Reviewerly/exHarmony huggingface dataset:
Design of a smart doorbell for a leader’s office with availability status notification and visitor recognition features

Smart doorbells have become a critical component of smart homes and modern offices. However, a smart doorbell, particularly designed for a leader’s office, has not been introduced. In this study, a smart doorbell is developed for a leader’s office. The system includes an application that allows availability status notification on the doorbell module and voice communication with the visitor from inside the office based on a private Wi-Fi network without an Internet connection to prevent the leader from potential privacy and security issues. It also features a live video capture of the visitor with face recognition by implementing a MobileNet model. In training and testing this model, 1,549 free face images of 125 people were augmented to generate training, validation, and testing datasets of 9,185, 2,500, and 5,000 face images, respectively. An additional authentication testing dataset of 1,068 AI-generated face images was also used to evaluate the system’s False Acceptance Rate (FAR). A high confidence level of 0.945 was selected for the developed MobileNet model to obtain zero FAR and high accuracy, recall, and F-score values of 0.960, 0.960, and 0.978, respectively. Therefore, the proposed doorbell could be used for an office leader, showing potential use for biometric authentication.
SKELTER: unsupervised skeleton action denoising and recognition using transformers

Unsupervised Human Action Recognition (U-HAR) methods currently leverage large-scale datasets of human poses to solve this challenging problem. As most of the approaches are dedicated to reaching the best recognition accuracies, no attention has been put into analyzing the resilience of such methods given perturbed data, a likely occurrence in real in-the-wild testing scenarios. Our first contribution is to systematically validate the decrease in performance of current U-HAR state-of-the-art using perturbed or altered data (e.g., obtained by removing some skeletal joints, rotating the entire pose, and injecting geometrical aberrations). Then, we propose a novel framework based on a transformer encoder–decoder with remarkable de-noising capabilities to counter such perturbations effectively. Moreover, we also present additional losses to have robust representations against rotation variances and provide temporal motion consistency. Our model, SKELTER, shows limited drops in performance when skeleton noise is present compared with previous approaches, favoring its use in challenging in-the-wild settings.
How Does the Brain Allow the Eyes to See?

Did you know that it is not only your eyes that allow you to see? Your brain functions with the eyes to process and make sense of all things that you see. A part of the brain called the visual cortex is responsible for vision. The brain contains over 100 billion brain cells called neurons, and they work in “levels” to help you see the world—from a basic level in which you perceive simple shapes up to higher levels where you understand complex patterns. When networks of brain neurons do not work properly, brain disorders can result. Doctors and scientists can use various techniques to measure the activity of neurons. For example, unusual patterns of brain waves can tell us about damaged neural networks and brain abnormalities. Computers can also be programmed to “see” visual information, and such computers can help us to learn about the vision process in humans.
Education in world history

Click to increase image sizeClick to decrease image size Additional informationFundingThis work was supported by the Lembaga Pengelola Dana Pendidikan [202201112007954].
How to use natural and base 10 log scales in ggplot2

I always forget how to deal with logged values in ggplot—particularly things that use the natural log.
A color image encryption scheme based on block compressive sensing and chaotic systems

The traditional compressive sensing theory does not take full advantage of the differences in image block information features for sampling, and the measurement matrix does not adequately measure the image based on the image information features. To solve these problems, we design a color image encryption scheme based on block compressive perception and chaotic systems. First, the color image is divided into three channels of RGB and into 8*8 blocks respectively. Then, a zigzag transform is applied to each block so that each pixel value is evenly distributed. Each block is sparsified by the discrete cosine transform, and the measurement matrix generated by the Chen chaotic system is sampled to compress the image blocks to CR times the original block size according to different compression ratios (CR). The resulting chunked compressed samples are further distributed across the pixel values of the image using a cross-channel confusion dislocation method. Finally, the size of the pixel values is changed by a dissimilarity operation. The experimental results show that the scheme can balance the security of image information transmission and the integrity of reception, and has good resistance to various attacks.
Image quality assessment based on aesthetic evaluation

Visual information accounts for about three-fourths of the total information received by humans, and image information is an important component of visual information. Usually, human beings get the most intuitive sense of "beauty" by directly observing images with the naked eye. However, with the development of technology, we are trying to use computers and image processing techniques to obtain an image quality assessment method that is close to the subjective consistency of human beings and meets the requirements of "beauty". Therefore, the general image quality assessment methods are not applicable, and the image aesthetic quality evaluation has become the most important research hotspot in the image processing field. With the progress of technology and the improvement of equipment, people's requirements for image aesthetic quality are also increasing. In this paper, we will introduce and analyze several deep learning-based image aesthetic quality evaluation algorithms, and finally present an outlook on the future of this field.
GEOLOGY AND STRATIGRAPHY OF HOD (ARTVİN) REGION, EASTERN PONTIDES, NE TURKEY

