Reviewerly/exHarmony huggingface dataset:
Image Compression Based on Hybrid Domain Attention and Postprocessing Enhancement

Deep learning-based image compression methods have made significant achievements recently, of which the two key components are the entropy model for latent representations and the encoder-decoder network. Both the inaccurate estimation of the entropy estimation model and the existence of information redundancy in latent representations lead to a reduction in the compression efficiency. To address these issues, the study suggests an image compression method based on a hybrid domain attention mechanism and postprocessing improvement. This study embeds hybrid domain attention modules as nonlinear transformers in both the main encoder-decoder network and the hyperprior network, aiming at constructing more compact latent features and hyperpriors and then model the latent features as parametric Gaussian-scale mixture models to obtain more precise entropy estimation. In addition, we propose a solution to the errors introduced by quantization in image compression by adding an inverse quantization module. On the decoding side, we also provide a postprocessing enhancement module to further increase image compression performance. The experimental results show that the peak signal-to-noise rate (PSNR) and multiscale structural similarity (MS-SSIM) of the proposed method are higher than those of traditional compression methods and advanced neural network-based methods.
Research on the Application of Artificial Intelligence Wireless Network Technology in the Optimization of University Resources

With the increase of teaching resources in universities, allocation for teaching resources has gradually attracted people’s attention. How to optimize and reorganize the numerous teaching resources so as to maximize the teaching efficiency and achieve the best teaching effect is a question that the majority of college management workers must think deeply. Due to the development of computer science, artificial neural networks and wireless networks have made considerable progress. This article optimizes the resources of colleges and universities from these two aspects. First, this work proposes an improved artificial neural network to optimize school resources. The algorithm pointed out that the adaptive genetic algorithm itself has problems such as slow evolution speed and poor individual diversity of the population. After that an improved adaptive genetic algorithm is proposed to optimize BP network training process. By introducing individual evolutionary trend adjustment parameters, a new calculation formula for crossover probability as well as mutation probability is constructed. Improved BP algorithm can effectively avoid the shortcomings of local minima. Second, additional momentum and adaptive learning rate strategies are used to optimize BP network to promote the efficiency for network training. Third, the artificial intelligence network is deployed in the wireless network, and the wireless network will complete the work of data collection, processing, and transmission, so as to optimize the resources of colleges and universities.
Prediction of News Popularity Based on Deep Neural Network

Due to the timeliness and short life cycle of news, the postrelease prediction is limited, and the prerelease prediction also faces huge challenges due to the diversity and difficulty of defining influencing factors. This paper uses the foreclosure. This paper proposes a news popularity prediction method based on GRU deep neural network. Firstly, a web crawler was designed to obtain news data of different types and structures from 10 information security portal websites in China. After data preprocessing, the Word2Vec method was used to extract features, extract key news sentences and construct a subset of content features. Establish a GRU neural network regression prediction model to predict hot news on the Internet. The experimental results show that, compared with the traditional processing method, the model can process the multi-source rough data set in this paper and greatly reduce the prediction error. At the same time, because the threshold recurrent unit structure used in this paper is simpler than the long-short-term memory network structure, it can shorten the prediction time and improve the computing performance.
Interpretable risk models for Sleep Apnea and Coronary diseases from structured and non-structured data

Machine learning-based risk models built from Electronic Health Records (EHR) can support medical decision-making. However, the lack of standardization of EHR data and the “black-box” nature of the machine learning approaches have imposed difficulties to their acceptance as support tools in a clinical environment. This paper presents a method able to predict and explain the diagnostic of Atrial Fibrillation (AF), Sleep Apnea (SA) and, Coronary Arterial Disease (CAD); the proposed model is learned using EHR’s structured data (commonly used screening variables) and non-structured data (textual data drawn from medical reports) of patients. An embedding scheme of variables together with a labeling approach is used to mimic the ability of an expert in categorizing the non-structured textual data. The method relies on complex models to predict such diseases combined with the SHAP approach to explaining the prediction. A comparison of prediction models with different settings of input variables has shown that the use of non-structured data improved the performances of CAD risk prediction models. Moreover, such a comparison pointed out that the patients’ medical histories is an important factor that should be considered during the data-driven learning process. • Novel method for risk prediction of diseases with accurate and explainable prediction. • Embedding and weak supervision method to extract knowledge from EHR unstructured data • Empirical evidence that the join of EHR unstructured data improves the risk model.
Object Counting Using a Refinement Network

