Reviewerly/exHarmony huggingface dataset:
Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c

Accurate and reliable prediction of clinical progression over time has the potential to improve the outcomes of chronic disease. The classical approach to analyzing longitudinal data is to use (generalized) linear mixed-effect models (GLMM). However, linear parametric models are predicated on assumptions, which are often difficult to verify. In contrast, data-driven machine learning methods can be applied to derive insight from the raw data without a priori assumptions. However, the underlying theory of most machine learning algorithms assume that the data is independent and identically distributed, making them inefficient for longitudinal supervised learning. In this study, we formulate an analytic framework, which integrates the random-effects structure of GLMM into non-linear machine learning models capable of exploiting temporal heterogeneous effects, sparse and varying-length patient characteristics inherent in longitudinal data. We applied the derived mixed-effect machine learning (MEml) framework to predict longitudinal change in glycemic control measured by hemoglobin A1c (HbA1c) among well controlled adults with type 2 diabetes. Results show that MEml is competitive with traditional GLMM, but substantially outperformed standard machine learning models that do not account for random-effects. Specifically, the accuracy of MEml in predicting glycemic change at the 1st, 2nd, 3rd, and 4th clinical visits in advanced was 1.04, 1.08, 1.11, and 1.14 times that of the gradient boosted model respectively, with similar results for the other methods. To further demonstrate the general applicability of MEml, a series of experiments were performed using real publicly available and synthetic data sets for accuracy and robustness. These experiments reinforced the superiority of MEml over the other methods. Overall, results from this study highlight the importance of modeling random-effects in machine learning approaches based on longitudinal data. Our MEml method is highly resistant to correlated data, readily accounts for random-effects, and predicts change of a longitudinal clinical outcome in real-world clinical settings with high accuracy.
An Emotional Response to the Value of Visualization

When assessing the value of visualizations, researchers traditionally focus on efficiency, comprehension, or insight. However, analyzing successful data physicalizations leads to a deep appreciation for hedonic qualities. Informed by the role of emotion in psychology, art, design, marketing, and HCI, we argue for an expanded definition of value, applicable to all forms of data visualization.
Automated Pap Smear Cervical Cancer Screening Using Deep Learning

This study aims to apply Mask Regional Convolutional Neural Network (Mask R-CNN) to cervical cancer screening using pap smear histological slides. Based on our current literature review, this is the first attempt of using Mask R-CNN to detect and analyze the nucleus of the cervical cell, screening for normal and abnormal nuclear features. The data set were liquid-based histological slides obtained from Thammasat University (TU) Hospital. The slides contained both cervical cells and various artifacts such as white blood cells, mimicking the slides obtained in actual clinical settings. The proposed algorithm achieved mean average precision (mAP) of 57.8%, accuracy of 91.7%, sensitivity of 91.7%, and specificity of 91.7% per image. As we needed to evaluate the efficiency of our algorithm in comparison to single cell classification algorithm (Zhang et al., IEEE JBHI, vol. 21, no. 6, pp. 1633, 2017), we modified our method to also classify single cells on TU dataset test using Mask R-CNN segmentation. The results obtained had an accuracy of 89.8%, sensitivity of 72.5%, and specificity of 94.3%.
From sci-fi to sci-fact: the state of robotics and AI in the hospitality industry

Purpose This paper aims to review the extant hospitality and tourism literature on the state of robotics and artificial intelligence (AI) in the service industry. The aim was to highlight the current areas of research on this emerging topic and identify areas for future application and study. Design/methodology/approach A list of hospitality and tourism journals was used to identify articles related to AI and robotics using the terms AI, robots, robotics, hospitality and tourism, and several combinations thereof. Additional sources were identified through the literature reviews from the identified works. Findings The findings revealed several studies on the current state of robotics and AI in hospitality and tourism. Additional research examines and discusses implications for internal and external customer service, legal and ethical issues and theory. Originality/value This paper provides a compilation of the current studies that examine the impact of robotics and AI in hospitality and tourism. It offers scholars an overview of the current knowledge in the field on this rapidly emerging and evolving topic.
From Offline to Online Learning: A Qualitative Study of Challenges and Opportunities as a Response to the COVID-19 Pandemic in the UAE Higher Education Context

