Reviewerly/exHarmony huggingface dataset:
LANGUAGE POLITENESS: APPLYING MADURESE CULTURE ROLE PLAY TO HABITUATE STUDENTS’ LANGUAGE MODESTY

This study aims to discover and explain how politeness maxims are employed in the Madurese theme role-playing. The study employs qualitative design of the research. It also employs observation technique and depth interview to obtain the data. In analyzing the data, it applies interactive model: data reduction, display, and/claim. This study finds ten politeness maxims in the using of Madurese theme role- playing.
Long Time Target Tracking Algorithm Based on Multi Feature Fusion and Correlation Filtering

This paper considers the problem of long-term target tracking in complex scenes when tracking failures are unavoidable due to illumination change, target deformation, scale change, motion blur, and other factors. More specifically, we propose a target tracking algorithm, called Re-detection Multi-feature Fusion (RDMF), based on the fusion of Scale-adaptive kernel correlation filtering and re-detection. The target tracking algorithm trains three kernel correlation filters based on HOG, CN and LBP features, and then obtains the fusion weight of response graphs corresponding to different features based on APCE criterion, and uses weighted Average to complete the position estimation of the tracked target. In order to deal with the problem that the target is occluded and disappears in the tracking process, a random fern classifier is trained to perform re-detection when the target is occluded. After comparing the OTB-50 target tracking data set, the RDMF algorithm improves the range accuracy by 10.1% compared with SAMF algorithm, and is better than KCF, DSST, CN and other algorithms.
Two-Step Domain Adaptation for Mitotic Cell Detection in Histopathology Images

Mitotic figure count is an important prognostic factor for breast cancer grading. However, the mitotic identification often suffers from the domain variations. We propose a two-step domain-invariant mitosis detection method based on Faster RCNN and a convolutional neural network (CNN). We generate various domain-shifted versions of existing histopathology images using a stain augmentation technique, enabling our method to effectively learn various stain domains and achieve better generalization. The performance of our method is evaluated on the preliminary test and final test sets of the MIDOG-2021 challenge, resulting in F1 score of 68.95% and 67.64% respectively. The experimental results demonstrate that the proposed mitosis detection method can achieve promising performance for domain-shifted histopathology images.
Image Retrieval Using Locality Preserving Projections: A Recent Study

Image retrieval algorithms are important in web surfing. There are many algorithms available, but they all have disadvantages and low retrieval rates. This work proposes a novel method for image retrieval called locality preserving projections (LPPs).The benefits of the LPP approach are that LPP is a linear technique that retains the image space’s local info. The method has a higher retrieval rate since it uses principal component analysis, nearest neighbour, and eigen features to extract hyper parameters for classification. This unique approach is tested using the Yale facial image dataset, and the algorithm's performance is evaluated using several classifiers such as Decision Tree.
Popup Observation Kit for Remote Usability Testing

The need for remote usability testing has increased during the ongoing COVID-19 global pandemic. However, lockdown and physical distancing regulations have affected how HCI researchers conduct in-person tests of systems and technologies under design. We present a Pop-Up Observation Kit, which serves as an affordable mobile usability lab. The kit is sent to participants of a study alongside the system they are testing. The Pop-Up Observation Kit provides a simple, unobtrusive form factor that enables the study participant to concentrate on the task itself and not on documenting the task they are performing. While initially developed to observe hand and finger gestures on a pressure sensor mat for the Rich Interactive Materials for everyday objects project, the Pop-up Observation Kit also applies to other use cases. Additionally, the kit is extensible with additional functionalities combined with the sensor mat to enable better data collection or unmoderated remote observations.
Testability Evaluation by Multi-sources Bayes Method Applied in Torpedo Weapon System with Improved Jaccard Similarity

Aiming at the small sample problem in the testability evaluation of complex equipment systems, this paper takes the application of a torpedo combat system as an example. Based on the Bayes theory, a hybrid pre-information weighted fusion based on the improved Jaccard coefficient to consider the similarity of historical models is proposed. This method not only solves the problem of "small samples" of complex equipment testability data, but also avoids the risk of field data being overwhelmed by the traditional Bayes method due to the different overall characteristics of multi-source information and field data. Comparative analysis of examples shows that the method used in this paper is more accurate and reasonable in test evaluation than the classical evaluation method and traditional Bayes evaluation method, and has better engineering application value.
Modeling Platform as an Innovation Playground for Advances in Characterization and Remediation of Contaminated Sites

Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]Modeling Platform as an Innovation Playground for Advances in Characterization and Remediation of Contaminated SitesAuthorsParisaJourabchiiDSee all authors Parisa JourabchiiDCorresponding Author• Submitting AuthorARIS Environmental Ltd.iDhttps://orcid.org/0000-0003-3058-2825view email addressThe email was not providedcopy email address
Facilitation in Education: Historical and Pedagogical Dimension

