Content-based Methods Research Articles

Effect of anisotropy, angle, and critical tensile stress and confining pressures on evaluation of shale brittleness index — Part 1: Methodology and laboratory study

Brittleness is an important evaluation parameter in shale fracturing. Current methods of brittleness evaluation can be classified into two categories: elastic parameter-based and mineral content-based methods. However, both categories neglect the effect of anisotropy on the brittleness index (BI) computation of shale resources. We have redefined a new BI by integrating failure criteria stress and anisotropy parameters estimated (BIac) from seismic waves. According to the new definition, the BI at one analysis point varies with the incident angle of the seismic wave and confining pressures. We applied the BIac method to laboratory-measured shale samples acquired from the Monterey Formation, Santa Maria Basin. We found that the delta parameter [Formula: see text] is more responsive to the BIac than the gamma [Formula: see text] and epsilon [Formula: see text] anisotropic parameters, and it indicates a good linear fit relationship with the BIac at different angles. The slope of the linear is variable with the angles, thus delta can be used to predict the BIac in the Monterey Formation, Santa Maria Basin.

Measuring similarity and relatedness using multiple semantic relations in WordNet

Semantic similarity and relatedness computation has attracted an increasing amount of attention among researchers. The majority of previous studies, including edge-based and information content-based methods, rely on a single semantic relationship in WordNet such as the “is-a” relation. However, a performance ceiling may have been created by semantic unicity and inadequate calculation in solely “is-a” relation-based measurements, i.e., the computed results for some word pairs are too small and significantly deviate from human judgments. For this problem, we propose the following solutions: (1) We introduce the notion of the nearest common descendant to provide a supplement for commonalities between concepts according to genetics theory. (2) We design various targeted methods for different incomplete semantic relations. Therefore, various semantic relations can participate in similarity and relatedness computations in their most appropriate manners. (3) We utilize the cross-use of incomplete semantic relations similar-to and antonymy to solve the challenge of adjective and adverb similarity/relatedness measurements in WordNet. (4) We propose a targeted independent computation and largest contribution aggregation method to break through the performance ceiling of similarity/relatedness measurements based on single “is-a” relations. We conduct evaluations of our proposed model using seven extensively employed datasets. These evaluations indicate that our method significantly improves the performance of the existing methods based on single “is-a” relations. Their best Pearson coefficient with human judgments on both the MC30 and RG65 is increased to 0.9. With the development and enrichment of semantic relations in WordNet, our proposed model can be expected to have a more prominent role.

A vocabulary recommendation method for spatiotemporal data discovery based on Bayesian network and ontologies

ABSTRACTIn the research field of spatiotemporal data discovery, how to utilize the semantic characteristics of spatiotemporal datasets is an important topic. This paper presented a content-based recommendation method, and applied Bayesian networks and ontologies into the vocabulary recommendation process for spatiotemporal data discovery. The source data of this research was from the MUDROD (Mining and Utilizing Dataset Relevancy from Oceanographic Datasets) search platform. From the historical search log, major keywords were extracted and organized according to ontologies in a hierarchical structure. Using the search history, the posterior probability between each subclass and their super class in the ontologies was calculated, indicating a recommendation likelihood. We created a Bayesian network model for inference based on ontologies. This model can address the following two objectives: (1) Given one class in the ontology, the model can judge which class has the biggest likelihood to be selected for recommendation. 2) Based on the search history of a user, the Bayesian network model can judge which class has the biggest probability to be recommended. Comparison experimentation with existing system and evaluation experimentation with expert knowledge show that this method is specifically helpful for spatiotemporal data discovery.

User community detection via embedding of social network structure and temporal content

Identifying and extracting user communities is an important step towards understanding social network dynamics from a macro perspective. For this reason, the work in this paper explores various aspects related to the identification of user communities. To date, user community detection methods employ either explicit links between users (link analysis), or users’ topics of interest in posted content (content analysis), or in tandem. Little work has considered temporal evolution when identifying user communities in a way to group together those users who share not only similar topical interests but also similar temporal behavior towards their topics of interest. In this paper, we identify user communities through multimodal feature learning (embeddings). Our core contributions can be enumerated as (a) we propose a new method for learning neural embeddings for users based on their temporal content similarity; (b) we learn user embeddings based on their social network connections (links) through neural graph embeddings; (c) we systematically interpolate temporal content-based embeddings and social link-based embeddings to capture both social network connections and temporal content evolution for representing users, and (d) we systematically evaluate the quality of each embedding type in isolation and also when interpolated together and demonstrate their performance on a Twitter dataset under two different application scenarios, namely news recommendation and user prediction. We find that (1) content-based methods produce higher quality communities compared to link-based methods; (2) methods that consider temporal evolution of content, our proposed method in particular, show better performance compared to their non-temporal counter-parts; (3) communities that are produced when time is explicitly incorporated in user vector representations have higher quality than the ones produced when time is incorporated into a generative process, and finally (4) while link-based methods are weaker than content-based methods, their interpolation with content-based methods leads to improved quality of the identified communities.

