Reviewerly/exHarmony huggingface dataset:
The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations

There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities—foreign-born, immigrant, refugee, and culturally marginalized—are at increased risk of poor health outcomes. Artificial intelligence (AI) is actively being researched as a means of improving diabetes management and care; however, several factors may predispose AI to ethnoracial bias. To better understand whether diabetes AI interventions are being designed in an ethnoracially equitable manner, we conducted a secondary analysis of 141 articles included in a 2018 review by Contreras and Vehi entitled “Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.” Two members of our research team independently reviewed each article and selected those reporting ethnoracial data for further analysis. Only 10 articles (7.1%) were ultimately selected for secondary analysis in our case study. Of the 131 excluded articles, 118 (90.1%) failed to mention participants’ ethnic or racial backgrounds. The included articles reported ethnoracial data under various categories, including race (n=6), ethnicity (n=2), race/ethnicity (n=3), and percentage of Caucasian participants (n=1). Among articles specifically reporting race, the average distribution was 69.5% White, 17.1% Black, and 3.7% Asian. Only 2 articles reported inclusion of Native American participants. Given the clear ethnic and racial differences in diabetes biomarkers, prevalence, and outcomes, the inclusion of ethnoracial training data is likely to improve the accuracy of predictive models. Such considerations are imperative in AI-based tools, which are predisposed to negative biases due to their black-box nature and proneness to distributional shift. Based on our findings, we propose a short questionnaire to assess ethnoracial equity in research describing AI-based diabetes interventions. At this unprecedented time in history, AI can either mitigate or exacerbate disparities in health care. Future accounts of the infancy of diabetes AI must reflect our early and decisive action to confront ethnoracial inequities before they are coded into our systems and perpetuate the very biases we aim to eliminate. If we take deliberate and meaningful steps now toward training our algorithms to be ethnoracially inclusive, we can architect innovations in diabetes care that are bound by the diverse fabric of our society.
Acceptance in incomplete argumentation frameworks

Abstract argumentation frameworks (AFs), originally proposed by Dung, constitute a central formal model for the study of computational aspects of argumentation in AI. Credulous and skeptical acceptance of arguments in a given AF are well-studied problems both in terms of theoretical analysis—especially computational complexity—and the development of practical decision procedures for the problems. However, AFs make the assumption that all attacks between arguments are certain (i.e., present attacks are known to exist, and missing attacks are known to not exist), which can in various settings be a restrictive assumption. A generalization of AFs to incomplete AFs was recently proposed as a formalism that allows the representation of both uncertain attacks and uncertain arguments in AFs. In this article, we explore the impact of allowing for modeling such uncertainties in AFs on the computational complexity of natural generalizations of acceptance problems to incomplete AFs under various central AF semantics. Complementing the complexity-theoretic analysis, we also develop the first practical decision procedures for all of the NP-hard variants of acceptance in incomplete AFs. In terms of complexity analysis, we establish a full complexity landscape, showing that depending on the variant of acceptance and property/semantics, the complexity of acceptance in incomplete AFs ranges from polynomial-time decidable to completeness for Σ3p. In terms of algorithms, we show through an extensive empirical evaluation that an implementation of the proposed decision procedures, based on boolean satisfiability (SAT) solving, is effective in deciding variants of acceptance under uncertainties. We also establish conditions for what type of atomic changes are guaranteed to be redundant from the perspective of preserving extensions of completions of incomplete AFs, and show that the results allow for considerably improving the empirical efficiency of the proposed SAT-based counterexample-guided abstraction refinement algorithms for acceptance in incomplete AFs for problem variants with complexity beyond NP.
Decodable quantum LDPC codes beyond the square root distance barrier using high dimensional expanders

Constructing quantum LDPC codes with a minimum distance that grows faster than a square root of the length has been a major challenge of the field. With this challenge in mind, we investigate constructions that come from high-dimensional expanders, in particular Ramanujan complexes. These naturally give rise to very unbalanced quantum error correcting codes that have a large X-distance but a much smaller Z-distance. However, together with a classical expander LDPC code and a tensoring method that generalises a construction of Hastings and also the Tillich-Zemor construction of quantum codes, we obtain quantum LDPC codes whose minimum distance exceeds the square root of the code length and whose dimension comes close to a square root of the code length. When the ingredient is a 3-dimensional Ramanujan complex, we show that its 2-systole behaves like a square of the log of the complex size, which results in an overall quantum code of minimum distance n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> logn, and sets a new record for quantum LDPC codes. When we use a 2-dimensional Ramanujan complex, or the 2-skeleton of a 3-dimensional Ramanujan complex, we obtain a quantum LDPC code of minimum distance n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> n. We then exploit the expansion properties of the complex to devise the first polynomial time algorithm that decodes above the square root barrier for quantum LDPC codes.
DP-cryptography

