Reviewerly/exHarmony huggingface dataset:
Ontology-based knowledge management framework: Toward CBR-supported risk response to hydrological cascading disasters

Given knowledge scarcity, this paper proposes a knowledge management framework using case-based reasoning (CBR) to respond to hydrological cascading disaster risk (HCDR). This framework consists of scenario modeling, scenario layout, scenario reuse, and capability assessment. First, scenario modeling is to identify critical scenario ontologies to build a multiphase nested genetic model for HCDR. Second, scenario layout presents a novel evolutionary risk forest to reorganize ontologies in historical cases and expand cases. Third, scenario reuse involves an ontology-supported four-stage scenario reuse method, including scenario filtration, deduction, copy, and adaptation. Finally, capability assessment is considered with gap analysis to guarantee the feasibility and effectiveness of the proposed framework. This framework can fully use historical cases and support HCDR response decision-making with a logical process. It is conducive to improving the preparedness system for coping with hydrological cascading disasters.
Practical Limitations of Ethereum’s Layer-2

Most permissionless blockchains inherently suffer from throughput limitations. Layer-2 systems, such as side-chains or Rollups, have been proposed as a possible strategy to overcome this limitation. Layer-2 systems interact with the main-chain in two ways. First, users can move funds from/to the main-chain to/from the layer-2. Second, layer-2 systems periodically synchronize with the main-chain to keep some form of log of their activity on the main-chain - this log is key for security. Due to this interaction with the main-chain, which is necessary and recurrent, layer-2 systems impose some load on the main-chain. The impact of such load on the main-chain has been, so far, poorly understood. In addition to that, layer-2 approaches typically sacrifice decentralization and security in favor of higher throughput. This paper presents an experimental study that analyzes the current state of Ethereum layer-2 projects. Our goal is to assess the load they impose on Ethereum and to understand their scalability potential in the long-run. Our analysis shows that the impact of any given layer-2 on the main-chain is the result of both technical aspects (how state is logged on the main-chain) and user behavior (how often users decide to transfer funds between the layer-2 and the main-chain). Based on our observations, we infer that without efficient mechanisms that allow users to transfer funds in a secure and fast manner directly from one layer-2 project to another, current layer-2 systems will not be able to scale Ethereum effectively, regardless of their technical solutions. Furthermore, from our results, we conclude that the layer-2 systems that offer similar security guarantees as Ethereum have limited scalability potential, while approaches that offer better performance, sacrifice security and lead to an increase in centralization which runs against the end-goals of permissionless blockchains.
The Airborne Redundant Sensors Management Method With Markov Set-Value Decision Strategy

The airborne sensor system with system-level redundancy architecture is a convenient and effective method to ensure mission reliability and safety. In practice, the management strategy of the redundant system mainly relies on the engineer’s experience rather than systematic analysis in theory. The Markov set-value decision strategy was proposed to provide an analyzable and systematic strategy for managing the redundant sensor system, such as the redundant inertial reference system. In this strategy, the set-value system is the framework. The Dempster-Shafer (D-S) theory and the Markov decision process are used to improve the accuracy and the stability of the decision results. The results of the simulation experiments certify the advantage of the new strategy over the traditional one with less miss alarm rate and similar false alarm rate.
A Group-Based Block Storage Model With Block Splitting and Unit Encoding for Consortium Blockchains

With the continuous development of consortium blockchains, the storage overhead of blocks and storage load of nodes are increasing. The existing storage models consider either low-reliability issues or high storage overhead but not both. This work proposes a storage model that combines group-based block storage with block splitting and unit encoding. The former is used to improve the reliability of a block, while the latter is adopted to reduce the storage overhead and increase the reliability of a block in a consortium blockchain. Based on this model, this work designs a storage method to minimize the storage overhead of blocks and storage load of nodes, and maximize reliability of a consortium blockchain. Experimental results show that the proposed method outperforms Multi-layer Practical Byzantine Fault Tolerance, RapidChain, and Byzantine Fault Tolerance-Store in terms of storage overhead and reliability, thus greatly advancing the field of consortium blockchains.
Progressive Moire Removal and Texture Complementation for Image Demoireing