Incorporating student population differences for effective online education: A content-based review and integrative model

The present study reviews and analyzes recent literature relevant to learning online in higher education. This study applied a content-based method for collecting and evaluating recently published studies relating to populations of students engaged in formal online programs and courses. Based on our findings, we identified key descriptors and processes that prove essential for defining online student populations and understanding their differences.This study offers a new conceptual model that consolidates certain prior typologies and frameworks of online learning. The model integrates key components and major processes derived from our study's findings. Our model thus synthesizes and classifies the major categories of antecedents and processes, and suggests relationships that directly or indirectly influence outcomes. The model also provides an integrated framework by which educators and institutions may develop better strategies and approaches for designing, developing, and delivering online education. This study and its model thus advance theory and contribute to educational practice and research in online learning. The study encourages more research specifically on student populations engaged in online learning. Our framework orients future studies that can generate more consistent, coherent data on antecedents of student populations and how population differences interrelate with processes to impact online learning outcomes.

Learning, Probability and Logic: Toward a Unified Approach for Content-Based Music Information Retrieval

Within the last fifteen years, the field of Music Information Retrieval (MIR) has made tremendous progress in the development of algorithms for organizing and analyzing the ever-increasing large and varied amount of music and music-related data available digitally. However, the development of content-based methods to enable or improve multimedia retrieval still remains a central challenge. In this perspective paper, we critically look at the problem of automatic chord estimation from audio recordings as a case study of content-based algorithms, and point out several bottlenecks in current approaches: expressiveness and flexibility are obtained to the expense of robustness and vice-versa; available multimodal sources of information are little exploited; modeling multi-faceted and strongly interrelated musical information is limited with current architectures; models are typically restricted to short-term analysis that does not account for the hierarchical temporal structure of musical signals. Dealing with music data requires the ability to handle both uncertainty and complex relational structure at multiple levels of representation. Traditional approaches have generally treated these two aspects separately, probability and learning being the standard way to represent uncertainty in knowledge, while logical representation being the standard way to represent knowledge and complex relational information. We advocate that the identified hurdles of current approaches could be overcome by recent developments in the area of Statistical Relational Artificial Intelligence (StarAI) that unifies probability, logic and (deep) learning. We show that existing approaches used in MIR find powerful extensions and unifications in StarAI, and we explain why we think it is time to consider the new perspectives offered by this promising research field.

Educational system for the development of collaborative Ill-structured problem-solving skills

Problem solving is indicated among the most important 21st century skills and therefore many educational practitioners and researchers suggest to include the development of this skill in diverse subjects across university studies. However, practice of this skill development is usually limited to the application of problem-based learning, which leaves it only among many other goals. The fact also remains that this popular curricular invention is most commonly considered as a content-based method, which puts greater emphasis on subject-related knowledge acquisition in the context of solving problems. Indeed, solving ill-structured problems is not an easy activity and what higher education needs is a more explicit focus on the development of this skill in students. The article seeks to scientifically and practically explain the system on how this educational aim can be attained. Based on literature review, we design a tripartite system for enabling students to learn collaborative problem solving. It comprises three main parts: preparation for solving ill-structured problems in collaboration, inclusion into the processes of solving such problems and evaluation of the processes as well as outcomes achieved. The designed system is universal and can be implemented across various disciplines and subjects in higher education.

