Content Classification Research Articles

Sixty Years of the Maximum Principle in Optimal Control: Historical Roots and Content Classification

This study examines the scientific production focused on the Maximum Principle between 1962 and 2021. Results indicate a consistent increase in the absolute number of publications over time. In relative terms, there is a resurgence of interest in this research field after the period between 2004 and 2009. Overall, these findings support the idea of strategic complementarity between the Maximum Principle and optimal control. However, there is a notable exception during the period 2010–2015, characterised by a decline in scientific production focused on the Maximum Principle and a simultaneous increase in focus on optimal control. Academic journals that play a role in promoting this research field tend to have high impact factors and interesting cite scores. Using a modified Boston Consulting Group matrix, the results reveal the persistence of two researchers labelled as stars and three as cash cows. A multiple linear regression analysis confirms that reputation significantly influences the clustering trends. A critical discussion is provided to highlight the dichotomy between popularity and effective contributions in this research field.

Mapping interpretations of the law in online content moderation in Germany

Content moderation is a vital condition that online platforms must facilitate, according to the law, to create suitable online environments for their users. By the law, we mean national or European laws that require the removal of content by online platforms, such as EU Regulation 2021/784, which addresses the dissemination of terrorist content online. Content moderation required by these national or European laws, summarised here as ‘the law’, is different from the moderation of pieces of content that is not directly required by law but instead is conducted voluntarily by the platforms. New regulatory requests create an additional layer of complexity of legal grounds for the moderation of content and are relevant to platforms’ daily decisions. The decisions made are either grounded in reasons stemming from different sources of law, such as international or national provisions, or can be based on contractual grounds, such as the platform's Terms of Service and Community Standards. However, how to empirically measure these essential aspects of content moderation remains unclear. Therefore, we ask the following research question: How do online platforms interpret the law when they moderate online content?To understand this complex interplay and empirically test the quality of a platform's content moderation claims, this article develops a methodology that facilitates empirical evidence of the individual decisions taken per piece of content while highlighting the subjective element of content classification by human moderators. We then apply this methodology to a single empirical case, an anonymous medium-sized German platform that provided us access to their content moderation decisions. With more knowledge of how platforms interpret the law, we can better understand the complex nature of content moderation, its regulation and compliance practices, and to what degree legal moderation might differ from moderation due to contractual reasons in dimensions such as the need for context, information, and time.Our results show considerable divergence between the platform's interpretation of the law and ours. We believe that a significant number of platform legal interpretations are incorrect due to divergent interpretations of the law and that platforms are removing legal content that they falsely believe to be illegal (‘overblocking’) while simultaneously not moderating illegal content (‘underblocking’). In conclusion, we provide recommendations for content moderation system design that takes (legal) human content moderation into account and creates new methodological ways to test its quality and effect on speech in online platforms.

Where and Why Travelers Visit? Classifying Coastal Tourism Activities Using Geotagged Image Content from Social Media Data

Accurate information regarding the size, activity, and distribution of coastal tourists is essential for the effective management and planning of coastal tourism. In this study, geotagged photos uploaded to social network services were classified to identify coastal tourism activities. These activities were linked with spatial-scale data on tourist numbers estimated from social media data. To classify the activities, which included recreation, appreciation, education, and other activities, an image-supervised classification model was trained using 12,229 images, and the test accuracy was found to be 0.7244. On the Flickr platform, 43% of the image data located in the coastal land of South Korea are other activities, 39% are appreciation activities, and 18% are recreation and education activities. Other activities are mainly located in urban areas with a high population density and are spatially concentrated, while appreciation activities are mainly located in the natural environment and tend to be spatially spread out. Data on tourist activity categorization through content classification, combined with traditional tourist volume estimates, can help us understand previously overlooked information and context about a space.

