Reviewerly/exHarmony huggingface dataset:
Active Prolonged Engagement EXpanded (APEX): A Toolkit for Supporting Evidence-Based Iterative Design Decisions for Collaborative, Embodied Museum Exhibits

This article presents Active Prolonged Engagement eXpanded (APEX), a framework and toolkit for informing evidence-based decisions about the iterative design of embodied, collaborative museum exhibits. We provide an overview of APEX, a framework that builds on both prior work and experimentally derived data to provide an understanding of how visitors' physical, social, emotional, and intellectual engagement transform during the course of their interaction with an exhibit. We present two case studies demonstrating how to apply APEX in practice, analyzing video recordings of participant interactions with different design iterations of TuneTable-an interactive exhibit for co-creative computational music-making-at both a macro- and micro-level. In the case studies, we explore how APEX reveals important features of participant interaction that suggest implications and directions for design. Finally, we present a toolkit of resources to aid researchers in operationalizing APEX as a framework for video analysis, in-situ observation, and iterative design and evaluation.
Attitude Control for Fixed-Wing Aircraft Using Q-Learning

In recent years, there have been many advances in the field of Reinforcement Learning (RL). RL algorithms have achieved human master abilities in games such as Go, chess, Atari games, etc. The capabilities of RL algorithms have also now been tested in the automated transportation field for self-driving cars and aerial vehicles, where they are used to aid drivers and pilots in various situations. In this paper we apply Reinforcement Learning models to simulated airplane flight. In particular, we develop and test a Reinforcement Learning based methodology for airplane stabilization. In essence, through reward functions and Q-Learning based modeling, we analyzed and evaluated how a trained agent can learn to control a simulated Cessna 172 to stabilize itself while in flight. Our results show that, after training, the agent learns to achieve a stable attitude for the airplane. We perform the experiments using QPlane, which incorporates two flight simulators (X-Plane 11 and JSBSim). X-Plane 11 and JSBSim are both independently developed realistic flight simulators. The trained agent will be trained in JSBSim and tested in both simulators. Results of the analysis are presented and discussed.
A Survey of Deep Learning Based Object Detection

Object detection based on deep learning has become one of the most widely used and popular research topics in the field of computer vision. This paper firstly reviews the development of target detection technology and analyzes and summarizes the advantages and disadvantages of traditional target detection methods. Then, several target detection technologies based on deep learning and their advantages and disadvantages are introduced in detail. Finally, the possible future development of object detection is discussed.
Design and Implementation of Stop Words Removal Method for Punjabi Language Using Finite Automata

With the ease in accessibility of Internet, data available online has become one of the main source of information. Large amount of data gets updated daily online. Although this data may be useful for research purposes, however, it cannot be used in its raw form. In general, unstructured data contains a lot of common irrelevant words which do not add to the semantic meaning of the document. These words are known as stop words, and removing them is an important requirement for efficient text processing as done in information retrieval systems and other natural language processing applications. A significant amount of research has been done for removing stop words in languages such as English, Chinese, Urdu, Arabic, Hindi. However, not enough work is done regarding removal of stop words in Punjabi language. Most of the available works utilize corpus-based methods for removing stop words, which tend to be time-consuming. Present paper proposes a method for removing stop words for Punjabi language using finite automata. The performance of the proposed method is compared with the classical method of stop words removal. The implementation results show that the proposed algorithm gives better results in terms of execution time.
Best of Both Worlds? Mapping Process Metadata in Digital Humanities and Computational Engineering

Process metadata constitute a relevant part of the documentation of research processes and the creation and use of research data. As an addition to publications on the research question, they capture details needed for reusability and comparability and are thus important for a sustainable handling of research data. In the DH project SDC4Lit we want to capture process metadata when researchers work with literary material, conducting manual and automatic processing steps, which need to be treated with equal emphasis. We present a content-related mapping between two process metadata schemas from the area of Digital Humanities (GRAIN, RePlay-DH) and one from Computational Engineering (EngMeta) and find that there are no basic obstacles preventing the use of any of them for our purposes. Actually a basic difference rather exists between GRAIN on the one hand and RePlay-DH and EngMeta on the other, regarding the treatment of tools and actors in a workflow step.
An Empirical Study of Fuzzy Decision Tree for Gradient Boosting Ensemble