Network Management of Multicluster RT-WiFi Networks

Applying wireless technologies in cyber-physical systems (CPSs) has received significant attention in recent years. In our previous work, a high-speed and flexible real-time wireless communication protocol called RT-WiFi was designed to support a wide range of CPSs, and we presented an implementation with a single access point (AP). To serve the CPS applications with communication nodes geographically distributed over a large area, multicluster RT-WiFi networks with multiple APs need to be deployed. Although effective scheduling algorithms have been designed to schedule tasks in RT-WiFi networks with a single AP, uncoordinated packet transmissions from multicluster RT-WiFi networks may suffer from cochannel interferences that cause performance degradation. The multicluster RT-WiFi network management problem is to resolve the cochannel interference through channel assignment for clusters and through phasing assignment for communication tasks. In this article, we first derive a conjunctive normal form encoding of the problem and design a TScheduler that searches feasible solutions through the SAT solver. A novel LRTree Scheduler is further designed to solve the problem in chain graphs while keeping the number of used channels small and the network management overhead low. A testbed of the multicluster RT-WiFi network is deployed to validate the design of the multicluster RT-WiFi network and evaluate the performance of the proposed scheduling algorithms compared to the contention-based methods in regular WiFi networks. Performance of these scheduling algorithms in large-scale networks is further evaluated through extensive simulations on both static and dynamic multicluster RT-WiFi networks.

Lay it Out: Detecting of Fake News Publishers through Website Structure Data

We propose a novel website structure based domain-level fake news detection model that has performance results surprisingly comparable to that of existing content-based methods. Through feature analysis, we highlight that fake news sites have more clustered subpages and more {\it ads} links, whereas traditional news sites are more substantive and more likely to contain {\it staff} links. We then illustrate that the structural model has a higher overall false positive rate compared to content-based methods, which have a higher false negative rate for domains that are more recent, more popular, and is conservative-leaning. Additionally, we also show that all model performance is dependent on the strictness in definitions of fake and traditional news sites. Specifically, model performance is higher when these definitions are more restrictive. Finally, we demonstrate that the performance of existing content-based models improve significantly by incorporating structural features, particularly when the definitions for fake and traditional news sites are lax.

Multi-classification analysis of large data based on knowledge element in micro-blog short text

ABSTRACT To solve the problem that the traditional emotion analysis method for microblog information adopting vocabulary target identification is unsatisfactory for identifying concrete concept application, a recommended system mixing behavioral habits of users and microblog user labels is proposed. Firstly, the content-based method is used to build a statistic model for emotion analysis of microblog information and experimental analysis was carried out for cluster coefficients of emotion analysis so as to acquire the best selected value of cluster coefficients; secondly, the decision classifier is used to build an emotion-based concrete method model for microblog information so as to realize the highest similar mode classification of microblog information and also use content and the method model to rectify it; finally, the performance advantage of the proposed method is verified in concrete statistic analysis through experiments.

Flower Image Retrieval Using LBP, Wavelet Moments, Gabor Wavelet Features and Multiple Distance Functions

Flower image retrieval is a significant and challenging problem in content-based image retrieval. In this paper, we propose a content-based method for retrieving flower images of specified species from a database of flower images of various species. Firstly, we use wavelet moment, Gabor wavelet and Local Binary Pattern (LBP) independently to characterize all flower images in the database. Secondly, we represent a query flower image with Gabor wavelet, wavelet moment, and LBP individually and search images in the database analogous to the query image. The retrieval is accomplished through calculating similarities between the query image and the database images by employing a set of distance functions. Experimental evaluation of the approach reveals that the Gabor wavelet achieves superiority over the wavelet moment and LBP considerably. It is also indicated that the retrieval outcome can be improved through concatenating the Gabor wavelet, wavelet moment, and LBP features rather than utilizing them individually.

Towards Serendipity for Content–Based Recommender Systems

Recommender systems are intelligent applications build to predict the rating or preference that a user would give to an item. One of the fundamental recommendation methods in the content-based method that predict ratings by exploiting attributes about the users and items such as users’ profile and textual content of items. A current issue faces by recommender systems based on this method is that the systems seem to recommend too similar items to what users have known. Thus, creating over-specialisation issues, in which a self-referential loop is created that leaves user in their own circle of finding and never get expose to new items. In order for these systems to be of significance used, it is important that not only relevant items been recommender, but the items must be also interesting and serendipitous. Having a serendipitous recommendation let users explore new items that they least expect. This has resulted in the issues of serendipity in recommender systems. However, it is difficult to define serendipity because in recommender system, there is no consensus definition for this term. Most of researchers define serendipity based on their research purposes. From the reviews, majority shows that unexpected as the important aspect in defining serendipity. Thus, in this paper, we aim to formally define the concept of serendipity in recommender systems based on the literature work done. We also reviewed few approaches that apply serendipity in the content-based methods in recommendation. Techniques that used Linked Open Data (LOD) approaches seems to be a good candidate to find relevant, unexpected and novel item in a large dataset.

