Relevant bibliographies by topics / Explicit content detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Explicit content detection'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Explicit content detection"

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Marcial Basilio, Jorge Alberto, Gualberto Aguilar Torres, Gabriel Sanchez Perez, Linda Karina Toscano Medina, Hector Manuel Perez Meana, and Enrique Escamilla Hernadez. "Explicit Content Image Detection." Signal & Image Processing : An International Journal 1, no. 2 (2010): 47–58. http://dx.doi.org/10.5121/sipij.2010.1205.

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Qamar Bhatti, Ali, Muhammad Umer, Syed Hasan Adil, Mansoor Ebrahim, Daniyal Nawaz, and Faizan Ahmed. "Explicit Content Detection System: An Approach towards a Safe and Ethical Environment." Applied Computational Intelligence and Soft Computing 2018 (July 4, 2018): 1–13. http://dx.doi.org/10.1155/2018/1463546.

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An explicit content detection (ECD) system to detect Not Suitable For Work (NSFW) media (i.e., image/ video) content is proposed. The proposed ECD system is based on residual network (i.e., deep learning model) which returns a probability to indicate the explicitness in media content. The value is further compared with a defined threshold to decide whether the content is explicit or nonexplicit. The proposed system not only differentiates between explicit/nonexplicit contents but also indicates the degree of explicitness in any media content, i.e., high, medium, or low. In addition, the system
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Appati, Justice Kwame, Kennedy Yaw Lodonu, and Richmond Chris-Koka. "A Review of Image Analysis Techniques for Adult Content Detection." International Journal of Software Innovation 9, no. 2 (2021): 102–21. http://dx.doi.org/10.4018/ijsi.2021040106.

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The fast growth of internet access globally without boundary has led to some negative impacts among children who are exposed to pornographic contents daily. Many parental control strategies have been put in place to protect these children; however, these strategies are usually inspired by political and social interventions. With the availability of computational tools, many automated explicit content detection methods though having their flaws have been proposed to support these social interventions. In this study, a review of the current automated adult content detectors is presented with ope
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Chen, Xiaoyuan, Turki Aljrees, Muhammad Umer, et al. "A novel approach for explicit song lyrics detection using machine and deep ensemble learning models." PeerJ Computer Science 9 (August 30, 2023): e1469. http://dx.doi.org/10.7717/peerj-cs.1469.

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The content of music is not always suitable for all ages. Industries that manage music content are looking for ways to help adults determine what is appropriate for children. Lyrics of songs have become increasingly inappropriate for kids and can negatively impact their mental development. However, it is difficult to filter explicit musical content because it is mostly done manually, which is time-consuming and prone to errors. Existing approaches lack the desired accuracy and are complex. This study suggests using a combination of machine learning and deep learning models to automatically scr
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Muhammad Fadzli, Muhammad Arif Haikal, Mohd Fadzil Abu Hassan, and Norazlin Ibrahim. "Explicit kissing scene detection in cartoon using convolutional long short-term memory." Bulletin of Electrical Engineering and Informatics 11, no. 1 (2022): 213–20. http://dx.doi.org/10.11591/eei.v11i1.3542.

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The main concern of this study is due to certain cartoon content consisting of explicit scenes such as kissing, sex, violence. That are somehow not suitable for kids and may contradict to some religions and cultures. There are some reasons the film industry does not expel the kissing scene in a cartoon movie. It is categorized as a romance sequence and love scene. These could be a double-edged weapon that will ruin an individual’s childhood through excessive exposure to explicit content. This paper proposes a deep learning-based classifier to detect the kissing scene in the cartoon by using Da
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Muhammad, Arif Haikal Muhammad Fadzli, Fadzil Abu Hassan Mohd, and Ibrahim Norazlin. "Explicit kissing scene detection in cartoon using convolutional long short-term memory." Bulletin of Electrical Engineering and Informatics 11, no. 1 (2022): 213–20. https://doi.org/10.11591/eei.v11i1.3542.

