Vector Space Model Research Articles

Discovery Model Based on Analogies for Teaching Computer Programming

Teaching the fundamentals of computer programming in a first course (CS1) is a complex activity for the professor and is also a challenge for them. Nowadays, there are several teaching strategies for dealing with a CS1 at the university, one of which is the use of analogies to support the abstraction process that a student needs to carry for the appropriation of fundamental concepts. This article presents the results of applying a discovery model that allowed for the extraction of patterns, linguistic analysis, textual analytics, and linked data when using analogies for teaching the fundamental concepts of programming by professors in a CS1 in university programs that train software developers. For that reason, a discovery model based on machine learning and text mining was proposed using natural language processing techniques for semantic vector space modeling, distributional semantics, and the generation of synthetic data. The discovery process was carried out using nine supervised learning methods, three unsupervised learning methods, and one semi-supervised learning method involving linguistic analysis techniques, text analytics, and linked data. The main findings showed that professors include keywords, which are part of the technical computer terminology, in the form of verbs in the statement of the analogy and combine them in quantitative contexts with neutral or positive phrases, where numerical examples, cooking recipes, and games were the most used categories. Finally, a structure is proposed for the construction of analogies to teach programming concepts and this was validated by the professors and students.

Text Mining of Research Articles Using Clustering Approach

Widening of research articles publication in various streams of research is epidemic. Tracking down of an appropriate article from the research archive is considered to be vast and also time consuming. Research articles are clustered based on their respective domain and it plays an important role for researchers to retrieve articles in a faster manner. Hence a commonly practiced search mechanism, namely domain name search has been applied to retrieve appropriate documents and articles. When new domains of documents are added to the repository it’s to spot keywords and boost the corresponding domains for proper classification. Classification techniques namely Random forest classifier, SVM and TF-IDF have been used to classify articles and compare its processing time. TF-IDF (Term Frequency-Inverse Document Frequency) has been further proposed to transform the corpus into vector space model. Clustering algorithm such as K-Means and Hierarchical have been used to cluster articles. Finally, the processing time of SVM is better than random forest classifier and TF-IDF and K-Means gives a better understanding than Hierarchical algorithm.

Web-Based Information Search System Development Using a Semantic Network

Finding information from a large collection of documents is a complicated task; therefore, we need a method called an information retrieval system. Several models that have been used in information retrieval systems include the Vector Space Model (VSM), DICE Similarity, Latent Semantic Indexing (LSI), Generalized Vector Space Model (GVSM), and semantic-based information retrieval systems. The purpose of this study was to develop a semantic network-based search system that will find information based on keywords and the semantic relationship of keywords provided by users. This cannot be done by most search systems that only work based on keyword matching or similarities. The Waterfall development model was used, which divides the development stages into five steps, namely: (1) requirements analysis and definition; (2) system and software design; (3) implementation and unit testing; (4) integration and system testing; and (5) operation and maintenance. The developed system/application was tested by trying to find information based on various combinations of keywords provided by the user. The results showed that the system can find information that matches the keyword, and other relevant information based on the semantic relationships of these keywords.
 Keywords: information retrieval, search system, semantic network, web-based application

Analysis and implementation of the bi-polar slope one algorithm with the content base filtering method in producing culinary place recommendations in kuningan regency

The recommendation system is an analysis model to obtain recommendations that use filtering techniques to produce recommendations as desired. One of the prediction algorithms is Bipolar Slope One which is a development of the Slope One algorithm to predict an event. Previous studies have shown that the results of the recommendations on the Bipolar Slope One algorithm show a mismatch between the type of recommended item and the type of item that has been rated by the active user, because the rating prediction process in the Bipolar Slope One algorithm does not pay attention to the type or content of the item, but pay more attention to the similarity of rating patterns. This study uses the Content-Based Filtering method to form a profile based on the attributes of an item which will make an assessment based on the analysis of the similarity of the user profile with the vector space model. Application users get information about which culinary delights have an item profile that is expected by him. Culinary players get information about what item profile I expect by the User.

