Reviewerly/exHarmony huggingface dataset:
Real Time Human Activity Detection using YOLOv7

The You Only Look Once (YOLO) algorithm is used in this research to demonstrate a sophisticated human activity detection system integrated with real-time applications. Computer vision, a subset of deep learning, is crucial in grasping the content of pictures and videos and having applications in security monitoring, healthcare, traffic control, and other fields. The suggested system focuses on three critical safety scenarios, fall detection, social distance detection, and drowsiness detection, to improve real-time safety, security, and efficiency. The system uses YOLO’s extraordinary strength of object identification and localization to deliver real-time processing, high accuracy, and flexibility to dynamic situations. The modules' accuracy and dependability are established through extensive testing and assessment, making them useful instruments for addressing challenges in the real world.
Educational Process in a Secondary School Using Distance Learning Technologies

The authors describe the results of an experiment on the introduction of additional distance learning methods in senior classes of some secondary schools in rural areas of the Stavropol Territory. Given the new digital educational environment, there is a wide range of tasks in secondary educational organizations in Russia. The goal of the digital educational environment is to ensure high quality and accessibility of education of all types and levels. The empirical study was conducted from 2013 to 2018 as part of dissertation research on the digitalization of general secondary education in schools of Russia. The authors consider the possibility of filling gaps in schoolchildren's knowledge through distance learning in chemistry, physics, mathematics, and computer science. Difficulties that arise when using methods of additional education are noted. Several educational and motivational difficulties that arise when implementing digital learning methods are shown. Aspects of health saving in the framework of mixed training are considered.
Secure User Recommendation using Machine Learning Techniques

A secure user recommendation is a significant and emerging service, which expands and enhances the recommendation service by actively recommending secure user to other users based on the trustworthiness. One primary reason for the need of a secure user recommendation system in current society is that people have too many opportunities to access other users due to the ubiquity of the internet. The existing recommendation system such as content based, collaborative filtering gives user to user recommendation based on their similar choices, activities and location preferences etc. These exiting methods have narrow scope of recommendation and are less efficient. This study proposes a new secure user recommendation system that overcomes the shortcoming of the existing system. The proposed secure user recommendation system determines the trustworthiness of user found in the dataset based on the various attributes like profile, interaction, intensity of relationship etc. The machine learning techniques are used for giving recommendation on various applications. To arrive at the goal of intensifying the user trustworthiness, here the focus is on building the trustworthy model that can predict the trustworthiness level of users under the multiple trust attribute conditions using machine learning techniques. To help the process of finding the most accurate machine learning technique in providing the secure user recommendations, the performance metrics such as precision, recall, F1 score and accuracy of various machine learning techniques are calculated and it is also observed that a random forest technique gives higher accuracy of 97.5% than the other existing techniques.
Research on Recognition Algorithm Based on Polarization Characteristics of Coal and Gangue Surface

In this paper, an intelligent identification method of gangue based on polarization imaging technology is proposed. Firstly, a gangue image acquisition system was built in the laboratory, and 2000 groups of coal and gangue samples were collected, including polarization intensity images, polarization angle images and polarization degree images of coal and gangue; secondly, after preprocessing the polarization images to obtain the regions of interest of coal and gangue in the images, the grayscale characteristics of the polarized images, the grayscale features of the grayscale histogram features and the texture features of the polarized images were extracted respectively; finally, The Relief-SVM and the single hidden layer neural networks were used respectively to train the classification model. Experimental results show that the recognition rate of the images acquired by polarization cameras can reach 99.28% and 100% respectively. The accuracy rate is improved compared to the method of classifying ordinary black and white images of coal and gangue with non-polarization characteristics.
Addressing Uncertainty in Imbalanced Histopathology Image Classification of HER2 Breast Cancer: An Interpretable Ensemble Approach With Threshold Filtered Single Instance Evaluation (SIE)

