Reviewerly/exHarmony huggingface dataset:
Cognitive Bias and Belief Revision

In this paper we formalise three types of cognitive bias within the framework of belief revision: confirmation bias, framing bias, and anchoring bias. We interpret them generally, as restrictions on the process of iterated revision, and we apply them to three well-known belief revision methods: conditioning, lexicographic revision, and minimal revision. We investigate the reliability of biased belief revision methods in truth tracking. We also run computer simulations to assess the performance of biased belief revision in random scenarios.
MTBERT-Attention: An Explainable BERT Model based on Multi-Task Learning for Cognitive Text Classification

In recent years, there has been a lot of focus on Bloom’s taxonomy-based classification of E-Learning materials. Researchers employ different methods and features. In our previous works, we have dealt with this problem via different techniques and algorithms. we started by boosting traditional machine learning algorithms then we moved on to deep learning algorithms by proposing a bidirectional GRU (BI-GRU) model combined with word2vec, finally we also proposed a fine-tuned model of bidirectional encoder representations from Transformers (BERT). The limitations of our model’s performance as well as the lack of an annotated dataset lead us to explore a novel approach to the cognitive classification of text. First, we propose MTBERT-Attention, a unique and explainable model based on multi-task learning (MTL), BERT, the co-attention mechanism. MTL enhances our primary task’s generalization capacity and permits data augmentation. The usage of BERT as a stack for knowledge transfer between activities has been improved. The co-attention mechanism put particular emphasis on the significant aspects of the Learning objective. We propose an explainability framework based on the attention mechanism. Lastly, we carry out in-depth tests to assess the viability and efficiency of the suggested model and the explainability Framework. Our proposed model outperforms the baseline models for loss, F1-score, and accuracy. It attained an overall classification accuracy of 97.71% with the test set and effectively classifies learning objectives that use unclear action verbs from Bloom’s taxonomy. On the other hand, to prove the performance of our explainability Framework, we conduct a qualitative and quantitative study on the quality of the explanations as well as on the computational cost. We adopt Local Interpretable Model-agnostic Explanations (LIME) as a baseline for comparison. Our experiments show that our proposed approach for explainability outperforms the LIME explainer in terms of fidelity and computational cost.
Extended-resolution of a single-camera synthetic Schlieren method for measurement of free liquid surfaces

In a synthetic Schlieren method of measuring the height of a dynamic free liquid surface, the smallest wavelength of the measured surface topography is determined by the spatial resolution of the displacement field of markers. Currently, a displacement vector is obtained for each interrogation window, including a few markers, with a cross-correlation algorithm. In this study, the measurement resolution is extended by obtaining the displacement of individual markers. This simple and rational change, however, brings technical difficulties in numerically solving the governing equation over spatially randomly distributed markers. For this, the governing equation, which associates the surface height to be measured with the virtual displacements of the markers, is solved by the finite-volume scheme for the unstructured meshes, where a marker locates at the center of each triangular mesh face. The present method is examined with the synthetic generated data, in laboratory experiments of a transparent solid carved with a sinusoidal surface, and in experiments of water ripples. The measurement uncertainty is discussed.
The Effectiveness of Picture Series in Improving Students’ Writing Skill at Mega Buana University

This research has two variables, namely the students’ writing skill as independent variable and picture series as dependent variable. The aim of the research is to find out the effectiveness of picture series in improving students’ writing skill at Mega Buana University. The research applied Pre-Experimental method, where the students were given both pre test and post test. The population of this research is the Fourth Semester Students of Midwifery Study Program at Mega Buana University with the total of population are 42 students. The researcher chose 15 students as sample. The sample was taken by using purposive sampling technique. The result of this research showed the probability was 0.000 &lt; 0.05. Based on this fact can be showed that H0 was rejected. Therefore there was different mean score of pre test and post test in this class. By looking at this result, can be understood that picture series was effective to improve students’ writing skill.&#x0D;
Improving attention network to realize joint extraction for the construction of equipment knowledge graph

