Reviewerly/exHarmony huggingface dataset:
Insulator Surface Breakage Recognition Based on Multiscale Residual Neural Network

Insulators are one of the widely used important devices in power systems, and proposing an accurate and effective online monitoring method for insulator defects is an urgent need for intelligent and informative power systems. We propose a multiscale residual neural network for insulator surface breakage recognition. The proposed multiscale convolution filter uses three different size convolution kernels for convolution filtering, and performs feature maps fusion, which can enrich the spatial correlation and channel correlation of feature maps. We investigated the effects of different learning rates on the model performance, and confirmed that the model performs best with learning rate of 10-6. Compared with VGG16, Inception_v3 and Resnet50, the multiscale residual neural network model has significant advantages in detection capability and occupies less computational resources, and the detection speed can meet the industrial real-time detection requirements. Through dark-light and noise experiments, it is demonstrated that the proposed method is robust and can effectively deal with low-quality insulator images.
Learning Transformation-Invariant Local Descriptors With Low-Coupling Binary Codes

Despite the great success achieved by prevailing binary local descriptors, they are still suffering from two problems: 1) vulnerable to the geometric transformations; 2) lack of an effective treatment to the highly-correlated bits that are generated by directly applying the scheme of image hashing. To tackle both limitations, we propose an unsupervised Transformation-invariant Binary Local Descriptor learning method (TBLD). Specifically, the transformation invariance of binary local descriptors is ensured by projecting the original patches and their transformed counterparts into an identical high-dimensional feature space and an identical low-dimensional descriptor space simultaneously. Meanwhile, it enforces the dissimilar image patches to have distinctive binary local descriptors. Moreover, to reduce high correlations between bits, we propose a bottom-up learning strategy, termed Adversarial Constraint Module, where low-coupling binary codes are introduced externally to guide the learning of binary local descriptors. With the aid of the Wasserstein loss, the framework is optimized to encourage the distribution of the generated binary local descriptors to mimic that of the introduced low-coupling binary codes, eventually making the former more low-coupling. Experimental results on three benchmark datasets well demonstrate the superiority of the proposed method over the state-of-the-art methods. The project page is available at https://github.com/yoqim/TBLD.
Hy-Demosaicing: Hyperspectral Blind Reconstruction From Spectral Subsampling

This article proposes a smart hyperspectral sensing strategy, implemented in the spectral domain, conceived for spaceborne sensor systems, where physical space, storage resources, and communication bandwidth are extremely scarce and expensive. Smart sensing means faster and hardware-friendly imaging. Instead of acquiring all band samples in the spectral domain, we randomly select a few band samples per spatial pixel location. A periodic structure of spectral band selector array (SBSA) is designed so that we can learn a subspace basis from subsamples, which is essential to the underlying hyperspectral image (HSI) recovery algorithm. This spectral subsampling sensing strategy yields a demosaicing problem. We propose a blind hyperspectral reconstruction technique termed hyperspectral demosaicing (Hy-demosaicing) exploiting spectral low-rankness and spatial correlation of HSIs. It is blind in the sense that the signal subspace is learned from measured spectral subsamples. The subspace basis is data-adaptive and provides a more compact representation than other non-adaptive representations. This adaptiveness leads to improved image recovery as illustrated in experiments with real data.
Deep Reinforcement Learning Framework for Category-Based Item Recommendation

Deep reinforcement learning (DRL)-based recommender systems have recently come into the limelight due to their ability to optimize long-term user engagement. A significant challenge in DRL-based recommender systems is the large action space required to represent a variety of items. The large action space weakens the sampling efficiency and thereby, affects the recommendation accuracy. In this article, we propose a DRL-based method called deep hierarchical category-based recommender system (DHCRS) to handle the large action space problem. In DHCRS, categories of items are used to reconstruct the original flat action space into a two-level category-item hierarchy. DHCRS uses two deep Q -networks (DQNs): 1) a high-level DQN for selecting a category and 2) a low-level DQN to choose an item in this category for the recommendation. Hence, the action space of each DQN is significantly reduced. Furthermore, the categorization of items helps capture the users' preferences more effectively. We also propose a bidirectional category selection (BCS) technique, which explicitly considers the category-item relationships. The experiments show that DHCRS can significantly outperform state-of-the-art methods in terms of hit rate and normalized discounted cumulative gain for long-term recommendations.
Securing biometric authentication system using blockchain

