Reviewerly/exHarmony huggingface dataset:
Cycle and Self-Supervised Consistency Training for Adapting Semantic Segmentation of Aerial Images

Semantic segmentation is a critical problem for many remote sensing (RS) image applications. Benefiting from large-scale pixel-level labeled data and the continuous evolution of deep neural network architectures, the performance of semantic segmentation approaches has been constantly improved. However, deploying a well-trained model on unseen and diverse testing environments remains a major challenge: a large gap between data distributions in train and test domains results in severe performance loss, while manual dense labeling is costly and not scalable. To this end, we proposed an unsupervised domain adaptation framework for RS image semantic segmentation that is both practical and effective. The framework is supported by the consistency principle, including the cycle consistency in the input space and self-supervised consistency in the training stage. Specifically, we introduce cycle-consistent generative adversarial networks to reduce the discrepancy between source and target distributions by translating one into the other. The translated source data then drive a pipeline of supervised semantic segmentation model training. We enforce consistency of model predictions across target image transformations in order to provide self-supervision for the unlabeled target data. Experiments and extensive ablation studies demonstrate the effectiveness of the proposed approach on two challenging benchmarks, on which we achieve up to 9.95% and 7.53% improvements in accuracy over the state-of-the-art methods, respectively.
Fairness-related performance and explainability effects in deep learning models for brain image analysis

Purpose: Explainability and fairness are two key factors for the effective and ethical clinical implementation of deep learning-based machine learning models in healthcare settings. However, there has been limited work on investigating how unfair performance manifests in explainable artificial intelligence (XAI) methods, and how XAI can be used to investigate potential reasons for unfairness. Thus, the aim of this work was to analyze the effects of previously established sociodemographic-related confounders on classifier performance and explainability methods.Approach: A convolutional neural network (CNN) was trained to predict biological sex from T1-weighted brain MRI datasets of 4547 9- to 10-year-old adolescents from the Adolescent Brain Cognitive Development study. Performance disparities of the trained CNN between White and Black subjects were analyzed and saliency maps were generated for each subgroup at the intersection of sex and race.Results: The classification model demonstrated a significant difference in the percentage of correctly classified White male (90.3 % ± 1.7 % ) and Black male (81.1 % ± 4.5 % ) children. Conversely, slightly higher performance was found for Black female (89.3 % ± 4.8 % ) compared with White female (86.5 % ± 2.0 % ) children. Saliency maps showed subgroup-specific differences, corresponding to brain regions previously associated with pubertal development. In line with this finding, average pubertal development scores of subjects used in this study were significantly different between Black and White females (p < 0.001) and males (p < 0.001).Conclusions: We demonstrate that a CNN with significantly different sex classification performance between Black and White adolescents can identify different important brain regions when comparing subgroup saliency maps. Importance scores vary substantially between subgroups within brain structures associated with pubertal development, a race-associated confounder for predicting sex. We illustrate that unfair models can produce different XAI results between subgroups and that these results may explain potential reasons for biased performance.
Artificial Intelligence Algorithms for Treatment of Diabetes

Artificial intelligence (AI) algorithms can provide actionable insights for clinical decision-making and managing chronic diseases. The treatment and management of complex chronic diseases, such as diabetes, stands to benefit from novel AI algorithms analyzing the frequent real-time streaming data and the occasional medical diagnostics and laboratory test results reported in electronic health records (EHR). Novel algorithms are needed to develop trustworthy, responsible, reliable, and robust AI techniques that can handle the imperfect and imbalanced data of EHRs and inconsistencies or discrepancies with free-living self-reported information. The challenges and applications of AI for two problems in the healthcare domain were explored in this work. First, we introduced novel AI algorithms for EHRs designed to be fair and unbiased while accommodating privacy concerns in predicting treatments and outcomes. Then, we studied the innovative approach of using machine learning to improve automated insulin delivery systems through analyzing real-time information from wearable devices and historical data to identify informative trends and patterns in free-living data. Application examples in the treatment of diabetes demonstrate the benefits of AI tools for medical and health informatics.
Focus! Rating XAI Methods and Finding Biases

