Negative Examples Research Articles

An SAR Target Detector Based on Gradient Harmonized Mechanism and Attention Mechanism

In this letter, a target detector based on gradient harmonized mechanism (GHM) and attention mechanism is proposed to realize synthetic aperture radar (SAR) target detection in complex scenes. Considering the imbalance of positive and negative examples in SAR target detection, we use RefineDet as our backbone network. RefineDet can mitigate this imbalance problem by introducing the idea of two-step classification and regression into the one-stage detector. However, RefineDet only selects a part of examples for training and does not make full use of the information of all examples. Therefore, we apply GHM to the classification loss function of RefineDet, so that the network can make full use of all examples and increase the weights of hard examples adaptively in the loss function to reduce the false alarms and the missing alarms. In addition, to achieve a better detection performance in SAR images with complex scenes, a multiscale feature attention module (MFAM) is embedded into the network. By applying channel and spatial attention mechanisms to the multiscale feature maps, the MFAM can highlight the significant information and suppress the interference caused by clutter. The extensive experimental results based on the measured SAR dataset verify the effectiveness of the proposed method.

God's Patients: Chaucer, Agency, and the Nature of Laws

God's Patients: Chaucer, Agency, and the Nature of Laws

...ALCOHOL ... MAKES A YOUNG SENSITIVE MAN A BRUTAL BEAST»: ALCOHOLISM IN STUDENT AND GYMNASIUM ENVIRONMENTS OF HALYCHYNA AND THE FIGHT AGAINST IT IN THE END OF THE XIX – EARLY XX CENTURY

The problem of spreading such deviations as drunkenness and alcoholism in the youth environment of Halychyna in the late XIX – early XX centuries is analyzed. In particular, studied the genesis and main causes of this phenomenon: financial difficulties of a large part of young people, changes in their lifestyle and living environment (relocation to the city), difficult economic situation, alcohol consumption as an attempt to «escape» from reality, limited ways of spending leisure time, lack of extracurricular life and total control over it by the school administration, public disregard for alcoholism and drunkenness, alcohol consumption for fun, family feast traditions, a negative example of seniors, especially teachers, cheapness of alcohol, availability of the one to students and gymnasists, even a kind of «fashion» for alcohol in youth circles. Emphasis is placed on the increasing scale of alcoholism in Halychyna society, the gradual awareness of young people of the complexity of the problem, and the search for ways out of it. The two main directions of fighting alcoholism in student and gymnasium environments are analyzed: the one implemented by the educational administration of the region and heads of educational institutions and the second initiated by the youth itself. Insignificant effectiveness of measures taken by school authorities to solve the problem was noted. They were either informative or authoritarian and were limited to strengthening control over the behavior of secondary school students. The main form of eradication of alcoholism among young people at the beginning of World War I was disciplinary proceedings with subsequent punishment of violators of the behavioral rules in and out of school. Detected the efforts of the educational institutions authorities not to advertise manifestations of alcoholism among students and solve the problem within the school. Studied the main methods of fighting alcoholism used by young people: educational activities (lectures, reports, exhibitions), expanding the information field on the problem, creating a network of anti-alcohol clubs in schools, spreading the idea of sobriety and abstinence movement, promoting healthy lifestyles and sports. The role of public initiative, in particular the Scout movement, «Plast», «Renaissance», «Eleusis», «Ukraine», «Sokil», etc. in the fight against alcoholism is analyzed.

The use of digital marketing in information transport in social media: the example of Turkish companies

The aim of this research is to examine the relationship between brands and consumers in the Turkish digital marketing business and to reveal results. aims. In this research, the definition of digital marketing, the definition of the development process, the development of digital marketing and the situation in Turkey are analyzed. The research mainly includes positive or negative examples of brand and consumer communication through social media channels and other digital channels. In addition, examples of these communication types and the evolution of ever-changing marketing concepts are given in the digital environment. Marketing efficiency, information transfer and consumer behavior of the digital platform were evaluated. The communication between consumers and brands through digital channels and the effect of this communication on consumer behavior in the purchasing process are examined. Then, the campaigns of various companies on social media and the results of these activities are explained. In this study, the concept of digital marketing, which is one of the leading forms of marketing today, is emphasized. For this, firstly, the concept of digital marketing is explained. Then, the campaigns that explain the advantages of digital marketing to the sales of different brands and the effects of these campaigns are explained.