This study reports a revised geological map and a stratigraphic section of the Hod region. It represents a synthesis of new data and earlier work. New results gathered from field geological mapping and petrographic investigation have clarified the lithological boundaries, textural characteristics, the stratigraphical relationships between various rock formations and spatial distribution and behavior of structural elements. Hod region, located in the eastern part of the eastern Pontides (Artvin district), comprises Jurassic to Cretaceous volcanic and sedimentary sequences with a roughly north-north-east trending, steeply east dipping stratigraphy. These lithological units are Liassic Berta Formation, Dogger Kırgülü Formation and Hızarlıdere Formation, Malm-Lower Cretaceous Öğdem Formation and İnanlı Member, Lower Cretaceous Aşağımaden Formation, Turonian Çatak Formation, Santonian Kızılkaya Formation and Santonian-Campanian Evliyatepe Formation. Among these formations, intermediate to felsic volcanic and volcanosedimentary rocks of Kızılkaya Formation are significant as they host several volcanogenic massive sulfide (VMS) and epithermal deposits throughout eastern Pontides. Hod Maden Au-Cu deposit which represents one of the highest-grade gold and copper discoveries made globally in recent years is also located in Hod region. Therefore, the revised geological map and stratigraphy presented here are important contributions to the regional geology and essential tools for exploration geologists working in the region.
Prediction of Human Body Orientation based on Voxel Using 3D Convolutional Neural Network

Robot interaction with humans requires intelligent robots that can understand human activities. The development of advanced 3D LiDAR sensors has greatly contributed to this capability. In this study, we specifically focus on the use of 3D LiDAR sensors to predict the orientation of the human body using 3D Convolutional Neural Networks (CNNs) based on voxelized datasets. The dataset used in this study was created using a 3D LiDAR sensor with 32-channel specifications. We divided the dataset into four categories representing different walking orientations. The goal was to explore the performance of four different 3D CNN architectures using independently generated datasets. Based on the experimental results and performance analysis, it was found that VGG16 outperformed the other three architectures in predicting body orientation. VGG16 achieved an accuracy of 0.95, which was higher than DenseNet121 with approximately 0.91, ResNet50V2 with 0.80, and ResNet50 with 0.73. In the future, this method will be developed with additional orientation and results of architectural testing so that it can be modified to be better for further research on understanding human activity by robots.
A YOLOX-CBAF Traffic Sign Detection Algorithm

Traffic sign detection plays a crucial role in autonomous driving tasks. However, in practical applications, the accuracy and real-time performance of general object detection algorithms are often compromised by factors such as scene variations, distance changes, and lighting conditions. To address these challenges, this paper proposes a YOLOX-CBAF algorithm.To improve the detection performance of small traffic sign targets, we propose the CBAM-PIM module, which enhances the channel attention part of the CBAM module. Instead of the original adaptive average pooling layer with forced dimension reduction, different direction pooling layer modules are used to effectively establish the correlation of channel attention modules with features in different directions, thus strengthening the channel representation ability.To address the issue of poor detection performance in complex backgrounds, we propose the ASFF-Dconv module, which replaces the nearest-neighbor interpolation operator in the sampling part of ASFF with a depth-wise separable transpose convolution module. We use a learnable sampling structure to enhance the network’s ability to extract edge information, and use a large convolution kernel’s depth-wise separable structure to reduce the computational complexity of transpose convolution and avoid the aliasing effect. This effectively solves the impact of complex backgrounds on traffic sign detection accuracy.Experimental results show that the proposed algorithm effectively addresses the challenges of detecting small targets and complex backgrounds, and improves the mean average precision (mAP0.5) by 4.33% compared to the YOLOX-tiny algorithm.
Developing audio-based pop-up book learning media for introducing numbers 1-10 for children with intellectual disability grade ii in SLB negeri 1 barru

This research aimed to develop a product in the form of an audio-based pop-up book learning media for introducing numbers 1-10. The focus of this research is to find out the process of developing learning media starting from analyzing teacher needs, designing pop-up book learning media, validating media content, to determining the effectiveness of pop-up book learning media based on audio introduction to numbers 1-10. This research utilized the 4D development model (four-D model) modified into 3 stages without reducing the value or development itself. Data collection techniques in this study used interview techniques and questionnaires with descriptive qualitative and quantitative data analysis. The results of the study on the needs analysis indicated that teachers and students need learning media for the introduction of numbers 1-10. The design of the media is a 3D book that has a speaker and is equipped with a user manual. Content validity was tested using the Gregory formula with the results that have been declared valid for use, and the effectiveness of this media was analyzed using the Recude into different variables transformation using the SPSS program
Making Middle Earth maps with R