To address the scale variance and uneven distribution of objects in scenarios of object-counting tasks, an algorithm called Refinement Network (RefNet) is exploited. The proposed top-down scheme sequentially aggregates multiscale features, which are laterally connected with low-level information. Trained by a multiresolution density regression loss, a set of intermediate-density maps are estimated on each scale in a multiscale feature pyramid, and the detailed information of the density map is gradually added through coarse-to-fine granular refinement progress to predict the final density map. We evaluate our RefNet on three crowd-counting benchmark datasets, namely, ShanghaiTech, UCF_CC_50, and UCSD, and our method achieves competitive performances on the mean absolute error and root mean squared error compared to the state-of-the-art approaches. We further extend our RefNet to cell counting, illustrating its effectiveness on relative counting tasks.
Computer Science Diagram Understanding with Topology Parsing

Diagram is a special form of visual expression for representing complex concepts, logic, and knowledge, which widely appears in educational scenes such as textbooks, blogs, and encyclopedias. Current research on diagrams preliminarily focuses on natural disciplines such as Biology and Geography, whose expressions are still similar to natural images. In this article, we construct the first novel geometric type of diagrams dataset in Computer Science field, which has more abstract expressions and complex logical relations. The dataset has exhaustive annotations of objects and relations for about 1,300 diagrams and 3,500 question-answer pairs. We introduce the tasks of diagram classification (DC) and diagram question answering (DQA) based on the new dataset, and propose the Diagram Paring Net (DPN) that focuses on analyzing the topological structure and text information of diagrams. We use DPN-based models to solve DC and DQA tasks, and compare the performances to well-known natural images classification models and visual question answering models. Our experiments show the effectiveness of the proposed DPN-based models on diagram understanding tasks, also indicate that our dataset is more complex compared to previous natural image understanding datasets. The presented dataset opens new challenges for research in diagram understanding, and the DPN method provides a novel perspective for studying such data. Our dataset can be available from https://github.com/WayneWong97/CSDia.
Torocó: A Subsumption Architecture Implementation

Writing control software for robots poses specific challenges. Robotic systems are inherently real-time, concurrent, and tightly coupled to the physical environment through sensing and actuation. To ease the development of control software multiple architectures and frameworks have been proposed. Of special historical and theoretical importance is R. Brooks’ Subsumption Architecture, a framework for developing Reactive Systems.Despite its influence, there have been very few actual practical implementations. In this work, we present Torocó, a highly compliant implementation. This framework is portable and lightweight and thus can be deployed on multiple low-cost robotic platforms. This makes it useful as a research and experimental platform to study Subsumption and Reactive robotics architectures in general. Also, the system is easy to use and is useful as an educational tool. All components and a step-by-step guide to install all necessary dependencies are publicly available in gitlab.fing.edu.uy/fbenavid/toroco. Finally, we show how this architecture can be used to solve typical robotic tasks, demonstrating Subsumption’s composability and incremental development.
Inverse problems, constraint satisfaction, reversible logic, invertible logic and Grover quantum oracles for practical problems

It is well-known that the “Unsorted Database” quantum algorithm by Grover gives quadratic speedup to several practically important combinatorial and enumerative problems, such as: SAT, Graph Coloring, Maximum Cliques, Traveling Salesman and many others. Recently, quantum programming languages such as QISKIT, QSHARP or Quipper start to be used to design, verify and simulate practical quantum algorithms for important problems in Quantum Machine Learning or optimization. So far, however, no methodologies have been created to program Grover's Oracles for particular classes of problems. In contrast, such methodologies have been already created for classical Constraint Satisfaction Problems (CSP). Invertible Logic was recently introduced by Supriyo Datta and his team. The goal of this paper is to present results of our research towards creating a systematic methodology to solve search problems in Artificial Intelligence, Logic Design and Machine Learning by repeated applications of modified hardware oracles. These oracles can use: (1) classical Boolean logic, (2) quantum logic, or (3) invertible logic. Our methodology to design and exercise all types of general oracles is based on bottom-up synthesis and technology transformations. For CSP problems we apply unified blocks which use various data representations and encodings.
Negative campaigning

Abstract This paper develops an argumentative perspective on attacks in political election campaigns. The perspective highlights the role that attack ads play in the justification of vote claims aiming to advance our understanding of the notorious practice and to bring about a nuanced assessment of its benefits and risks. In the examination, special attention is paid to the argumentative potential that links a certain criticism of an adversary to the defense of the negative vote against the adversary as well as to the defense of the positive vote claim in favor of a campaign’s candidate. Considering the argumentative potential is especially beneficial for capturing the role of attacks in important political processes, including accountability and the stimulation of an informed public political participation.
Quick Classification of Xception And Resnet-50 Models on Deepfake Video Using Local Binary Pattern