HRS-CE: A hybrid framework to integrate content embeddings in recommender systems for cold start items

Recommender systems (RSs) provide the personalized recommendations to users for specific items in a wide range of applications such as e-commerce, media recommendations and social networking applications. Collaborative Filtering (CF) and Content Based (CB) Filtering are two methods which have been employed in implementing the recommender systems. CF suffers from Cold Start (CS) problem where no rating records (Complete Cold Start CSS) or very few records (Incomplete Cold Start ICS) are available for newly coming users and items. The performance of CB methods relies on good feature extraction methods so that the item descriptions can be used to measure items similarity as well as for user profiling. This paper addresses the CS problem by providing a novel way of integrating content embeddings in CF. The proposed algorithm (HRS-CE) generates the user profiles that depict the type of content in which a particular user is interested. The word embedding model (Word2Vec) is used to produce distributed representation of items descriptions. The higher representation for an item description, obtained using content embeddings, are combined with similarity techniques to perform rating predictions. The proposed method is evaluated on two public benchmark datasets (MovieLens 100k and MovieLens 20M). The results demonstrate that the proposed model outperforms the state of the art recommender system models for CS items.

Multilingual SMS-based author profiling: Data and methods

AbstractIn the recent years, many benchmark author profiling corpora have been developed for various genres including Twitter, social media, blogs, hotel reviews and e-mail, etc. However, no such standard evaluation resource has been developed for Short Messaging Service (SMS), a popular medium of communication, which is very useful for author profiling. The primary aim of this study is to develop a large multilingual (English and Roman Urdu) benchmark SMS-based author profiling corpus. The proposed corpus contains 810 author profiles, wherein each profile consists of an aggregation of SMS messages as a single document of an author, along with seven demographic traits associated with each author profile: gender, age, native language, native city, qualification, occupation and personality type (introvert/extrovert). The secondary aims of this study include the following: (1) annotating the proposed corpus for code-switching annotations at the lexical level (approximately 0.69 million tokens are manually annotated for code-switching) and (2) applying the stylometry-based method (groups of sixty-four features) and the content-based method (twelve features) for gender identification in order to demonstrate how our proposed corpus can be used for the development and evaluation of various author profiling methods. The results show that the content-based character 5-gram feature outperformed all the other features by obtaining the accuracy score of 0.975 andF1score of 0.947 for gender identification while using the entire corpus. Furthermore, our proposed corpora (SMS–AP–18 and code-switched SMS–AP–18) are freely and publicly available for research purpose.

Constrained User Exposure Matrix Factorization in Recommendation System

Due to the massive items and users limited scope, many existing recommendation systems suffer a more and more serious sparse problem on rating matrix. Most of the recommendation systems employ the content-based method or the collaborative filter model to abbreviate the sparse problem. While in our paper, based on the assumption that users who have rated similar sets of items are likely to have similar preference information, we present a constrained user exposure matrix factorization collaborative filter model and apply it to deal with the matrix sparsity problem, which it makes a better recommendation to users who have very few ratings. In detail, for the sake of capturing the effect of user having rated a particular item, we introduce a constrained matrix to user latent vector space. This effect will have an impact on the prior mean of user latent feature vector. After that, using the user feature vector and item feature vector to estimate the values of unrated items. The experimental results show that the proposed model gets higher prediction precision than the state-of-the-art models in providing recommendation for user having very few ratings.

Funding map using paragraph embedding based on semantic diversity

Maps of science representing the structure of science can help us understand science and technology (S&T) development. Studies have thus developed techniques for analyzing research activities’ relationships; however, ongoing research projects and recently published papers have difficulty in applying inter-citation and co-citation analysis. Therefore, in order to characterize what is currently being attempted in the scientific landscape, this paper proposes a new content-based method of locating research projects in a multi-dimensional space using the recent word/paragraph embedding techniques. Specifically, for addressing an unclustered problem associated with the original paragraph vectors, we introduce paragraph vectors based on the information entropies of concepts in an S&T thesaurus. The experimental results show that the proposed method successfully formed a clustered map from 25,607 project descriptions of the 7th Framework Programme of EU from 2006 to 2016 and 34,192 project descriptions of the National Science Foundation from 2012 to 2016.

