Reviewerly/exHarmony huggingface dataset:
ReSyRIS - A Real-Synthetic Rock Instance Segmentation Dataset for Training and Benchmarking

The exploration of our solar system for understanding its creation and investigating potential chances of life on other celestial bodies is a fundamental drive of human mankind.After early telescope-based observation, Apollo 11 was the first space mission able to collect samples on the lunar surface and take them back to earth for analysis.Especially in recent years this trend accelerates again, and many successors were (or are in the process of being) launched into space for extra-terrestrial sample extraction.Yet, the abundance of potential failures makes these missions extremely challenging.For operations aimed at deeper parts of the solar system, the operational working distance extends even further, and communication delay and limited bandwidth increase complexity.Consequently, sample extraction missions are designed to be more autonomous in order to carry out large parts without human intervention.One specific sub-task particularly suitable for automation is the identification of relevant extraction candidates.While there exists several approaches for rock sample identification, there are often limiting factors in the form of applicable training data, lack of suitable annotations of the very same, and unclear performance of the algorithms in extra-terrestrial environments because of inadequate test data.To address these issues, we present ReSyRIS (Real-Synthetic Rock Instance Segmentation Dataset), which consists of real-world images together with their manually created synthetic counterpart.The real-world part is collected in a quasi-extra-terrestrial environment on Mt.Etna in Sicily, and focuses recordings of several rock sample sites.Every scene is re-created in OAISYS, a Blender-based data generation pipeline for unstructured outdoor environments, for which the required meshes and textures are extracted from the volcano site.This allows not only precise re-construction of the scenes in a synthetic environment, but also generation of highly realistic training data with automatic annotations in similar fashion to the real recordings.We finally investigate the generalization capability of a neural network trained on incrementally altered versions of synthetic data to explore potential sim-toreal gaps.The real-world dataset together with the OAISYS config files to create its synthetic counterpart are publicly available at https://rm.dlr.de/resyris_en.With this novel benchmark on extra-terrestrial stone instance segmentation we hope to further push the boundaries of autonomous rock sample extraction.
Adaptive Particle Filter With Randomized Quasi-Monte Carlo Sampling for Unbalanced Distribution System State Estimation

Particle filter (PF) is an effective state estimation method for nonlinear unbalanced distribution systems with non-Gaussian noises. However, the computational inefficiency limits its real-time application in large-scale distribution systems. This paper proposes an adaptive PF (APF) method by using randomized quasi-Monte Carlo (RQMC) sampling to enhance computational efficiency. Firstly, an RQMC sampling method is introduced to enable uniform sampling based on the low discrepancy Sobol’ sequence. This even sampling accelerates the convergence rate of numerical integrations. Secondly, the standard PF is extended to be an RQMC-PF estimator by using the RQMC sampling instead of the MC sampling. Benefiting from the higher convergence rate, fewer particles are required to obtain precise state estimates, thus notably improving computation efficiency. Thirdly, to further make a trade-off between estimation accuracy and computational efficiency, the number of particles used in the RQMC-PF is adaptively determined with the root mean square of the estimated state covariance, yielding an APF. Test results on unbalanced distribution systems with different scales demonstrate that the proposed method can efficiently provide accurate estimates as well as achieve good scalability.
Flexible and Scalable BLAKE/BLAKE2 Coprocessor for Blockchain-Based IoT Applications

This article proposes a flexible and scalable BLAKE/BLAKE2 coprocessor aiming for high flexibility, high performance, and low power for blockchain-based IoT applications.
Effects of public expenditure on education on gender inequality in education in sub‐Saharan Africa (SSA)

Abstract The study examines the effect of public education expenditures on gender inequality in education in sub‐Saharan Africa. It covers 25 countries in sub‐Saharan Africa for the period 2010–2019. The generalised method of moments was used. The estimator developed by Blundell and Bond was chosen. The following results are found: (i) public expenditure on education has a positive effect on gender equality in education at the primary, secondary and tertiary levels; (ii) remittances have a negative effect on gender equality in education at the primary, secondary and tertiary levels; (iii) population growth has a negative effect on gender equality in education at the primary and tertiary levels, but at the secondary level, the effect is positive. The main political implication is that sub‐Saharan African countries must continue to invest in inclusive school to ensure gender equality, which remains a goal to be achieved in the region.
Top-N Knowledge Concept Recommendations in MOOCs Using a Neural Co-Attention Model