AI explainability improves the transparency and trustworthiness of models. However, in the domain of images, where deep learning has succeeded the most, explainability is still poorly assessed. In the field of image recognition many feature attribution methods have been proposed with the purpose of explaining a model’s behavior using visual cues. However, no metrics have been established so far to assess and select these methods objectively. In this paper we propose a consistent evaluation score for feature attribution methods—the Focus—designed to quantify their coherency to the task. While most previous work adds out-of-distribution noise to samples, we introduce a methodology to add noise from within the distribution. This is done through mosaics of instances from different classes, and the explanations these generate. On those, we compute a visual pseudo-precision metric, Focus. First, we show the robustness of the approach through a set of randomization experiments. Then we use Focus to compare six popular explainability techniques across several CNN architectures and classification datasets. Our results find some methods to be consistently reliable (LRP, GradCAM), while others produce class-agnostic explanations (SmoothGrad, IG). Finally we introduce another application of Focus, using it for the identification and characterization of biases found in models. This empowers bias-management tools, in another small step towards trustworthy AI.
Improved Traffic Sign Detection Algorithm Based on Faster R-CNN

The traffic sign detection algorithm based on Faster Region-Based Convolutional Neural Network (R-CNN) has been applied to various intelligent-vehicles driving scenarios. However, the model of the current detection algorithm has certain shortcomings, which include the influence of weather and light, the detection of distance traffic signs, and the detection of similar traffic signs. To solve these problems, this paper proposes an improved traffic sign detection method based on Faster R-CNN. First, we propose a fusion method that fuses the feature pyramid into the Faster R-CNN algorithm. This fusion method can extract object features with precision and decrease the influence of weather and light. Second, a deformable convolution (DCN) which can train the algorithm to identify traffic signs with precision and make similar signs more distinguishable, and in particular make it work better with distorted images, is added to the backbone network. Lastly, we apply ROI align to replace the ROI pooling, which can avoid the distant traffic sign detail loss caused by pooling and increase the detection precision of distant traffic signs. The experimental results on both the TT100k dataset and real intelligent vehicle tests demonstrate that the algorithm is superior to the original Faster R-CNN algorithm and four other state-of-the-art methods in traffic sign detection, specifically in small-target traffic sign detection and low-intensity environments such as sunset time and rainy days. Therefore, the proposed method is helpful to improve the traffic sign detection performance in extreme environments (low-light intensity or rainy weather).
Teaching a Lean masterclass in the metaverse

Purpose This paper reports exploratory findings about teaching in the metaverse using a virtual reality (VR) platform and head-mounted displays. This paper addresses three research questions: Is the metaverse an appropriate platform for teaching Lean? What are the benefits and problems of this approach over traditional classroom methods? What are the future implications for teaching Lean in the VR-enabled metaverse? Design/methodology/approach The research presented in this paper is exploratory. A critical realism approach is adopted using action research through the preparation, delivery and review of the first four masterclasses entitled Leading Intelligent Lean offered by Gemba (formerly The Leadership Network). The data collection methods included a pre-workshop questionnaire, participant observation, focus groups and end-of-course surveys. Findings It was found that Lean can be taught successfully in the metaverse. There are many related benefits, particularly in terms of the high levels of immersion and the “wow” factor. Remaining technological limitations are reduced by continually improving the metaverse platform and the course content and delivery. The authors conclude that the metaverse is a useful addition to teaching Lean – and is especially promising when used in a blended teaching approach. Originality/value To the best of the authors’ knowledge, this is the first report of teaching Lean in a VR-enabled metaverse and one of the first of teaching and learning in the metaverse regardless of topic. The respective problems and benefits of this approach versus teaching in a traditional classroom setting are discussed.
A Performance Evaluation of the Alpha-Beta (α-β) Filter Algorithm with Different Learning Models: DBN, DELM, and SVM

In this paper, we present a new Multiple learning to prediction algorithm model that used three different combinations of machine-learning methods to improve the accuracy of the α-β filter algorithm. The parameters of α and β were tuned in dynamic conditions instead of static conditions. The proposed system was designed to use the deep belief network (DBN), the deep extreme learning machine (DELM), and the SVM as three different learning algorithms. Then these learned parameters were trained by the machine-learning algorithms tuned to the α-β filter algorithm as a prediction module, and they gave the final predicted results. The MAE and RMSE were used to evaluate the performance of the proposed α-β filter with different learning algorithms. Each algorithm recorded different best-case accuracy results; for the DBN, we achieved 3.60 and 2.61; for the DELM, we obtained the best-case result of 3.90 and 2.81; and finally, for the SVM, 4.0 and 3.21 were attained in terms of the RMSE and MAE, respectively, as compared to 5.21 and 3.95. When assessed in comparison with the typical alpha–beta filter algorithm, the proposed system provided results with better accuracy.
Lightweight Two-Factor-Based User Authentication Protocol for IoT-Enabled Healthcare Ecosystem in Quantum Computing