Biometric authentication systems have major security weaknesses: risk of biometric information leakage, unreliability of authentication modules, and non-transparency of biometric information management. This paper presents BDAS, a new biometric authentication system using blockchain, which provides a decentralized and distributed mechanism for processing biometric authentication and an auditable mechanism for managing biometric information. BDAS’s evaluation demonstrates that it provides reliable and secure authentication compared to existing methods while introducing negligible performance overhead in real-world scenarios.
DBHC: A DBSCAN-based hierarchical clustering algorithm

Clustering is the process of partitioning objects of a dataset into some groups according to similarities and dissimilarities between its objects. DBSCAN is one of the most important clustering algorithms in the density based approach of clustering. In spite of the numerous advantages of the DBSCAN algorithm, it has two important input parameters, MinPts and Eps, which determining their values is still a great challenge. This problem arises because values of these parameters are heavily dependent on data distribution. To overcome this challenge, firstly features of these parameters are investigated and the data distribution are analyzed. Then a DBSCAN-based hierarchical clustering (DBHC) method is proposed in this paper in order to fix this challenge. For this purpose, DBHC first determines values of these parameters using the notion of k nearest neighbor and k-dist plot. Because most of the real world data is not distributed uniformly, it is needed to be produced several values for the Eps parameter. Then, DBHC executes the DBSCAN algorithm several times based on the number of Eps produced earlier. Finally, DBHC method merges obtained clusters if the number of produced clusters is larger than the number which has estimated by the user. To evaluate the performance of the DBHC method, several experiments were performed on some of benchmark datasets of UCI database. Obtained results were compared with other previous works. The obtained results consistently showed that the DBHC method led to better results in comparison to the other works.
PANet: Patch-Aware Network for Light Field Salient Object Detection

Most existing light field saliency detection methods have achieved great success by exploiting unique light field data-focus information in focal slices. However, they process light field data in a slicewise way, leading to suboptimal results because the relative contribution of different regions in focal slices is ignored. How we can comprehensively explore and integrate focused saliency regions that would positively contribute to accurate saliency detection. Answering this question inspires us to develop a new insight. In this article, we propose a patch-aware network to explore light field data in a regionwise way. First, we excavate focused salient regions with a proposed multisource learning module (MSLM), which generates a filtering strategy for integration followed by three guidances based on saliency, boundary, and position. Second, we design a sharpness recognition module (SRM) to refine and update this strategy and perform feature integration. With our proposed MSLM and SRM, we can obtain more accurate and complete saliency maps. Comprehensive experiments on three benchmark datasets prove that our proposed method achieves competitive performance over 2-D, 3-D, and 4-D salient object detection methods. The code and results of our method are available at https://github.com/OIPLab-DUT/IEEE-TCYB-PANet.
The effects of visualization on judgment and decision-making: a systematic literature review

Abstract The visualization of information is a widely used tool to improve comprehension and, ultimately, decision-making in strategic management decisions as well as in a diverse array of other domains. Across social science research, many findings have supported this rationale. However, empirical results vary significantly in terms of the variables and mechanisms studied as well as their resulting conclusion. Despite the ubiquity of information visualization with modern software, there is little effort to create a comprehensive understanding of the powers and limitations of its use. The purpose of this article is therefore to review, systematize, and integrate extant research on the effects of information visualization on decision-making and to provide a future research agenda with a particular focus on the context of strategic management decisions. The study shows that information visualization can improve decision quality as well as speed, with more mixed effects on other variables, for instance, decision confidence. Several moderators such as user and task characteristics have been investigated as part of this interaction, along with cognitive aspects as mediating processes. The article presents integrative insights based on research spanning multiple domains across the social and information sciences and provides impulses for prospective applications in the realm of managerial decision-making.
Selective encryption and optimization based watermarking for robust transmission of landslide images

In this paper, a secure watermarking algorithm is proposed to efficiently enhance invisibility and robustness simultaneously. First, the combination of transform domain scheme is used to identify the appropriate coefficient for data embedding. To offer a good balance between robustness and invisibility, we computed optimal embedding factors by using the fusion of particle swarm optimisation (PSO) and the Firefly algorithm. Subsequently, the watermark is scrambled by using a step space-filling curve, and the scrambled watermark is embedded into the wavelet domain of the cover image via the optimal embedding factor. Furthermore, selective encryption is applied to the original host image so that the proposed algorithm can also provide an additional level of security at low cost. We achieved average scores of peak signal-to-noise ratio (PSNR), normalised coefficient (NC), number of changing pixel rate (NPCR) and unified averaged changed intensity (UACI) as 55.1471 dB, 0.9956, 0.9801 and 0.3471, respectively.
A Variable Importance-Based Differential Evolution for Large-Scale Multiobjective Optimization

