Reviewerly/exHarmony huggingface dataset:
Distributed hierarchical reinforcement learning in multi-agent adversarial environments

We develop a hierarchical approach for controlling a team of aircraft in multi-agent adversarial environments. Each individual aircraft is equipped with a high-level agent that is solely responsible for target assignment decisions, and a low-level agent that generates actions based only on the selected target. We use distributed deep reinforcement learning to train the high-level agents, and neuroevolution to train the low-level agents. This approach leverages centralized training for decentralized execution to enable individual autonomy when communication is limited. Simulation results confirm the superiority of our proposed approach as compared to non-hierarchical multi-agent reinforcement learning methods.
ASAE Annual Meeting and Expo

Nonprofit Business AdvisorVolume 2022, Issue 394 p. 4-4 Conferences & Events ASAE Annual Meeting and Expo First published: 10 June 2022 https://doi.org/10.1002/nba.31256Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume2022, Issue394July 2022Pages 4-4 RelatedInformation
Bibliography

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
Issues raised by monetary theory concerning bitcoin and cryptocurrencies

In terms of the quality of money, bitcoin/cryptocurrency enthusiasts are largely correct - they may be or become a better form of money and could replace sovereign fiat currencies. The major unresolved controversies are and no doubt will be over who should control the quantity of money; the source of capitalist economic instability; the main threat to freedom and liberties - governments in general or concentrated private labour command/wealth-holders and their enterprises; and the threat posed by the U.S state in particular, issuer of the world's major universal equivalent, the U.S dollar. For various reasons, politics regarding the political economy and feasibility of bitcoin and cryptocurrencies make for and most likely will continue to make for strange bedfellows.
Transmission map optimization for single image dehazing

In hazy weather, images and videos captured outdoor scenes often suffer from inadequate visibility, low contrast, and color shift due to the atmospheric light scattering from the atmosphere particles. In general, the haze is not uniformly distributed. It is a high challenge in computer vision-based applications to visualize matters behind hazy scenes like haze-free images. This paper aims to: i) develop a new optimal-based transmission map for removing haze or fog from a single image and a video; and ii) demonstrate the utility and effectiveness of the developed technique. The proposed method offers a single image de-hazing algorithm based on transmission map optimization and novel enhancement techniques. Intensive computer simulation results of natural Live-Haze dataset and synthetic image datasets such as O-HAZY, dataset show that: 1. The presented approach effectively removes haze and prevents color distortion from undesirable de-hazing. 2. The resulting dehazed images illustrate realistic colors and remarkable details. 3. The proposed method achieves to restore the visibility of hazy scenes and illustrates colorful and natural appearances.
Technical Perspective

Model counting is the problem of approximately counting the number |Sol(Φ)| of satisfying assignments to a given model Φ, which could, for example, be a formula in conjunctive normal form (CNF) or a formula in disjunctive normal form (DNF).
Comprehensive analysis and benchmarking of de-raining techniques through a rain model approach

Adverse weather conditions, such as rain, impact the visual quality of images and significantly impact the performance of vision systems for drone-based video surveillance and self-driving car applications. It is essential to develop algorithms that can automatically remove these artifacts and not degrade the rest of the image. Several methods have been proposed in literature and practice to address this problem. They mainly focus on specific rain models, such as droplets, streaks, mist, or a combination of these. Real-life rain images are largely randomized with diverse rain sizes, types, densities, and directions. Furthermore, rain impacts various image parts differently and is often randomly distributed. Most existing de-raining algorithms can't remove drops, streaks, and mist from images simultaneously. This paper addresses this issue by reviewing existing algorithms and datasets through a rain model lens. We present surveys and quantitative benchmarking of state-of-the-art intelligence algorithms based on the rain types they aim to remove. While other review papers exist on single image de-raining, our work looks at and outlines the different algorithms and datasets available for each specific rain model. Finally, the paper makes the following contributions: • Select the most recent state of the art algorithms and show their performance for each rain type on our combination dataset called the Combination Rain Model Dataset • Offers insights on the issues that still exist in the developing field of image de-raining and future steps in the field
Evaluating adaptive differential privacy model