The healthcare ecosystem is migrating from legacy systems to the Internet of Things (IoT), resulting in a digital environment. This transformation has increased importance on demanding both secure and usable user authentication methods. Recently, a post-quantum fuzzy commitment scheme (PQFC) has been constructed as a reliable and efficient method of biometric template protection. This paper presents a new two-factor-based user authentication protocol for the IoT-enabled healthcare ecosystem in post-quantum computing environments using the PQFC scheme. The proposed protocol is proved to be secure using random oracle model. Furthermore, the functionality and security of the proposed protocol are analyzed, showing that memoryless-effortless, user anonymity, mutual authentication, and resistance to biometric templates tampering and stolen attacks, stolen smart card attack, privileged interior attack are fulfilled. The costs of storage requirement, computation, communication and storage are estimated. The results demonstrate that the proposed protocol is more efficient than Mukherjee et al., Chaudhary et al., and Gupta et al. protocols.
A blockchain-based IoT data marketplace

Abstract The (IoT) is growing steadily, and so is the number of data that is generated by (IoT) devices. This makes it difficult to find and leverage relevant data (and data sources) without a data marketplace. Such a marketplace provides a platform to enable different parties, e.g., sensor operators and service providers, to trade their data. Today, most data marketplaces are based on centralized solutions, which may become a single point of failure and come with expensive infrastructure, trust problems, and privacy issues. Therefore, we propose the application of blockchain technology to implement a data marketplace for the IoT. Within the proposed marketplace, smart contracts are used to implement various functionalities and enforce the rules of the data exchange. The marketplace also includes a proxy, a broker, and (GUIs) to enable data trading. To show the applicability of the proposed data marketplace, we analyze the costs arising from the utilization of smart contracts.
Finite-sample analysis of nonlinear stochastic approximation with applications in reinforcement learning

Motivated by applications in reinforcement learning (RL), we study a nonlinear stochastic approximation (SA) algorithm under Markovian noise, and establish its finite-sample convergence bounds under various stepsizes. Specifically, we show that when using constant stepsize (i.e., αk≡α), the algorithm achieves exponential fast convergence to a neighborhood (with radius O(αlog(1/α))) around the desired limit point. When using diminishing stepsizes with appropriate decay rate, the algorithm converges with rate O(log(k)/k). Our proof is based on Lyapunov drift arguments, and to handle the Markovian noise, we exploit the fast mixing of the underlying Markov chain. To demonstrate the generality of our theoretical results on Markovian SA, we use it to derive the finite-sample bounds of the popular Q-learning algorithm with linear function approximation, under a condition on the behavior policy. Importantly, we do not need to make the assumption that the samples are i.i.d., and do not require an artificial projection step in the algorithm. Numerical simulations corroborate our theoretical results.
Coarse-to-Fine Contrastive Self-Supervised Feature Learning for Land-Cover Classification in SAR Images With Limited Labeled Data

Contrastive self-supervised learning (CSSL) has achieved promising results in extracting visual features from unlabeled data. Most of the current CSSL methods are used to learn global image features with low-resolution that are not suitable or efficient for pixel-level tasks. In this paper, we propose a coarse-to-fine CSSL framework based on a novel contrasting strategy to address this problem. It consists of two stages, one for encoder pre-training to learn global features and the other for decoder pre-training to derive local features. Firstly, the novel contrasting strategy takes advantage of the spatial structure and semantic meaning of different regions and provides more cues to learn than that relying only on data augmentation. Specifically, a positive pair is built from two nearby patches sampled along the direction of the texture if they fall into the same cluster. A negative pair is generated from different clusters. When the novel contrasting strategy is applied to the coarse-to-fine CSSL framework, global and local features are learned successively by forcing the positive pair close to each other and the negative pair apart in an embedding space. Secondly, a discriminant constraint is incorporated into the per-pixel classification model to maximize the inter-class distance. It makes the classification model more competent at distinguishing between different categories that have similar appearance. Finally, the proposed method is validated on four SAR images for land-cover classification with limited labeled data and substantially improves the experimental results. The effectiveness of the proposed method is demonstrated in pixel-level tasks after comparison with the state-of-the-art methods.
Generation of cat states by a weak parametric drive and a transitionless tracking algorithm