This paper aims to explore the challenges, opportunities and implications of COVID-19 pandemic in the UAE higher education context. It uses the qualitative study approach and websites methodologies to enrich the aims and results of the study. It focuses on the influence of COVID-19 pandemic on higher education institutions and their progressions respectively. As higher education institutions not only provide knowledge but also develop interpersonal skills and help mold an individual in all aspects to face real-world challenges. Institutions should, therefore, get back to traditional teaching methods as soon as possible by incorporating the benefits of virtual learning into traditional learning methods, as virtual learning cannot replace traditional methods. Consequently, over some time as the crisis continues globally, the higher educational institutions worldwide can take into development better modes of communication and continue to provide various forms of delivering their services, and as the COVID-19 with progress in medical fields can fade to a disease of history we can improve what was once traditional learning with the online learning experience from this time. As the COVID-19 crisis continues globally, the higher education institutions can develop better modes of communication and forms of providing their services and as the COVID 19 fades to a disease of history, so it can improve what was known as traditional learning into the online learning over the time. After substantiating the multiple benefits and consequences of having virtual learning in the higher education system, it is clear that there is a lot of room for improvement over the time. This study contributes to the COVID 19 concept and higher education tools as it introduces you to the various impacts of online educational tools used by higher education universities in the UAE during the spread of COVID-19. It can be considered as a pioneer study in UAE in exploration the relationship between COVID-19 and higher education field.
BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction

Background subtraction (BGS) is a fundamental video processing task which is a key component of many applications. Deep learning-based supervised algorithms achieve very good performance in BGS, however, most of these algorithms are optimized for either a specific video or a group of videos, and their performance decreases dramatically when applied to unseen videos. Recently, several papers addressed this problem and proposed video-agnostic supervised BGS algorithms. However, nearly all of the data augmentations used in these algorithms are limited to the spatial domain and do not account for temporal variations that naturally occur in video data. In this work, we introduce spatio-temporal data augmentations and apply them to one of the leading video-agnostic BGS algorithms, BSUV-Net. We also introduce a new cross-validation training and evaluation strategy for the CDNet-2014 dataset that makes it possible to fairly and easily compare the performance of various video-agnostic supervised BGS algorithms. Our new model trained using the proposed data augmentations, named BSUV-Net 2.0, significantly outperforms state-of-the-art algorithms evaluated on unseen videos of CDNet-2014. We also evaluate the cross-dataset generalization capacity of BSUV-Net 2.0 by training it solely on CDNet-2014 videos and evaluating its performance on LASIESTA dataset. Overall, BSUV-Net 2.0 provides a ~5% improvement in the F-score over state-of-the-art methods on unseen videos of CDNet-2014 and LASIESTA datasets. Furthermore, we develop a real-time variant of our model, that we call Fast BSUV-Net 2.0, whose performance is close to the state of the art.
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect the impact of user historical sessions while modeling user preference, which often leads to non-personalized recommendation. Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions. To address these issues, we propose a novel Heterogeneous Global Graph Neural Networks (HG-GNN) to exploit the item transitions over all sessions in a subtle manner for better inferring user preference from the current and historical sessions. To effectively exploit the item transitions over all sessions from users, we propose a novel heterogeneous global graph that contains item transitions of sessions, user-item interactions and global co-occurrence items. Moreover, to capture user preference from sessions comprehensively, we propose to learn two levels of user representations from the global graph via two graph augmented preference encoders. Specifically, we design a novel heterogeneous graph neural network (HGNN) on the heterogeneous global graph to learn the long-term user preference and item representations with rich semantics. Based on the HGNN, we propose the Current Preference Encoder and the Historical Preference Encoder to capture the different levels of user preference from the current and historical sessions, respectively. To achieve personalized recommendation, we integrate the representations of the user current preference and historical interests to generate the final user preference representation. Extensive experimental results on three real-world datasets show that our model outperforms other state-of-the-art methods.
Artificial intelligence in early childhood education: A scoping review