Проверка прокурором соблюдения требований закона при разрешении вопроса об отказе в возбуждении уголовного дела

The article is devoted to the study of the issues of the implementation of prosecutorial inspections of compliance with the requirements of the law when the bodies of preliminary investigation make a procedural decision to refuse to initiate criminal proceedings. It reveals the basic, fundamental concepts on the issues of prosecutorial inspections, raises some of the most pressing problems today. In particular, attention is paid to the subtleties of prosecutorial checks when checking decisions on refusal to initiate criminal proceedings on reports of crimes committed in the field of entrepreneurship, ways to strengthen prosecutorial supervision in this area. Both positive and negative examples of interdepartmental interaction of the prosecutor's office with the bodies of pre-liminary investigation and inquiry are also considered. The relevant statistical information of both the Prosecutor's Office of the Republic of Dagestan and the investigative Department of the Investigative Committee of the Russian Federation for the Republic of Dagestan is provided on these issues. The authors come to the conclusion that the protection of the rights of victims of crimes, the restoration of the violated security of society, the violated interests of the state are extremely important and responsible tasks assigned to the prosecutor by law. Accordingly, the prosecutor is obliged to act proactively, clearly and clearly indicate his legal position on certain issues, orienting other law enforcement agencies accordingly to specific actions.

Социальные девиации в СССР периода Великой Отечественной войны: от супружеской измены до измены Родине

Methodology. The authors studied a set of sources, phrased and verified the hypothesis of the article based on the analysis of authentic facts with the involvement of a representative historiographical sample.Results. Social deviations were an integral and quite significant part of everyday life and sociopolitical development of the USSR during the Great Patriotic War. They were distinguished by a significant type and species diversity. Given the extreme historical conditions, it may be stated that deviant behavior was an additional risk factor that significantly reduced the stability and resistance of the Soviet Union in the face of external aggression. At the same time, Soviet society and the state developed a wide range of educational and repressive measures that allowed not only to stop the negative social consequences of deviant behavior, but also to increase the viability of the social system. The absolutization of negative examples as an argument in favor of a negative assessment of the entire Soviet era is unacceptable. The prevention of the development of social deviations into a destructive pathogenesis of society and the state was the result of the successful synthesis of values and principles of traditional society with the technologies of the Modern era in management.Research implications. The data and scientific results systematized in the article help to gain a deeper understanding of the Soviet era, allowing in the future to build a unified theory of the history of Russia in the twentieth century. In addition, the materials of the article can be used in teaching students of humanities and patriotic education of schoolchildren.

Detecting Pine Trees Damaged by Wilt Disease Using Deep Learning Techniques Applied to Multi-Spectral Images

Pine wilt disease (PWD) is responsible for significant damage to East Asia’s pine forests, including those in Korea, Japan, and China. Preventing the spread of wilt disease requires early detection and removal of damaged trees. This paper proposes a method of detecting disease-damaged pines using ortho-images corrected from 5-band multi-spectral images captured by unmanned aviation vehicles. The proposed method relies on a ResNet18 backbone network connected to a modified DenseNet module, classifies the 5-band multispectral (RGB, NIR, Red_Edge) ortho-image patches, and visualizes the results as a heat map. The patch-based classifier was retrained with hard negative examples, after which it achieved 98.66% accuracy, an improvement over the 96.0% accuracy associated with the same method applied to RGB images. The resulting heat map reflects the approximate distribution, and movement of the disease. Disease locations are also predicted by local maximums in the heat map. When the distance between a ground truth and the predicted location is less than visible distance, e.g. about 5m, it is counted as a correct detection. The proposed detection which consists of heat map generation followed by localization achieves Recall of 93.39%, Precision of 88.26%, and F1-score of 90.75%.