Classification of domestic violence Persian textual content in social media based on topic modeling and ensemble learning

ObjectiveDue to the importance of monitoring social networks to categorize domestic violence content and extract practical knowledge for conducting preventive interventions, as well as analyzing the extensive Persian textual content related to domestic violence generated in social networks following the COVID-19 pandemic, primarily, this research aims to create the best domestic violence Persian textual content classification model using topic modeling content at first and then combining algorithms using ensemble learning to achieve the best model performance. MethodBy collecting Persian textual data using hashtags related to domestic violence equally and randomly from Telegram, Twitter, and Instagram networks between April 2020 and April 2023, the content were considered for topic modeling using the LDA algorithm. By extracting the probabilities of each topic for each document in our dataset, we considered the topic that had the highest probability to be a label for that document. Following feature extraction from labeled datasets, the Stacking and Voting ensemble learning methods were applied. ResultThe analysis of 337,287 textual data revealed five topics: family crime news, war violence, women's rights, and violent reactions. Also, compared to the voting method, the stacking method performed better with 96.4577 precision, 96.4499 accuracy, 96.4499 recall, and 96.4475 F-score. ConclusionAccording to the study findings, practical knowledge of the extracted topics can assist mental health centers in making preventive decisions. Moreover, the built model has the most efficient performance among the built models for the multi-class classification of DV texts in the Persian language for social media monitoring.

The Path Towards Personal Growth in Spousal Caregiving

Caregiver support is necessary for many long-term ill or disabled people to enable them to live at home. However, the impact of this care relationship on the caregiver is still partly unknown. This article focuses on the process of achieving personal growth in spousal caregiving. The aim of our study was to conduct an integrative literature review to describe the aspects associated with achieving personal growth as well as to analyze the relationship between the aspects involved. The thematic analysis method was used with inductive content classification to modify the themes. The analysis highlighted three main aspect categories connected with achieving personal growth: the means, coping styles, and personal or caregiving resources. In addition, the path from adversities and challenges towards personal growth and positive experiences in spousal caregiving was revealed by analyzing the relationship between the identified aspects. We found that the means, based on social support and self-care, help caregivers to adopt positive coping strategies, reframing, and resilience, which are associated with achieving personal growth. Also, personal or caregiving resources, such as meaningful caring experiences, religiosity, and spirituality, hope, positive personality characteristics, family functioning, and disease severity of the care recipient, were associated with achieving growth. Finally, our findings suggest that it is important to further study the path towards personal growth in spousal caregiving and to find the best possible ways to support caregivers in difficult moments and periods of caregiving.

Instagram Content Analysis on @kualanamu.airport Account as Company Branding Media

Kualanamu International Airport Deli Serdang utilizes Instagram social media as the main media in disseminating information and education related to airports and flights to the public consistently. in disseminating information and education related to airports and flights to the public consistently and periodically through Instagram. and periodically through account @kualanamu.airport. The utilization of social media is carried out as a branding effort the company to the public. So the author focuses on this analysis on the Instagram content presented by the @kualanamu.airport account which aims to find out the content classification and follower algorithm in accessing the content provided. content provided. This research uses qualitative methods of non-participant observation and literature study. The results of the analysis found entertainment, education, collaboration, and information content during the observation period of the @kualanamu.airport account, Kualanamu Airport Public Relations through this account is considered successful in shaping brand awareness to the public. brand awareness to the public, thus increasing public satisfaction and public understanding in using air transportation. However, the infographics displayed on information content on @kualanamu.airport are not all equipped with easily recognizable thumbnails. that are easily recognizable.

Amina: an Arabic multi-purpose integral news articles dataset

AbstractElectronic newspapers are one of the most common sources of Modern Standard Arabic. Existing datasets of Arabic news articles typically provide a title, body, and single label. Ignoring important features, like the article author, image, tags, and publication date, can degrade the efficacy of classification models. In this paper, we propose the Arabic multi-purpose integral news articles (AMINA) dataset. AMINA is a large-scale Arabic news corpus with over 1,850,000 articles collected from 9 Arabic newspapers from different countries. It includes all the article features: title, tags, publication date and time, location, author, article image and its caption, and the number of visits. To test the efficacy of the proposed dataset, three tasks were developed and validated: article textual content (classification and generation) and article image classification. For content classification, we experimented the performance of several state-of-the-art Arabic NLP models including AraBERT and CAMeL-BERT, etc. For content generation, the reformer architecture is adopted as a character text generation model. For image classification applied on Al-Sharq and Youm7 news portals, we have compared the performance of 10 pre-trained models including ConvNeXt, MaxViT, ResNet18, etc. The overall study verifies the significance and contribution of our newly introduced Arabic articles dataset. The AMINA dataset has been released at https://huggingface.co/datasets/MohamedZayton/AMINA.