I’ve taught a course on data visualization with R since 2017, and it’s become one of my more popular classes, especially since it’s all available asynchronously online with hours of Creative Commons-licensed videos and materials.
Managing Suspects

This chapter describes the management of suspects, which requires management of risk and community impact and exploitation of evidence-gathering opportunities available, from arrest and forensic recovery through to detention management and interviewing. The term ‘suspect’ implies there are reasonable grounds to suspect a person's involvement in an offence. It affords them protection and rights under the Police and Criminal Evidence Act 1984 (PACE), including legal advice and being cautioned before questioning. The investigative interviewing of a suspect is a vitally important part of the process in any criminal investigation. The interview allows the suspect to establish their position in relation to the investigation, the investigator to develop further lines of enquiry, and provides an opportunity to present the gathered investigative material to the suspect. In those circumstances where a guilty person admits their involvement, it enables the investigator to probe for the required detail that allows the correct means of case disposal to be taken.
Investigative Strategies

This chapter explores some of the most common investigative strategies. These include House-to-House (H2H) enquiries, financial investigation, and proactive investigation. Some strategies may require inputs from specialists such as Interview Advisers, Media/Press Liaison Officers, Police Search Advisers (PolSAs), forensic and digital evidence recovery experts, and Crime Scene Investigators/Managers (CSI/CSMs). Ownership may be assigned for each one and those with supervisory responsibility are usually best placed to take on large or substantial projects, up to and including a Deputy SIO (DSIO). Those nominated are responsible for setting the objectives and drawing up associated policies, together with agreed terms of reference, aims and objectives, methods, parameters, and staffing and resourcing levels. Whatever strategies are used, they remain under review to ensure they are proportionate, relevant, justifiable, and on course. The chapter then defines passive data and digital data, before discussing the vital role of criminal intelligence in any investigation.
Early detection of breast cancer in mammograms using the lightweight modification of efficientNet B3

Breast cancer is one of the leading causes of death in women worldwide and early detection is critical to improving survival rates. In this study, we present a modified deep learning method for automatic feature detection for breast mass classification on mammograms. We propose to use EfficientNet, a Convolutional Neural Network (CNN) architecture that requires minimal parameters. The main advantage of EfficientNet is the small number of parameters, which allows efficient and accurate classification of mammogram images. Our experiments show that EfficientNet, with an overall accuracy of 86.5%, has the potential to be the fundamental for a fully automated and effective breast cancer detection system in the future. Our results demonstrate the potential of EfficientNet to improve the accuracy and efficiency of breast cancer detection compared to other approaches.
Ten Key Problems of Pre-trained Models: An Outlook of Representation Learning

Abstract The aforementioned representation learning methods have shown their effectiveness in various NLP scenarios and tasks. Large-scale pre-trained language models (i.e., big models) are the state of the art of representation learning for NLP and beyond. With the rapid growth of data scale and the development of computation devices, big models bring us to a new era of AI and NLP. Standing on the new giants of big models, there are many new challenges and opportunities for representation learning. In the last chapter, we will provide a 2023 outlook for the future directions of representation learning techniques for NLP by summarizing ten key open problems for pre-trained models.
Im2win: An Efficient Convolution Paradigm on GPU

Convolution is the most time-consuming operation in deep neural network operations, so its performance is critical to the overall performance of the neural network. The commonly used methods for convolution on GPU include the general matrix multiplication (GEMM)-based convolution and the direct convolution. GEMM-based convolution relies on the im2col algorithm, which results in a large memory footprint and reduced performance. Direct convolution does not have the large memory footprint problem, but the performance is not on par with GEMM-based approach because of the discontinuous memory access. This paper proposes a window-order-based convolution paradigm on GPU, called im2win, which not only reduces memory footprint but also offers continuous memory accesses, resulting in improved performance. Furthermore, we apply a range of optimization techniques on the convolution CUDA kernel, including shared memory, tiling, micro-kernel, double buffer, and prefetching. We compare our implementation with the direct convolution, and PyTorch’s GEMM-based convolution with cuBLAS and six cuDNN-based convolution implementations, with twelve state-of-the-art DNN benchmarks. The experimental results show that our implementation 1) uses less memory footprint by 23.1% and achieves 3.5 $$\times $$ TFLOPS compared with cuBLAS, 2) uses less memory footprint by 32.8% and achieves up to 1.8 $$\times $$ TFLOPS compared with the best performant convolutions in cuDNN, and 3) achieves up to 155 $$\times $$ TFLOPS compared with the direct convolution. We further perform an ablation study on the applied optimization techniques and find that the micro-kernel has the greatest positive impact on performance.
Crowd Counting Based on Multiscale Spatial Guided Perception Aggregation Network