Comparison of two methods on vector space model for trust in social commerce

The study of dealing with searching information in documents within web pages is Information retrieval (IR). The user needs to describe information with comments or reviews that consists of a number of words. Discovering weight of an inquiry term is helpful to decide the significance of a question. Estimation of term significance is a basic piece of most information retrieval approaches and it is commonly chosen through term frequency-inverse document frequency (TF-IDF). Also, improved TF-IDF method used to retrieve information in web documents. This paper presents comparison of TF-IDF method and improved TF-IDF method for information retrieval. Cosine similarity method calculated on both methods. The results of cosine similarity method on both methods compared on the desired threshold value. The relevance documents of TF-IDF method are more extracted than improved TF-IDF method.

Comparative Study on Feature-Based Scoring Using Vector Space Modelling System

This paper shows the importance of automated scoring (AS) and that it is better than human graders in terms of degree of reproducibility. Considering the potential of the automated scoring system, there is further a need to refine and develop the existing system. The paper goes through the state of the art. It presents the results concerning the problems of existing systems. The paper also presents the semantic features that are indispensable in the scoring system as they have complete content. Moreover, in the present research, a huge deviation has been exhibited by the system which has been shown later in performance analysis of the study, and this clearly indicates the novelty and improved results of the system. It explains the algorithms included in the methodology of this proposed system. The novelty of our work consists in the use of its own similarity function and its notation mechanism. It does not use the cosine similarity function between two vectors. This paper describes and develops a more accurate system which employs a statistical method for scoring. This system adopts and integrates rule-based semantic feature analysis.

Dynamic malware attack dataset leveraging virtual machine monitor audit data for the detection of intrusions in cloud

AbstractIn this new era of cloud computing, Intrusion Detection System (IDS) is very essential for the continual monitoring of computing resources for signs of compromise since the number of attack vectors and malware are in increase. Only few IDS datasets are publicly available and those available are outdated, lack cloud‐specific attacks. This article presents a novel dataset based on Virtual Machine Introspected data for the implementation of IDS in cloud. The dataset was generated from the behavioral characteristics of malware and benign sample execution traces on virtual machines using Virtual Machine Introspection (VMI) technique. A vector space model based on system call approach is applied to analyze the behavioral characteristics for the generation of proposed dataset. The purpose of this study is to compare the proposed dataset with existing datasets and evaluate the effectiveness of these datasets by applying Machine Learning (ML) algorithms with 10‐fold cross‐validation. The ML algorithms used in the experiments are C4.5, Random Forest, JRip, NaiveBayes, K‐Nearest Neighbors (KNN), and Support Vector Machine (SVM). The effectiveness of detecting intrusions using proposed dataset is promising compared with other datasets in‐terms of intrusion detection accuracy, recall value, precision, and F1‐score metrics. For example, the intrusion detection accuracy in proposed dataset is 0.11% improved than UNM dataset, 6.28% higher than ADFA dataset, and 1.88% higher than LID dataset with C4.5 algorithm. Therefore, the proposed dataset is best suitable for implementing IDS for cloud.

Frequency-Dependent Cortical Interactions during Semantic Processing: An Electrocorticogram Cross-spectrum Analysis Using a Semantic Space Model.

Convergent evidence has demonstrated that semantics are represented by the interaction between a multimodal semantic hub at the anterior temporal lobe (ATL) and other modality-specific association cortical areas. Electrocorticogram (ECoG) recording with high spatiotemporal resolutions is efficient in evaluating such cortical interactions; however, this has not been a focus of preceding studies. The present study evaluated cortical interactions during picture naming using a novel ECoG cross-spectrum analysis, which was formulated from a computational simulation of neuronal networks and combined with a vector space model of semantics. The results clarified three types of frequency-dependent cortical networks: 1) an earlier-period (0.2-0.8s from stimulus onset) high-gamma-band (90-150Hz) network with a hub at the posterior fusiform gyrus, 2) a later-period (0.4-1.0s) beta-band (15-40Hz) network with multiple hubs at the ventral ATL and posterior middle temporal gyrus, and 3) a pre-articulation theta-band (4-7 Hz) network distributed over widely located cortical regions. These results suggest that frequency-dependent cortical interactions can characterize the underlying processes of semantic cognition, and the beta-band network with a hub at the ventral ATL is especially associated with the formation of semantic representation.