In the context of promoting autonomous decision, intelligent interaction, and concept recognition of the equipment, constructing an equipment Knowledge Graph (KG) is the key technology to facilitate this. In this article, we propose a method that combines design rules and automatic extraction to construct equipment KG from equipment manuals. We define that equipment KG is scheme-based and depicts scheme in the form of KG triples (a.k.a design rules, scheme layer of KG, or concept KG). Our contributions include designing an initial concept KG for the equipment domain and improving the BERT (Bidirectional Encoder Representations from Transformers) model to jointly extract knowledge from texts to enrich the KG. The BERT model was improved from internal calculations so that joint extraction could be achieved directly without extra additional parts. We applied our model to a standard dataset “SemEval2010 Task 8” and achieved the F1 score of 89.55 which demonstrates its rationality. We also established a dataset called “Equipment Manuals Corpus for KG” based on the concept KG and applied the joint model in the dataset to extract knowledge. The result was visualized in the form of a graph.
Development of Simulation Models of Cloud Computing Infrastructures with Automatic Scaling Based on Thresholds

In this paper, the open access toolkit CloudSim Plus was used to evaluate the impact of computing power scaling on service level and cost of using cloud resources. Focusing on automatic scaling policy based on thresholds, static power-aware distribution scenarios, horizontal scaling scenarios based on thresholds, horizontal scaling scenarios based on power-aware thresholds, vertical scaling scenarios based on thresholds, vertical scaling scenarios based on power-aware thresholds were simulated. The experiments monitored the average job waiting time, the amount of energy consumed by the data center, the number of hosts that participate in workload processing, the average CPU load, the number of virtual machines created to process the workload, the number of local processing clusters created. It is found that setting the thresholds requires a deep understanding of the workload patterns. Incorrect setting of CPU load, response time, and threshold latency leads to fluctuations in the number of virtual machines and, as a consequence, performance degradation. #CSOC1120.
Moving Object 3D Detection and Segmentation Using Optical Flow Clustering

Deep learning methods have recently improved the performance and accuracy of prediction using big data and abundant computing resources. However, many complex problems in Computer Vision, including 3D detection, motion estimation, and image segmentation, cannot be easily solved. All the same, it is still possible to benefit from using ‘traditional’ methods. This article analyzes the relationship between optical flow and object localization. The proposed algorithm works with moving objects and performs not only instance segmentation but also determines the type of objects and estimates 3D bounding boxes using geometric constraints. Since the method is based only on an algorithmic understanding of three-dimensional data, it does not require data and time for the training process and can be deployed on constrained devices.
Fraud Detection in Mobile Banking Based on Artificial Intelligence

The advent of modern technological advances has led to traditional Banks and other financial institutions to reposition their financial services and introduce new transaction channels and modes of payments. Mobile Banking (M-Banking) usage has been on the rise in the recent years and has provided opportunities for growth and financial inclusion. However, this substantial increase in the volume of mobile banking transactions, has had a corresponding increase in fraud cases recorded in millions of dollars every year by banks and financial institutions. Managing vast amounts of mobile banking transactional data and making choices about risk, client retention, fraud detection, and marketing management are crucial tasks in the banking industry. This study explores an effective application of Artificial Intelligence data mining algorithms fraud detection in mobile banking transactions. This research aims to provide a robust, cost effective, efficient yet accurate data mining-based solution to detect mobile banking fraud. We propose Artificial Intelligence (AI) model using K-Means clustering and Anomaly detection algorithms to detect “fraudulent” and “genuine” transactions in near- real time. The results of applying K-means clustering and Isolation Forest anomaly detection produced a detection rate of 5% of the volume of transactions analyzed.
Optimizing Data Analysis Tasks Scheduling Based on Resource Utilization Prediction

In order to reduce the average response time of data analysis tasks in a decision support system and maximize the utilization of cluster resources, we proposed the Upsa algorithm for task scheduling, which is based on the prediction of resource utilization. It aims to balance the workload of analytical data processing tasks in the cluster and increase task execution efficiency. In Upsa, the random forest (RF) model is employed to accurately determine the resource utilization rate for the subsequent period to provide a rational basis for task allocation. The Upsa method can ignore a server that is about to become overloaded and assign new duties to the most suitable server. As soon as the server’s resource utilization rate returns to normal levels, it will resume receiving tasks. We evaluate the effectiveness of Upsa using the TPC-H benchmark. Compared to the well-known scheduling solution Round Robin, our experimental results show that the Upsa method substantially reduces the average response time.
Nonlinear holographic image inpainting via deep learning