Massive Open Online Courses (MOOCs), which provide learners with a large-scale, openaccess learning opportunity, have drawn a lot of attention recently.The amount of information available on MOOCs is increasing, making it challenging for learners to choose the best course materials and leading to low learning efficiency and high dropout rates.To address these problems, Recommendation Systems (RSs) have been researched as a direct approach of delivering educational content to learners while also attracting their interest.However, a course often consists of various learning concepts, each of which covers a different topic.Explicitly proposing courses can cause the lack of learners' attention to a particular knowledge concept.We introduce a Top-N Knowledge Concept Recommendations in MOOCs using a Neural Co-Attention Model, called NCO-A, that integrates significant heterogeneous data with recommendations based on knowledge concepts.The NCO-A model's effectiveness has been proven by extensive experiments on three real-world datasets.
A domain-theoretic framework for robustness analysis of neural networks

Abstract A domain-theoretic framework is presented for validated robustness analysis of neural networks. First, global robustness of a general class of networks is analyzed. Then, using the fact that Edalat’s domain-theoretic L -derivative coincides with Clarke’s generalized gradient, the framework is extended for attack-agnostic local robustness analysis. The proposed framework is ideal for designing algorithms which are correct by construction. This claim is exemplified by developing a validated algorithm for estimation of Lipschitz constant of feedforward regressors. The completeness of the algorithm is proved over differentiable networks and also over general position ${\mathrm{ReLU}}$ networks. Computability results are obtained within the framework of effectively given domains. Using the proposed domain model, differentiable and non-differentiable networks can be analyzed uniformly. The validated algorithm is implemented using arbitrary-precision interval arithmetic, and the results of some experiments are presented. The software implementation is truly validated, as it handles floating-point errors as well.
Flow-Edge-Net: Video Saliency Detection Based on Optical Flow and Edge-Weighted Balance Loss

Optical flow networks have been widely utilized for video saliency detection (VSD) due to their effective performance in capturing the motion of objects. However, the use of optical flow blurs the edges of salient objects and leads to the problems of poorly defined object boundaries. To address this issue, we propose an optical flow-based edge-weighted loss function, to train a network called Flow-Edge-Net, which can balance the weights of the foreground and background information at the edges of video frames. It has achieved superior performance in detecting salient boundaries. Specifically, we propose two complementary encoding and decoding networks based on the concept of decoupling. That is, the optical flow network focuses on moving objects, while the edge network, based on the encoder-decoder structure, focuses on edge information. As the two networks output features of the same dimension and are from the same input, our proposed self-designed adaptive weighted feature fusion module can compare and integrate the edge information and location information from the two networks through adaptive weighting. The proposed method has been evaluated on five widely used databases. Experiment results demonstrate the superior performance of the proposed Flow-Edge-Net in locating salient objects, with accurate and refined edges. The proposed method achieves superior performance over the state-of-the-art methods in detecting salient objects in videos.
Entropy-Constrained Implicit Neural Representations for Deep Image Compression

Implicit neural representations (INRs) for various data types have gained popularity in the field of deep learning owing to their effectiveness. However, previous studies on INRs have only focused on recovering original representations. This paper investigated an image compression model based on INRs using a model compression technique for entropy-constrained neural networks. Specifically, the proposed model trains a multilayer perceptron (MLP) to overfit a single image and then uses its weights to optimize its compressed representation using additive uniform noise. Accordingly, the proposed model efficiently minimizes the size of the model weight in an end-to-end manner. This training optimization process is fairly desirable for adjusting the rate of distortion for image compression. In contrast to other model compression techniques, the proposed model is implemented without additional training process or memory cost. By introducing entropy loss, this paper demonstrated that the proposed model can be used to preserve high image quality while maintaining smaller model size. The experimental results demonstrated that the proposed model achieved comparable performance to conventional image compression models without incurring high storage costs.
The Influence of Emotion and Emotion Regulation on Complex Problem-Solving Performance