A social context-aware graph-based multimodal attentive learning framework for disaster content classification during emergencies

In times of crisis, the prompt and precise classification of disaster-related information shared on social media platforms is of paramount importance for effective disaster response and public safety. During such critical events, people utilize social media as a medium for communication, sharing multimodal textual and visual content. However, due to the substantial influx of unfiltered and diverse data, humanitarian organizations face challenges in effectively leveraging this information. Numerous methods have been proposed for classifying disaster-related content, but these methods lack modeling users’ credibility, emotional context, and social interaction information, which is crucial for classification. In this context, we propose a method, CrisisSpot, that leverages a Graph-based Neural Network to comprehend intricate relationships between textual and visual modalities and Social Context Features to incorporate user-centric and content-centric information. We also propose Inverted Dual Embedded Attention, which captures both harmonious and contrary patterns present in the data to harness complex interactions and facilitate richer insights in multimodal data. We have developed a multimodal disaster dataset, TSEqD (Turkey-Syria Earthquake Dataset), which is a large annotated dataset for a single disaster event containing 10,352 data samples. Through extensive experimentation, CrisisSpot has demonstrated significant improvements, achieving an average gain of 9.45% and 5.01% in F1-score compared to the state-of-the-art methods on the publicly available CrisisMMD dataset and TSEqD dataset, respectively.

Classification of Histamine Content in Fish Using Near-Infrared Spectroscopy and Machine Learning Techniques

Near-infrared (NIR) spectroscopy has emerged as a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on histamine content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes, Safe and Unsafe, based on their histamine content. A comprehensive NIR dataset comprising 11,360 spectra collected at eight distinct positions within the fish body was obtained from 284 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their histamine content exceeded the permissible limit of 100 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques as well as ML algorithms were explored. The results demonstrated that utilizing NIR data specifically obtained from the tail’s flesh, a specific location within the fish, yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their second derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers with an accuracy of approximately 93%.

Identifying motivational interviewing techniques in Quitline smoking cessation counselling sessions from Queensland, Australia.

Motivational interviewing (MI) is a common approach for smoking cessation counselling, yet little is known about the use of MI techniques in practice. This qualitative content analysis applied a published classification of content and relational MI techniques to a sample of 30 Quitline transcripts (January-March 2019) from Queensland, Australia. Overall, 36 MI techniques (94.7%) were identified at least once within the total sample. On average, 20 techniques (52.6%) were used in an individual conversation with a small difference observed between initial and follow-up calls. Techniques most frequently applied across conversations were largely relational, while techniques addressing client ambivalence/resistance were less frequently/never applied. Variability in techniques between individual initial and follow-up calls highlights the high degree of personalisation when applying MI to smoking cessation. Further investigations exploring associations of individual techniques and cessation outcomes are warranted. The classification may prove useful for assessments of fidelity for training and monitoring activities.

Carbohydrate Content Classification Using Postprandial Heart Rate Responses from Non-Invasive Wearables.