Crowd counting has received extensive attention in the field of computer vision, and methods based on deep convolutional neural networks (CNNs) have made great progress in this task. However, challenges such as scale variation, nonuniform distribution, complex background, and occlusion in crowded scenes hinder the performance of these networks in crowd counting. In order to overcome these challenges, this article proposes a multiscale spatial guidance perception aggregation network (MGANet) to achieve efficient and accurate crowd counting. MGANet consists of three parts: multiscale feature extraction network (MFEN), spatial guidance network (SGN), and attention fusion network (AFN). Specifically, to alleviate the scale variation problem in crowded scenes, MFEN is introduced to enhance the scale adaptability and effectively capture multiscale features in scenes with drastic scale variation. To address the challenges of nonuniform distribution and complex background in population, an SGN is proposed. The SGN includes two parts: the spatial context network (SCN) and the guidance perception network (GPN). SCN is used to capture the detailed semantic information between the multiscale feature positions extracted by MFEN, and improve the ability of deep structured information exploration. At the same time, the dependence relationship between the spatial remote context is established to enhance the receptive field. GPN is used to enhance the information exchange between channels and guide the network to select appropriate multiscale features and spatial context semantic features. AFN is used to adaptively measure the importance of the above different features, and obtain accurate and effective feature representations from them. In addition, this article proposes a novel region-adaptive loss function, which optimizes the regions with large recognition errors in the image, and alleviates the inconsistency between the training target and the evaluation metric. In order to evaluate the performance of the proposed method, extensive experiments were carried out on challenging benchmarks including ShanghaiTech Part A and Part B, UCF-CC-50, UCF-QNRF, and JHU-CROWD ++ . Experimental results show that the proposed method has good performance on all four datasets. Especially on ShanghaiTech Part A and Part B, CUCF-QNRF, and JHU-CROWD ++ datasets, compared with the state-of-the-art methods, our proposed method achieves superior recognition performance and better robustness.
Intrapulse Recognition of Radar Signals Via Bicubic Interpolation WVD

In regards to electromagnetic warfare application, this work proposes a radar intra-pulse recognition algorithm based on bicubic interpolation Wigner-Ville distribution (WVD). The proposed method is aimed at overcoming the complexity of traditional feature extraction
Blackstone’s Police Operational Handbook

This book is designed for police officers while out on patrol. Covering a wide range of common offences, it clearly explains and interprets the relevant legislation, providing offence definitions, points to prove, and practical considerations. It allows readers to make a quick, informed decision in a host of everyday policing situations. The sixteenth edition of has been updated to include all recent legislative developments and further changes to the law. Topics covered include police powers, assaults and violence, sexual offences, drugs, public disorder, and firearms. There is also coverage of road traffic issues. The book finishes with five appendices on topics such as human rights and traffic data.
Quick and easy ways to deal with long labels in ggplot2

In one of the assignments for my data visualization class, I have students visualize the number of essential construction projects that were allowed to continue during New York City’s initial COVID shelter-in-place order in March and April 2020. It’s a good dataset to practice visualizing amounts and proportions and to practice with &lt;strong&gt;dplyr&lt;/strong&gt;’s group_by() and summarize() and shows some interesting trends.
Half a dozen frequentist and Bayesian ways to measure the difference in means in two groups

(See this notebook on GitHub) Taking a sample from two groups from a population and seeing if there’s a significant or substantial difference between them is a standard task in statistics.
Daytime and Nighttime Image Haze Removal based on Improved Dark Channel Prior, Multiple-Scale Retinex, and Sparrow Search Optimization

Image haze removal is a very essential preprocessing step in machine vision systems. Due to different imaging mechanisms of daytime hazy images and nighttime hazy images (such as different light sources), it is difficult to have a common method to achieve the dehazing effect of daytime and nighttime hazy images at the same time. Therefore, we propose a daytime image dehazing method based on a physical model and image enhancement, and a nighttime hazy image dehazing method based on image enhancement. For daytime hazy images, we propose an image dehazing method with improved dark channel priors, and image enhancement through an improved sparrow search algorithm. For nighttime hazy images, we propose an image dehazing method based on the improved multiple-scale Retinex, and finally use the improved sparrow search algorithm for post-processing to improve the visual effect of dehazing and enhance the details. Quantitative and qualitative experimental results prove that our method can achieve excellent sharpening effects on daytime and nighttime hazy images.