Nonlinear photonic crystals can be employed to generate holographic images in nonlinear optical processes. However, due to the limit of binary structure, the process of holographic imaging will lose part of the amplitude information and cause image hollowing, thus the imaging quality is reduced. Generative adversarial network, a deep learning network based on game theory, is used to restore images. The results of restoration show high similarity to the original images, effectively weakening the effect of image hollowing, suppressing the diffraction effect, and restoring grayscale values. This image post-processing approach completes the field of application of nonlinear holographic imaging, which is useful for non-visible source imaging, crosstalk avoidance, optical encryption, and so on.
Ontology based Feature Selection and Weighting for Text classification using Machine Learning

Text classification consists in attributing text (document) to its corresponding class (category). It can be performed using an artificial intelligence technique called machine learning. However, before training the machine learning model that classifies texts, three main steps are also mandatory: (1) Preprocessing, which cleans the text; (2) Feature selection, which chooses the features that significantly represent the text; and (3) Feature weighting, which aims at numerically representing text through feature vector. In this paper, we propose two algorithms for feature selection and feature weighting. Unlike most existing works, our algorithms are sense-based since they use ontology to represent, not the syntax, but the sense of a text as a feature vector. Experiments show that our approach gives encouraging results compared to existing works. However, some additional suggested improvements can make these results more impressive.
Traveling Salesman Problem

A problem is the frame into which the solutions fall. The design of a search system to solve an optimization problem and study of the related search behavior are based on how the problem is defined. This chapter defines the TSP and describes different solution representations. The role of the edge matrix E in solving the TSP is explained in detail. Computational complexity of the TSP is reviewed from a different perspective, and the concept of search space reduction is raised.
Analysis of the Attractor-Based Search System

This chapter analyzes the search behavior of the ABSS in detail to answer three fundamental questions for the ABSS: (1) how can we construct the edge configuration of the attractor using a small set of local search trajectories? (2) what is the relationship between the size of the constructed attractor and the size of the TSP instance? and (3) is the best tour in the attractor the optimal tour in the solution space?
A multimodal feature fusion image dehazing method with scene depth prior

Abstract Current dehazing networks usually only learn haze features in a single‐image colour space and often suffer from uneven dehazing, colour, and edge degradation when confronted with different scales of ground objects in the depth space of the scene. The authors propose a multimodal feature fusion image dehazing method with scene depth prior based on a decoder–encoder backbone network. The multimodal feature fusion module was first designed. In this module, affine transformation and polarized self‐attention mechanism are used to realize the fusion of image colour and depth prior feature, to improve the representation ability of the model for different scale ground haze feature in‐depth space. Then, the feature enhancement module (FEM) is added, and deformable convolution and difference convolution methods are used to enhance the representation ability of the model for the geometric and texture feature of the ground objects. The publicly available dehazing datasets are used for comparison and ablation experiments. The results show that compared with the existing classical dehazing networks, the peak signal‐to‐noise ratio (PSNR) and SSIM of the authors’ proposed method have been significantly improved, have a more uniform dehazing effect in different depth spaces, and maintain the colour and edge details of the ground objects very well.
Rethinking one-shot face reenactment: A spatial–temporal reconstruction view

One-shot face reenactment aims to animate a source facial image into a different pose and expression provided by a driving image. Despite recent advancements showing promising results, challenges still exist in accurately transferring facial expressions and being robust to large-scale head movements. Additionally, the current method’s lack of interpretability in the network’s generalization remains a concern. We propose a novel and interpretable framework for one-shot face reenactment based on spatial–temporal reconstruction views to address these challenges. To achieve precise transfer of facial expressions and intuitive control over the generated images, we have opted for the powerful 3D Morphable Face Model (3DMM) as an immediate representation. Transferring motion parameters such as pose and expression from the driving image to the target face allows us to generate a 3D reconstructed face image that allows explicit control. To enhance the robustness of motion reconstruction, we introduce the concept of dense pseudo-temporal flow and propose a cross-modal cascade temporal motion reconstruction network to predict it. Our experimental results demonstrate its effectiveness in improving the robustness of motion reconstruction, especially for large-scale head movements. In addition, to further improve the generated image quality, we propose the motion-modulative residual fusion block as the information path to transfer the motion information from the motion reconstruction network to the local texture reconstruction network. Finally, we discussed the generalization ability of our framework based on its interpretable generation process. Extensive experiments demonstrate that our framework can generate highly realistic facial images and can be comparable with state-of-the-art methods.
Super-resolution image visual quality assessment based on structure–texture features