Large-scale multiobjective optimization problems (LMOPs) bring significant challenges for traditional evolutionary operators, as their search capability cannot efficiently handle the huge decision space. Some newly designed search methods for LMOPs usually classify all variables into different groups and then optimize the variables in the same group with the same manner, which can speed up the population's convergence. Following this research direction, this article suggests a differential evolution (DE) algorithm that favors searching the variables with higher importance to the solving of LMOPs. The importance of each variable to the target LMOP is quantized and then all variables are categorized into different groups based on their importance. The variable groups with higher importance are allocated with more computational resources using DE. In this way, the proposed method can efficiently generate offspring in a low-dimensional search subspace formed by more important variables, which can significantly speed up the convergence. During the evolutionary process, this search subspace for DE will be expanded gradually, which can strike a good balance between exploration and exploitation in tackling LMOPs. Finally, the experiments validate that our proposed algorithm can perform better than several state-of-the-art evolutionary algorithms for solving various benchmark LMOPs.
Ship Object Detection of Remote Sensing Image Based on Visual Attention

At present, reliable and precise ship detection in high-resolution optical remote sensing images affected by wave clutter, thin clouds, and islands under complex sea conditions is still challenging. At the same time, object detection algorithms in satellite remote sensing images are challenged by color, aspect ratio, complex background, and angle variability. Even the results obtained based on the latest convolutional neural network (CNN) method are not satisfactory. In order to obtain more accurate ship detection results, this paper proposes a remote sensing image ship object detection method based on a brainlike visual attention mechanism. We refer to the robust expression mode of the human brain, design a vector field filter with active rotation capability, and explicitly encode the direction information of the remote sensing object in the neural network. The progressive enhancement learning model guided by the visual attention mechanism is used to dynamically solve the problem, and the object can be discovered and detected through time–space information. To verify the effectiveness of the proposed method, a remote sensing ship object detection data set is established, and the proposed method is compared with other state-of-the-art methods on the established data set. Experiments show that the object detection accuracy of this method and the ability to capture image details have been improved. Compared with other models, the average intersection rate of the joint is 80.12%, which shows a clear advantage. The proposed method is fast enough to meet the needs of ship detection in remote sensing images.
What is The Best Data Augmentation For 3D Brain Tumor Segmentation?

Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain. Despite this fact, data augmentation has in our opinion not been fully explored for brain tumor segmentation. In this project we apply different types of data augmentation (flipping, rotation, scaling, brightness adjustment, elastic deformation) when training a standard 3D U-Net, and demonstrate that augmentation significantly improves the network’s performance in many cases. Our conclusion is that brightness augmentation and elastic deformation work best, and that combinations of different augmentation techniques do not provide further improvement compared to only using one augmentation technique. Our code is available at https://github.com/mdciri/3D-augmentation-techniques.
A Structure Constraint Matrix Factorization Framework for Human Behavior Segmentation

This article presents a structure constraint matrix factorization framework for different behavior segmentation of the human behavior sequential data. This framework is based on the structural information of the behavior continuity and the high similarity between neighboring frames. Due to the high similarity and high dimensionality of human behavior data, the high-precision segmentation of human behavior is hard to achieve from the perspective of application and academia. By making the behavior continuity hypothesis, first, the effective constraint regular terms are constructed. Subsequently, the clustering framework based on constrained non-negative matrix factorization is established. Finally, the segmentation result can be obtained by using the spectral clustering and graph segmentation algorithm. For illustration, the proposed framework is applied to the Weiz dataset, Keck dataset, mo_86 dataset, and mo_86_9 dataset. Empirical experiments on several public human behavior datasets demonstrate that the structure constraint matrix factorization framework can automatically segment human behavior sequences. Compared to the classical algorithm, the proposed framework can ensure consistent segmentation of sequential points within behavior actions and provide better performance in accuracy.
Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition

Sign languages are visual languages produced by the movement of the hands, face, and body. In this paper, we evaluate representations based on skeleton poses, as these are explainable, person-independent, privacy-preserving, low-dimensional representations. Basically, skeletal representations generalize over an individual’s appearance and background, allowing us to focus on the recognition of motion. But how much information is lost by the skeletal representation? We perform two independent studies using two state-of-the-art pose estimation systems. We analyze the applicability of the pose estimation systems to sign language recognition by evaluating the failure cases of the recognition models. Importantly, this allows us to characterize the current limitations of skeletal pose estimation approaches in sign language recognition.
Design and Implementation of an Ontology for Semantic Labeling and Testing: Automotive Global Ontology (AGO)