Breast Cancer (BC) is among women’s most lethal health concerns. Early diagnosis can alleviate the mortality rate by helping patients make efficient treatment decisions. Human Epidermal Growth Factor Receptor (HER2) has become one the most lethal subtype of BC. According to the College of American Pathologists/American Society of Clinical Oncology (CAP/ASCO), the severity level of HER2 expression can be classified between 0 and 3+ range. HER2 can be detected effectively from immunohistochemical (IHC) and, hematoxylin & eosin (HE) images of different classes such as 0, 1+, 2+, and 3+. An ensemble approach integrated with threshold filtered single instance evaluation (SIE) technique has been proposed in this study to diagnose BC from the multi-categorical expression of HER2 subtypes. Initially, DenseNet201 and Xception have been ensembled into a single classifier as feature extractors with an effective combination of global average pooling, dropout layer, dense layer with a swish activation function, and l2 regularizer, batch normalization, etc. After that, extracted features has been processed through single instance evaluation (SIE) to determine different confidence levels and adjust decision boundary among the imbalanced classes. This study has been conducted on the BC immunohistochemical (BCI) dataset, which is classified by pathologists into four stages of HER2 BC. This proposed approach known as DenseNet201-Xception-SIE with a threshold value of 0.7 surpassed all other existing state-of-art models with an accuracy of 97.12%, precision of 97.15%, and recall of 97.68% on H&E data and, accuracy of 97.56%, precision of 97.57%, and recall of 98.00% on IHC data respectively, maintaining momentous improvement. Finally, Grad-CAM and Guided Grad-CAM have been employed in this study to interpret, how TL-based model works on the histopathology dataset and make decisions from the data.
Next-Gen Security: YOLOv8 for Real-Time Weapon Detection

The swift and accurate identification of weaponry holds paramount importance in military operations to ensure the safety of personnel and the effectiveness of missions. In recent times, deep learning models have emerged as robust solutions for object detection tasks, rendering them valuable tools for enhancing military security. This research study delves into the realm of weapon detection by presenting a novel approach utilizing YOLOv8-Small, a streamlined variant of the renowned You Only Look Once (YOLO) detection framework. The study's primary objective revolves around harnessing the capabilities of YOLOv8-Small for precise weapon detection within military contexts. Through a meticulous design process and rigorous training, the proposed model demonstrates its competence in identifying a diverse range of weapons with remarkable accuracy and efficiency. The experimental results validate the potential applicability of YOLOv8-Small in bolstering military operations, underscoring its utility as a force multiplier on the battlefield. Moreover, the research delves into the model's adaptability to varying environmental conditions, a critical factor in real-world military scenarios. The findings reveal the model's capacity to maintain consistent performance across different terrains, lighting conditions, and weather situations. This adaptability significantly enhances its operational viability, ensuring reliable weapon detection capabilities even under challenging circumstances. The implications of this research extend to broader military strategies and tactics, where rapid and accurate weapon detection can tip the scales in favor of mission success. The potential integration of YOLOv8-Small with existing military systems holds promise for enhancing situational awareness and proactive threat mitigation. In conclusion, this research study presents a pioneering contribution to the field of military weapon detection by leveraging YOLOv8-Small's efficiency and adaptability. The study's insights provide valuable guidance for military stakeholders seeking innovative solutions to enhance security, thereby paving the way for more effective and safeguarded military operations.
Action Recognition Based on Dense Action Captioning

Considering that visually similar actions may be easily distinguished in textual description, it provides an opportunity to introduce textual description to assist action recognition. This paper proposes a novel action recognition method based on dense action captioning. Considering that an action may include multiple sub-actions with temporal relationship, this paper extends the thought of video captioning into actions. Multiple descriptions are generated from an action video sequence and each description corresponds to one sub-action sequence. In this paper, temporal constraints are added into a dense video captioning model for dense action captioning. With the generated descriptions, actions are recognized by a decision fusion strategy on both visual and textual presentation. The classification of visually similar actions can be refined based on textual classification of generated descriptions. The proposed method could be used in other action recognition models based on only visual representation. Experiments conducted on WorkoutUOW-18 and TAPOS datasets demonstrate the effectiveness of the proposed method on action captioning and classification.
Cyclostationary signal sensing algorithm based on principal component analysis and AdaBoost

At present, with the development of 5G technology, the bandwidth requirement of data communication is increasing, which makes the limited spectrum resources become more and more tight. Based on the current situation of the utilization and allocation of radio spectrum resources, the unused frequency bands have become less and less, and the shortage of radio spectrum resources is very serious. In this paper, a spectrum sensing algorithm based on principal component analysis (PCA) and AdaBoost is proposed to solve the problem of low detection rate of main user signal in wireless channel environment. Firstly, we extract the feature parameters of the signal by using the cyclostationary PCA algorithm, obtain the principal components of the signal, generate the samples, and construct the sample set, then the AdaBoost algorithm is used to classify and detect the signals in the presence and absence of the main user. The simulation results show that the proposed algorithm has better classification and detection performance compared with the artificial neural network and the max-min eigenvalue algorithm under low signal-to-noise ratio, the sensing of primary user signal is realized effectively.
Semi-supervised Object Detection for Remote Sensing Images via Pseudo Labeling and Consistency Learning