The rising incidence of type 2 diabetes underscores the need for technological innovations aimed at enhancing diabetes management by aiding individuals in monitoring their dietary intake. This has resulted in the development of technologies capable of tracking the timing and content of an individual's meals. However, the ability to use non-invasive wearables to estimate or classify the carbohydrate content of the food an individual has just consumed remains a relatively unexplored area. This study investigates carbohydrate content classification using postprandial heart rate responses from non-invasive wearables. We designed and developed timeStampr, an iOS application for collecting timestamps essential for data labeling and establishing ground truth. We then conducted a pilot study in controlled, yet naturalistic settings. Data were collected from 23 participants using an Empatica E4 device worn on the upper arm, while each participant consumed either low-carbohydrate or carbohydrate-rich foods. Due to sensor irregularities with dark skin tones and non-compliance with the study's health criteria, we excluded data from three participants. Finally, we configured and trained a Light Gradient Boosting Machine (LGBM) model for carbohydrate content classification. Our classifiers demonstrated robust performance, with the carbohydrate content classification model consistently achieving at least 84% in accuracy, precision, recall, and AUCROC within a 60 s window. The results of this study demonstrate the potential of postprandial heart rate responses from non-invasive wearables in carbohydrate content classification.

Content Classification of the Official Website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) using Vector Space Model (VSM)

The official website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) is an important platform for disseminating information to a diverse audience. Efficiently categorizing the vast amount of content available on the website is essential for enhancing user experience and optimizing information retrieval. These categories will also become an identifier and topic classification based on the content inside the article. This study presents a systematic approach to content classification of the Official Website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) using the Vector Space Model (VSM). The methodology involves preprocessing the text data, constructing a term-document matrix, and implementing cosine similarity to measure the relevance of documents to predefined categories. The study demonstrates the effectiveness of VSM in accurately classifying content, thus facilitating streamlined access to information for users navigating the website. Furthermore, the findings offer insights into enhancing the organization and accessibility of governmental online platforms, contributing to improved user experience and information dissemination.

Exploring Factors Affecting the Rural Poor's Access to Resources and Public Services, Thailand

Public service involves the state's or administrative department's operations in matters aimed at meeting the collective needs of all citizens. The objectives of the research were to: 1) study access to resources and public services of the rural poor, 2) examine the factors that affected access to resources and public services, and 3) propose a policy to solve the problems related to access to resources and public services. This study employed a mixed research method. The study population included 7,520 recipients of the state welfare cards. The samples consisted of 379 state welfare cardholders. The key informants made up the 12 subjects, while the 15 participated in a group focus. The following statistics were used: percentage, mean, standard deviation, and multiple regression analysis. Qualitative data were evaluated using content classification and description. The research found that 1) the overall access to resources and public services of the rural poor was high level, 2) political, economic, social, technological, and cultural factors affected access to resources and public services of the rural poor at a high level. The five variables could predict access to resources and public services of the rural poor by 85.40 (R2.40 = 0.854). An error in the prediction was statistically significant at .05 level, and 3) the recommendations were as follows: the state sectors should focus on services and reduce formality as much as possible. Increase opportunities for the poor and their communities, strengthen public-state agency relationships, make technological equipment accessible, and focus on cultural capital-based community strengthening.

Multi-instrument spectroscopic study for authentication of curcumin content in commercial turmeric powders using machine learning algorithms

Adulteration during processing of turmeric powder not only causes health risks for the consumers but also affects its quality. There is a need for rapid and non-invasive analysis of its active ingredient, curcumin, during the supply-chain. In the present study a total six IR instruments ranging from hand-held (NIR), portable (NIR) and standalone (FTNIR and FTIR) were used to obtain spectral data of 160 different turmeric samples. The curcumin content quantified using HPLC procedure was used as the response variable for analytical model using machine learning tools. Real coded genetic algorithm (RCGA) as the variable selection procedure provided most critical variables in the sets of 10, 20, 30 and 40 variables. Sensitivity analysis has revealed the most critical fingerprint(s) in authenticating curcumin across all the instruments. The hand-held (NIR) device with only 20 spectral variables resulted in 93 % accuracy using SVM classifier, and RP (regression co-efficient of prediction) values of 0.970 and 0.997 using RF and XGBoost, respectively. In case of FTNIR and FTIR instruments 100 % classification accuracy was achieved using SVM, whereas RF and XGBoost resulted in RP values greater than 0.93. This study enables classification and quantification of curcumin content in commercial turmeric powders using non-destructive methodology.