In this paper, we present an experimentally feasible protocol to generate the cat states in the microwave resonator coupled to a superconducting qubit. The setup employs a detuned, time-dependent parametric drive to squeeze the resonator mode so that an adjustable qubit-resonator coupling strength can be obtained. Therefore, based on the transitionless tracking algorithm, we can design control pulses to generate the qubit-resonator entangled states with high fidelity in the laboratory frame. Then, the even and odd cat states can be further obtained by performing measurement on the superconducting qubit. Compared to the scheme [Chen et al., Phys. Rev. Lett. 126, 023602 (2021)], the present protocol is realized in the regime of weak parametric drive. In the case, squeezing-induced noise can be reduced so that the fidelity of the generated state can be improved. Numerical simulations indicate that the present protocol is well executed under experimentally available parameters. Thus, the protocol is feasible with the present state of the art in microwave superconducting circuits.
Prediction model for methanation reaction conditions based on a state transition simulated annealing algorithm optimized extreme learning machine

Methanation is the core process of synthetic natural gas, the performance of the entire reaction system depends on precise values of the reaction condition parameters. Accurate predictions of the CO conversion rate of the methanation reaction can eliminate time-consuming and complex steps in experiments and speed up the discovery of the best reaction conditions. However, the methanation reaction is an uncertain, highly complex, and highly nonlinear process. Thus, this paper proposes a machine learning prediction model for the methanation reaction to facilitate the subsequent search for optimal reaction conditions. The reaction temperature, pressure, hydrogen–carbon ratio, water vapor content, CO2 content, and space velocity were selected as the condition variables. The CO conversion rate was the optimization objective. An extreme learning machine (ELM) was selected as a prediction model. Because the input weights and bias matrices of the ELM are randomly generated, an ELM based on a state transition simulated annealing (STASA-ELM) algorithm is proposed. The STASA algorithm was used to optimize the ELM to improve the accuracy and stability of the model. Five additional sets of experimental data were designed for the experiment, and the error between the experimental and predicted values was small. Thus, the STASA-ELM algorithm can accurately predict the conversion of CO for different values of reaction conditions.
Masked face recognition with convolutional visual self-attention network

With the global outbreak of COVID-19, wearing face masks has been actively introduced as an effective public measure to reduce the risk of virus infection. This measure leads to the failure of face recognition in many cases. Therefore, it is very necessary to improve the recognition performance of masked face recognition (MFR). Inspired by the successful application of self-attention in computer vision, we propose a Convolutional Visual Self-Attention Network (CVSAN), which uses self-attention to augment the convolution operator. Specifically, this is achieved by connecting a convolutional feature map, which enforces local features, to a self-attention feature map that is capable of modeling long-range dependencies. Since there is currently no publicly available large-scale masked face data, we generate a Masked VGGFace2 dataset based on the face detection algorithm to train the CVSAN model. Experiments show that the CVSAN algorithm significantly improves the performance of MFR compared to other algorithms.
Video prediction by efficient transformers

Video prediction is a challenging computer vision task that has a wide range of applications. In this work, we present a new family of Transformer-based models for video prediction. Firstly, an efficient local spatial–temporal separation attention mechanism is proposed to reduce the complexity of standard Transformers. Then, a full autoregressive model, a partial autoregressive model and a non-autoregressive model are developed based on the new efficient Transformer. The partial autoregressive model has a similar performance with the full autoregressive model but a faster inference speed. The non-autoregressive model not only achieves a faster inference speed but also mitigates the quality degradation problem of the autoregressive counterparts, but it requires additional parameters and loss function for learning. Given the same attention mechanism, we conducted a comprehensive study to compare the proposed three video prediction variants. Experiments show that the proposed video prediction models are competitive with more complex state-of-the-art convolutional-LSTM based models. The source code is available at https://github.com/XiYe20/VPTR.
Reap the Harvest on Blockchain: A Survey of Yield Farming Protocols

Yield farming represents an immensely popular asset management activity in decentralized finance (DeFi). It involves supplying, borrowing, or staking crypto assets to earn an income in forms of transaction fees, interest, or participation rewards at different DeFi marketplaces. In this systematic survey, we present yield farming protocols as an aggregation-layer constituent of the wider DeFi ecosystem that interact with primitive-layer protocols such as decentralized exchanges (DEXs) and loanable funds (PLFs) protocol for loanable funds (PLF). We examine the yield farming mechanism by first studying the operations encoded in the yield farming smart contracts, and then performing stylized, parameterized simulations on various yield farming strategies. We conduct a thorough literature review on related work, and establish a framework for yield farming protocols that takes into account pool structure, accepted token types, and implemented strategies. Using our framework, we characterize major yield aggregators in the market including Yearn Finance, Beefy, and Badger DAO. Moreover, we discuss anecdotal attacks against yield aggregators and generalize a number of risks associated with yield farming.