Differential privacy is a statistical disclosure control that is gaining popularity in recent years due to easy application for the data collection mechanisms. Many variants of differential privacy are being developed for specific use cases and environments. One of them is adaptive differential privacy that modulates the generated noise in such a way, that the retrieved result is affected according to the risk profile of the asked query and the risk-accuracy tradeoff required for the queried database. This paper intends to evaluate the adaptive differential privacy using VIOLAS Framework and through assessing how the security characteristics satisfied by the adaptive differential privacy mitigate the risk of selected inference attacks.
Learning spatio-temporal attention for multi-object tracking and re-identification in wide area motion imagery

Multi-object tracking in wide-area motion imagery (WAMI) is facilitating great interest in the field of image processing that leads to numerous real-world applications. Among them, aircraft and unmanned aerial vehicles (UAV) with real-time robust visual trackers for long-term aerial maneuvering are currently attracting attention and have remarkably broadened the scope of applications of object tracking. In this paper, we present a novel attention-based feature fusion strategy, which effectively combines the template and searching region features. Our results demonstrate the efficacy of the proposed system on CLIF and UNICORN datasets.
Student‐led creation and sharing of e‐learning resources

Medical EducationEarly View REALLY GOOD STUFF Student-led creation and sharing of e-learning resources Kyong-Jee Kim, Kyong-Jee Kim orcid.org/0000-0002-1936-5128 Search for more papers by this authorYoungjoon Kang, Youngjoon KangSearch for more papers by this authorJinwoo Jeong, Jinwoo JeongSearch for more papers by this authorGiwoon Kim, Corresponding Author Giwoon Kim flyingguy0202@daum.net orcid.org/0000-0003-2720-7442 Correspondence Giwoon Kim, Department of Emergency Medicine, Soonchunhyang University School of Medicine, Asan, South Korea. Email: flyingguy0202@daum.netSearch for more papers by this author Kyong-Jee Kim, Kyong-Jee Kim orcid.org/0000-0002-1936-5128 Search for more papers by this authorYoungjoon Kang, Youngjoon KangSearch for more papers by this authorJinwoo Jeong, Jinwoo JeongSearch for more papers by this authorGiwoon Kim, Corresponding Author Giwoon Kim flyingguy0202@daum.net orcid.org/0000-0003-2720-7442 Correspondence Giwoon Kim, Department of Emergency Medicine, Soonchunhyang University School of Medicine, Asan, South Korea. Email: flyingguy0202@daum.netSearch for more papers by this author First published: 27 May 2022 https://doi.org/10.1111/medu.14837Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Designing an Ensemble Classifier for Classifying Multi-label Data

In today’s categorization challenges, it is critical to organize the ever-increasing amount of available data, and numerous labels are commonly connected with single occurrence. Multi-label classification is real-world problem that can be found in a variety of settings. Most traditional learning methods are developed for single-label problems because they involve more than one class, multi-label classification challenges. To address multi-label problems, this paper proposes a new model based on the binary relevance (BR) methodology and classifier chain (CC) techniques, which employs traditional classifiers such as k-nearest neighbor (KNN), Gaussian Naïve Bayes (GNB), and decision trees (DT) as well as ensemble classifiers such as random forest (RF) and stacking classifiers (SC). In this research, it is demonstrated that the two ensemble classifiers outperform the three traditional classifiers.
Smart Geyser Approach using Machine Learning Techniques

Water heating has become an important part in our daily lives in a variety of ways, and the quality of water geyser technology is rapidly improving. A water heater is a device that uses thermodynamics to heat water from its initial state to a specified high temperature. Hot water is used for a variety of household and industrial uses, including heating, washing, bathing, and cleaning. But a basic geyser is less effective and more energy consuming. So, the technology should be used in a way which reduces electricity consumption in more effective way and also should be economically feasible. Artificial intelligence can be deployed in a variety of ways, one of which is the Decision Tree algorithm. The algorithm automatically adjusts the water temperature according to the current weather conditions, saving energy by avoiding irregular temperature fluctuations.
Face Mask Detection Using Machine Learning