The article deals with the origins of facilitation as a way of organizing the joint activities of participants in the educational process, outlines the direction of its transformation in the historical dynamics. It is shown that the beginnings of the facilitative approach can be traced in the dialogical method of teaching used by Socrates. The basis for the application of the ideas of facilitation in education were also the principle of naturalness of education and Ya. A. Komenskyi’s classroom system, J. Locke’s empirical-sensualist theory of knowledge, J.-J. Rousseau’s concept of free education, J. H Pestalozzi’s theory of elementary education. The significance of foreign reformist pedagogy of the end of the XIX – beginning of the XX century, which offered new systems and methods of teaching, including group education of children, is revealed. This required from the teacher new qualities, the ability to organize effective interaction with students, i.e. the presence of certain facilitative skills. The contribution of the famous psychologist K. Rogers, an ardent supporter of human-centered learning in substantiating the idea of facilitation in education, is considered. The scientist proceeded from the fact that the disadvantage of the traditional system of education is the overestimation of the role of teaching and teachers and the underestimation of the role of conscious learning and student. He insisted on the need to shift the emphasis from teaching to learning and advised teaching not as a translation of information, but as a facilitation of the processes of conscious learning.
Introduction to AI, Data Science, and Machine Learning

This chapter will dive into the world of Artificial Intelligence, Machine Learning, and Data Science. It will talk about the relationship between these components. When we talk about AI, it is generally defined as intelligent systems that mimic human intelligence. There is a certain element of autonomous behavior to the systems in that they tend to not need any manual intervention once the systems are implemented. The AI systems may learn ways to perform certain actions based on historical behavior. Machine Learning algorithms help the AI system to learn from past data that are collected using data acquisition systems or sensors connected to the AI system.
A Novel Method of Trajectory Data Visualization to Analyze the Current Traffic Situation in Smart Cities

As modern society develops, cities become the major living places where people are living in. This phenomenon leads to the high population density in cities, which causes many problems including urban congestion. To better analyze the traffic, models for trajectory data visualization have come up by researchers. Although there have been a lot of studies on trajectory visualization, some studies have performed poorly in terms of user experience. Therefore, in this paper, an improvement in one existing visualization method will be proposed to a more perceptional way to visualize, giving the end-users a clearer perception. Technically, the data used in this research is collected from the existing method, including vehicle speed, traffic direction, location of the vehicle, and traffic volume. Further, the color system used is of better perception and the visualization method is based on JavaScript Object Notation (JSON) and Google Map. With these methods, a final prototype with high effectiveness and better perception will be proposed. Moreover, an evaluation of user study is designed to test user acceptance. By using the improved traffic visualization, drivers will have a better user experience and it is more efficient for traffic observers to analyze the macro traffic.
Implementation of a constrained quantum optimisation method in resource distribution management with considering queueing scenarios

Cloud resource demands are rapidly increasing exponentially with the development of cloud computing. For this sake, new modern methods and techniques are needed to improve its quality of service. In this work, the problem of resource distribution management in cloud computing is formulated as an optimisation problem. To this end, we exploit the constrained quantum optimisation algorithm (CQOA) in resource distribution management by considering queueing aspect in order to optimise and improve its performance in terms of computational complexity, accuracy, and energy consumption. Next, we study the implementation and the configuration of the CQOA. After, we investigate the computational complexity of selecting the optimal distribution scenario that corresponds to minimum energy consumption. We also express the lower and upper bounds of the overall possible assignment scenarios within the database. Finally, we validate the efficiency of the proposed implemented CQOA by constructing a simulation environment.
Semantic segmentation with transfer learning for self-driving cars

Self-driving cars and autonomous robotic systems rely on the precise perception and understanding of the surrounding environment. Semantic segmentation is a crucial component of road scene perception that offers semantic information. Recently, numerous approaches have been proposed for semantic segmentation. Despite their enhanced performance, these models need a large dataset for training, and they are afflicted by high computation complexity. Fine-tuning deep pretrained models has shown state-of-the-art performances. To this end, we present novel and functional deep fully convolutional neural networks (FCN) architectures based on transfer learning for semantic pixel-wise segmentation. The first one is a fine-tuned FCN-based VGG16, where the second uses this first model as an encoder and concatenates its output with a fine-tune-based Xception model, which works as a decoder. The second model breaks an input image into different coherent semantically meaningful regions. These regions are then classified into predetermined classes. Exhaustive experiments are executed on three real-world datasets that confirm the effectiveness of our models trained on large or small datasets. Our models have significantly improved road segmentation for self-driving cars compared to the state-of-the-art methods.
Research on Wireless Network Consumer Satisfaction Prediction Method

