Reviewerly/exHarmony huggingface dataset:
Personas-based Student Grouping using reinforcement learning and linear programming

Group discussions and assignments play a pivotal role in the classroom and online study. Existing research has mainly focused on exploring the educational impact of group learning, while the study on automated grouping still remains under-explored. This paper proposes a principled method that aims to achieve personalized, accurate, and efficient grouping outcomes. Dubbed as Personas-based Student Grouping (PSG), our method first applies unsupervised clustering techniques to assign personas to students based on their behavioral characteristics. Based on their personas, we then utilize deep reinforcement learning to search for appropriate grouping rules and perform linear programming to obtain a suitable grouping scheme. Finally, the teaching effectiveness is fed back as the rewards to the reinforcement learning model to optimize future grouping scheme selections. Extensive experiments conducted on MOOCs datasets show that PSG can achieve more advantageous performance in both efficiency and effectiveness compared to the manual or random grouping mechanism. We hope PSG can provide students with a more enhanced learning experience and contribute to the future development of education. Our project homepage is available at https://PSG-project.pages.dev.
Deep learning-based correction of defocused fringe patterns for high-speed 3D measurement

Digital fringe projection profilometry often faces a trade-off between measurement accuracy and efficiency. Defocus technology is commonly employed to address this challenge and improve the efficiency of high-speed three-dimensional (3D) measurement. This technology uses 1-bit binary fringe patterns instead of traditional 8-bit sinusoidal patterns, but accurately measuring 3D shapes with both high speed and accuracy remains a challenge due to defocus errors. These errors are introduced by the manual adjustment of lens focal length and reduce both fringe pattern quality and measurement accuracy. To overcome this limitation, we propose a multi-stage generative adversarial network with a self-attention mechanism to correct inaccurate fringe patterns and transform them into more ideal sinusoidal fringe patterns. Our generation network comprises a multi-stage feature extraction network with a self-attention mechanism and an encoder-decoder network. A multi-stage network integrating residual and transformer modules is constructed to mine global feature information. The self-attention mechanism accurately detects key areas for correction, and the encoder-decoder network generates rectified sinusoidal fringe patterns by combining the feature information with the attention area. We use a discriminative network to evaluate whether the output of the generative network is good enough to be true. In our experiments, we considered different fringe widths and measured objects of various types and colors. The results show that our proposed method improves the quality of defocus fringe patterns and the accuracy of subsequent 3D reconstruction compared to existing direct defocus methods.
Auto encoder generative adversarial networks - based mineral prospectivity mapping in Lhasa area, Tibet

When conducting Mineral Potential Mapping (MPM) using multiple sources of data such as geology, geochemistry, and geophysics, it often encounters the challenges of complex and diverse data distributions within these datasets. Enhancing the capability to extract nonlinear data features and further uncover metallogenic information is a crucial research objective. This study utilizes the C-A multifractal approach to extract anomalous information related to metallogenic elements, employs compositional data analysis methods for quantitatively extracting geochemical associations of mineralization, and utilizes GIS spatial analysis techniques to quantitatively extract predictive indicators from various data sources, including geology, geophysics, and remote sensing, to construct an MPM prediction dataset. Building upon the foundation of AutoEncoder (AE), this study introduces a discriminator and employs the AEGAN (Auto Encoder Generative Adversarial Network) algorithm, which combines AutoEncoder and Generative Adversarial Network (GAN), for metallogenic prospectivity prediction in the Lhasa region. Compared to AE algorithms, AEGAN combines the strengths of AE and GAN, significantly improving the model's ability to reconstruct input data through the interaction between the generator and discriminator. Additionally, this study designs comparative experiments with AE, and the results demonstrate that the AEGAN model can more accurately identify the correlation between high anomaly areas and polymetallic deposits, providing a more precise delineation of anomalous extents. The Area Under the Receiver Operating Characteristic Curve (AUC) further validates the superior performance of the AEGAN model. These findings indicate that the AEGAN model exhibits outstanding capabilities in learning the internal connections and features among multiple data sources, holding significant potential for practical applications in mineral exploration.
A semantically enhanced dual encoder for aspect sentiment triplet extraction