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The main concern of this study is due to certain cartoon content consisting of explicit scenes such as kissing, sex, violence. That are somehow not suitable for kids and may contradict to some religions and cultures. There are some reasons the film industry does not expel the kissing scene in a cartoon movie. It is categorized as a romance sequence and love scene. These could be a double-edged weapon that will ruin an individual’s childhood through excessive exposure to explicit content. This paper proposes a deep learningbased classifier to detect the kissing scene in the cartoon by usi
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Marcial Basilio, Jorge A., Gualberto Aguilar Torres, Gabriel Sánchez Pérez, Karina Toscano Medina, and Héctor M. Pérez Meana. "Novel method for pornographic image detection using HSV and YCbCr color models." Revista Facultad de Ingeniería Universidad de Antioquia, no. 64 (October 3, 2012): 79–90. http://dx.doi.org/10.17533/udea.redin.13117.

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In this paper a novel method to explicit content or pornographic images detection is proposed, using the transformation from RGB to HSV or YCbCr color model, which is the most usual format to images that exists on Internet, moreover the using of a threshold to skin detection applying the color models HSV and YCbCr is proposed. Using the proposed threshold the image is segmented, once the image segmented, the skin quantity localized in that image is calculated. The obtained results using the proposed system are compared with two programs which carry out with the same goal, the Forensic Toolkit
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Naik, Deepak. "Fake Media Forensics:AI – Driven Forensic Analysis of Fake Multimedia Content." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47208.

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Abstract—With the rapid advancement of deep learning techniques, the generation of synthetic media—commonly Research and development on deepfakes technology have reached new levels of sophistication. Digital security along with misinformation face serious threats because of these sophisticated methods. and privacy. Existing deepfake detection models primarily the detection methods primarily analyze either video or audio or image-based forgeries yet they seldom employ unified multi-modal examination methods. The authors introduce here a multi-modal deepfake detection system. The proposed framew
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Meguellati, Elyas, Assaad Zeghina, Shazia Sadiq, and Gianluca Demartini. "LLM-Based Semantic Augmentation for Harmful Content Detection." Proceedings of the International AAAI Conference on Web and Social Media 19 (June 7, 2025): 1190–209. https://doi.org/10.1609/icwsm.v19i1.35868.

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Recent advances in large language models (LLMs) have demonstrated strong performance on simple text classification tasks, frequently under zero-shot settings. However, their efficacy declines when tackling complex social media challenges such as propaganda detection, hateful meme classification, and toxicity identification. Much of the existing work has focused on using LLMs to generate synthetic training data, overlooking the potential of LLM-based text preprocessing and semantic augmentation. In this paper, we introduce an approach that prompts LLMs to clean noisy text and provide context-ri
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Shahriari, Mohsen, Sabrina Haefele, and Ralf Klamma. "Using Content to Identify Overlapping Communities in Question Answer Forums." JUCS - Journal of Universal Computer Science 23, no. (9) (2017): 907–31. https://doi.org/10.3217/jucs-023-09-0907.

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Nowadays, people use online social networks almost every day. They activate either due to their interests, or to search or catch their desirable information. Users of online social networks generate structural and contextual traces that can be analyzed by, i.e., network science researchers. Researchers can describe networks fabricated out of online traces from different perspectives that one of them is communities. Overlapping communities are overlapped structures, in which nodes have denser connections with each other than the rest of the network. Different approaches have addressed this prob
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Dissertations / Theses on the topic "Explicit content detection"

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Vaglio, Andrea. "Leveraging lyrics from audio for MIR." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT027.

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Les paroles de chansons fournissent un grand nombre d’informations sur la musique car ellescontiennent une grande partie de la sémantique des chansons. Ces informations pourraient aider les utilisateurs à naviguer facilement dans une large collection de chansons et permettre de leur offrir des recommandations personnalisées. Cependant, ces informations ne sont souvent pas disponibles sous leur forme textuelle. Les systèmes de reconnaissance de la voix chantée pourraient être utilisés pour obtenir des transcriptions directement à partir de la source audio. Ces approches sont usuellement adaptée
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Book chapters on the topic "Explicit content detection"

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Gutfeter, Weronika, Joanna Gajewska, and Andrzej Pacut. "Detecting Sexually Explicit Content in the Context of the Child Sexual Abuse Materials (CSAM): End-to-End Classifiers and Region-Based Networks." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74627-7_11.