With 5G network globalization, consumers have higher requirements for telecom operators' services. It is necessary to predict consumer satisfaction for analyzing consumer requirements. Based on the understanding of telecommunications services, the wireless network consumer satisfaction prediction is divided into three sub-predictive models: network quality, promotional activities, and tariff packages. At the same time, a hybrid sampling algorithm based on support vector machine (HS-SVM) which is used to classify the consumer satisfaction imbalance dataset is proposed to predict the consumer satisfaction of these three sub-predictive models, and the consumer's overall satisfaction is obtained by merging the results of the three sub-predictive models. The validity of the model is verified by wireless network consumer satisfaction dataset compared with the popular five separate classification algorithms and SMOTE combined with the five classification algorithms. The experimental results show that the F-value and G-mean of the proposed algorithm are improved. The proposed method has better classification performance and stronger robustness in the prediction of wireless network consumer satisfaction.
Four-state reference-frame-independent quantum key distribution with non-qubit source flaws

The discrepancy between theory and experiment severely limits the development of quantum key distribution (QKD). Reference-frame-independent (RFI) protocols have been proposed to avoid alignment of the reference frame with the assumption of qubit systems. However, Trojan horse attacks and high-dimensional information leakage caused by equipment loopholes inevitably result in an imperfect non-qubit emitted signal. In this paper, we analyze the security of the RFI-QKD protocol with non-qubit source flaws based on a generalizing loss-tolerant method. The simulation results show that it is feasible to employ non-qubit flaw sources without compromising the security of the QKD system. Moreover, it only requires the preparation of four quantum states, which reduces the complexity of the experiment.
An improved anomaly detection process in smart vehicle using a supervised ensemble-learning framework

As the complexity of autonomous transportation systems increases with advances in connected and automated vehicle (CAV) technology, they are increasingly suffering from performance variations due to software- and hardware-related issues. Such problems may result in breakdowns and inefficiencies and are part of the critical challenges of system resilience. Reducing performance degradation requires timely and reliable detection and diagnosis of the anomalies causing the degradation, along with mitigating actions. However, identifying anomalies from the large, highly dimensional, and noisy data collected by system monitoring infrastructures is problematic. This chapter presents a machine learning-based framework for automatically detecting and diagnosing anomalous behavior in vehicles. Our model leverages historical data collected from various vehicle sensors to extract signatures of previously observed anomalies. By selecting the required features, our framework identifies anomalies and extracts their profiles. We evaluate the proposed model using simulations and demonstrate that it successfully identifies 98% of the injected anomalies.
Identity Management in Internet of Things with Blockchain

Internet of Things (ΙοΤ) is characterized by heterogeneity of devices, software and communication protocols when it comes to the implementation of any practical solutions. Especially in use cases such as smart cities, where scalability is very important and increased, the complexity of an Internet of Things system introduces issues on the management and privacy of both (smart) devices and users. IdentityIdentity and access management is the set of policies applied on a system restricting or allowing access of acting entities (devices and users) to the system’s services. OAuth and Single-Sign on are two of the widest used implementations for identity and access managementAccess Management. This book chapter targets exploring the new ways in which blockchain technology can significantly improve identity management in IoT by utilizing decentralized identity structures and specific cryptographicCryptographic techniques applied by it.
Hybrid Chinese Grammar Error Checking Model Based on Transformer

Chinese grammar error correction (CGEC) is a basic application of natural language processing. It can will detect and correct various grammatical errors in the texts. In this paper, we propose a Hybrid Chinese Grammar error checking Model based on Transformer. First, the model introduces Bert pre-training model to process text sequences at the Chinese character level. Next, we integrate point mutual information (PMI) to enable the model to deal with the semantic collocation information between Chinese characters. We benchmark a baseline model against our model on CGED_2018 dataset and Corrupt dataset. The proposed method is better than the baseline model, and obtains state-of-the-art results on CGED_2018 datasets.
How to Use the Recipes

This chapter will contain details of how to use the recipes we present to get the expected result. It will outline the system requirements needed for the recipe, followed by where the reader can find the code. It will also talk about how the auditors can use the recipe and tweak it based on their needs. The implementers will need other granular details and may need to use the recipe in a different way than the auditors themselves, which will be discussed at the end of the chapter. Finally, implementation considerations are highlighted at the end of the chapter, including operationalizing the ML model.
Unsupervised Volumetric Displacement Fields Using Cost Function Unrolling