INCEPT: A Framework for Duplicate Posts Classification with Combined Text Representations

Dealing with many of the problems related to the quality of textual content online involves identifying similar content. Algorithmic solutions for duplicate content classification typically rely on text vector representation, which maps textual information into a set of features. Ideally, this representation would capture all aspects of the underlying text, including length, word frequencies, syntax, and semantics. While recent advancements in text representation have led to improved performance, a comprehensive approach that explicitly incorporates all text features has not yet been proposed. In this study, we present the INCEPT framework that utilizes multiple representation methods to detect duplicate text pairs, taking advantage of their individual strengths. The core of our approach involves using a stacking ensemble of pairwise vector distance measurements that are computed from multiple text representation methods. A stacking classifier then utilizes these distance scores as input and learns to identify duplicate posts. We assess the proposed framework’s effectiveness in identifying duplicate posts in an online Question and Answer platform. By combining several text representation methods, INCEPT performs well in the duplicate posts classification task. Our experiments demonstrate that specific framework configurations outperform the accuracy scores obtained from individual text representation methods. Therefore, we also infer that no single text representation method can independently capture a text’s features.

SPD Manifold Deep Metric Learning for Image Set Classification.

By characterizing each image set as a nonsingular covariance matrix on the symmetric positive definite (SPD) manifold, the approaches of visual content classification with image sets have made impressive progress. However, the key challenge of unhelpfully large intraclass variability and interclass similarity of representations remains open to date. Although, several recent studies have mitigated the two problems by jointly learning the embedding mapping and the similarity metric on the original SPD manifold, their inherent shallow and linear feature transformation mechanism are not powerful enough to capture useful geometric features, especially in complex scenarios. To this end, this article explores a novel approach, termed SPD manifold deep metric learning (SMDML), for image set classification. Specifically, SMDML first selects a prevailing SPD manifold neural network (SPDNet) as the backbone (encoder) to derive an SPD matrix nonlinear representation. To counteract the degradation of structural information during multistage feature embedding, we construct a Riemannian decoder at the end of the encoder, trained by a reconstruction error term (RT), to induce the generated low-dimensional feature manifold of the hidden layer to capture the pivotal information about the visual data describing the imaged scene. We demonstrate through theory and experiments that it is feasible to replace the Riemannian metric with Euclidean distance in RT. Then, the ReCov layer is introduced into the established Riemannian network to regularize the local statistical information within each input feature matrix, which enhances the effectiveness of the learning process. The theoretical analysis of the activation function used in the ReCov layer in terms of continuity and conditions for generating positive definite matrices is beneficial for network design. Inspired by the fact that the single cross-entropy loss used for training is unable to effectively parse the geometric distribution of the deep representations, we finally endow the suggested model with a novel metric learning regularization term. By explicitly incorporating the encoding and processing of the data variations into the network learning process, this term can not only derive a powerful Riemannian representation but also train an effective classifier. The experimental results show the superiority of the proposed approach on three typical visual classification tasks.

디지털 영상 합성촬영 방법의 차이와 활용에 따른 영상 콘텐츠 분류의 목적과 방향성에 대한 고찰

This study aims to classify and define video content according to composite shooting methods to minimize confusion becauseXR and virtual production are used interchangeably in video content production sites without a distinction of terms.XR Studio (or Stage)—a video compositing system that combines the physical reality world with the digital virtual world—and Virtual Production are video compositing technologies that use the same software, rendering technology, real-time engines, and media technology systems that are synchronized to the LED wall. Communication at the video content production site is therefore unclear. Although XR Studio and Virtual Production are connected and may use some of the same technologies, they are ultimately separate entities. XR Studio and Virtual Production should be used separately,depending on the type of video content being produced. This study examines the similarities and differences in the technical approaches of XR and virtual production systems and presents a classification of video content according to the use of XR and virtual production.