Complex problem solving (CPS) research has focused on cognitive variables, but in recent years, the influential role of emotions and motivation during the CPS process has been highlighted. In the current study, we focus on emotion regulation during CPS. Eighty-three university students worked on a simulated chocolate-producing company. Initially, they completed a survey on emotion regulation and demographics. Then, they were randomly assigned to four conditions where emotions were induced with short video clips: anger, fear, happiness, and trust. A manipulation check assessed the successful priming of emotions. While working individually on the microworld, CPS behavior and performance were saved in log files. We hypothesized that happiness and trust would lead to better performance than fear and anger. We also hypothesized that emotion regulation would be positively related to performance. There were no differences in performance at the beginning and at the end of the simulation among the four emotion groups. Regression analyses showed that emotion-regulation strategies significantly predicted CPS performance. Aggression was positively related to performance. Results show that it is more the regulation of emotions than the emotion per se that influences CPS performance.
HAL-IA: A Hybrid Active Learning framework using Interactive Annotation for medical image segmentation

High performance of deep learning models on medical image segmentation greatly relies on large amount of pixel-wise annotated data, yet annotations are costly to collect. How to obtain high accuracy segmentation labels of medical images with limited cost (e.g. time) becomes an urgent problem. Active learning can reduce the annotation cost of image segmentation, but it faces three challenges: the cold start problem, an effective sample selection strategy for segmentation task and the burden of manual annotation. In this work, we propose a Hybrid Active Learning framework using Interactive Annotation (HAL-IA) for medical image segmentation, which reduces the annotation cost both in decreasing the amount of the annotated images and simplifying the annotation process. Specifically, we propose a novel hybrid sample selection strategy to select the most valuable samples for segmentation model performance improvement. This strategy combines pixel entropy, regional consistency and image diversity to ensure that the selected samples have high uncertainty and diversity. In addition, we propose a warm-start initialization strategy to build the initial annotated dataset to avoid the cold-start problem. To simplify the manual annotation process, we propose an interactive annotation module with suggested superpixels to obtain pixel-wise label with several clicks. We validate our proposed framework with extensive segmentation experiments on four medical image datasets. Experimental results showed that the proposed framework achieves high accuracy pixel-wise annotations and models with less labeled data and fewer interactions, outperforming other state-of-the-art methods. Our method can help physicians efficiently obtain accurate medical image segmentation results for clinical analysis and diagnosis.
Image Patch-Matching With Graph-Based Learning in Street Scenes

Matching landmark patches from a real-time image captured by an on-vehicle camera with landmark patches in an image database plays an important role in various computer perception tasks for autonomous driving. Current methods focus on local matching for regions of interest and do not take into account spatial neighborhood relationships among the image patches, which typically correspond to objects in the environment. In this paper, we construct a spatial graph with the graph vertices corresponding to patches and edges capturing the spatial neighborhood information. We propose a joint feature and metric learning model with graph-based learning. We provide a theoretical basis for the graph-based loss by showing that the information distance between the distributions conditioned on matched and unmatched pairs is maximized under our framework. We evaluate our model using several street-scene datasets and demonstrate that our approach achieves state-of-the-art matching results.
Lightweight attention‐guided redundancy‐reuse network for real‐time semantic segmentation

Abstract Semantic segmentation is a critical topic in computer vision, and it has numerous practical applications, including mobile devices, autonomous driving, and many other fields. However, in these application scenarios, it is often essential for the segmentation models to achieve a balance between efficiency and performance. A lightweight attention‐guided redundancy‐reuse network (LARNet) was proposed to address this challenge in this paper. Specifically, the multi‐scale asymmetric redundancy reuse (MAR) module was designed as the main component of the encoder for dense encoding of contextual semantic features. Furthermore, the efficient attention fusion (EAF) module was established for multi‐scale information fusion via the channel and spatial attention mechanisms in the decoder. A series of experiments were conducted to verify the proposed network. The results of tests on multiple datasets suggest that the network has higher accuracy and faster speed than the existing real‐time semantic segmentation methods.
Lazy Code Transformations in a Formally Verified Compiler

Translation validation verifies the results of an untrusted translator—called an oracle—at the compiler’s runtime using a validator. This approach enables validating intricate optimizations without having to prove them directly. Parametrizing such a validator with hints provided by oracles greatly simplifies its integration within a formally verified compiler—such as CompCert. However, generating those hints requires adapting state-of-the-art optimizations.
Asynchronous adaptive federated learning for distributed load forecasting with smart meter data