Gradient boosting has been proved to be an effective ensemble learning paradigm to combine multiple weak learners into a strong one. However, its improved performance is still limited by decision errors caused by uncertainty. Fuzzy decision trees are designed to solve the uncertainty problems caused by the collected information's limitation and incompleteness. This paper investigates whether the robustness of gradient boosting can be improved by using fuzzy decision trees even when the decision conditions and objectives are fuzzy. We first propose and implement a fuzzy decision tree (FDT) by referring to two widely cited fuzzy decision trees. Then we propose and implement a fuzzy gradient boosting decision tree (FGBDT), which integrates a set of FDTs as weak learners. Both the algorithms can be set as non-fuzzy algorithms by parameters. To study whether fuzzification can improve the proposed algorithms in classification tasks, we pair the algorithms with their non-fuzzy algorithms and run comparison experiments on UCI Repository datasets in the same settings. The experiments show that the fuzzy algorithms perform better than their non-fuzzy algorithms in many classical classification tasks. The code is available at github.com/ZhaoqingLiu/FuzzyTrees .
Deep Unfolded Sparse Refinement Network Based Detection in Uplink Massive MIMO

Massive multiple-input multiple-output (mMIMO) is a promising technique to realize the ever-increasing demand for high-speed data, quality of service (QoS), and energy efficiency for 5G and beyond wireless systems. However, the increased number of users in mMIMO systems significantly affects performance of the existing approximate matrix inversion based and matrix inversion less iterative symbol detection techniques. Conventional detection algorithms cannot learn the inter-relations of input-output parameters based on available data without having specific mathematical models of communication scenarios. Moreover, existing deep learning (DL) based symbol detection models lack in-network compression, resulting in large training time and high computational load while expected to be deployed in a low latency communication system. In this article, a sparse refinement architecture is proposed for symbol detection in uplink mMIMO. The proposed DL architecture requires less trainable parameters as compared to a conventional fully connected detection network and refines the estimated symbol vector in each layer. Convergence of the proposed symbol detection technique is analytically justified. An expression for the approximate upper bound on the BER is derived which is supported by simulations. The obtained results prove viability of the proposed symbol detection model as compared to the several existing state-of-art uplink mMIMO detection techniques, in terms of superior the error performance and low computational complexity.
Weakly Supervised Correspondence Learning

Correspondence learning is a fundamental problem in robotics, which aims to learn a mapping between state, action pairs of agents of different dynamics or embodiments. However, current correspondence learning methods either leverage strictly paired data-which are often difficult to collect-or learn in an unsupervised fashion from unpaired data using regularization techniques such as cycle-consistency-which suffer from severe misalignment issues. We propose a weakly supervised correspondence learning approach that trades off between strong supervision over strictly paired data and unsupervised learning with a regularizer over unpaired data. Our idea is to leverage two types of weak supervision: i) temporal ordering of states and actions to reduce the compounding error, and ii) paired abstractions, instead of paired data, to alleviate the misalignment problem and learn a more accurate correspondence. The two types of weak supervision are easy to access in real-world applications, which simultaneously reduces the high cost of annotating strictly paired data and improves the quality of the learned correspondence. We show the videos of the experiments on our website.
Keyword Spotting in Modern Handwritten Documents Using oBIFs

Spotting keywords in modern handwritten documents is an interesting problem that allows to search, index, and classify document images. This paper investigates the word spotting problem in a segmentation-based framework where features extracted from word images are employed to match a query keyword with those in a reference base. More specifically, we employ oriented basic image features (oBIFs) to characterize the word images while matching is carried out by computing the distance (similarity) between word images in the feature space. Experimental study of the system is carried out using the dataset of the ICFHR 2014 word spotting competition and promising results are reported in terms of P@K precision and mean average precision (mAP).
Introduction to String Theory

Throughout the course of human history, scientists and philosophers alike have attempted to provide conclusive evidence of what really makes up the universe at the most fundamental, or smallest level. Today, the hunt continues to unravel this mystery, but we may be very close to a final answer: string theory. The world of string theory is difficult to grasp, so this paper helps to provide an introduction to the basic ideas of string theory in order to show why it is a successful candidate for 'The Theory of Everything'.
Leveraging blockchain for cross-institution data sharing and authentication in mobile healthcare