Aspect sentiment triplet extraction (ASTE) is a crucial subtask of aspect-based sentiment analysis (ABSA) that aims to comprehensively identify sentiment triplets. Previous research has focused on enhancing ASTE through innovative table-filling strategies. However, these approaches often overlook the multi-perspective nature of language expressions, resulting in a loss of valuable interaction information between aspects and opinions. To address this limitation, we propose a framework that leverages both a basic encoder, primarily based on BERT, and a particular encoder comprising a Bi-LSTM network and graph convolutional network (GCN). The basic encoder captures the surface-level semantics of linguistic expressions, while the particular encoder extracts deeper semantics, including syntactic and lexical information. By modeling the dependency tree of comments and considering the part-of-speech and positional information of words, we aim to capture semantics that are more relevant to the underlying intentions of the sentences. An interaction strategy combines the semantics learned by the two encoders, enabling the fusion of multiple perspectives and facilitating a more comprehensive understanding of aspect–opinion relationships. Experiments conducted on benchmark datasets demonstrate the state-of-the-art performance of our proposed framework.
Improving defocus blur detection via adaptive supervision prior-tokens

The Defocus Blur Detection (DBD) technique is devised to accurately identify regions of blurriness within images. The prediction difficulty of defocused pixels is closely associated with their spatial location. Owing to the cluttered background, pixels near the edges are more prone to erroneous predictions. To address the issue of uneven pixel distribution at the edges of defocused regions, we deliberately decouple the original labels into Prior-Tokaens: Edge Transition Detail Region (EDR) and Structure Body Region (SBR). Subsequently, we propose a novel adaptive multi-supervised network comprising a feature extraction module, a feature fusion network (FFN), and a Multi-scale Channel Attention Module (MCAM). This method harnesses complementary features between SBR and EDR, furnishing a tailored feature learning strategy that outperforms traditional single-supervised techniques. Furthermore, considering that features generated with varying receptive fields contain information at different levels, we introduce MCAM to identify feature pixels at different scales, enhancing semantic relevance. Moreover, for images with complex scenes, an adaptive learning scheme is developed to selectively fuse low-level detail features and high-level semantic information, thereby enhancing the model's generalization capability. The proposed approach outperforms state-of-the-art techniques on various evaluation metrics, as demonstrated through qualitative and quantitative analyses of popular public datasets.
AdaBoost based Random forest model for Emotion classification of Facial images

Posting of visual data in the social network has now become a common trend. Mainly, users are posting selfies or facial images over the social media that depict various moods at different instances. This has attracted the attention of researchers to come up with facial expression mining from social media images. Aim of the present work is to improve the performance of emotion analysis in a more efficient way in terms of accuracy and reliability. Developing new strategies for carrying out emotion analysis on posts containing images in social media. In this work, a novel model has been presented that focuses on transformed features for the purpose. Six distinct sentimental emotion classes (labeled 0 through 5) are considered in this work. They are 0: Sad, 1: Fear, 2: Awful, 3: Happy, 4: Surprised, 5: Satisfied. This model consists of three major stages: Feature extraction, Feature selection, and Class labeling.•This work incorporates the use of 2D Ortho-normal Stockwell Transformation (DOST) method is used for feature extraction of facial images.•Following the feature extraction model, feature selection is implemented through 'bi-variate t-test'.•Finally, these selected features are subjected to a AdaBoost based Random Forest classifier for Emotion Classification(ARFEC) for the purpose of class labeling towards different classes of expression. The Flickr8k, CK+ and FER2013 image databases are utilized for validating the efficiency of the developed ARFEC model. Analysis of results shows the effectiveness of ARFEC model with overall rates of accuracy of 89.5 %, 92.5 % and 89.5 % respectively for the databases taken. Performance of ARFEC model when compared with other existing methods such as Support Vector Machine and K-Nearest Neighbors yielded better results in terms of overall rate of accuracy.
Multimedia IoT-surveillance optimization model using mobile-edge authentic computing