The ease of deepfake videos are created indicates that there is an increasing need for authentication methods to classify deepfake videos. This issue is very sensitive, deepfakes make it possible to make someone say or do something they never said or did in a video, this puts a person&#x2019;s dignity at risk. Another problem arises with the large size of the deepfake video dataset, because large datasets require longer training times and high computational specifications. This paper describes an approach to deepfake video classification using a small dataset and image processing. We propose a method that applies MTCNN (Multi-task Cascaded Convolutional Networks) to capture face data on video frames, image processing in the form of Gaussian filters and Local Binary Pattern (LBP) with Xception model for deepfake video classification and ResNet-50 as comparison. We use a dataset totaling 2000 frames from the entire Celeb-DF(V2) dataset. The results show that the proposed method, the Xception model, has better performance with an AUC value of 0.87 and an accuracy value of 0.79 compared to the ResNet-50 model.
Audio Signal Denoising Based on Laplacian Filter and Sparse Signal Reconstruction

Impulsive disturbance commonly appears in audio signals. During the last few decades, a number of denoising approaches has been proposed for the removal of this particular type of noise. An advanced class of denoising algorithms emerged from the recent compressive sensing (CS) paradigm. Sparsity or high concentration of audio signals in some specific transformation domains, such as, for example, the discrete cosine transform (DCT) domain, can be engaged in procedures for the detection of corrupted samples. Since in the case of impulsive noise only a subset of samples is highly corrupted, upon detection of their positions, these disturbed samples can be further considered as unavailable, and reconstructed using sophisticated CS procedures. In this paper, we investigate the possibility to apply Laplacian filter in conjunction with the compressive sensing reconstruction, in the removal of impulsive disturbance from audio signals.
Ownership Protection on Digital Elevation Model (DEM) Using Transform-Based Watermarking

This research aims to protect Digital Elevation Model (DEM) data from piracy or counterfeiting. An invisible watermark inserted into the data, which will not considerably change the data value, is necessary. The proposed method involves the use of the two-dimensional discrete cosine transform (2D DCT), a combination of 2D DCT and discrete wavelet transform (DWT), and two-dimensional discrete Fourier transform (2D DFT) in the frequency domain. The data used include a National DEM file downloaded from the geoportal of the Geospatial Information Agency (Badan Informasi Geospasial—BIG). Three files represent mountainous, lowland/urban, and coastal areas. An “attack” is also conducted on the watermarked DEM by cropping. The results indicate that the watermarked DEM is well recognized. The watermark can be read 100% for 2D DCT, while that for 2D DFT can be read 90.50%. The distortion value of the elevation data under the DCT technique demonstrates the smallest maximum value of 0.1 m compared with 4.5 and 1.1 m for 2D DFT and 2D DCT–DWT. Meanwhile, the height difference (Max Delta), the peak signal-to-noise ratio, and the root mean squared error (RMSE) are highest in mountainous, lowland, and coastal areas, respectively. Overall, the 2D DCT is also superior to the 2D DFT and the2D DCT–DWT. Although only one can recognize the nine watermarks inserted on each sheet, DEMs attacked by the cropping process can still be identified. However, this finding can sufficiently confirm that DEMs belong to BIG.
Analysis of Bank Customer Default Risk Based on Embedded Microprocessor Wireless Communication

Bank personal credit is affected by factors such as inadequate management and lagging risk information management system. Bank default risk analysis is needed to improve the ability of bank credit risk management. Therefore, a bank customer default risk analysis based on embedded microprocessor wireless communication is proposed. Firstly, it analyzes the risk assessment parameter evaluation system of personal credit, constructs the quantitative analysis model of personal credit risk, calculates the grade gradient value in the bank’s personal credit risk standard, carries out the mathematical modeling of bank customer default risk assessment, strengthens the implementation of “three checks,” tracks and manages the borrower, supervises the use of credit funds, improves the social credit investigation system, and on this basis, optimizes the mathematical model of bank customer default risk assessment. The experimental results show that this method can realize the bank’s quantitative evaluation and control of personal credit risk, improve the bank’s credit risk prevention and control ability, and provide a reliable basis for the bank’s credit risk evaluation operation and management.
Tutorial: how to access, process, and label PDS image data for machine learning