Load forecasting is essential for the operation and planning of a utility company. Recent large-scale smart meter deployments enabled the collection of fine-grained load data and created opportunities for sensor-based load forecasting. Machine learning has achieved great successes in load forecasting; however, conventional machine learning techniques require data transfer to the cloud or another centralized location for model training. This not only exposes data to privacy and security risks but also, with a large number of smart meters, increases network traffic. Federated Learning (FL) has a potential to alleviate mentioned concerns by training a single ML model in a distributed manner without requiring participants to share their data. Consequently, this paper proposes FedNorm, a novel asynchronous FL approach for load forecasting with smart meter data. While most FL strategies are synchronous and require all clients to complete local training in each round of aggregation, FedNorm is asynchronous and aggregates updates without waiting for lagging clients. To achieve this, FedNorm measures the clients contributions considering similarities of local and global models as well as the loss function magnitudes. The experiments demonstrate that FedNorm achieves higher accuracy than seven state-of-the-art FL techniques. Moreover, experiments show that FedNorm converges in fewer communication rounds compared to other FL models.
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control With Action Constraints

This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms. In action-constrained RL, each action taken by the learning system must comply with certain constraints. These constraints are crucial for ensuring the feasibility and safety of actions in real-world systems. We evaluate existing algorithms and their novel variants across multiple robotics control environments, encompassing multiple action constraint types. Our evaluation provides the first in-depth perspective of the field, revealing surprising insights, including the effectiveness of a straightforward baseline approach. The benchmark problems and associated code utilized in our experiments are made available online at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">github.com/omron-sinicx/action-constrained-RL-benchmark</uri> for further research and development.
Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration

Previous works have extensively studied the transferability of adversarial samples in untargeted black-box scenarios. However, it still remains challenging to craft targeted adversarial examples with higher transferability than non-targeted ones. Recent studies reveal that the traditional Cross-Entropy (CE) loss function is insufficient to learn transferable targeted adversarial examples due to the issue of vanishing gradient. In this work, we provide a comprehensive investigation of the CE loss function and find that the logit margin between the targeted and untargeted classes will quickly obtain saturation in CE, which largely limits the transferability. Therefore, in this paper, we devote to the goal of continually increasing the logit margin along the optimization to deal with the saturation issue and propose two simple and effective logit calibration methods, which are achieved by downscaling the logits with a temperature factor and an adaptive margin, respectively. Both of them can effectively encourage optimization to produce a larger logit margin and lead to higher transferability. Besides, we show that minimizing the cosine distance between the adversarial examples and the classifier weights of the target class can further improve the transferability, which is benefited from downscaling logits via L2-normalization. Experiments conducted on the ImageNet dataset validate the effectiveness of the proposed methods, which outperform the state-of-the-art methods in black-box targeted attacks. The source code is available at Link.
An Examination of the Blockchain Technology: Challenges and Future Opportunities

One of the most recent technological developments, block chain technology has the potential to help a lot of organizations. In order to give readers a better understanding of what block chain technology is and how it works, this paper's main goal is to give an overview of it. The questions that should be asked to determine whether Block chain Technology is the best option for organizations are included in another section of this paper. This section will also provide a summary of how Block chain Technology can benefit businesses. The discussion that follows demonstrates how various industries are currently mastering the technology in order to dominate their fields. This essay will also look at the findings of a study that was done to gauge public awareness of block chain technology.
BSDRM: A Machine Learning Based Bug Triaging Model to Recommend Developer Team

Bug triage assigns bugs to developers by mapping properties with past experiences. Without this process, many experienced developers may overwhelm with more assignments, whereas new developers are underwhelmed. Therefore, we propose a machine learning bug triaging approach called Bug Solving Developer Recommendation Model (BSDRM) to solve this issue by assigning developers. We first gather several datasets to combine and split them into train and test sets. Then, we create a sentence-embedded model from training set and generate a bag of developer’s words. Instead, test set is transformed into a vocabulary list using an embedded model. BSDRM recommends some eligible developers by matching the developer’s bag of words and vocabulary list of bug reports using K-Nearest Neighbour (KNN). A developer classification model categorizes these developers into experienced, newly experienced, and fresh graduate developers. It consists of Decision Tree (DT), Extra Tree (ET), AdaBoost (AdC), Bagging Classifier (BC), Gradient Boosting (GB), KNN, Nearest Centroid (NC), Bernoulli Naïve Bayes (BNB), Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CoNB), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Perceptron (Pr), and Multi-Layer Perceptron (MLP) produces individual outcomes of these classifiers. In this case, BC shows 96.59% accuracy to categorize developers with different experience levels. Based on these results, a developer team is recommended to solve testing bugs.