Reviewerly/exHarmony huggingface dataset:
Semantic decision Trees: A new learning system for the ID3-Based algorithm using a knowledge base

The decision tree (DT) is a well-established tool used to classify information. Its efficiency in accessing data and the accuracy of its outcomes is largely dependent on the characteristics of the input data and the efficacy of the construction processes and algorithms. In this study, we are introducing an innovative approach called the Semantic Decision Tree (SDT). The SDT builds upon the concept of the Iterative Dichotomiser 3 (ID3) algorithm. Our proposed SDT's first application involves utilizing a knowledge base, intentionally designed to support the DT construction process. The primary goal here is to effectively address the multi-value bias selection issue, a recurring problem found in a number of DT algorithms. Building on this, we adopt an ontology summarization technique to fine-tune the splitting condition of the DT. This adjustment is influenced by the attribute's importance value, extracted directly from the ontology. This process leads to the creation of nodes that align more precisely with the dataset than those generated by traditional DT methods. To validate the robustness and precision of our proposed SDT, we evaluated it across four distinct datasets. In addition, we benchmarked the classification accuracy of our SDT against several leading baseline algorithms; the traditional ID3, C4.5, CART, and the Mutual Information Decision Tree (MIDT). The findings demonstrate that our proposed SDT outperforms these industry-standard algorithms in terms of accuracy. Further in-depth qualitative analysis showed that the modifications in information gain resulted in DT structures that align more naturally with human decision-making logic.
The Synergy of Artificial Intelligence (AI) and Geographic Information Systems (GIS) for Enhanced Disaster Management: Opportunities and Challenges

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Quantifying U‐Net uncertainty in multi‐parametric MRI‐based glioma segmentation by spherical image projection

Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground-truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand.To develop a U-Net segmentation uncertainty quantification method based on spherical image projection of multi-parametric MRI (MP-MRI) in glioma segmentation.The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection-based U-Net (SPU-Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel-wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU-Net model was implemented on the basis of 369 glioma patients with MP-MRI scans (T1, T1-Ce, T2, and FLAIR). Three SPU-Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU-Net model was compared with (1) the classic U-Net model with test-time augmentation (TTA) and (2) linear scaling-based U-Net (LSU-Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score).The developed SPU-Net model successfully achieved low uncertainty for correct segmentation predictions (e.g., tumor interior or healthy tissue interior) and high uncertainty for incorrect results (e.g., tumor boundaries). This model could allow the identification of missed tumor targets or segmentation errors in U-Net. Quantitatively, the SPU-Net model achieved the highest uncertainty scores for three segmentation targets (ET/TC/WT): 0.826/0.848/0.936, compared to 0.784/0.643/0.872 using the U-Net with TTA and 0.743/0.702/0.876 with the LSU-Net (scaling factor = 2). The SPU-Net also achieved statistically significantly higher Dice coefficients, underscoring the improved segmentation accuracy.The SPU-Net model offers a powerful tool to quantify glioma segmentation uncertainty while improving segmentation accuracy. The proposed method can be generalized to other medical image-related deep-learning applications for uncertainty evaluation.
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution

Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images. Prior attempts at applying the DPM to image super-resolution (SR) have shown that iteratively refining a pure Gaussian noise with a conditional image using a U-Net trained on denoising at various-level noises can help obtain a satisfied high-resolution image for the low-resolution one. To further improve the performance and simplify current DPM-based super-resolution methods, we propose a simple but non-trivial DPM-based super-resolution post-process framework, i.e., cDPMSR. After applying a pre-trained SR model on the to-be-test LR image to provide the conditional input, we adapt the standard DPM to conduct conditional image generation and perform super-resolution through a deterministic iterative denoising process. Our method surpasses prior attempts on both qualitative and quantitative results and can generate more photo-realistic counterparts for the low-resolution images with various benchmark datasets including Set5, Set14, Urban100, BSD100, and Manga109. Code will be published after accepted.
Exploring Diffusion Models for Unsupervised Video Anomaly Detection