Synthesizing the emerging directions of research at the intersection of differential privacy and cryptography.
Evaluation and analysis of Data Management Plan tools: A parametric approach

This paper explores the openly available DMP tools and forms a comparative analysis aimed at assisting researchers and data managers to formulate effective data management plans. Based on a literature review 14 DMP tools were selected and were evaluated using 45 selected parameters. The study enlists and enunciates the features of DMP tools, spots several gaps in DMP practices, and provides a few recommendations that can improve the existing tools and DMP practices. Compared to other related works, present work sheds extra light on percentage coverage of parameters by each tool and percentage coverage of tools by each parameter. It is identified that selected tools cover 50%–84% parameters, whereas 78% parameters are covered by half the selected DMP tools. Moreover, 28% of the tools cover 60% of the DMP assisting parameters. Additionally, co-occurrence of parameters and correlation among the tools are illustrated using matrices. It was found that co-occurrence of data description/summary/collection, documentation and metadata, findability, and accessibility parameters are relatively higher and all the selected tools are positively correlated to each other. The study is impactful for the researchers, librarians, data managers, and funding agencies for selecting an appropriate DMP tool as per their requirement.
Know what? How digital technologies undermine learning and remembering

Are digital technologies affecting how we learn, what we know, and how we remember? We examine three aspects of these questions: the impact of the internet on cognitive skills, the way GPS is reshaping abilities to navigate physical space, and differences between digital and print reading. The goal of the analysis is to understand potential effects of digital technologies on educational practices and goals. We begin by reviewing research demonstrating the growing tendency to depend on digital devices and the internet to do our remembering for us. Regarding GPS, there is mounting evidence that reliance on GPS devices reduces our ability to navigate physical space on our own. Worryingly, studies indicate correlations between decreased navigational skills and dementia. Finally, the article documents how digital reading tends to lead to a more superficial approach to text than traditional print. Technologies have consequences, including in education. If digital technologies lead to diminished memory, lower our ability to find our way in physical space, and foster shallower reading, we need to consider the consequences for both formal and informal learning.
Customizing SVM as a base learner with AdaBoost ensemble to learn from multi-class problems: A hybrid approach AdaBoost-MSVM

Learning from a multi-class problem has not been an easy task for most of the classifiers, because of multiple issues. In the complex multi-class scenarios, samples of different classes overlap with each other by sharing attribute, and hence the visibility of least represented samples decrease even more. Learning from imbalanced data studied extensively in the research community, however, the overlapping issues and the co-occurrence impact of overlapping with data imbalance have received comparatively less attention, even though their joint impact is more thoughtful on classifiers’ performance. In this paper, we introduce a modified SVM, MSVM to use as a base classifier with the AdaBoost ensemble classifier (MSVM-AdB) to enhance the learning capability of the ensemble classifier. To implement the proposed technique, we divide the multi-class dataset into overlapping and non-overlapping region. The overlapping region is further filter into the Critical and less Critical region depending upon their sample contribution in the overlapped region. The MSVM is designed to map the overlapped samples in a higher dimension by modifying the kernel mapping function of the standard SVM by using the mean distance of the Critical region samples. To highlight the learning enhancement of the MSVM-AdB, we use 20 real datasets with varying imbalance ratio and the overlapping degree to compare the significance of the AdaBoost-MSVM with the standard SVM, and AdaBoost with standard base classifiers. Experimental results show the superiority of the MSVM-AdB on a collection of benchmark datasets to its standard counterpart classifiers.
Early Warning for Great Earthquakes From Characterization of Crustal Deformation Patterns With Deep Learning

Abstract Although infrequent, large (Mw7.5+) earthquakes can be extremely damaging and occur on subduction and intraplate faults worldwide. Earthquake early warning (EEW) systems aim to provide advanced warning before strong shaking and tsunami onsets. These systems estimate earthquake magnitude using the early metrics of waveforms, relying on empirical scaling relationships of abundant past events. However, both the rarity and complexity of great events make it challenging to characterize them, and EEW algorithms often underpredict magnitude and the resulting hazards. Here, we propose a model, M‐LARGE, that leverages deep learning to characterize crustal deformation patterns of large earthquakes for a specific region in real‐time. We demonstrate the algorithm in the Chilean Subduction Zone by training it with more than six million different simulated rupture scenarios recorded on the Chilean GNSS network. M‐LARGE performs reliable magnitude estimation on the testing data set with an accuracy of 99%. Furthermore, the model successfully predicts the magnitude of five real Chilean earthquakes that occurred in the last 11 years. These events were damaging, large enough to be recorded by the modern high rate global navigation satellite system instrument, and provide valuable ground truth. M‐LARGE tracks the evolution of the source process and can make faster and more accurate magnitude estimation, significantly outperforming other similar EEW algorithms. This is the first demonstration of our approach. Future work toward generalization is outstanding and will include the addition of more training and testing data, interfacing with existing EEW methods, and applying the method to different tectonic settings to explore performance in these regions.
Quantum Approximation for Wireless Scheduling