Object detection for remote sensing images (RSI) has made significant progress in recent years. However, existing supervised methods typically require a large amount of labeled data, whose acquisition, and particularly that of RSI, can be quite expensive. For natural images, many semi-supervised methods have been proposed to tackle the lack of labeled data, while few studies have focused on semi-supervised object detection for RSI. To address this problem, this paper proposes RS-PCL, which is a semi-supervised method applying Pseudo labeling and Consistency Learning to RSI. The RS-PCL method uses the teacher-student training scheme to generate high-quality pseudo labels for self-training. To deal with biased pseudo boxes, Noisy Pseudo box Assignment (NPA) is proposed to consider the teacher predictions during the label assignments of the unlabeled student. To ensure scale invariance, Scale-Invariant Learning (SIL) is proposed to carry out feature-consistency training on multi-scale images. This study conducted experiments using the DOTA-v1.0 dataset as labeled data and newly added images in the DOTA-v2.0 dataset (DOTA-v2.0 was constructed by adding extra images to DOTA-v1.0) as unlabeled data. The results reveal that there is obvious improvement over the supervised baseline and state-of-the-art semi-supervised methods, which demonstrates the effectiveness of the proposed method.
Educational Standards in the Social Digitalization Context

The chapter focuses on the main patterns of social development, causing the modernization of existing educational standards for training modern specialists. The authors analyze multiple paradigms existing in different educational systems in the context of the ongoing globalization and social transition to digitalization. The chapter reviews the development of educational standards and concepts in their retrospect over the past 30 years. The authors describe the features and tasks of Russian higher education in the transition from one generation of standards to another under the influence of the fourth industrial revolution. Nowadays, the chapter argues, one of the strategic goals of modern education is the formation of subject and metacognition knowledge. The authors emphasize that educational tasks related to the creation of critical activities for the digital society and their corresponding digital competencies do not lose their particular relevance.
Police Whistleblowers, the Lamplighter Project, and Twitter: Exposing Misconduct and Corruption in American Law Enforcement

As a result of George Floyd's senseless killing, countless individuals took to the streets to protest. Coinciding with the protests, a number of police whistleblowers utilized social media to speak out about the transgressions and brutality that they had witnessed firsthand. For these individuals, social media provides a space to voice their concerns with law enforcement in America and is a place to tell their stories and critique the police. Moreover, social media empowers whistleblowers by allowing anonymity while affording a safe space for effective community building. This article is a case study involving seven whistleblowers interviewed by the author. Social media has become the primary communication tool they use to expose malfeasance and corruption in policing. The pervasive culture of law enforcement condemns any kind of vocal critique of policing, so social media functions in strong juxtaposition to the silencing of complaints, placing it in direct opposition to the thin blue line and the blue wall of silence. This article highlights the Lamplighter Project and Twitter as resources that assist police whistleblowers who speak out against wrongdoings perpetrated by other members of law enforcement.
Panoramic three-dimensional optical digitization system assisted by a bi-mirror

The digitization of objects' full surfaces finds widespread applications in fields such as virtual reality, art and design, and medical and biological sciences. For the realization of three-dimensional full-surface digitization of objects within complex sceneries, we propose a straightforward, efficient, and robust panoramic three-dimensional optical digitization system. This system contains a laser-based optical three-dimensional measurement system and a bi-mirror. By integrating mirrors into the system, we enable the illumination of the object from all angles using the projected laser beam in a single scanning process. Moreover, the main camera employed in the system can acquire three-dimensional information of the object from several different viewpoints. The rotational scanning method enhances the efficiency and applicability of the three-dimensional scanning process, enabling the acquisition of surface information of large-scale objects. After obtaining the three-dimensional data of the sample from different viewpoints using laser triangulation, mirror reflection transformation was employed to obtain the full-surface three-dimensional data of the object in the global coordinate system. The proposed method has been subjected to precision and validity experiments using samples with different surface characteristics and sizes, resulting in the demonstration of its capability for achieving correct three-dimensional digitization of the entire surface in diverse complex sceneries.
Analysis of Image Pattern Classification using Hopfield Network on Letters and Thai Banknotes via MATLAB Implementation