This paper investigates the performance of diffusion models for video anomaly detection (VAD) within the most challenging but also the most operational scenario in which the data annotations are not used. As being sparse, diverse, contextual, and often ambiguous, detecting abnormal events precisely is a very ambitious task. To this end, we rely only on the information-rich spatio-temporal data, and the reconstruction power of the diffusion models such that a high reconstruction error is utilized to decide the abnormality. Experiments performed on two large-scale video anomaly detection datasets demonstrate the consistent improvement of the proposed method over the state-of-the-art generative models while in some cases our method achieves better scores than the more complex models. This is the first study using a diffusion model and examining its parameters’ influence to present guidance for VAD in surveillance scenarios.
Efficient multi-view semi-supervised feature selection

Multi-view semi-supervised feature selection can identify a feature subset from heterogeneous feature spaces of data. However, existing methods fail in handling large-scale data since they have to calculate the inverses of high-order dense matrices. Moreover, traditional methods often pre-construct graphs to mine the similarity structure of data, such that the interaction between graph construction and feature selection is directly ignored, degrading their effectiveness in practice. To address these issues, we propose an efficient multi-view feature selection method (EMSFS), which combines graph learning, label propagation as well as multi-view feature selection within a unified framework. Specifically, EMSFS can adaptively learn a bipartite graph between training samples and generated anchors, not only reducing the cost of graph computation but also tactfully avoiding the inverse of a high-order matrix. As a result, the main computational complexity of EMSFS is approximately linear to the number of training samples. Meanwhile, EMSFS simultaneously selects important features and exploits the similarity structure in the projected feature space, which enhances the reliability of the graph and positively facilitates feature selection. To solve the formulated objective function, we developed an alternating optimization, and experiments validate the effectiveness and the efficiency of EMSFS.
A Multi-Strategy Dung Beetle Optimization Algorithm for Optimizing Constrained Engineering Problems

The dung beetle optimization (DBO) algorithm is one of newly excellent swarm intelligent algorithm while its exploration capability is still insufficient. For this, a multi-strategy DBO algorithm (GODBO) by utilizing the optimal value in the current population directed shift and the opposition-based learning (OBL) is proposed. In GODBO, the OBL is used to increase the likelihood of finding a better solution in the early stage of the algorithm so that the algorithm can find the optimal solution faster. Meanwhile, the current optimal value (Gbest) is used to guide the solution to search a new solution later in the algorithm, and the improved algorithm will be searched near a better solution at the later stage to get a better solution. Therefore, both are used to enhance exploration capabilities. 29 famous mathematical benchmark functions as test objects are applied to evaluate the abilities of the GODBO algorithm, and the experimental results demonstrate that GODBO performs better in the light of convergence speed and convergence accuracy in comparison with other competitors. Furthermore, two constrained engineering optimization problems are employed in GODBO to validate the effectiveness to solve practice problems, and the experiment results show that it can make tools to tackling them.
The effect of chatbots on learning: a meta-analysis of empirical research

This meta-analysis aimed to comprehensively review empirical studies on the effect of chatbots on learning and quantitatively synthesize their findings to produce an overall effect size. Searching several databases yielded 28 eligible reports with 31 individual effect sizes. The results revealed a significant and medium effect (g = .48) of chatbots on learning. Further analyses indicated four significant moderators: the type of instruction in the comparison group, experimental duration, chatbot type, and chatbot tasks. The highest effect sizes emerged when the comparison group had no specific support, the experiment lasted only one session, and chatbots were task-focused and took care of frequently asked questions. These results suggest that chatbots can be more effective in certain cases within their overall contribution area to learning.
Via Digitalis: From the Postdigital to the Hyperdigital

Abstract The way beyond the digital can be discovered, not outside, but rather in and through the digital at its highest point. Since its introduction, the postdigital has neither been adequately defined nor scientifically demonstrated to be indefinable. If, on the contrary, the postdigital can be defined by a conceptual and genealogical analysis as a circuit of reflections upon the essential rupture of the digital, then the way that, in the digital, we calculate in writing can, in the postdigital, be assumed into a hyperbolic reflection in and from its more originary creative source. In this theological critique of Maggi Savin-Baden and John Reader’s volume Postdigital Theologies , I prosecute three theses: first, the postdigital has failed; second, postdigital theology is incompatible with Christian theology; and third, for mystical theology, the hyperdigital is the truth of the postdigital. The postdigital is not ‘ineffable’ but rather only appears indefinable because of a prior and contestable refusal to analyse the digital, as it predicates the ‘postdigital’ of the plural ‘theologies’, and as it speaks past theology. Once, however, it has been analysed into a circuit of reflections upon the rupture of the digital, the truth of the postdigital can be discovered in the hyper-negative or apophatic judgements of mystical theology. This theological critique of the postdigital has thus been written to prepare the mystical way of the digital or ‘via digitalis’ to read the digital and the postdigital on a spiritual ascent towards a hyperbolic cybernetic grammar, which, following Pseudo-Dionysius the Areopagite, should be named the ‘hyperdigital’.
Adaptive learning: a cluster-based literature review (2011-2022)