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Jimenez-Martinez, Miguel, Gibran Benitez-Garcia, Linda Karina Toscano-Medina, and Jesus Olivares-Mercado. "Frame-Level Deepfake Detection on Explicit Content with ID-Unaware Binary Classification." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240353.

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The rapid advancement in deepfake technology has enabled the creation of highly realistic fake images and videos, posing significant risks, especially in the context of explicit content. Such content, which often involves the alteration of an individual’s identity in sexually explicit material, can lead to defamation, harassment, and blackmail. This paper focuses on the detection of deepfakes in explicit content using a state-of-the-art ID-unaware Binary Classification method. We evaluate its effectiveness in real-world scenarios by analyzing three versions of the model with different backbone
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Vajda, Peter, Ivan Ivanov, Lutz Goldmann, Jong-Seok Lee, and Touradj Ebrahimi. "Robust Duplicate Detection of 2D and 3D Objects." In Methods and Innovations for Multimedia Database Content Management. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1791-9.ch007.

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In this paper, the authors analyze their graph-based approach for 2D and 3D object duplicate detection in still images. A graph model is used to represent the 3D spatial information of the object based on the features extracted from training images to avoid explicit and complex 3D object modeling. Therefore, improved performance can be achieved in comparison to existing methods in terms of both robustness and computational complexity. Different limitations of this approach are analyzed by evaluating performance with respect to the number of training images and calculation of optimal parameters
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Vadivukarasi, L., L. Ganesh Babu, Dler Salih Hasan, Rajesh Sharma R., N. Durga Devi, and L. Karthick. "Nurturing Trust in Human-Robot Interaction and the Crucial Role of Dialogue and Explicit AI." In Advances in Mechatronics and Mechanical Engineering. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1966-6.ch015.

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Modern technology has improved to the level that robots are possible to interact like human beings. Therefore, human-robot interaction has gradually become a part of human society. At the same time, technology is still in the process of refinement for better interaction with humans. Therefore, AI for content detection and dialogue AI has developed as a device for improving human and robot interface. An increase in the online presence of people triggered a change of gratified and explicit AI helps to analyse and filter explicit content according to age set. Conversely, dialoguer AI aids in the
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Khribi, Mohamed Koutheaïr, Mohamed Jemni, and Olfa Nasraoui. "Automatic Personalization in E-Learning Based on Recommendation Systems." In Intelligent and Adaptive Learning Systems. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-842-2.ch002.

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Web based learning environments are being increasingly used at a large scale in the education area. This situation has brought a dramatic growth in the amount of educational resources and services incorporated continuously in these systems, and related access and usage of this educational content by a diversity of learners. However, the delivery of this educational content is generally done in the same way for all learners without giving any special attention to the different consumption styles or differences between their profiles and individual needs. Therefore, providing personalization in
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Liu Lanbo, Zhang Fengkai, Huang Qinghua, Li Shucai, and Sun Huaifeng. "Fast Model of Transient Electromagnetic Response to Geological Structures Ahead of Tunnel Face." In Studies in Applied Electromagnetics and Mechanics. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-767-2-211.

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Safety is the key issue during the process of tunnel construction. High pressure water and mud burst and the resultant tunnel face collapse is one of the major hazards in any tunneling projects. Detection and forecasting of potential water-bearing geological structures (usually one or another kind of zone of weakness, such as faults and shear zones) in front of the tunnel face, consequently, is the first step to reduce the risk in tunnel construction. Transient (or time domain) electromagnetic (TEM) survey is a major tool to detect and image conductive targets in the geologic formation so that
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Sanchez-Pinsach David, Arcos Josep Lluis, Laxe Sara, Bernabeu Montserrat, and Tormos Josep Maria. "Using Community Detection Techniques to Discover Non-Explicit Relationships in Neurorehabilitation Treatments." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-806-8-26.

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The interaction between patients and professionals in complex clinical domains, as in the case of Neurorehabilitation, is always a complex process where crucial decision making in a short period of time is required, and where every decision has a serious impact on the patient. In this situation, deciding which are the most appropriate interventions is not an easy task because these patients simultaneously present several impairments, multiple diagnoses, and required complex interdisciplinary approaches. In this context, a methodology and a tool based on ICF have been developed to explore the r
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Bitsch Jó Ágila, Ramos Roann, Ix Tim, Ferrer-Cheng Paula Glenda, and Wehrle Klaus. "Psychologist in a Pocket: Towards Depression Screening on Mobile Phones." In Studies in Health Technology and Informatics. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-516-6-153.