Abstract: COVID-19 pandemic has rapidly affected ourday-to-day life disrupting the world trade and movements. Wearing a protective face mask hasbecome a new normal. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. Therefore, face maskdetection has become a crucial task to help global society. This paper presents a simplified approach to achieve this purpose using some basic Machine Learning packages like TensorFlow, Keras and OpenCV. The application of “machine learning” and “artificial intelligence” has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents.The proposed method detects the face from the image correctly and then identifies if it has a mask on it or not. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 95.77% and 94.58% respectively on two different datasets. We explore optimized values of parameters using the mobileNetV2 which is a Convolutional Neural Network architecture to detect the presence of masks correctly without causing over- fitting.
Electronic reference and bibliographic sources in universal scientific libraries as a basis of social development

The article discusses the issue of changing the structure and expanding the content of the reference and bibliographic apparatus of the library (RBA) in connection with the widespread use of electronic bibliographic sources. The instructions for bibliographic databases or traditional library catalogs, electronic media, and information resources as part of an electronic RBA are considered. The RBA composition is studied in accordance with modern requirements, and the resources that make up the RBA composition are also involved in the study. The experience of universal scientific libraries is analyzed.
Multi-class cell line classification using digital holographic microscopy and machine learning

Non-invasive classification of cultured cells is an important task for in vitro drug panel screening, phenotypic profiling for manufacturing therapeutic cell lines/proteins, and the evaluation of culture health and crosscontamination. Holographic microscopy and machine learning (ML) are attractive tools for these applications but must address two challenges: high performance in multi-class classification problems (&lt; 2 classes), and identification of important image-derived features for a given classification problem. This paper aims to achieve high performance classification of four cell lines, two breast cancer (MDA-MB-231 and MCF-7) and two noncancer (human gingival fibroblast, HGF; and human gingival keratinocytes, GIE no3B11), with varied epithelial and mesenchymal morphologies; to determine the best machine learning model for this classification; and to identify features most strongly influencing model performance. We trained and evaluated three ML algorithms: Support Vector Machines (SVMs) with various kernels, Random Forest, and AdaBoost, using a previously defined set of 17 features derived from holographic microscopy images; selected components after Principal Component Analysis (PCA); and a subset of original features after feature selection. Grid searching was conducted to determine the optimal set of hyperparameters for each machine learning algorithm before training. The multi-class ML model created using the Random Forest algorithm was reliable and had an average F1 score of 0.89, 0.86, 0.84, and 0.84 for GIE, HGF, MCF7, and MDA cell lines respectively. Moreover, with the feature selection technique, the model performance for each cell line can be further improved. Top important features were geometric (area, perimeter, eccentricity), histogram-based (skew) and textural (contrast, correlation, homogeneity).
Organisational and Ethical Perspectives

In this chapter we welcome the reader and explain the unique perspectives of Organisational and Ethical Perspectives on AI and Big Data in Accounting and Auditing that this work brings with. We further provide an overview of the structure and flow of this book and detail its chapters.
Synthesizing Configuration Tactics for Exercising Hidden Options in Serverless Systems

A proper configuration of an information system can ensure accuracy and efficiency, among other system objectives. Conversely, a poor configuration can have a significant negative impact on the system’s performance, reliability, and cost. Serverless systems in particular, which are comprised of many functions and managed services, can quickly risky the danger of misconfiguration, with many provider- and platform-specific, often intransparent and ‘hidden’ settings. In this paper, we argue to pay close attention to the configuration of serverless systems to exercise options with known accuracy, cost and time. Based on a literature study and long-term serverless systems development experience, we present nine tactics to unlock potentially neglected and unknown options in serverless systems.
The Bayesian Approach and Gaussian Processes

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
Homotopic Affine Transformations in the 2D Cartesian Grid