This tutorial provides instructions for how to access PDS image data, convert PDS image data into commonly used image formats, and obtain labels to enable the training of a machine learning image classifier. We also walk through an example of an operational PDS image classifier and provide details about how it was trained and evaluated. This example can serve as a template for similar projects.
Binary segmentation based on visual attention consistency under background-change

Good visual perception capability for object plays an important role in binary segmentation task, such as the segmentation for portraits and pulmonary nodules. When facing the same object in different backgrounds, humans always keep consistent visual perception. This has motivated semantic data augmentation strategies widely used in segmentation tasks. For example, ‘Cut-Paste’ strategy creates many images by changing background and assigns them to the same segmentation ground-truths for enhancing training. However, even if using these strategies, there are still differences among the segmentation results of images with the same object and different backgrounds. Hence, this paper proposes to adopt image-level classification and visual attention consistency under background-change to enhance the training of binary segmentation. The combination of image-level classification and class activation mapping can activate and visualize certain regions, which are related to classification label. The visual attention consistency requires the activated object attention to keep consistent when background of the input image changes. Based on this purpose, we augment the dataset by changing backgrounds with ‘Cut-Paste’. Afterwards, we adopt a shared triple-branch network to make original image, background-cut-out image and background image as inputs, and then propose image-level classification and attention consistency to train the binary segmentation network. Experimental results based on two datasets demonstrate that our method achieves new state-of-the-art binary segmentation performance.
Exploring Bioinspired Feature Engineering Technique for Online Hate Speech Detection

The spreading of hate speech and toxicity on social media and other online platforms has increased severely in the past decade. In the current scenario also, when the whole world is suffering with outspread of COVID-19 online hate speech spreading more than before. The spread of such hate can jeopardize the mental and physical health of many people and is thus necessary to stop its spread on online social media. This paper aims to explore bioinspired algorithms like PSO and GA to detect online hate speech on social media and other online platforms. We explore the hybrid feature selection approach to select valuable and meaningful features from the hate speech dataset to classify between hate and not hate posts efficiently. Our experiments indicate the random behavior of Particle Swarm Optimization and Genetic Algorithm and the decrease in accuracy when applied individually to the experiments. The proposed hybrid approach gives the comparative results as TF-IDF when applied with the baseline machine learning models.
YOLO Based Model for Garbage Detection

Air and water pollution are common cause of rotten rubbish. It produces harmful gases that comes in contact with the air and can cause respiratory problems in humans. Also, untreated waste leads to damage the vegetation around the landfill. Since, water resources are important to people and need to be protected by all means. In this regard, garbage collection is an important step in monitoring the environment and circulating in a large area, because manual monitoring can be time consuming. Therefore, identifying the garbage and monitoring with the minimum human intervention enables to remove them at appropriate times for a healthy environment. In this context, we explore yolo based model for garbage detection by collecting the pictures of garbage on the road or in bodies of water in real time. For further analysis the the YOLO frame that works on darknet is considered for positive effect on the visual perception of the object.
Training Adaptive Neuro Fuzzy Inference System Using Genetic Algorithms for Predicting Labor Productivity

The construction industry is labor-intensive and providing an accurate estimate for labor productivity is crucial for managing construction projects. In this regard, this research develops an adaptive neuro fuzzy inference system (ANFIS) model trained using genetic algorithms (GA) to enhance the prediction accuracy and reliability for construction labor productivity. The model is validated using data acquired from two high-rise buildings in Montreal, Canada. The performance of the optimized model is evaluated against that of the classic ANFIS model using correlation coefficient (R), mean bias error (MBE), and root mean square error (RMSE). The results of the evaluation metrics reveal that the ANFIS-GA model (R = 0.121, MBE = 0.331, and RMSE = 0.411) outperforms the classical ANFIS model (R = 0.040, MBE = 0.426, and RMSE = 0.537) for formwork labor productivity. It can be concluded that combining the ANFIS model with metaheuristic algorithms improves the forecasting accuracy of construction labor productivity.
Design of an optical linear discriminant filter for classification of subwavelength concave and convex defects on dielectric substrates

Identification of the defect type on substrate materials is essential for enhancing their yield ratio. We propose a novel optical filter to discriminate between subwavelength-order concave and convex defects on flat surfaces. This filter performs Fisher's linear discriminant analysis using light wave diffraction. The defect type is discriminated by simply comparing the irradiance at an observation point with the threshold value. Neither the defect image nor phase data, nor a large amount of data processing by a computer, is necessary. Numerical discrimination simulations indicate a discrimination error of 0.85%, and the noise tolerance of the proposed system is also discussed.