Smart technologies are advancing the development of cutting-edge systems by exploring the future network. The Internet of Things (IoT) and many multimedia sensors interact with each other for collecting and transmitting visual data. However, managing enormous amounts of data from numerous network devices is one of the main research challenges. In this context, various IoT systems have been investigated and have provided efficient data retrieval and processing solutions. For multimedia systems, however, controlling inefficient bandwidth utilization and ensuring timely transmission of vital information are key research concerns. Moreover, to transfer multimedia traffic while balancing communication costs for the IoT system, a sustainable solution with intelligence in real-life applications is demanded. Furthermore, trust must be formed for technological advancement to occur; such an approach provides the smart communication paradigm with the incorporation of edge computing. This study proposed a model for optimizing multimedia using a combination of edge computing intelligence and authentic strategies. Mobile edges analyze network states to discover the system's status and minimize communication disruptions. Moreover, direct and indirect authentication determines the reliability of data forwarders and network stability. The proposed authentication approach minimizes the possibility of data compromise and increases trust in multimedia surveillance systems. Using simulation testing, the proposed model outperformed other comparable work in terms of byte delivery, packet overhead, packet delay, and data loss metrics.
Notation3 as an Existential Rule Language

Notation3 Logic ( $$\text {N}_3$$ ) is an extension of RDF which allows the user to write rules introducing new blank nodes to RDF graphs. Many applications (e.g., ontology mapping) rely on this feature as blank nodes – used directly or in auxiliary constructs – are omnipresent on the Web. However, the number of fast $$\text {N}_3$$ reasoners fully supporting blank node introduction is rather limited. On the other hand, there are engines like VLog or Nemo not directly supporting Semantic Web rule formats but developed for very similar constructs: existential rules. In this paper we investigate the relation between $$\text {N}_3$$ rules with blank nodes in their heads and existential rules. We identify a subset of $$\text {N}_3$$ which can be mapped directly to existential rules and define such a mapping preserving the equivalence of $$\text {N}_3$$ formulae. To also illustrate that $$\text {N}_3$$ reasoning could benefit from our translation, we employ this mapping in an implementation to compare the performance of the $$\text {N}_3$$ reasoners EYE and cwm to VLog and Nemo on $$\text {N}_3$$ rules and their mapped counterparts. Our tests show that the existential rule reasoners perform particularly well for use cases containing many facts while the EYE reasoner is very fast when dealing with a high number of dependent rules. We thus provide a tool enabling the Semantic Web community to directly use existing and future existential rule reasoners and benefit from the findings of this active community.
Combining Proofs for Description Logic and Concrete Domain Reasoning

Logic-based approaches to AI have the advantage that their behavior can in principle be explained with the help of proofs of the computed consequences. For ontologies based on Description Logic (DL), we have put this advantage into practice by showing how proofs for consequences derived by DL reasoners can be computed and displayed in a user-friendly way. However, these methods are insufficient in applications where also numerical reasoning is relevant. The present paper considers proofs for DLs extended with concrete domains (CDs) based on the rational numbers, which leave reasoning tractable if integrated into the lightweight DL $$\mathcal {E}\mathcal {L} _\bot $$ . Since no implemented DL reasoner supports these CDs, we first develop reasoning procedures for them, and show how they can be combined with reasoning approaches for pure DLs, both for $$\mathcal {E}\mathcal {L} _\bot $$ and the more expressive DL $$\mathcal {ALC}$$ . These procedures are designed such that it is easy to extract proofs from them. We show how the extracted CD proofs can be combined with proofs on the DL side into integrated proofs that explain both the DL and the CD reasoning.
Enhancing classification performance in imbalanced datasets: A comparative analysis of machine learning models