Adaptive learning is a personalized instruction system that adjusts to the needs, preferences, and progress of learners. This paper reviews the current and future developments of adaptive learning in higher education, especially in relation to the digital education strategy of the European Union. It also uses a cluster analysis framework to explore the main themes and their relationships in the academic literature on adaptive learning. The paper highlights the potential of emerging technologies such as AI, eye-tracking, and physiological measurements to improve the personalization and effectiveness of adaptive learning systems. It presents various methods, algorithms, and prototypes that incorporate learning styles into adaptive learning. It also stresses the importance of continuous professional development in e-learning, media literacy, computer security, and andragogy for teachers who use adaptive learning systems. The paper concludes that adaptive learning can promote creativity, innovation, and lifelong learning in Ukrainian higher education, but it also acknowledges the challenges and suggests further research to assess its impact.
Quantum simulation of partial differential equations: Applications and detailed analysis

We study a recently introduced simple method [S. Jin, N. Liu, and Y. Yu, Quantum simulation of partial differential equations via Schr\"odingerisation, arXiv:2212.13969] for solving general linear partial differential equations with quantum simulation. This method converts linear partial differential equations into a Hamiltonian system, using a simple transformation called the warped phase transformation. Here we provide a more-in-depth technical discussion and expand on this approach in a more detailed and pedagogical way. We apply this to examples of partial differential equations, including heat, convection, Fokker-Planck, linear Boltzmann, and Black-Scholes equations. This approach can also be extended to general linear partial differential equations, including the Vlasov-Fokker-Planck equation and the Liouville representation equation for nonlinear ordinary differential equations. Extension to higher-order time derivatives is also possible.
A bibliometric analysis of immersive technology in museum exhibitions: exploring user experience

Introduction: This study aims to comprehensively understand the existing literature on immersive technology in museum exhibitions, focusing on virtual reality (VR), augmented reality (AR), and the visitor experience. The research utilizes a bibliometric approach by examining a dataset of 722 articles with two main research objectives. Firstly, it seeks to analyze current trends in immersive technology literature, specifically emphasizing VR and the user experience in museum exhibitions through co-citation analysis. Secondly, it aims to identify emerging research trends using co-word analysis. Methods: The study employs a bibliometric approach, specifically co-citation and co-word analysis, to investigate trends and forecast emerging areas in the field, particularly the role of VR in the museum context. Results: The analysis reveals the presence of five interconnected thematic clusters in the literature. These clusters include (1) VR and AR-enhanced heritage tourism, (2) VR and AR-enabled virtual museums, (3) interactive digital art education in immersive environments, (4) immersive storytelling in virtual heritage spaces, and (5) mobile AR heritage revival. Discussion: The article highlights influential works within these areas, showcasing the historical evolution of the field and the current emphasis on utilizing VR to create immersive, educational, and engaging experiences for museum visitors. The findings indicate that research on VR applications for museum exhibitions has predominantly focused on profound game-driven experiences and interactive 3D heritage, resulting in improved visitor engagement and access to cultural content. The adoption of VR technology holds the potential to revolutionize user experiences within the cultural heritage sector and reshape the overall landscape of museums and exhibitions. By presenting these research trends, this study contributes to a deeper understanding of the vital role of VR in enhancing visitor experiences in museum settings. Furthermore, it paves the way for further exploration and innovation in immersive technology.
The mid-level vision toolbox for computing structural properties of real-world images