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Depression is the most prevalent clinical disorder and one of the main causes of disability. This makes early detection of depressive symptoms critical in its prevention and management. This paper presents and discusses the development of Psychologist in a Pocket (PiaP), a mental mHealth application for Android which screens and monitors for these symptoms, and–given the explicit permission of the user–alerts a trusted contact such as the mental health professional or a close friend, if it detects symptoms.
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Wadhwani, Arun Kumar, Sulochana Wadhwani, and Tripty Singh. "Computer Aided Diagnosis System for Breast Cancer Detection." In Medical Imaging. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0571-6.ch040.

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Management of breast cancer in elder patients is challenging due to a lack of good quality evidence regarding the role of adjuvant chemotherapy. Mammograms can depict most of the significant changes of breast disease. The primary radiographic signs of breast cancer are masses (its density, site, shape, borders), spicular lesions and calcification content. The basic idea is to convert the mammogram image and convert into 3-D matrix. Obtained matrix is used to convert the mammogram into binary image. Several techniques like detecting cell, filling gaps, dilating gaps, removing border, smoothing
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Wadhwani, Arun Kumar, Sulochana Wadhwani, and Tripty Singh. "Computer Aided Diagnosis System for Breast Cancer Detection." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9530-6.ch015.

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Management of breast cancer in elder patients is challenging due to a lack of good quality evidence regarding the role of adjuvant chemotherapy. Mammograms can depict most of the significant changes of breast disease. The primary radiographic signs of breast cancer are masses (its density, site, shape, borders), spicular lesions and calcification content. The basic idea is to convert the mammogram image and convert into 3-D matrix. Obtained matrix is used to convert the mammogram into binary image. Several techniques like detecting cell, filling gaps, dilating gaps, removing border, smoothing
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Conference papers on the topic "Explicit content detection"

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Ghatol, Prathmesh, Yash Shah, Rucha Gavaskar, Monika Mangla, and Richa Sharma. "Employing Machine Learning based Majority Voting Classifier for Explicit Content Detection in Music." In 2024 IEEE 5th India Council International Subsections Conference (INDISCON). IEEE, 2024. http://dx.doi.org/10.1109/indiscon62179.2024.10744255.

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Vaglio, Andrea, Romain Hennequin, Manuel Moussallam, Gael Richard, and Florence d'Alche-Buc. "Audio-Based Detection of Explicit Content in Music." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054278.

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Chin, Hyojin, Jayong Kim, Yoonjong Kim, Jinseop Shin, and Mun Y. Yi. "Explicit Content Detection in Music Lyrics Using Machine Learning." In 2018 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2018. http://dx.doi.org/10.1109/bigcomp.2018.00085.

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Dubettier, Adrien, Tanguy Gernot, Emmanuel Giguet, and Christophe Rosenberger. "A Comparative Study of Tools for Explicit Content Detection in Images." In 2023 International Conference on Cyberworlds (CW). IEEE, 2023. http://dx.doi.org/10.1109/cw58918.2023.00077.

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Wang, Supeng, Yuxi Li, Ming Xie, et al. "Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/166.

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Change detection is a widely adopted technique in remote sense imagery (RSI) analysis in the discovery of long-term geomorphic evolution. To highlight the areas of semantic changes, previous effort mostly pays attention to learning representative feature descriptors of a single image, while the difference information is either modeled with simple difference operations or implicitly embedded via feature interactions. Nevertheless, such difference modeling can be noisy since it suffers from non-semantic changes and lacks explicit guidance from image content or context. In this paper, we revisit
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Babonnaud, William, Estelle Delouche, and Mounir Lahlouh. "The Bias that Lies Beneath: Qualitative Uncovering of Stereotypes in Large Language Models." In 14th Scandinavian Conference on Artificial Intelligence SCAI 2024, June 10-11, 2024, Jönköping, Sweden. Linköping University Electronic Press, 2024. http://dx.doi.org/10.3384/ecp208022.