The Fourth Industrial Revolution and the Implications for Security Management : A Case Study of South Korea

This paper explores the implications of the Fourth Industrial Revolution (4IR) for security management using a case study of South Korea. The paper examines what the 4IR is and explores some of the technological innovations in security, before speculating on where these could lead using both positive and negative examples. The paper then explores what the implications of 4IR will be for security management using a case study of South Korea based upon a survey of security managers in a specific industrial zone. The paper argues the 4IR will accelerate further trends in the ‘convergence’ of security and related functions such as safety.

Idegen tulajdonnevek ejtése a magyar szinkronban

The paper investigates Hungarian voice actors’ attitudes to the language quality of texts they have to read for dubbing and narrating foreign language audiovisual products as part of an overall study that is theoretically and practically supported by the most popular voice actors of present-day Hungary and the trade union of Hungarian voice actors. Besides summarizing some general results, the study focuses on a research topic emphasized by many participants: the problems of the pronunciation of foreign proper names in Hungarian dubbing and narration. The results are based on surveyanswers from 69 Hungarian voice actors with different personal and professional backgrounds. The participants name many factors that affect the final quality of the products and reveal many negative examples of topics presented in the paper. Since most foreign language audiovisual products released for Hungarian television, cinemas, film theatres, and even to the Hungarian consumers of online streaming services are dubbed or narrated in Hungarian, and this being the only accessible way of watching these products for most people, the quality of their language should be studied, regardless of the dichotomy of attitudes on watching audiovisual products in Hungarian or original (or other) languages. The results and the dissemination of these can help develop a quality assurance system for Hungarian dubbing that serves many millions of consumers.

Latent-Insensitive Autoencoders for Anomaly Detection

Reconstruction-based approaches to anomaly detection tend to fall short when applied to complex datasets with target classes that possess high inter-class variance. Similar to the idea of self-taught learning used in transfer learning, many domains are rich with similar unlabeled datasets that could be leveraged as a proxy for out-of-distribution samples. In this paper we introduce the latent-insensitive autoencoder (LIS-AE) where unlabeled data from a similar domain are utilized as negative examples to shape the latent layer (bottleneck) of a regular autoencoder such that it is only capable of reconstructing one task. We provide theoretical justification for the proposed training process and loss functions along with an extensive ablation study highlighting important aspects of our model. We test our model in multiple anomaly detection settings presenting quantitative and qualitative analysis showcasing the significant performance improvement of our model for anomaly detection tasks.

A Computer Vision-Based Yoga Pose Grading Approach Using Contrastive Skeleton Feature Representations.

The main objective of yoga pose grading is to assess the input yoga pose and compare it to a standard pose in order to provide a quantitative evaluation as a grade. In this paper, a computer vision-based yoga pose grading approach is proposed using contrastive skeleton feature representations. First, the proposed approach extracts human body skeleton keypoints from the input yoga pose image and then feeds their coordinates into a pose feature encoder, which is trained using contrastive triplet examples; finally, a comparison of similar encoded pose features is made. Furthermore, to tackle the inherent challenge of composing contrastive examples in pose feature encoding, this paper proposes a new strategy to use both a coarse triplet example—comprised of an anchor, a positive example from the same category, and a negative example from a different category, and a fine triplet example—comprised of an anchor, a positive example, and a negative example from the same category with different pose qualities. Extensive experiments are conducted using two benchmark datasets to demonstrate the superior performance of the proposed approach.

Detecting Credit Card Fraud using Machine Learning

Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. Customary techniques use rule-based master frameworks to identify fraud practices, ignoring assorted circumstances, the outrageous lopsidedness of positive and negative examples. In this paper, we propose a CNN-based fraud detection system, to catch the natural examples of fraud practices gained from named information. Bountiful exchange information is spoken to by an element lattice, on which a convolutional neural organization is applied to recognize a bunch of idle examples for each example. Trials on true monstrous exchanges of a significant business bank show its boss presentation contrasted and some best-in-class strategies. The aim of this paper is to merge between Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Auto-encoder (AE) to increase credit card fraud detection and enhance the performance of the previous models. By using these four models; CNN, AE, LSTM, and AE&LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy =0.99, the CNN model has accuracy =0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&LSTM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models.