Deep phenotyping unstructured data mining in an extensive pediatric database to unravel a common KCNA2 variant in neurodevelopmental syndromes

PurposeElectronic health records are gaining popularity to detect and propose interdisciplinary treatments for patients with similar medical histories, diagnoses, and outcomes. These files are compiled by different nonexperts and expert clinicians. Data mining in these unstructured data is a transposable and sustainable methodology to search for patients presenting a high similitude of clinical features. MethodsExome and targeted next-generation sequencing bioinformatics analyses were performed at the Imagine Institute. Similarity Index (SI), an algorithm based on a vector space model (VSM) that exploits concepts extracted from clinical narrative reports was used to identify patients with highly similar clinical features. ResultsHere we describe a case of “automated diagnosis” indicated by Dr. Warehouse, a biomedical data warehouse oriented toward clinical narrative reports, developed at Necker Children’s Hospital using around 500,000 patients’ records. Through the use of this warehouse, we were able to match and identify two patients sharing very specific clinical neonatal and childhood features harboring the same de novo variant in KCNA2. ConclusionThis innovative application of database clustering clinical features could advance identification of patients with rare and common genetic conditions and detect with high accuracy the natural history of patients harboring similar genetic pathogenic variants.

A knowledge management-based engineering design system for highway design projects

ABSTRACT Construction engineering design is highly dependent on previous experience so experienced engineers normally produce the design. This practice allows the leakage of knowledge when experienced engineers leave and is costly in terms of the time required for design work. This study proposes a knowledge management–based engineering design system (KMEDS) that supports engineering design of highways and roads. A text mining technique using a vector space model (VSM) is used to retrieve documents. Nine historical design cases containing 1,683 engineering documents are collected as the knowledge base. A real-world express highway design project is used to test the feasibility of the proposed method. A questionnaire survey is then conducted to evaluate the proposed KMEDS. The approval rate is 88% in terms of system correctness and 86% in terms of time savings.

LeDoCl : A Semantic Model for Legal Documents Classification using Ensemble Methods

NLP is one of the components of Machine Learning. Topic Modeling is a sub component of information retrieval Information Retrieval is a broad domain research in Natural Language Processing (NLP). This downside has been broadly studied in the perspective of cluster algorithms like K–means and K-fold, that tends to converge to at least one of diverse native issue counting on the selection of format method. To overcome the instabilities and assumptions in existing systems such as Vector Space Model (VSM) and SVD, Semantic based topic modeling (SLDA) and ensemble model with generation and integration is proposed. In the case of topic modelling, instability is visible in two distinct aspects. First, when the topic descriptors are examined over multiple runs. During which there will be considerable change in the term rankings and few terms may appear or disappear completely as well. Next, there could be instability due to the extent to which topics have association with document, through several executions. In the proposed system, ensemble learning comprises of algorithms Kernel Support Vector Machine (KSVM) and Random Forest algorithm which overcomes the instability. The first issue of appearance and disappearance of words between multiple runs is overcome by Gibbs Sampling based Semantic LDA (GSLDA). The second issue of alignment of topics with document is aided by using ESLDA. This ensemble SLDA algorithm show increased accuracy in terms of retrieval and reduced time interval compared to conventional models. The accuracy increases up to 98% using ESLDA compared to SLDA (82%) and term frequency methods (78%).

A graphical decomposition and similarity measurement approach for topic detection from online news