Assessing the visual quality of super-resolution images (SRIs) is crucial for advancing algorithm development, but it remains an unsolved problem. In this paper, we present a novel reduced-reference image quality assessment (RR-IQA) method specifically suited for evaluating SRIs. Our approach leverages information from the input low-resolution (LR) image as a reference signal to extract features that are most relevant to modeling the visual quality of SRIs. We analyze the artifact characteristics of SRIs and demonstrate that features describing edge orientations, high frequency components, and textures are the most important for this task. To extract these features, we first perform structure–texture decompositions (STD) on both the SRI and its LR input, then obtain the edge orientation feature through a traditional hand-crafted approach, and deep neural networks to extract features related to high frequency components and textures. We employ a shallow multilayer perceptron (MLP) to predict an image quality score based on these quality-relevant features. To improve feature representation ability and prevent overfitting, we pretrain the feature extraction module using a large number of unlabeled samples, which accounts for over 99.8% of the total model parameters. We use precious samples with mean opinion score (MOS) labels to train a high-quality shallow MLP predictor. Our experimental results show that the proposed method outperforms classical and state-of-the-art models.
Use of Visual Learning Media to Increase Student Learning Motivation

The development of the digitalization era today has a great impact on the world of education, this development can be seen from the many developments in learning media that can be used to increase student learning motivation. with increased motivation can foster students' confidence in this visual learning. One of the learning media used today is visual learning media. This study aims to determine the use of visual learning media to increase student motivation. The method used in this study is a quantitative method using Google Chrome and conducting in-depth interviews. The results of this study show how many digitalization learning media are used by educators to be able to improve the quality of student learning to be easier and better. this can make it easier for educators and students to be able to understand an ongoing learning between the two. Because with the help of this visual learning media, it will make learning interesting for students. with interesting learning it will make students enthusiastic in learning. The drawback of this study is that researchers cannot conduct interviews directly on the use of visual learning media used by educators to students, therefore researchers hope that future researchers can be better than on the current one.
A cross-domain recommendation model by unified modelling high-order information and rating information

Cross-domain recommendation models are proposed to enrich the knowledge in the target domain by taking advantage of the data in the auxiliary domain to mitigate sparsity and cold-start user problems. However, most of the existing cross-domain recommendation models are dependent on rating information of items, ignoring high-order information contained in the graph data structure. In this study, we develop a novel cross-domain recommendation model by unified modelling high-order information and rating information to tackle the research gaps. Different from previous research work, we apply heterogeneous graph neural network to extract high-order information among users, items and features; obtain high-order information embeddings of users and items; and then use neural network to extract rating information and obtain user rating information embeddings by a non-linear mapping function MLP (Multilayer Perceptron). Moreover, high-order information embeddings and rating information embeddings are fused in a unified way to complete the final rating prediction, and the gradient descent method is adopted to learn the parameters of the model based on the loss function. Experiments conducted on two real-world data sets including 3,032,642 ratings from two experimental scenarios demonstrate that our model can effectively alleviate the problems of sparsity and cold-start users simultaneously, and significantly outperforms the baseline models using a variety of recommendation accuracy metrics.
Cascaded face super‐resolution with shape and identity priors

Abstract Despite impressive progress in face super‐resolution (SR), it is an open challenge to reconstruct a reliable SR face that preserves authentic facial characteristics. Here, the problem of super‐resolving low‐resolution (LR) faces to high‐resolution (HR) ones is addressed. To tackle the ill‐posed nature of face SR, the cascaded super‐resolution network (CSRNet) is proposed to utilize shape and identity priors jointly and progressively, the first to explore multiple priors. Specifically, CSRNet adopts a cascaded structure to transform an LR face to HR face progressively via multiple stages. At each stage, CSRNet forces its output face image to match both the shape priors and identity priors extracted from the ground‐truth HR face. The shape priors estimated in one stage are merged into the inputs of its subsequent stage to provide rich information for the face SR. To generate realistic yet discriminative faces, the cascaded super‐resolution generative adversarial network (CSRGAN) is also proposed to incorporate the adversarial loss and identification loss into CSRNet. Extensive experiments on popular benchmarks show that the CSRNet and CSRGAN outperform existing face SR state‐of‐the‐art methods, both quantitatively and qualitatively, and detailed ablation studies show the advantage of this method.