This paper proposes an application algorithm based on a quantum approximate optimization algorithm (QAOA) for wireless scheduling problems. QAOA is one of the promising hybrid quantum-classical algorithms to solve combinatorial optimization problems and it provides great approximate solutions to non-deterministic polynomial-time (NP) hard problems. QAOA maps the given problem into Hilbert space, and then it generates the Hamiltonian for the given objective and constraint. Then, QAOA finds proper parameters from the classical optimization loop in order to optimize the expectation value of the generated Hamiltonian. Based on the parameters, the optimal solution to the given problem can be obtained from the optimum of the expectation value of the Hamiltonian. Inspired by QAOA, a quantum approximate optimization for scheduling (QAOS) algorithm is proposed. The proposed QAOS designs the Hamiltonian of the wireless scheduling problem which is formulated by the maximum weight independent set (MWIS). The designed Hamiltonian is converted into a unitary operator and implemented as a quantum gate operation. After that, the iterative QAOS sequence solves the wireless scheduling problem. The novelty of QAOS is verified with simulation results implemented via Cirq and TensorFlow-Quantum.
SA-LuT-Nets: Learning Sample-Adaptive Intensity Lookup Tables for Brain Tumor Segmentation

In clinics, the information about the appearance and location of brain tumors is essential to assist doctors in diagnosis and treatment. Automatic brain tumor segmentation on the images acquired by magnetic resonance imaging (MRI) is a common way to attain this information. However, MR images are not quantitative and can exhibit significant variation in signal depending on a range of factors, which increases the difficulty of training an automatic segmentation network and applying it to new MR images. To deal with this issue, this paper proposes to learn a sample-adaptive intensity lookup table (LuT) that dynamically transforms the intensity contrast of each input MR image to adapt to the following segmentation task. Specifically, the proposed deep SA-LuT-Net framework consists of a LuT module and a segmentation module, trained in an end-to-end manner: the LuT module learns a sample-specific nonlinear intensity mapping function through communication with the segmentation module, aiming at improving the final segmentation performance. In order to make the LuT learning sample-adaptive, we parameterize the intensity mapping function by exploring two families of non-linear functions (i.e., piece-wise linear and power functions) and predict the function parameters for each given sample. These sample-specific parameters make the intensity mapping adaptive to samples. We develop our SA-LuT-Nets separately based on two backbone networks for segmentation, i.e., DMFNet and the modified 3D Unet, and validate them on BRATS2018 and BRATS2019 datasets for brain tumor segmentation. Our experimental results clearly demonstrate the superior performance of the proposed SA-LuT-Nets using either single or multiple MR modalities. It not only significantly improves the two baselines (DMFNet and the modified 3D Unet), but also wins a set of state-of-the-art segmentation methods. Moreover, we show that, the LuTs learnt using one segmentation model could also be applied to improving the performance of another segmentation model, indicating the general segmentation information captured by LuTs.
Studying Robustness of Semantic Segmentation Under Domain Shift in Cardiac MRI

Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in many heart related diseases. Recently, deep neural networks have demonstrated successful automatic segmentation, thus alleviating the burden of time-consuming manual contouring of cardiac structures. Moreover, frameworks such as nnU-Net provide entirely auto- matic model configuration to unseen datasets enabling out-of-the-box application even by non-experts. However, current studies commonly neglect the clinically realistic scenario, in which a trained network is applied to data from a different domain such as deviating scanners or imaging protocols. This potentially leads to unexpected performance drops of deep learning models in real life applications. In this work, we systematically study challenges and opportunities of domain transfer across images from multiple clinical centres and scanner vendors. In order to maintain out-of-the-box usability, we build upon a fixed U-Net architecture configured by the nnU-net framework to investigate various data augmentation techniques and batch normalization layers as an easy-to-customize pipeline component and provide general guidelines on how to improve domain generalizability abilities in existing deep learning methods. Our proposed method ranked first at the Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms).
A Data Mining Based System For Transaction Fraud Detection

