinder: A hybrid model for expert finding from text-based expertise data

Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques such as vector space models, document language models, and graph-based models. In this paper, we propose ExpFinder , a new hybrid model for expert finding, that integrates a novel N-gram vector space model, denoted as nVSM, and a graph-based model, denoted as μCO-HITS, that is a proposed variation of the CO-HITS algorithm. The key of nVSM is to exploit recent inverse document frequency weighting method for N-gram words, and ExpFinder incorporates nVSM into μCO-HITS to achieve expert finding. We comprehensively evaluate ExpFinder on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that ExpFinder is an highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.

Multi-objective optimization of angles-only navigation and closed-loop guidance control for spacecraft autonomous noncooperative rendezvous

In this paper, the angles-only navigation and closed-loop guidance control for spacecraft autonomous noncooperative rendezvous in geosynchronous Earth orbit (GEO) are studied, and a multi-objective optimization model is established considering various constraints. The optimization objectives include the total flight time, the total fuel cost, and the observability of angles-only navigation. First of all, the angles-only navigation model is established, including relative dynamics and Line-of-Sight (LoS) measurement models. Then, the closed-loop guidance control covariance is analyzed, and the navigation and control covariance analysis models are established. And then, a multi-objective optimization model is established, including the objective functions, optimization variables, constraints, and the mathematical model. After that, a crowding mechanism based on vector space model (VSM) is proposed to improve the fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, and a VSM-NSGA-II is constructed to solve the proposed multi-objective optimization model. Finally, a set of Pareto optimal solutions are simulated and analyzed by using closed-loop guidance control covariance analysis and Monte Carlo simulation, thereby proving the effectiveness of the multi-objective optimization model proposed in this paper.

Design of Personalized Push Algorithm of Hot Social News Based on User Interest Model

As a result of rapid advances in information technology, the volume of information on the Internet is expanding at a breakneck rate. The World Wide Web has evolved into a vast and intricate information space. People have shifted from information deficiency to information overload. The characteristics of Internet information are dispersion, disorder, and mass. A challenging research topic is how to quickly, accurately, and efficiently extract vital information from vast information resources. Web search is becoming one of the Internet field’s study centers and focal points. Traditional web search algorithms focus on the link structure of the web and the hierarchical weight of web pages while ignoring the behavior of users, resulting in some search results that are insufficient and inaccurate. In addition, because each web page's hub value and authority value are calculated iteratively, web search is inefficient and susceptible to dispersion and generalization. This study fully integrates the user’s interest behavior and relevant, intelligent optimization algorithms to address the shortcomings of the traditional World Wide Web search algorithm, based on a synthesis and analysis of relevant domestic and international research. A method of user interest model construction and update for news recommendation is proposed to address the problem of user interest model construction and user interest drift in the news recommendation system. Initially, the original user interest model is constructed using a bisection K-means clustering algorithm and a vector space model. Subsequently, the forgetting function is constructed using the Ebbinghaus forgetting curve, and the user interest model is time-weighted to achieve the goal of updating the user interest model. User-based collaborative filtering recommendations and item-based collaborative filtering suggestions serve as the experiment’s baseline. The experimental results suggest that the recommendation performance of the original user interest model is enhanced, with the F value increasing by 4%. The modified model’s F value has increased by 1.3% compared to the previous version.

Text Similarity Detection Between Documents Using Case Based Reasoning Method with Cosine Similarity Measure (Case Study SIMNG LPPM Universitas Sriwijaya)

LPPM Universitas Sriwijaya is an institution that coordinates academic research and community service inside Universitas Sriwijaya. In carrying out the duty, LPPM assesses every proposal’s originality which would be impossible to do manually in the future due to massive data growth. Thus, automatization for the proposal's originality check is needed. The Case Based Reasoning method is used in this research because it allows the system to reuse the information that has been obtained to find documents that are similar to the test document. In this study, the data is represented in the form of the Vector Space Model and uses Cosine Similarity to measure document to document similarity. The data is represented by giving weight for each part of the tested documents. In this study, four formulas from previous research will be used for term weighting then the final result will be compared. The process begins by extracting data, separating parts of the document, figuring the similarity value of the test document to the case base utilizing Cosine Similarity Measure, results filtering with a certain threshold, summarizing the calculation results, and finally preserving the results obtained to be reused in the next calculation. The results of this study indicate that the text-similarity detection between documents has been successfully carried out using the proposed method with the best sensitivity level and the fastest computation time achieved in configuration II.