Artificial intelligence (AI) tools are increasingly being used in the field of early childhood education (ECE) to enhance learning and development among young children. Previous proof-of-concept studies have demonstrated that AI can effectively improve teaching and learning in ECE; however, there is a scarcity of knowledge about how these studies are conducted and how AI is used across these studies. We conducted this scoping review to evaluate, synthesize and display the latest literature on AI in ECE. This review analyzed 17 eligible studies conducted in different countries from 1995 to 2021. Although few studies on this critical issue have been found, the existing references provide up-to-date insights into different aspects (knowledge, tools, activities, and impacts) of AI for children. Most studies have shown that AI has significantly improved children's concepts regarding AI, machine learning, computer science, and robotics and other skills such as creativity, emotion control, collaborative inquiry, literacy skills, and computational thinking. Future directions are also discussed for researching AI in ECE.
The Market for Fake Reviews

This paper studies the market for fake product reviews on Amazon.com.
Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge

Chinese relation extraction is conducted using neural networks with either character-based or word-based inputs, and most existing methods typically suffer from segmentation errors and ambiguity of polysemy.To address the issues, we propose a multi-grained lattice framework (MG lattice) for Chinese relation extraction to take advantage of multi-grained language information and external linguistic knowledge.In this framework, (1) we incorporate word-level information into character sequence inputs so that segmentation errors can be avoided.(2) We also model multiple senses of polysemous words with the help of external linguistic knowledge, so as to alleviate polysemy ambiguity.Experiments on three realworld datasets in distinct domains show consistent and significant superiority and robustness of our model, as compared with other baselines.The source code of this paper can be obtained from https://github.com/thunlp/Chinese_NRE.
Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff

Complex data visualization design projects often entail collaboration between people with different visualization-related skills. For example, many teams include both designers who create new visualization designs and developers who implement the resulting visualization software. We identify gaps between data characterization tools, visualization design tools, and development platforms that pose challenges for designer-developer teams working to create new data visualizations. While it is common for commercial interaction design tools to support collaboration between designers and developers, creating data visualizations poses several unique challenges that are not supported by current tools. In particular, visualization designers must characterize and build an understanding of the underlying data, then specify layouts, data encodings, and other data-driven parameters that will be robust across many different data values. In larger teams, designers must also clearly communicate these mappings and their dependencies to developers, clients, and other collaborators. We report observations and reflections from five large multidisciplinary visualization design projects and highlight six data-specific visualization challenges for design specification and handoff. These challenges include adapting to changing data, anticipating edge cases in data, understanding technical challenges, articulating data-dependent interactions, communicating data mappings, and preserving the integrity of data mappings across iterations. Based on these observations, we identify opportunities for future tools for prototyping, testing, and communicating data-driven designs, which might contribute to more successful and collaborative data visualization design.
dMazeRunner

Dataflow accelerators feature simplicity, programmability, and energy-efficiency and are visualized as a promising architecture for accelerating perfectly nested loops that dominate several important applications, including image and media processing and deep learning. Although numerous accelerator designs are being proposed, how to discover the most efficient way to execute the perfectly nested loop of an application onto computational and memory resources of a given dataflow accelerator ( execution method ) remains an essential and yet unsolved challenge. In this paper, we propose dMazeRunner -- to efficiently and accurately explore the vast space of the different ways to spatiotemporally execute a perfectly nested loop on dataflow accelerators (execution methods). The novelty of dMazeRunner framework is in: i) a holistic representation of the loop nests, that can succinctly capture the various execution methods, ii) accurate energy and performance models that explicitly capture the computation and communication patterns, data movement, and data buffering of the different execution methods, and iii) drastic pruning of the vast search space by discarding invalid solutions and the solutions that lead to the same cost. Our experiments on various convolution layers (perfectly nested loops) of popular deep learning applications demonstrate that the solutions discovered by dMazeRunner are on average 9.16× better in Energy-Delay-Product (EDP) and 5.83× better in execution time, as compared to prior approaches. With additional pruning heuristics, dMazeRunner reduces the search time from days to seconds with a mere 2.56% increase in EDP, as compared to the optimal solution.
Stochastic Gradient Descent for Nonconvex Learning Without Bounded Gradient Assumptions

Stochastic gradient descent (SGD) is a popular and efficient method with wide applications in training deep neural nets and other nonconvex models. While the behavior of SGD is well understood in the convex learning setting, the existing theoretical results for SGD applied to nonconvex objective functions are far from mature. For example, existing results require to impose a nontrivial assumption on the uniform boundedness of gradients for all iterates encountered in the learning process, which is hard to verify in practical implementations. In this article, we establish a rigorous theoretical foundation for SGD in nonconvex learning by showing that this boundedness assumption can be removed without affecting convergence rates, and relaxing the standard smoothness assumption to Hölder continuity of gradients. In particular, we establish sufficient conditions for almost sure convergence as well as optimal convergence rates for SGD applied to both general nonconvex and gradient-dominated objective functions. A linear convergence is further derived in the case with zero variances.
An Image Encryption Algorithm Based on Random Walk and Hyperchaotic Systems