Topology preservation is a property of affine transformations in $${{{\mathbb {R}}}^2}$$ , but not in $${{\mathbb {Z}}}^2$$ . In this article, given a binary object $${\mathsf {X}} \subset {{\mathbb {Z}}}^2$$ and an affine transformation $${{\mathcal {A}}}$$ , we propose a method for building a binary object $$\widehat{{\mathsf {X}}} \subset {{\mathbb {Z}}}^2$$ resulting from the application of $${{\mathcal {A}}}$$ on $${\mathsf {X}}$$ . Our purpose is, in particular, to preserve the homotopy type between $${\mathsf {X}}$$ and $$\widehat{{\mathsf {X}}}$$ . To this end, we formulate the construction of $$\widehat{{\mathsf {X}}}$$ from $${{\mathsf {X}}}$$ as an optimization problem in the space of cellular complexes, and we solve this problem under topological constraints. More precisely, we define a cellular space $${{\mathbb {H}}}$$ by superimposition of two cellular spaces $${{\mathbb {F}}}$$ and $${{\mathbb {G}}}$$ corresponding to the canonical Cartesian grid of $${{\mathbb {Z}}}^2$$ where $${{\mathsf {X}}}$$ is defined, and a regular grid induced by the affine transformation $${{{\mathcal {A}}}}$$ , respectively. The object $$\widehat{{\mathsf {X}}}$$ is then computed by building a homotopic transformation within the space $${{\mathbb {H}}}$$ , starting from the complex in $${{\mathbb {G}}}$$ resulting from the transformation of $${\mathsf {X}}$$ with respect to $${{\mathcal {A}}}$$ and ending at a complex fitting $$\widehat{{\mathsf {X}}}$$ in $${{\mathbb {F}}}$$ that can be embedded back into $${{\mathbb {Z}}}^2$$ .
Non-parametric spatio-temporal activity learning from overhead imagery

Analysis of imagery derived from low-earth orbiting satellites has a long history going back into the midst of Cold War era. For a long time, these imagery have been provided by military satellites and mainly used by the Department of Defense (dod) and Intelligence Community (ic) analysts. Since the mid of 1990s, the international constellation of commercial satellites has been growing with increasing temporal and spatial resolutions such as Maxar constellation currently provides nearly four million square kilometers per day translating into a staggering 100TB of imagery every day. These satellites thus enable tremendous opportunities for various government and commercial tasks. In this paper, we present our software framework combining state-of-the-art object detection and change identification algorithms with statistical learning techniques to detect various objects-of-interest (permanent- and semipermanent-structures and vehicles) and learn their behaviors. Our approach is applicable for detecting both macro- and micro- scale changes by turning vast amount of imagery collected by commercial satellites into information and information into actionable insight.
BioFace-3D

Over the last decade, facial landmark tracking and 3D reconstruction have gained considerable attention due to their numerous applications, such as human-computer interactions, facial expression analysis, emotion recognition, etc. However, existing camera-based solutions require users to be confined to a particular location and face a camera at all times without occlusions, which largely limits their usage in practice. To overcome these limitations, we propose the first single-earpiece lightweight biosensing system, Bioface-3D, that can unobtrusively, continuously, and reliably sense the entire facial movements, track 2D facial landmarks, and further render 3D facial animations. Without requiring a camera positioned in front of the user, this paradigm shift from visual sensing to biosensing would introduce new opportunities in many emerging mobile and IoT applications.
Technical Perspective of TURL

Several efforts aim at representing tabular data with neural models for supporting target applications at the intersection of natural language processing (NLP) and databases (DB) [1-3]. The goal is to extend to structured data the recent neural architectures, which achieve state of the art results in NLP applications. Language models (LMs) are usually pre-trained with unsupervised tasks on a large text corpus. The output LM is then fine-tuned on a variety of downstream tasks with a small set of specific examples. This process has many advantages, because the LM contains information about textual structure and content, which are used by the target application without manually defining features.