&lt;abstract&gt;&lt;p&gt;In the realm of machine learning, where data-driven insights guide decision-making, addressing the challenges posed by class imbalance in datasets has emerged as a crucial concern. The effectiveness of classification algorithms hinges not only on their intrinsic capabilities but also on their adaptability to uneven class distributions, a common issue encountered across diverse domains. This study delves into the intricate interplay between varying class imbalance levels and the performance of ten distinct classification models, unravelling the critical impact of this imbalance on the landscape of predictive analytics. Results showed that random forest (RF) and decision tree (DT) models outperformed others, exhibiting robustness to class imbalance. Logistic regression (LR), stochastic gradient descent classifier (SGDC) and naïve Bayes (NB) models struggled with imbalanced datasets. Adaptive boosting (ADA), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and k-nearest neighbour (kNN) models improved with balanced data. Adaptive synthetic sampling (ADASYN) yielded more reliable predictions than the under-sampling (UNDER) technique. This study provides insights for practitioners and researchers dealing with imbalanced datasets, guiding model selection and data balancing techniques. RF and DT models demonstrate superior performance, while LR, SGDC and NB models have limitations. By leveraging the strengths of RF and DT models and addressing class imbalance, classification performance in imbalanced datasets can be enhanced. This study enriches credit risk modelling literature by revealing how class imbalance impacts default probability estimation. The research deepens our understanding of class imbalance's critical role in predictive analytics. Serving as a roadmap for practitioners and researchers dealing with imbalanced data, the findings guide model selection and data balancing strategies, enhancing classification performance despite class imbalance.&lt;/p&gt;&lt;/abstract&gt;
A SURVEY ON VARIOUS APPROACHES FOR SEGMENTATION AND CLASSIFICATION OF WHITE BLOOD CELLS

Traditionally, White Blood Cell identification is performed by experienced pathologists manually. Peripheral blood smear analysis is a laboratory procedure that helps in diagnosis of various pathological disorders such as malaria, anemia, leukemia, etc. This process is performed at a microscopic level and therefore is required to be precise. Manually performing microscopic evaluation is a challenging task. This task can be automated with the help of computer-aided systems. This work discusses various methods being used in a variety of cellular image segmentation and classification tasks with the field of the survey being concentrated on leukocyte segmentation and classification.
Symbolic violence and discrimination in a social media comment section: A study on discriminatory discursive strategies targeting non-binary gender identity in the context of Indonesia

This study examines the presence of discriminatory discourses and symbolic violence in the digital realm within the context of Indonesia. The objective of this research is to identify the discriminatory strategies towards non-heteronormative gender identity within the comment section of an Instagram post published by VICE Indonesia and to comprehend how these strategies function as mechanisms of symbolic violence. The study was conducted qualitatively by analyzing notable comments in the primary data source, which is an Instagram post addressing the issue of non-binary gender in the Indonesian context. To ensure a comprehensive analysis of the comments, only representative comments were included. The data was categorized based on four discriminatory discursive strategies, and subsequently interpreted through the lens of symbolic violence. The results show that the utilization of discriminatory discursive strategies includes the negative portrayal of the other, which involves labeling the out-group using words such as penyimpangan and gangguan jiwa, scare tactics, which are used to stir worries, blaming the victim, by justifying discrimination towards the out-group by emphasizing their negative qualities, and delegitimation, by highlighting the illegitimate status of the out-group. Additionally, the employment of these strategies is argued to transmit symbolic violence as a way to maintain doxa. Furthermore, the transmission of symbolic violence is discussed further and related to the sociocultural context of Indonesia.
UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.
Phishing Email Detection Model Using Deep Learning

Email phishing is a widespread cyber threat that can result in the theft of sensitive information and financial loss. It uses malicious emails to trick recipients into providing sensitive information or transferring money, often by disguising themselves as legitimate organizations or individuals. As technology advances and attackers become more sophisticated, the problem of email phishing becomes increasingly challenging to detect and prevent. In this research paper, the use of deep learning techniques, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, recurrent neural networks (RNNs), and bidirectional encoder representations from transformers (BERT), are explored for detecting email phishing attacks. A dataset of phishing and benign emails was utilized, and a set of relevant features was extracted using natural language processing (NLP) techniques. The proposed deep learning model was trained and tested using the dataset, and it was found that it can achieve high accuracy in detecting email phishing compared to other state-of-the-art research, where the best performance was seen when using BERT and LSTM with an accuracy of 99.61%. The results demonstrate the potential of deep learning for improving email phishing detection and protecting against this pervasive threat.