In this paper, we propose an image encryption algorithm based on random walk and two hyperchaotic systems. The random walk method is adopted to scramble the position of pixels within a block. Furthermore, the permutation operation between blocks is presented to enhance the scramble effect. Thus, high correlation among pixels of original image is broken by permutation. Moreover, the chosen plaintext attack is used to test the anti-attack ability of the proposed algorithm. By analyzing experimental results and comparing with other image encryption algorithms, we show that the proposed algorithm has better performance and higher security.
High-Contrast <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>Z</mml:mi><mml:mi>Z</mml:mi></mml:mrow></mml:math> Interaction Using Superconducting Qubits with Opposite-Sign Anharmonicity

For building a scalable quantum processor with superconducting qubits, ZZ interaction is of great concern because its residual has a crucial impact to two-qubit gate fidelity. Two-qubit gates with fidelity meeting the criterion of fault-tolerant quantum computation have been demonstrated using ZZ interaction. However, as the performance of quantum processors improves, the residual static ZZ can become a performance-limiting factor for quantum gate operation and quantum error correction. Here, we introduce a superconducting architecture using qubits with opposite-sign anharmonicity, a transmon qubit, and a C-shunt flux qubit, to address this issue. We theoretically demonstrate that by coupling the two types of qubits, the high-contrast ZZ interaction can be realized. Thus, we can control the interaction with a high on-off ratio to implement two-qubit controlled-Z gates, or suppress it during two-qubit gate operation using XY interaction (e.g., an iSWAP gate). The proposed architecture can also be scaled up to multiqubit cases. In a fixed coupled system, ZZ crosstalk related to neighboring spectator qubits could also be heavily suppressed.
A Dynamic Fusion Pathfinding Algorithm Using Delaunay Triangulation and Improved A-Star for Mobile Robots

Although many studies exist on mobile robot path planning, the disadvantages of complex algorithms and many path nodes in logistics warehouses and manufacturing workshops are obvious, mainly due to the inconsistency of map environment construction and pathfinding strategies. In this study, to improve the efficiency of mobile robot path planning, the Delaunay triangulation algorithm was used to process complex obstacles and generate Voronoi points as pathfinding priority nodes. The concept of the grid was used to extract obstacle edges to provide obstacle avoidance strategies for robot pathfinding. Subsequently, the search for priority and regular path nodes used the improved A-star (A*) algorithm. The dynamic fusion pathfinding algorithm (DFPA), based on Delaunay triangulation and improved A*, was designed, which realizes the path planning of mobile robots. MATLAB 2016a was used as the simulation software, to firstly verify the correctness of the DFPA, and then to compare the algorithm with other methods. The results show that under the experimental environment with the same start point, goal point, and number of obstacles, the map construction method and pathfinding strategy proposed in this paper reduce the planned path length of the mobile robot, the number of path nodes, and the cost of overall turn consumption, and increase the success rate of obtaining a path. The new dynamic map construction method and pathfinding strategy have important reference significance for processing chaotic maps, promoting intelligent navigation, and site selection planning.
Data Shapley: Equitable Valuation of Data for Machine Learning

As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual data. In this work, we develop a principled framework to address data valuation in the context of supervised machine learning. Given a learning algorithm trained on $n$ data points to produce a predictor, we propose data Shapley as a metric to quantify the value of each training datum to the predictor performance. Data Shapley value uniquely satisfies several natural properties of equitable data valuation. We develop Monte Carlo and gradient-based methods to efficiently estimate data Shapley values in practical settings where complex learning algorithms, including neural networks, are trained on large datasets. In addition to being equitable, extensive experiments across biomedical, image and synthetic data demonstrate that data Shapley has several other benefits: 1) it is more powerful than the popular leave-one-out or leverage score in providing insight on what data is more valuable for a given learning task; 2) low Shapley value data effectively capture outliers and corruptions; 3) high Shapley value data inform what type of new data to acquire to improve the predictor.