• We propose a Capsule Semantic Graph (CSG) to represent the news documents. • The CSG can effectively capture the relationship between words and semantic of news documents. • We introduce the graph kernel to measure the similarity between CSGs. • Our method can better solve the problems of news representation and similarity measurement. • Our method has great significance for topic detection from news. Topic detection aims to discover valuable topics from the massive online news. It can help people to capture what is happening in real world and alleviate the burden of information overload. It also has great significance since the online news is experiencing an explosive growth. Topic detection is typically transformed into a document clustering problem, whose core idea is to cluster news documents that report on the same topic to the same group based on document similarity. Due to the complex structure and long length of news documents, the similarity measurement of news is very challenging. Existing term-based methods represent news documents based on a set of informative keywords in the document with a vector space model (VSM) and then the relationship between documents is calculated by cosine similarity . However, VSM ignores the relationship between words and has sparse semantics, which leads to low precision of topic detection. In recent years, the probabilistic methods and the graph analytical methods have been proposed for topic detection. However, both of them have high time complexity. To cope with these problems, we first present a novel document representation approach based on graphical decomposition, which decomposes each news document into different semantic units and then relationship between the semantic units is constructed to form a capsule semantic graph (CSG). The CSG can retain the relationship between words and alleviate the sparse semantics compared to VSM representation. We next introduce the graph kernel to measure the similarity between the CSGs based on their substructures. Finally, we use an incremental clustering method to cluster the news documents, in which the documents are represented by CSGs and the similarity between documents is calculated by graph kernel. The experiment results on three standard datasets show that our method obtains higher precision, recall and F1 score than several state-of-the-art methods. Moreover, the experiment results on a large news dataset show that our CSG-SM has lower time complexity than probabilistic methods and graph analytical methods.

Exploring semantic differences between the Indonesian prefixesPE-andPEN-using a vector space model

AbstractIndonesian has two prefixes,PE-andPEN-, that are similar in form and meaning, but are probably not allomorphs. In this study, we applied a distributional vector space model to clarify whether these prefixes have discriminable semantics. Comparisons of pairs of words within and across morphologically defined sets of words revealed that cosine similarities of pairs consisting of a word withPE-and a word withPEN-were reduced compared to pairs of onlyPE-words, or of onlyPEN-words. Furthermore, nouns withPE-were more similar to their base words than was the case for words withPEN-. The specialized use ofPE-for words denoting agents, and the specialized use ofPEN-for denoting instruments, was also visible in the semantic vector space. These differences in the semantics ofPE-andPEN-thus provide further quantitative support for the independent status ofPE-as opposed toPEN-.

The search for science and technology verses in Qur’an and hadith

Currently, the Vector Space Model algorithm has been widely implemented for the document search feature because of its reliability in retrieving information. One of them in the search for verses of the Qur'an based on the translation. However, if the phrase or word used is different (even though it has one meaning) with the word in the document in the database, the system will not display the verse. As we know that the Qur'an has a very deep meaning, so an interpretation of the verse is needed. Therefore, this research focuses on implementing the Vector Space Model (VSM) algorithm for searching verses and hadiths in science and technology by using the discussion parameters of these verses or hadiths. The test results obtained with 20 keyword samples using metric recall were 81% with an average time of 2.24 seconds.

An open-source framework for ExpFinder integrating N-gram vector space model and μCO-HITS

<h2>Abstract</h2> Finding experts drives successful collaborations and high-quality product development in academic and research domains. To contribute to the expert finding research community, we have developed ExpFinder which is a novel ensemble model for expert finding by integrating an <mml:math><mml:mi>N</mml:mi></mml:math>-gram vector space model (<mml:math><mml:mi>n</mml:mi></mml:math>VSM) and a graph-based model (<mml:math><mml:mi>μ</mml:mi></mml:math>CO-HITS). This paper provides descriptions of ExpFinder's architecture, key components, functionalities, and illustrative examples. ExpFinder is an effective and competitive model for expert finding, significantly outperforming a number of expert finding models as presented in Kang et al. (2021).

Idea plagiarism detection with recurrent neural networks and vector space model

PurposeNatural languages have a fundamental quality of suppleness that makes it possible to present a single idea in plenty of different ways. This feature is often exploited in the academic world, leading to the theft of work referred to as plagiarism. Many approaches have been put forward to detect such cases based on various text features and grammatical structures of languages. However, there is a huge scope of improvement for detecting intelligent plagiarism.Design/methodology/approachTo realize this, the paper introduces a hybrid model to detect intelligent plagiarism by breaking the entire process into three stages: (1) clustering, (2) vector formulation in each cluster based on semantic roles, normalization and similarity index calculation and (3) Summary generation using encoder-decoder. An effective weighing scheme has been introduced to select terms used to build vectors based on K-means, which is calculated on the synonym set for the said term. If the value calculated in the last stage lies above a predefined threshold, only then the next semantic argument is analyzed. When the similarity score for two documents is beyond the threshold, a short summary for plagiarized documents is created.FindingsExperimental results show that this method is able to detect connotation and concealment used in idea plagiarism besides detecting literal plagiarism.Originality/valueThe proposed model can help academics stay updated by providing summaries of relevant articles. It would eliminate the practice of plagiarism infesting the academic community at an unprecedented pace. The model will also accelerate the process of reviewing academic documents, aiding in the speedy publishing of research articles.