РЕПРЕЗЕНТАЦІЇ ІГРОВОГО МОДЕЛЮВАННЯ ЖИТТЄДІЯЛЬНОСТІ ЛЮДИНИ В УМОВАХ НЕВИЗНАЧЕНОСТІ

Purpose. Features of modernity that correspond to the features of a world dominated by volatility, uncertainty, complexity and ambiguity, threats of using such features as means of geopolitical influence, conditions of a full-scale russian-ukrainian war, the necessity restoration for a safe environment throughout the territory of Ukraine requires effective proposals from domestic psychology to ensure human life in the given conditions. A well-founded system-forming position for this increasingly appears as a game as a universal socio-cultural phenomenon and an integral factor of human self-realization, and game modeling as a way and method of human life activity in uncertainty. The purpose of the article is a theoretical study of the psychological regularities of game representation in the aspects of modeling human life, substantiation of psychological content, functional essence, classification of representations as an attributive factor of game modeling of human life in uncertainty conditions. Methods. The research uses methods of analysis and systematization for psychological and interdisciplinary approaches in the study of representation and modeling of human life activities, theoretical reconstruction and classification of the psychological content and functional essence for the game modeling representation of human life activities in current living conditions. Results. Based on the results of the research, the categorical and conceptual content and functional essence for game modeling representations of human life activity in uncertainty conditions were specified. The attribution of representation in the game modeling aspects is updated and substantiated. The definition of "game modeling representations of human life activity in uncertainty conditions" and the corresponding classification are proposed. Scientific ideas about representation, game and game modeling are deepened. It was established that the game modeling representations of human life activity in uncertainty conditions is a universal, pragmatic, attributive phenomenon; non-specific mental formation; the way of life of a person and a social group, which occurs simultaneously in two complementary dimensions: a) the intrasubjective space of a person as an interiorizational mental representation and b) in the external activity dimension as a deed, activity representation of exteriorization. Conclusions. The results of the study allow us to draw conclusions about the relevance of continuing psychological explorations for game modeling representations of human life activity in uncertainty conditions. The clarifying regularities tasks of influence for representations on psychological well-being and productivity of individual and group activities are considered promising.

A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

AbstractOver the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. Despite the popularity of ML techniques, only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering (RE) activities to solve the problems that occur in RE activities. The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023. The authors aim to investigate the research trends, main RE activities, ML algorithms, and data sources that were studied during this period. Forty‐five research studies were selected based on our exclusion and inclusion criteria. The results show that the scientific community used 57 algorithms. Among those algorithms, researchers mostly used the five following ML algorithms in RE activities: Decision Tree, Support Vector Machine, Naïve Bayes, K‐nearest neighbour Classifier, and Random Forest. The results show that researchers used these algorithms in eight major RE activities. Those activities are requirements analysis, failure prediction, effort estimation, quality, traceability, business rules identification, content classification, and detection of problems in requirements written in natural language. Our selected research studies used 32 private and 41 public data sources. The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA, Predictor Models in Software Engineering, and iTrust Electronic Health Care System.

Deteksi Email Spam dengan Continuous Bag-Of-Words dan Random Forest

Spam email poses a significant cyber threat, as scammers employ various tactics to deceive individuals into divulging sensitive information or downloading harmful content. For instance, in June 2023, Indonesia encountered approximately 6.51 thousand spam attacks, underscoring the widespread nature of this issue. These attacks frequently involve deceptive strategies, such as impersonation or false promises of rewards, to ensnare unsuspecting victims. Succumbing to spam can result in financial losses and other grave repercussions. To address this concern, this research addresses this pressing problem by focusing on email content classification to detect phishing attempts. The proposed solution leverages runtime platforms such as Google Colab and uses Continuous Bag of Words (CBOW) analysis and Random Forest methods. CBOW is selected for its effectiveness in capturing semantic relationships between words, allowing the model to extract meaningful features from the email content. Random Forest, on the other hand, is chosen for its ability to handle imbalanced datasets commonly encountered in email classification tasks, ensuring fair representation of both spam and ham emails during model training. By combining these two techniques, we aim to develop a robust classification model capable of accurately distinguishing between phishing (spam) and legitimate (ham) emails, thus enhancing email security measures. Through our approach, we aim to classify the SpamAssassin dataset into ham or spam categories, with an anticipated precision rate of 0.98, demonstrating the model's effectiveness in accurately identifying phishing emails.