This paper presents the analysis of Image Pattern Detection using the Hopfield algorithm. Initially two simple letter patterns, L and T are used for mathematical and graphical illustration of pattern classification using Hopfield Algorithm. Mathematical analysis with simple and comprehensive elaboration helps reader to better understand and implement the algorithm in its applications. The analysis is further extended for the patterns L, T, C, U and Y. For each of the patterns, the analysis is done for matrix size of 3 × 3, 5 × 5, 10 × 10 and 28 × 28 with the noise ranging from 10% to 80%. The result of comparative analysis done for different patterns, matrix sizes, presence of noise for the algorithm presented in this paper shows that the convergence ratio decreases with the increase in noise percentage. Additionally, this paper explains the affect of Hebbian learning rule in the convergence ratio of patterns. Finally, Hopfield algorithm is applied for the classification of 20 Baht and 50 Baht Thai banknotes. With image processing in MATLAB and application of Hopfield algorithm, the classification of banknotes is successfully done in the presence of different noise levels.
Multi-decoder Networks for Semi-supervised Medical Image Segmentation

To improve the performance of semi-supervised image segmentation, it is important to effectively generate pseudo-labels from unlabeled images. However, the impact of pseudo-label confidence on segmentation performance is often overlooked. Low-confidence pseudo-labels can misguide the model and lead to overfitting, making it challenging to use them effectively. To address this issue, we propose a consistency constraint-based network that employs one encoder and three decoders () to generate distinct pseudo-labels. To assess the confidence of the generated pseudo-labels, we introduce a critic network that learns relevant features and effectively regularizes the confidence of -generated pseudo-labels. For evaluating the unlabeled images, we define a loss function that minimizes entropy, consisting of three sets of losses. We compare the performance of our model with two other semi-supervised segmentation algorithms using Dice, MAE, and F1 indicators. Our results demonstrate that the model outperforms the comparison models on all three metrics. In summary, our proposed consistency constraint-based network with a critic network and entropy-based loss function can effectively generate high-confidence pseudo-labels for semi-supervised image segmentation and improve the overall performance of the model.
Element-Wise Layer Normalization for Continuous Signal Representation

Implicit neural representation (INR), sometimes also referred to coordinate-based representation or fitting, has gained the state-of-the-art performance in numerous research fields including computer vision and computer graphics due to the powerful continuous representation ability. Recent researches indicate that Fourier embedding is critical for INR to fit realistic images with high-frequency details. However, is Fourier embedding all we need for high-frequency coordinate (image) fitting? In this paper, we revisit the problem of coordinate fitting from a novel perspective of distribution mapping. Fourier embedding, as a preprocessing step of coordinate fitting, essentially performs the operation of mapping the uniform coordinate distribution to a normal distribution, and makes the learning of mapping function between two similar smooth distribution become easier. However, the number of discrete Fourier basis function affects the fitting performance dramatically and cannot be determined automatically. Based on the above analysis, a simple yet efficient INR coordinate fitting method is proposed in this paper, which demonstrates that the Fourier embedding is not the only way to improve INR. The proposed method only adds an element-wise layer normalization (ELN) module to the vanilla multi-layer perception (MLP) with ReLU activation. Experimental results on public database demonstrate that the proposed method outperforms the state-of-the-art methods using Fourier embedding.
Group-housed pigs tracking based on the attention mechanism

The estimation of motion behavior of group-housed pigs can be used to predict their health condition statistically. A feasible motion detection method is to use contact sensors, such as adding wireless inertial sensors to pigs, which will cause stress responses in pigs and bring high costs. Different from traditional methods, video Multi-Object Tracking (MOT) technology studies the daily movement of pigs indirectly via deep learning methods. However, MOT for group-housed pigs faces many research difficulties, such as deformation, adhesion, occlusion, appearance similarity, light disturbance and background interference. In order to increase the robustness of the tracking algorithm, an improved FairMOT algorithm based on attention mechanism is proposed, in which Convolutional Block Attention Modules (CBAM) are integrated into the DLA34 backbone as dlaCBAM and Multi-Object Tracking Accuracy (MOTA) rose to 90.7%. The data association applies Kalman filter and Hungarian algorithm, and euclidean distance is adopted to characterize the amount of movement and construct the daily trajectory of pigs. Test results show that the improved FairMOT algorithm has a better tracking effect on pigs, which provides new ideas for intelligent pig motion estimation.
Improving Data for Managing Cyber Risk and Building Resilience

Gaps in the data available for assessing cyber risk have limited the development of metrics that would help the public and private sectors prevent and recover from cyberattacks and reduce systemic risk. Cyber incident disclosure rules, introduced to close the data gaps, help but fall short in supporting the effective management of cyber risk. This article examines current and proposed reporting requirements, especially in the financial sector, where they are the most advanced. It describes the data gaps that remain and discusses how moving toward a better and harmonized cyber incident data collection rule could improve cybersecurity and reduce the risk of catastrophic cyber incidents.