A semantic and intelligent focused crawler based on semantic vector space model and membrane computing optimization algorithm

A semantic and intelligent focused crawler based on semantic vector space model and membrane computing optimization algorithm

Anti-monopoly supervision model of platform economy based on big data and sentiment.

With the advent of the cloud computing era, big data technology has also developed rapidly. Due to the huge volume, variety, fast processing speed and low value density of big data, traditional data storage, extraction, transformation and analysis technologies are not suitable, so new solutions for big data application technologies are needed. However, with the development of economic theory and the practice of market economy, some links in the industrial chain of natural monopoly industries already have a certain degree of competitiveness. In this context, the article conducts a research on the anti-monopoly supervision mode of platform economy based on big data and sentiment analysis. This paper introduces the main idea of MapReduce, the current software implementation specifies a Map function that maps a set of key-value pairs into a new set of key-value pairs. It specifies a concurrent Reduce function that guarantees that each of all mapped key-value pairs share the same set of keys. establishes a vector space model, and basically realizes the extraction of text emotional elements. It introduces the theoretical controversy of antitrust regulation of predatory pricing behavior of third-party payment platforms, and conducted model experiments. The experimental results show that the throughput of 40 test users in 1 h of test is determined by two factors, QPS and the number of concurrent, where QPS = 40/(60*60) transactions/second. The time for each test user to log in to the system is 10 min, and the average response time is 10*60 s, then the number of concurrency = QPS*average response time = 40/(60*60)*10*60 = 6.66. This paper has successfully completed the research on the anti-monopoly supervision model of platform economy based on big data and sentiment analysis.

An Analysis of the Historical Process of Cultural Confidence in Ideological and Political Education Based on Deep Learning

Ideological and political education can improve people’s ideological and moral character and psychological quality, improve social governance, and promote the harmonious development of society. However, the current data of ideological and political education have the trend of mass and diversification. In order to improve the effect of ideological and political education, this paper proposes an analysis method of the historical process of cultural self-confidence of ideological and political education based on in-depth learning. By preprocessing the ideological and political education data to obtain text keywords, the principal component analysis method is improved to reduce the dimension of the ideological and political education data, and the vector space model is used to complete the text balance processing of the reduced dimension data. The data sensitivity after dimensionality reduction is analyzed by data training, the historical text data mining model is constructed to extract data features, and the bidirectional recurrent neural network is used to complete data extraction. The semantic features of the extracted data are obtained by using the forward and reverse feature generation methods, and the historical process of the cultural confidence of ideological and political education is analyzed by using the two-way generation method. The experimental results show that the accuracy rate of the historical process analysis method of cultural self-confidence in ideological and political education based on deep learning is 95%, the recall rate is 94%, and the degree of collaborative performance is good.

Word embedding dimensionality reduction using dynamic variance thresholding (DyVaT)

Natural language processing (NLP) provides a framework for large-scale text analysis. One common processing method uses vector space models (VSMs) which embed word attributes, called features, into highly dimensional vectors. Comprehensive VSMs are generated on sources such as the GoogleNews archive. A thesaurus, a collection of semantically-related words, can be created for a particular root word using cosine similarity with a given VSM. Many methods have been developed to reduce the complexity of these models by maintaining useful semantic information while discarding non-informative features. One such method, variance thresholding, retains high-variance features above a manually-determined threshold, providing higher differentiation between words for classification purposes. Our research developed a dimension-reducing methodology called dynamic variance thresholding (DyVaT). DyVaT reduces the specificity of word embeddings by maintaining low-variance features, allowing for a broader thesaurus preserving semantic similarity. A dynamic variance threshold, determining which low-variance features are retained, is selected using the kneedle algorithm, improving the current results. Our test case for examining the efficiency of DyVat in creating a contextual thesaurus is the visual, auditory and kinesthetic learning style context. We conclude that DyVaT is a valid method for generating loosely-connected word collections with potential uses in NLP classification or clustering tasks.

Professional English Translation Corpus Under the Binomial Theorem Coefficient

Abstract This paper proposes a professional English translation corpus based on the binomial theorem coefficients. This paper combines the binomial theorem with the vector space model to analyze the similarity between machine translation and human translation in English translation. The study results show that both human translation and machine translation can experience translation inaccuracies. The English translation based on the binomial theorem is more objective and accurate. The professional English translation corpus established by this method can help us solve the problem of professional terminology translation. The research in this paper has a specific reference value for improving the accuracy of the English translation.