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The rapid growth of Large Language Models (LLMs), such as ChatGPT and Mistral, has raised concerns about their ability to generate inappropriate, toxic and ethically problematic content. This problem is further amplified by LLMs' tendency to reproduce the prejudices and stereotypes present in their training datasets, which include misinformation, hate speech and other unethical content. Traditional methods of automatic bias detection rely on static datasets that are unable to keep up with society's constantly changing prejudices, and so fail to capture the large diversity of biases, especially
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Zheng, Li, Zhenpeng Li, Jian Li, Zhao Li, and Jun Gao. "AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/614.

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Anomaly detection in dynamic graphs becomes very critical in many different application scenarios, e.g., recommender systems, while it also raises huge challenges due to the high flexible nature of anomaly and lack of sufficient labelled data. It is better to learn the anomaly patterns by considering all possible features including the structural, content and temporal features, rather than utilizing heuristic rules over the partial features. In this paper, we propose AddGraph, a general end-to-end anomalous edge detection framework using an extended temporal GCN (Graph Convolutional Network) w
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Veleshnja, Ina, and Xhei Çeliku. "Calls for Regulation in AI: Examining the Impact of Artificial Intelligence on Contemporary Issues Surrounding Sexual Abuse." In 8th International Scientific Conference – EMAN 2024 – Economics and Management: How to Cope With Disrupted Times. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2024. https://doi.org/10.31410/eman.2024.571.

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Artificial Intelligence holds significant influence in the realm of on­line sexual abuse, revolutionizing both perpetration and prevention. Through sophisticated algorithms and machine learning, AI facilitates the prolifera­tion of explicit content, grooming techniques, and online exploitation. Its rap­id evolution enables predators to exploit vulnerabilities, evade detection, and manipulate victims. Conversely, AI-driven technologies offer promising av­enues for detection, intervention, and victim support. The following paper highlights the dual nature of AI’s impact on online sexual abuse, e
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Yu, Zhiyuan, and Kwun-Lon Ting. "Explicit Dynamics Analysis for Harmonic Drives." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34759.

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Harmonic drive can have a high nonlinear dynamic behavior. In order to find a way to simulate its operating process and evaluate its performance, this paper gave an introduction of different methods of tooth contact analysis (TCA) and found out that explicit dynamics as a newly used tool for TCA is the most suitable one. Because the harmonic drive’s high contact ratio and uncertainty of contact boundary match with explicit dynamics’ features of explicit algorithm, trajectory detection to deal with contact. A harmonic drive with a new tooth profile has been modeled in Ansys Workbench and solved
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Acharya, Manoj, Anirban Roy, Kaushik Koneripalli, Susmit Jha, Christopher Kanan, and Ajay Divakaran. "Detecting Out-Of-Context Objects Using Graph Contextual Reasoning Network." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/89.

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This paper presents an approach for detecting out-of-context (OOC) objects in images. Given an image with a set of objects, our goal is to determine if an object is inconsistent with the contextual relations and detect the OOC object with a bounding box. In this work, we consider common contextual relations such as co-occurrence relations, the relative size of an object with respect to other objects, and the position of the object in the scene. We posit that contextual cues are useful to determine object labels for in-context objects and inconsistent context cues are detrimental to determining
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Reports on the topic "Explicit content detection"

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Rankin, Nicole, Deborah McGregor, Candice Donnelly, et al. Lung cancer screening using low-dose computed tomography for high risk populations: Investigating effectiveness and screening program implementation considerations: An Evidence Check rapid review brokered by the Sax Institute (www.saxinstitute.org.au) for the Cancer Institute NSW. The Sax Institute, 2019. http://dx.doi.org/10.57022/clzt5093.

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Background Lung cancer is the number one cause of cancer death worldwide.(1) It is the fifth most commonly diagnosed cancer in Australia (12,741 cases diagnosed in 2018) and the leading cause of cancer death.(2) The number of years of potential life lost to lung cancer in Australia is estimated to be 58,450, similar to that of colorectal and breast cancer combined.(3) While tobacco control strategies are most effective for disease prevention in the general population, early detection via low dose computed tomography (LDCT) screening in high-risk populations is a viable option for detecting asy
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