The rapid development of the Internet of Things (IoT) has promoted the wide adoption of mobile medical devices, which monitor patients’ body conditions in real-time. The collected health-related data are highly sensitive, requiring careful protection during the accessing and transmission for specialized analysis. Yet, existing efforts either rely on centralized authentication for the numerous end devices or are designed to be partially distributed for a closed institution, both lacking scalability for mobile healthcare scenarios. In this paper, we propose HealthTrust that provides a generalized and flexible authentication scheme for distributed medical devices in the cross-institution context. With blockchain as the building block, HealthTrust jointly exploits smart contracts and secure authentication to attain controlled transmission and secure exchanging of healthcare data between institutions. We have implemented the system functions with a prototype based on Ethereum. Experimental results and safety analysis demonstrate that HealthTrust can well satisfy both the safety and feasibility requirements.
Facemask Detection to Prevent COVID-19 Disease Using Computer Vision and Deep Learning: Algorithms, Frameworks, Research and Implementation Challenges

The ongoing global COVID-19 pandemic has impacted everyone’s life and brought economies to a standstill. World Health Organization (WHO) and governments all over the world have found that social distancing and donning a mask in public places has been instrumental in reducing the rate of COVID-19 transmission. Stepping out of her homes in a face mask is a social obligation and a law mandate that is often violated by people, and hence, a face mask detection model that is accessible and efficient will aid in curbing the spread of disease. Detecting and identifying a face mask on an individual in real time can be a daunting and challenging task, but using deep learning and computer vision, establish tech-based solutions that can help combat COVID-19 pandemic. In this paper, we have established various deep learning architecture, designs, models and parameters that are needed to establish a real-time working model to identify a person who is donning a face mask and a face mask violator. The main outcome of this survey is to bring light to the various deep transfer learning algorithms and parameters that help to build a face detection model in real time for various public environment or places.
Automated Diagnosis of Breast Cancer: An Ensemble Approach

Breast cancer is the most dominant cancer among women and has caused millions of deaths in the world. Automated learning techniques make a significant contribution to cancer prediction studies. In this study, we focused on ensemble learning techniques for breast cancer diagnosis prediction. The dataset used in this work is publicly available at the University of California, Irvine repository. To achieve more consistent results, our study advocates the use of ensemble approaches. The proposed methodology increased prediction accuracy from 76% with the “RBF” kernel to 81% with ensemble learning. The simulation results prove that the proposed model can serve as a cancer prediction model. This paper presents an ensemble approach to integrate the simulation results of multiple classification models.
Enhanced Movie Recommender System Using Hybrid Approach

Current COVID-19 pandemic scenario, people's exposure toward movies and series has increased phenomenally. At present, each family has a Netflix/Amazon Prime account. But the question is "What to watch?" Few movie recommendation sites exists, but these are predominantly based on user's watch history. Our project differs by utilizing reviews, comments, watch history and also information from similar users. Based on these, we recommend movies. We are using collaborative filtering, content filtering, and demographic filtering to recommend movies for the users. The machine learning algorithms are implementing K-means and KNN algorithm.
Mixed-Precision Kernel Recursive Least Squares

Kernel recursive least squares (KRLS) is a widely used online machine learning algorithm for time series predictions. In this article, we present the mixed-precision KRLS, producing equivalent prediction accuracy to double-precision KRLS with a higher training throughput and a lower memory footprint. The mixed-precision KRLS applies single-precision arithmetic to the computation components being not only numerically resilient but also computationally intensive. Our mixed-precision KRLS demonstrates the 1.32, 1.15, 1.29, 1.09, and 1.08× training throughput improvements using 24.95%, 24.74%, 24.89%, 24.48%, and 24.20% less memory footprint without losing any prediction accuracy compared to double-precision KRLS for a 3-D nonlinear regression, a Lorenz chaotic time series, a Mackey-Glass chaotic time series, a sunspot number time series, and a sea surface temperature time series, respectively.
Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories.

Gaussian processes (GPs) are a versatile nonparametric model for nonlinear regression and have been widely used to study spatiotemporal phenomena. However, standard GPs offer limited interpretability and generalizability for datasets with naturally occurring hierarchies. With large-scale, rapidly-updating electronic health record (EHR) data, we want to study patient trajectories across diverse patient cohorts while preserving patient subgroup structure. In this work, we partition our cohort of over 2000 COVID-19 patients by sex and ethnicity. We develop and apply a hierarchical Gaussian process and a mixture of experts (MOE) hierarchical GP model to fit patient trajectories on clinical markers of disease progression. A case study for albumin, an effective predictor of COVID-19 patient outcomes, highlights the predictive performance of these models. These hierarchical spatiotemporal models of EHR data bring us a step closer toward our goal of building flexible approaches to capture patient data that can be used in real-time systems*.
BooDet: Gradient Boosting Object Detection With Additive Learning-Based Prediction Aggregation