Soil-Moisture-Sensor-Based Automated Soil Water Content Cycle Classification With a Hybrid Symbolic Aggregate Approximation Algorithm

This article proposes a hybrid symbolic aggregate approximation and vector space model (SAX-VSM) method for automatically classifying soil water content cycles. In the proposed method, a novel similarity measure, the distance weighted cosine (DWC) similarity measure, is introduced to improve the classification performance of the SAX-VSM. The DWC similarity measure incorporates both direction and distance information of feature vectors. Meanwhile, a mixed-integer optimization problem is formulated to determine hyperparameters. An extended Rao-1 algorithm, I-Rao-1 algorithm, is developed to solve such optimization problems. To verify the feasibility and effectiveness of the proposed method, three soil moisture data sets collected from the Florida research trials are employed. Compared with state-of-the-art methods, the proposed method has achieved the best performance based on all data sets in terms of the highest accuracy, precision, and recall values. Therefore, it is promising to apply the proposed method into real applications in the smart irrigation system.

Identifying themes in fiction: A centroid-based lexical clustering approach

In recent years, numerous computational methods have been developed that have been widely used in humanities and literary studies. In spite of the potential of such methods in providing workable solutions to various inherent problems in research within these domains, including selectivity, objectivity, and replicability, very little empirical work has been done on thematic studies in literature. Such studies are almost entirely undertaken through traditional methods based on individual researchers’ reading of texts and intuitive abstraction of generalizations from their reading. This has negative implications in terms of issues of objectivity and replicability. Furthermore, there are challenges in dealing effectively with the hundreds of thousands of new novels that are published every year using traditional methods. In the face of these problems, this study proposes an integrated computational model for the thematic classification of literary texts based on lexical clustering methods. This study is based on a corpus comprising Thomas Hardy’s novels and short stories. The study employs computational semantic analysis based on a vector space model (VSM) representation of the lexical content of the texts. The results indicate that the selected texts could be grouped thematically based on their semantic content. Thus, there is now evidence that text clustering approaches, which have long been used in computational theory and data mining applications, can be usefully applied in literary studies.

EMR2vec: Bridging the gap between patient data and clinical trial

The human suffering from diseases caused by life-threatening viruses such as SARS, Ebola, and COVID-19 motivated many of us to study and discover the best means to harness the potential of data integration to assist clinical researchers to curb these viruses. Integrating patients data with clinical trials data is enormously promising as it provides a comprehensive knowledge base that accelerates the clinical research response-ability to tackle emerging infectious disease outbreaks. This work introduces EMR2vec, a platform that customises advanced NLP, machine learning and semantic web techniques to link potential patients to suitable clinical trials. Linking these two different but complementary datasets allows clinicians and researchers to compare patients to clinical research opportunities or to automatically select patients for personalized clinical care. The platform derives a ’bag of medical terms’ (BoMT) from eligibility criteria by normalizing extracted entities through SNOMED-CT ontology. With the usage of BoMT, an ontological reasoning method is proposed to represent EMR and clinical trials in a vector space model. The platform presents a matching process that reduces vector dimensionality using a neural network, then applies orthogonality projection to measure the similarity between vectors. Finally, the proposed EMR2vec platform is evaluated with an extendable prototype based on Big data tools.

Einstein tori and crooked surfaces

Abstract In hyperbolic space, the angle of intersection and distance classify pairs of totally geodesic hyperplanes. A similar algebraic invariant classifies pairs of hyperplanes in the Einstein universe. In dimension 3, symplectic splittings of a 4-dimensional real symplectic vector space model Einstein hyperplanes and the invariant is a determinant. The classification contributes to a complete disjointness criterion for crooked surfaces in the 3-dimensional Einstein universe.