Mid-level vision is the intermediate visual processing stage for generating representations of shapes and partial geometries of objects. Our mechanistic understanding of these operations is limited, in part, by a lack of computational tools for analyzing image properties at these levels of representation. We introduce the Mid-Level Vision (MLV) Toolbox, an open-source software that automatically processes low- and mid-level contour features and perceptual grouping cues from real-world images. The MLV toolbox takes vectorized line drawings of scenes as input and extracts structural contour properties. We also include tools for contour detection and tracing for the automatic generation of vectorized line drawings from photographs. Various statistical properties of the contours are computed: the distributions of orientations, contour curvature, and contour lengths, as well as counts and types of contour junctions. The toolbox includes an efficient algorithm for computing the medial axis transform of contour drawings and photographs. Based on the medial axis transform, we compute several scores for local mirror symmetry, local parallelism, and local contour separation. All properties are summarized in histograms that can serve as input into statistical models to relate image properties to human behavioral measures, such as esthetic pleasure, memorability, affective processing, and scene categorization. In addition to measuring contour properties, we include functions for manipulating drawings by separating contours according to their statistical properties, randomly shifting contours, or rotating drawings behind a circular aperture. Finally, the MLV Toolbox offers visualization functions for contour orientations, lengths, curvature, junctions, and medial axis properties on computer-generated and artist-generated line drawings. We include artist-generated vectorized drawings of the Toronto Scenes image set, the International Affective Picture System, and the Snodgrass and Vanderwart object images, as well as automatically traced vectorized drawings of set architectural scenes and the Open Affective Standardized Image Set (OASIS).
Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation

Archimedes' optimization algorithm (AOA) is a recently proposed physics-based optimization algorithm inspired by Archimedes' Principle. However, for optimization of complex engineering problems, it shows slow convergence and can easily fall into local minima. Chaotic maps have non-repeatability properties they can effectively scan the search space with fast convergence in optimization algorithms. In this study, knacks of physics-based optimization along with chaotic maps are exploited for the parameter estimation of autoregressive exogenous (ARX), nonlinear input ARX (NI-ARX), LD-Didactic temperature process plant models and electrically stimulated muscle model. Ten chaotic maps have been applied to AOA. The fitness function of both models is constructed based on the square error between true and estimated response. The chaotic variants indicate that AOA with a chaotic Circle map (CAOA2) gives the best performance in terms of accuracy and convergence against reptile search heuristics, sine cosine optimization approach, harris hawks optimizer (HHO) and whale optimization algorithm (WOA) for different variations of generation and noise levels. Statistics based on mean square error, convergence plots, statistical plots, Taguchi test, average fitness, t-test, and Friedman test for repeated measures approve the reliability of CAOA2 for parameter estimation of ARX, NI-ARX, LD-Didactic temperature process plant models and electrically stimulated muscle model.
Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

The success of Transformer models has pushed the deep learning model scale to billions of parameters, but the memory limitation of a single GPU has led to an urgent need for training on multi-GPU clusters. However, the best practice for choosing the optimal parallel strategy is still lacking, as it requires domain expertise in both deep learning and parallel computing. The Colossal-AI system addressed the above challenge by introducing a unified interface to scale your sequential code of model training to distributed environments. It supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer. Compared to the baseline system, Colossal-AI can achieve up to 2.76 times training speedup on large-scale models.
An improved deep learning-based optimal object detection system from images

Abstract Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.
Bootstrapped Personalized Popularity for Cold Start Recommender Systems

Recommender Systems are severely hampered by the well-known Cold Start problem, identified by the lack of information on new items and users. This has led to research efforts focused on data imputation and augmentation models as predominantly data pre-processing strategies, yet their improvement of cold-user performance is largely indirect and often comes at the price of a reduction in accuracy for warmer users. To address these limitations, we propose Bootstrapped Personalized Popularity (B2P), a novel framework that improves performance for cold users (directly) and cold items (implicitly) via popularity models personalized with item metadata. B2P is scalable to very large datasets and directly addresses the Cold Start problem, so it can complement existing Cold Start strategies. Experiments on a real-world dataset from the BBC iPlayer and a public dataset demonstrate that B2P (1) significantly improves cold-user performance, (2) boosts warm-user performance for bootstrapped models by lowering their training sparsity, and (3) improves total recommendation accuracy at a competitive diversity level relative to existing high-performing Collaborative Filtering models. We demonstrate that B2P is a powerful and scalable framework for strongly cold datasets.