Likelihood landscape and maximum likelihood estimation for the discrete orbit recovery model

AbstractWe study the nonconvex optimization landscape for maximum likelihood estimation in the discrete orbit recovery model with Gaussian noise. This is a statistical model motivated by applications in molecular microscopy and image processing, where each measurement of an unknown object is subject to an independent random rotation from a known rotational group. Equivalently, it is a Gaussian mixture model where the mixture centers belong to a group orbit.We show that fundamental properties of the likelihood landscape depend on the signal‐to‐noise ratio and the group structure. At low noise, this landscape is “benign” for any discrete group, possessing no spurious local optima and only strict saddle points. At high noise, this landscape may develop spurious local optima, depending on the specific group. We discuss several positive and negative examples, and provide a general condition that ensures a globally benign landscape at high noise. For cyclic permutations of coordinates on (multireference alignment), there may be spurious local optima when , and we establish a correspondence between these local optima and those of a surrogate function of the phase variables in the Fourier domain.We show that the Fisher information matrix transitions from resembling that of a single Gaussian distribution in low noise to having a graded eigenvalue structure in high noise, which is determined by the graded algebra of invariant polynomials under the group action. In a local neighborhood of the true object, where the neighborhood size is independent of the signal‐to‐noise ratio, the landscape is strongly convex in a reparametrized system of variables given by a transcendence basis of this polynomial algebra. We discuss implications for optimization algorithms, including slow convergence of expectation‐maximization, and possible advantages of momentum‐based acceleration and variable reparametrization for first‐ and second‐order descent methods. © 2021 Wiley Periodicals LLC.

Detection of tumour infiltrating lymphocytes in CD3 and CD8 stained histopathological images using a two-phase deep CNN

BackgroundImmuno-score, a prognostic measure for cancer, employed in determining tumor grade and type, is generated by counting the number of Tumour-Infiltrating Lymphocytes (TILs) in CD3 and CD8 stained histopathological tissue samples. Significant stain variations and heterogeneity in lymphocytes’ spatial distribution and density make automated counting of TILs’ a challenging task. MethodsThis work addresses the aforementioned challenges by developing a pipeline “Two-Phase Deep Convolutional Neural Network based Lymphocyte Counter (TDC-LC)” to detect lymphocytes in CD3 and CD8 stained histology images. The proposed pipeline sequentially works by removing hard negative examples (artifacts) in the first phase using a custom CNN “LSATM-Net” that exploits the idea of a split, asymmetric transform, and merge. Whereas, in the second phase, instance segmentation is performed to detect and generate a lymphocyte count against the remaining samples. Furthermore, the effectiveness of the proposed pipeline is measured by comparing it with the state-of-the-art single- and two-stage detectors. The inference code is available at GitHub Repository https://github.com/m-mohsin-zafar/tdc-lc. ResultsThe empirical evaluation on samples from LYSTO dataset shows that the proposed LSTAM-Net can learn variations in the images and precisely remove the hard negative stain artifacts with an F-score of 0.74. The detection analysis shows that the proposed TDC-LC outperforms the existing models in identifying and counting lymphocytes with high Recall (0.87) and F-score (0.89). Moreover, the commendable performance of the proposed TDC-LC in different organs suggests a good generalization. ConclusionThe promising performance of the proposed pipeline suggests that it can serve as an automated system for detecting and counting lymphocytes from patches of tissue samples thereby reducing the burden on pathologists.

PreBINDS: An Interactive Web Tool to Create Appropriate Datasets for Predicting Compound–Protein Interactions

Given the abundant computational resources and the huge amount of data of compound–protein interactions (CPIs), constructing appropriate datasets for learning and evaluating prediction models for CPIs is not always easy. For this study, we have developed a web server to facilitate the development and evaluation of prediction models by providing an appropriate dataset according to the task. Our web server provides an environment and dataset that aid model developers and evaluators in obtaining a suitable dataset for both proteins and compounds, in addition to attributes necessary for deep learning. With the web server interface, users can customize the CPI dataset derived from ChEMBL by setting positive and negative thresholds to be adjusted according to the user’s definitions. We have also implemented a function for graphic display of the distribution of activity values in the dataset as a histogram to set appropriate thresholds for positive and negative examples. These functions enable effective development and evaluation of models. Furthermore, users can prepare their task-specific datasets by selecting a set of target proteins based on various criteria such as Pfam families, ChEMBL’s classification, and sequence similarities. The accuracy and efficiency of in silico screening and drug design using machine learning including deep learning can therefore be improved by facilitating access to an appropriate dataset prepared using our web server (https://binds.lifematics.work/).