In recent years, the community of object detection has witnessed remarkable progress with the development of deep neural networks. But the detection performance still suffers from the dilemma between complex networks and single-vector predictions. In this paper, we propose a novel approach to boost the object detection performance based on aggregating predictions. First, we propose a unified module with adjustable hyper-structure to generate multiple predictions from a single detection network. Second, we formulate the additive learning for aggregating predictions, which reduces the classification and regression losses by progressively adding the prediction values. Based on the gradient Boosting strategy, the optimization of the additional predictions is further modeled as weighted regression problems to fit the Newton-descent directions. By aggregating multiple predictions from a single network, we propose the BooDet approach which can Bootstrap the classification and bounding box regression for high-performance object Detection. In particular, we plug the BooDet into Cascade R-CNN for object detection. Extensive experiments show that the proposed approach is quite effective to improve object detection. We obtain a 1.3%~2.0% improvement over the strong baseline Cascade R-CNN on COCO val dataset. We achieve 56.5% AP on the COCO test-dev dataset with only bounding box annotations.
Islamophobic Hate Speech Detection from Electronic Media Using Deep Learning

Islamophobic hate speech is the indiscriminate negative attitude and behavior towards Muslims and Islam. Speech indicating prejudice against Muslims has negatively impacted the perceptions of Islam. Online platforms like Twitter have carved out policies to stop users from promoting Islamophobic hate speech, however, such content still exists which causes problems for Muslim communities globally. Hence, it becomes pivotal to find solutions to eradicate such speech from social media platforms. This paper presents an effective methodology for Islamophobic hate speech identification in online tweets using deep learning techniques. The proposed technique relies on feature extraction using a one-dimensional Convolutional Neural Network and classification using Long Short-Term Memory network based classifier. The proposed technique is validated on a dataset comprising of 1290 pre-processed online tweets and an accuracy of more than 90% is reported.
Analysis of Different Methodologies for Sentiment in Hindi Language

Sentiment analysis is an important task for companies, data analysis, and many other areas. Until now, a lot of research has been done in English and other predominant languages. This paper presents a comparative analysis of different approaches for sentiment analysis in the Hindi language. In the first method, Hindi language sentences are translated into the English language, and sentiment analysis is performed on the translated data. Contrary to this approach, in the second method, the sentiment analysis is done without translation of Hindi sentences. In the next method, linear discriminant analysis (LDA) is applied to the features to reduce the dimensions which makes a probabilistic linear classification model. With recent developments of neural networks in NLP, a convolution neural network-based approach as a classification method is implemented to perform sentiment analysis. A complete comparative analysis is done in this method to find the best sentiment classification system. Various evaluation metrics with different classifiers are also discussed in this paper.
A Brief History of Updates of Answer-Set Programs

Abstract Over the last couple of decades, there has been a considerable effort devoted to the problem of updating logic programs under the stable model semantics (a.k.a. answer-set programs) or, in other words, the problem of characterising the result of bringing up-to-date a logic program when the world it describes changes. Whereas the state-of-the-art approaches are guided by the same basic intuitions and aspirations as belief updates in the context of classical logic, they build upon fundamentally different principles and methods, which have prevented a unifying framework that could embrace both belief and rule updates. In this paper, we will overview some of the main approaches and results related to answer-set programming updates, while pointing out some of the main challenges that research in this topic has faced.
A Decentralized Federated Learning Architecture for Intrusion Detection in IoT Systems

Internet of Things (IoT) systems are vulnerable to several attacks, mainly due to the weakness of IoT devices, which have little computational and memory power, necessary for more sophisticated security features. In addition, IoT systems are distributed systems and thus inherit all problems related to the need to guarantee confidentiality, integrity, and availability. One of the traditional strategies to deal with these problems involves intrusion detection and prevention techniques. It is usual to implement them in a centralized way. In addition to not being scalable for IoT systems with an increasing number of devices, it implies an unacceptable single point of failure. Besides, sending all collected data to a centralized server in the cloud poses a great risk to the privacy of information. This paper presents a decentralized architecture for intrusion detection in IoT-based systems, which is based on federated machine learning, combined with distributed ledger technologies for access control, allowing a mechanism to minimize security risks.