Exploit latent Dirichlet allocation for collaborative filtering

Previous work on the one-class collaborative filtering (OCCF) problem can be roughly categorized into pointwise methods, pairwise methods, and content-based methods. A fundamental assumption of these approaches is that all missing values in the user-item rating matrix are considered negative. However, this assumption may not hold because the missing values may contain negative and positive examples. For example, a user who fails to give positive feedback about an item may not necessarily dislike it; he may simply be unfamiliar with it. Meanwhile, content-based methods, e.g. collaborative topic regression (CTR), usually require textual content information of the items, and thus their applicability is largely limited when the text information is not available. In this paper, we propose to apply the latent Dirichlet allocation (LDA) model on OCCF to address the above-mentioned problems. The basic idea of this approach is that items are regarded as words, users are considered as documents, and the user-item feedback matrix constitutes the corpus. Our model drops the strong assumption that missing values are all negative and only utilizes the observed data to predict a user’s interest. Additionally, the proposed model does not need content information of the items. Experimental results indicate that the proposed method outperforms previous methods on various ranking-oriented evaluation metrics. We further combine this method with a matrix factorization-based method to tackle the multi-class collaborative filtering (MCCF) problem, which also achieves better performance on predicting user ratings.

FOSTERING AUTONOMOUS LEARNERS OF VOCABULARY ACQUISITION USING CONTENT-BASED ICT METHODS

Purpose: This paper investigated that (1) the effective use of content-based ICT methods of vocabulary acquisition through reading activities, and (2) the encouragement of learner's vocabulary building up autonomous learning. A considerable number of studies have been conducted on vocabulary acquisition in the EFL field in Japan,and researchers advocate vocabulary knowledge is the most important factor contributing to reading comprehension, yet, a firm effective pedagogy has not been established. In fact, university students encounter difficulties in reading comprehension because of their deficiencies in vocabulary knowledge during English reading class.
 Methodology: The free applications, Quizlet and Kahoot!, were adopted to incorporate language-focused learning while adding some gamification aspects to aid in vocabulary acquisition. The experiment was conducted in a Japanese undergraduate first-year reading class over an 11-week period. Quizlet was used for vocabulary learning prior to the reading class. Students were given multiple-choice vocabulary Cloze tests of new words from the textbook using Kahoot!, a free game-based educational platform.
 Main Findings: The results of this study indicated that content-based vocabulary instruction using ICT is effective and improves learner’s academic performance in vocabulary acquisition. Moreover, questionnaires were thoroughly reviewed and uncovered that students felt they developed more autonomy and this enhanced their motivation for vocabulary learning.
 Implications: In view of this study, ICT methods closely related to reading contexts and a variety of applications for vocabulary acquisition and improvement of reading performance should be introduced in EFL classrooms.
 Originality: This study was conducted in a Japanese undergraduate first-year reading class by author researcher.

Study on Hybrid Image Search Technology Based on Texts and Contents

Image search was studied first here based on texts and contents, respectively. The text-based image feature extraction was put forward by integrating the statistical and topic features in view of the limitation of extraction of keywords only by means of statistical features of words. On the other hand, a search-by-image method was put forward based on multi-feature fusion in view of the imprecision of the content-based image search by means of a single feature. The layered-searching method depended on primarily the text-based image search method and additionally the content-based image search was then put forward in view of differences between the text-based and content-based methods and their difficult direct fusion. The feasibility and effectiveness of the hybrid search algorithm were experimentally verified.

A Semantic Information Content Based Method for Evaluating FCA Concept Similarity

Probability information content-based FCA concepts similarity computation method relies on the frequency of concepts in corpus, it takes only the occurrence probability as information content metric to compute FCA concept similarity, which leads to lower accuracy. This article introduces a semantic information content-based method for FCA concept similarity evaluation, in addition to the occurrence probability, it takes the superordinate and subordinate semantic relationship of concepts to measure information content, which makes the generic and specific degree of concepts more accurate. Then the semantic information content similarity can be calculated with the help of an ISA hierarchy which is derived from the domain ontology. The difference between this method and probability information content is that the evaluation of semantic information content is independent of corpus. Furthermore, semantic information content can be used for FCA concept similarity evaluation, and the weighted bipartite graph is also utilized to help improve the efficiency of the similarity evaluation. The experimental results show that this semantic information content based FCA concept similarity computation method improves the accuracy of probabilistic information content based method effectively without loss of time performance.