Distributed Monitoring for Energy Infrastructures: A Two-Tier Analysis over Wireless Networks

Wireless networks (e.g., 5G networks) enable distributed energy infrastructures to be connected even when they are geometrically isolated. Intelligent monitoring from remote sites therefore becomes possible, allowing decision makers to examine the status of distributed energy infrastructures from a central location. The major challenge is when local devices cannot perform the monitoring independently; transmitting every signal back to the central server triggers enormous amounts of wireless communication. To address this, we propose a two-tier AI system by offloading computations to multiple devices. Specifically, we build lightweight AI models for deployment on edge clients (i.e., edge sensors) and a large-scale AI model for the central server. These two types of AI models are trained with different criteria: the models on the edges act as the filtering tools to detect abnormal events and maximally avoid making false negative predictions, whereas the server model is supposed to be an expert for accurate predictions. By validating on a power theft dataset, we show that such a cascading methodology could filter out sufficient negative examples on the edge side while still being able to provide precise predictions on the second-round analysis.

The Doctrine of Creation: A Constructive Kuyperian Approach

The Doctrine of Creation: A Constructive Kuyperian Approach

Wheel fault diagnosis model based on multichannel attention and supervised contrastive learning

As wheels are important components of train operation, diagnosing and predicting wheel faults are essential to ensure the reliability of rail transit. Currently, the existing studies always separately deal with two main types of wheel faults, namely wheel radius difference and wheel flat, even though they are both reflected by wheel radius changes. Moreover, traditional diagnostic methods, such as mechanical methods or a combination of data analysis methods, have limited abilities to efficiently extract data features. Deep learning models have become useful tools to automatically learn features from raw vibration signals. However, research on improving the feature-learning capabilities of models under noise interference to yield higher wheel diagnostic accuracies has not yet been conducted. In this paper, a unified training framework with the same model architecture and loss function is established for two homologous wheel faults. After selecting deep residual networks (ResNets) as the backbone network to build the model, we add the squeeze and excitation (SE) module based on a multichannel attention mechanism to the backbone network to learn the global relationships among feature channels. Then the influence of noise interference features is reduced while the extraction of useful information features is enhanced, leading to the improved feature-learning ability of ResNet. To further obtain effective feature representation using the model, we introduce supervised contrastive loss (SCL) on the basis of ResNet + SE to enlarge the feature distances of different fault classes through a comparison between positive and negative examples under label supervision to obtain a better class differentiation and higher diagnostic accuracy. We also complete a regression task to predict the fault degrees of wheel radius difference and wheel flat without changing the network architecture. The extensive experimental results show that the proposed model has a high accuracy in diagnosing and predicting two types of wheel faults.

Michael Genovese. The Problem of Profit: Finance and Feeling in Eighteenth-Century British Literature.

Michael Genovese. <i>The Problem of Profit: Finance and Feeling in Eighteenth-Century British Literature</i>.

How Constitutional Drafters Use Comparative Evidence

This article analyzes the transcripts of constitutional deliberations in two settings of third-wave democratization, Brazil and Ukraine. The focus is on the extent and kind of references to foreign countries and political institutions. Such references are relevant to the micro-foundations of theories of institutional diffusion. The evidence suggests that foreign references in constitutional debate are as frequent as are references to core concepts such as “democracy” and “freedom”. Also, actors employ foreign references mostly in order to attempt analytic comparisons across institutional models. These references mostly take the form of “endorsements” of the speaker’s favored policy, but a full third of them are negative examples (“warnings”), which lends credence to arguments about “aversive” diffusion mechanisms. Finally, the identity of countries referenced by Brazilian and Ukrainian constitution makers is analyzed. The ordering and profile of these target countries is remarkably similar despite differences in the cultural and geographic character of the two countries. Actors in both countries focused their attention on a small set of countries in the democratic “core”.