With the deepening of world trade informationization degree, transaction fraud has been endangering the security of world finance and commerce. The frequency and scale of transaction fraud are expanding day by day, which makes the vast number of users and financial practitioners suffer huge economic losses. With the increasing maturity of data mining and machine learning in the field of computer science, the detection of transaction fraud gradually finds a practical solution. This paper adopts a transaction fraud detection system based on random forest and manual detection. The experimental results of IEEE CIS fraud dataset show that the method of this model is better than the benchmark model, such as logistic regression, support vector machine. Finally, the accuracy of our model reached 96.8%, and the AUC ROC score was 92.5%.
Matching-space Stereo Networks for Cross-domain Generalization

End-to-end deep networks represent the state of the art for stereo matching. While excelling on images framing environments similar to the training set, major drops in accuracy occur in unseen domains (e.g., when moving from synthetic to real scenes). In this paper we introduce a novel family of architectures, namely Matching-Space Networks (MS-Nets), with improved generalization properties. By replacing learning-based feature extraction from image RGB values with matching functions and confidence measures from conventional wisdom, we move the learning process from the color space to the Matching Space, avoiding over-specialization to domain specific features. Extensive experimental results on four real datasets highlight that our proposal leads to superior generalization to unseen environments over conventional deep architectures, keeping accuracy on the source domain almost unaltered. Our code is available at https://qithub.com/ccj5351/MS-Nets.
NSDH: A Nonlinear Supervised Discrete Hashing framework for large-scale cross-modal retrieval

Hashing technology has been widely used in approximate nearest neighbor search algorithms for large-scale cross-modal retrieval due to its significantly reduced storage and high-speed search capabilities. However, most existing supervised cross-modal hashing methods either mainly rely on binary pairwise similarity and fail to exploit the rich semantic information contained in the label matrix, or the use of single linear projections which suffer from limited information completeness. In this paper, we propose a novel method, named Nonlinear Supervised Discrete Hashing (NSDH). Specifically, NSDH consists of two components, (1) a semantic enhancement descriptor consisting of multiple linear projections that is used to extract comprehensive latent representations of heterogeneous multimedia data, which aligns the original heterogeneous features and integrates the rich semantic label matrix; (2) a fast discrete optimization module used to learn discriminative compact hash codes, which preserves the similarity information using an inner product between the real-valued embeddings of the output of the semantic enhancement descriptors. Therefore, NSDH leverages both the label matrix and similarity information in order to enhance the semantic information of the learned hash codes. In this way, the representation learning capability of the output layer of semantic enhancement descriptors can be greatly enhanced and as a result the learned hash codes are more discriminative. In addition, we present a fast discrete optimization algorithm to efficiently learn the binary hash codes. Results from our experiments on two benchmark datasets highlight the superiority of NSDH in comparison to many state-of-the-art cross-modal hashing methods.
Federated Learning on the Road Autonomous Controller Design for Connected and Autonomous Vehicles

The deployment of future intelligent transportation systems is contingent upon seamless and reliable operation of connected and autonomous vehicles (CAVs). One key challenge in developing CAVs is the design of an autonomous controller that can accurately execute near real-time control decisions, such as a quick acceleration when merging to a highway and frequent speed changes in a stop-and-go traffic. However, the use of conventional feedback controllers or traditional learning-based controllers, solely trained by each CAV’s local data, cannot guarantee a robust controller performance over a wide range of road conditions and traffic dynamics. In this paper, a new federated learning (FL) framework enabled by large-scale wireless connectivity is proposed for designing the autonomous controller of CAVs. In this framework, the learning models used by the controllers are collaboratively trained among a group of CAVs. To capture the varying CAV participation in the FL training process and the diverse local data quality among CAVs, a novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, as well as the unbalanced and non-independent and identically distributed data across CAVs. A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal autonomous controller. In particular, the impacts of varying CAV participation in the FL process and diverse CAV data quality on the convergence of the proposed DFP algorithm are explicitly analyzed. Leveraging this analysis, an incentive mechanism based on contract theory is designed to improve the FL convergence speed. Simulation results using real vehicular data traces show that the proposed DFP-based controller can accurately track the target CAV speed over time and under different traffic scenarios. Moreover, the results show that the proposed DFP algorithm has a much faster convergence compared to popular FL algorithms such as federated averaging (FedAvg) and federated proximal (FedProx). The results also validate the feasibility of the contract-theoretic incentive mechanism and show that the proposed mechanism can improve the convergence speed of the DFP algorithm by 40% compared to the baselines.