Steepest descent algorithms, which are commonly used in deep learning, use the gradient as the descent direction, either as-is or after a direction shift using preconditioning. In many scenarios calculating the gradient is numerically hard due to complex or non-differentiable cost functions, specifically next to singular points. In this work, we focus on the derivation of the Total Variation regularizer commonly used in unsupervised displacement fields cost functions. Specifically, we derive a differentiable proxy to the hard $$L^1$$ smoothness constraint in an iterative scheme, which we refer to as Cost Unrolling. We show that our unrolled cost function enables more accurate gradients in regions where the gradients are hard to evaluate or even undefined without increasing the complexity of the original model. We demonstrate the effectiveness of our method in synthetic tests, as well as in the task of unsupervised learning of displacement fields between corresponding 3DCT lung scans. We report improved results compared to standard TV in all tested scenarios, achieved without modifying model architecture but simply through improving the gradients during training.
Cyber-Enabled Crime as an Enabler in Market Manipulation Schemes

Online disinformation is a method of offending that is increasingly recognized as a key driver of corporate risk. What is less clear is how companies should conceive of, and mitigate, the risks posed by this type of cyber-enabled crime. There is currently a gap in literature on how such attacks create harm and are able to manipulate markets. Several recent cases are examined and compared here to assess the role of online disinformation or information theft in these crimes. There is an essential role played by cybercrime in these operations. This is what makes this type of crime different from traditional types of market manipulations and cybercrimes. These cases illustrate also that there is often no financial gain by the perpetrator and a distribution of social harm that goes beyond the targeted victim. The implications for investigation, policy, and practice are that there is a breadth and complexity of social challenges resulting from just a single variant of cyber-enabled crime. There are unique impacts of these crimes, both at firm and market level. Therefore, firms should be specifically prepared for cyber-enabled incidents, which can demand a different approach compared to other “traditional” types of cyber incidents, like network intrusions and malware infections.
Contemporary American Drama: Socio-Political Aspect

The article deals with the topical issues of American drama based on the analysis of works by contemporary US playwrights, many of whom are representatives of groups classified as oppressed (in a white patriarchal society). The author examines popular topics and trends in cultural life in the context of public and political life in the United States over the past 70 years - from the Civil Rights Movement, &amp;quot;Women&amp;apos;s Liberation&amp;quot; and Stonewall to &amp;quot;Black Lives Matter&amp;quot;, &amp;quot;#MeToo&amp;quot; and LGBTQ prides. The author concludes that the current repertoire was directly influenced by the progressive agenda with its ambitious plan for large-scale social transformations that affected all cultural institutions, including theater. By highlighting the struggle for social equality and justice, the rights of blacks, women, ethnic and sexual minorities, diversity and inclusion, as well as condemnation of capitalism and American imperialism, progressivism has placed art at the service of ideology, once again turning cultural figures into &amp;apos;engineers of human souls&amp;apos; - but also contributed to the expansion of opportunities for members of under-represented groups, integration, the development of intercultural dialogue and the emergence of new dramatic voices.
The impact of consumer perceived value on purchase intention under Blockchain Technology

This paper takes Sichuan consumers who have bought commodities in Tmall global after 2018 as the research object and takes Blockchain Technology and online shopping as the research background From the perspective of supply chain information integration, this paper analyzed the perceived value of consumers under Blockchain Technology and established a structural equation model to explore the relationship among perceived value, consumer trust and purchase intention.
Detection of signals in noise

Chapter Contents: 9.1 Introduction 9.2 Radar noise 9.2.1 Noise probability density functions 9.3 Infrared detection and lidar noise 9.3.1 Thermal noise 9.3.2 Shot noise 9.3.3 Avalanche noise 9.3.4 1/f noise 9.3.5 Total noise contribution 9.4 Sonar noise 9.4.1 Thermal noise 9.4.2 Noise from the sea 9.5 Effects of signal-to-noise ratio 9.5.1 Probability of false alarm 9.5.2 Probability of detection 9.5.3 Detector loss relative to an ideal system 9.6 The matched filter 9.7 Coherent detection 9.8 Integration of pulse trains 9.9 Detection of fluctuating signals 9.10 Detecting targets in clutter 9.11 Constant false alarm rate (CFAR) processors 9.12 Target detection analysis 9.12.1 Worked example: target detection with an air surveillance radar 9.12.2 Range analysis software packages 9.12.3 Detection range in rain 9.13 Noise jamming 9.13.1 Noise jamming example References
Cultural Control

Bureaucratic control has many advantages and is proven to work in practice but also has many problems. One is that it only creates conformity, where people do their job but do not care much about it. Naturally, managers prefer if subordinates care about their work and try to do their best. The question is how to foster commitment to organizational goals like high productivity in members of organizations. The answer that has emerged is cultural control. In this chapter, we describe its main elements and explain its role in software development processes.