Modern Artificial Intelligence (AI) methods can produce a large quantity of accurate and richly described data, in domains such as surveillance or automation. As a result, the need to organize data at a large scale in a semantic structure has arisen for long-term data maintenance and consumption. Ontologies and graph databases have gained popularity as mechanisms to satisfy this need. Ontologies provide the means to formally structure descriptive and semantic relations of a domain. Graph databases allow efficient and well-adapted storage, manipulation, and consumption of these linked data resources. However, at present, there is no a universally defined strategy for building AI-oriented ontologies for the automotive sector. One of the key challenges is the lack of a global standardized vocabulary. Most private initiatives and large open datasets for Advanced Driver Assistance Systems (ADASs) and Autonomous Driving (AD) development include their own definitions of terms, with incompatible taxonomies and structures, thus resulting in a well-known lack of interoperability. This paper presents the Automotive Global Ontology (AGO) as a Knowledge Organization System (KOS) using a graph database (Neo4j). Two different use cases for the AGO domain ontology are presented to showcase its capabilities in terms of semantic labeling and scenario-based testing. The ontology and related material have been made public for their subsequent use by the industry and academic communities.
DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning

It is increasingly suggested to identify Software Vulnerabilities (SVs) in code commits to give early warnings about potential security risks. However, there is a lack of effort to assess vulnerability-contributing commits right after they are detected to provide timely information about the exploitability, impact and severity of SVs. Such information is important to plan and prioritize the mitigation for the identified SVs. We propose a novel Deep multi-task learning model, DeepCVA, to automate seven Commit-level Vulnerability Assessment tasks simultaneously based on Common Vulnerability Scoring System (CVSS) metrics. We conduct large-scale experiments on 1,229 vulnerability-contributing commits containing 542 different SVs in 246 real-world software projects to evaluate the effectiveness and efficiency of our model. We show that DeepCVA is the best-performing model with 38% to 59.8% higher Matthews Correlation Coefficient than many supervised and unsupervised baseline models. DeepCVA also requires 6.3 times less training and validation time than seven cumulative assessment models, leading to significantly less model maintenance cost as well. Over-all, DeepCVA presents the first effective and efficient solution to automatically assess SVs early in software systems.
Improving the Performance of Student Teams in Project-Based Learning with Scrum

The purpose of this paper is to analyze the effectiveness of Scrum for project and team management in PBL teams in higher education. To attain this goal, a study was carried out to analyze students’ perceptions about Scrum as an effective method for PBL teams. Based on two different editions of PBL that used the Scrum method with different characteristics in each approach, this paper aims to identify the best practices for effective team and project management and draw recommendations for successful use of scrum in PBL approaches. The authors used an exploratory case study carried out within an engineering program at the University of Minho, Portugal. The research design was based on an explorative quantitative and qualitative approach. Implementing Scrum in PBL teams helps students to keep the project running smoothly and draws greater awareness on how to manage the project and teams in a more effective way. Findings show that task assignment, performance monitoring, visual management and regular feedback were considered the main advantages of using Scrum in PBL teams, which had a positive impact on student performance. However, for the success of Scrum, students recognize the role of the Scrum Master and Project Owner as vital to guide the teams in a sustainable way. Research on the application of Scrum in Education is scarce and mostly exploratory. This paper is among the very few empirical studies consolidating knowledge on the implementation of Scrum approaches to improve learning in higher education. More specifically, it brings a valuable contribution on how to improve specifically team performance in PBL teams with the use of agile approaches such as Scrum.
Federated Adversarial Debiasing for Fair and Transferable Representations

Federated learning is a distributed learning framework that is communication efficient and provides protection over participating users' raw training data. One outstanding challenge of federate learning comes from the users' heterogeneity, and learning from such data may yield biased and unfair models for minority groups. While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework. In this work, we study these barriers and address them by proposing a novel approach Federated Adversarial DEbiasing (FADE). FADE does not require users' sensitive group information for debiasing and offers users the freedom to opt-out from the adversarial component when privacy or computational costs become a concern. We show that ideally, FADE can attain the same global optimality as the one by the centralized algorithm. We then analyze when its convergence may fail in practice and propose a simple yet effective method to address the problem. Finally, we demonstrate the effectiveness of the proposed framework through extensive empirical studies, including the problem settings of unsupervised domain adaptation and fair learning. Our codes and pre-trained models are available at: https://github.com/illidanlab/FADE.