Taking photos of digital screens often produces color-distorting moire patterns caused by inconsistency between the color filter array of cameras and the sub-pixel layout of screens, which severely degrades the quality of photos. Most existing demoireing methods employ multi-stream network architecture to simultaneously process the same moire image with different resolutions, but they neglect the complementarity among different resolutions. In this paper, we propose a novel moire removal model to address this issue. Unlike the existing multi-stream based approaches, in this model, we present a progressive texture complementation block to exploit the complementary information from different resolutions in order to progressively remove moire textures and restore image content. Additionally, we propose a residual moire removal block, in which the depthwise separable convolution is utilized to remove moire from image while reducing computation overhead. This block also includes a local color correction structure, which is used to correct color shifts presented in the moire images. Experimental results on two public datasets show that our method outperforms state-of-the-art methods. Besides, the quantity of parameters and FLOPs of our model are tens of times fewer than the off-the-shelf models. Furthermore, our network framework can adapt well to another low-level vision task, rain removal, in which our model also achieves state-of-the-art performance.
Meta Generative Attack on Person Reidentification

Adversarial attacks have been recently investigated in person re-identification. These attacks perform well under cross dataset or cross model setting. However, the challenges present in cross-dataset cross-model scenario does not allow these models to achieve similar accuracy. To this end, we propose our method with the goal of achieving better transferability against different models and across datasets. We generate a mask to obtain better performance across models and use meta learning to boost the generalizability in the challenging cross-dataset cross-model setting. Experiments on Market-1501, DukeMTMC-reID and MSMT-17 demonstrate favorable results compared to other attacks.
UIALN: Enhancement for Underwater Image With Artificial Light

Since underwater images are seriously degraded due to the attenuation of light, artificial light (AL) is often used to assist photography in underwater. However, the normal underwater imaging process is changed by the AL. It is observed that the AL source typically alters the light condition to a large extent, resulting in non-uniform illumination of images. In addition, the color distortion of the area affected by AL is little because the AL close to the object suffers little attenuation. However, most existing underwater image enhancement algorithms ignore this phenomenon. In their results, the areas affected by AL tend to be over-enhanced or over-exposed and even affect the overall enhancement effect. To this end, we propose a novel underwater image enhancement algorithm (UIALN) based on luminance correction and AL area color self-guided restoration. The underwater image is converted into the LAB color space, where the AL and pseudo-blur effect on the L channel are removed based on the luminance correction network, and the color casts on the AB channels is removed by the guidance of the AL area. Specifically, a luminance correction network is first designed based on the retinex decomposition to correct luminance, where the uneven luminance caused by AL is corrected in the illumination layer decomposed by the L channel because AL is usually white light. After that, the AL area is detected by the difference between before and after luminance correction. Second, an AL area self-guidance network is designed to assist the restoration of the color channels AB. The color restoration module utilizes the internal characteristics of the image, where the characteristics of the AL area are utilized as the prior to make the color easy to be restored. In addition, to facilitate the training and testing of the algorithm, a method of synthetic underwater images with AL is proposed based on underwater image imaging model, and a new underwater image dataset with artificial light (UIDWAL) is provided. Experimental results show that our UIALN outperforms the existing state-of-the-art approaches for the enhancement of both synthetic and real underwater images with AL.
An Efficient Framework of Energy Status Reporting for BLE Beacon Networks

With growing demands for Internet of Things (IoT) applications, BLE beacon networks are rapidly being adopted. Periodic battery replacement operations and onsite maintenance are required to ensure continuous and reliable service. These operations are labor intensive and resource exhaustive. Therefore, Bluetooth gateways/mobile devices are often employed to monitor/collect the energy status. However, the gateways consume a considerable amount of network requests, and the user existence influences the data collection, thus the data accuracy, based on mobile devices. Reducing the number of energy status reports and maintaining the high accuracy of the energy status monitoring service is essential to catalyze a generic adoption of beacon networks and IoT infrastructure of similar nature in more businesses and real-life applications. In this article, we proposed a novel energy status monitoring framework that will dynamically change the energy status report interval based on the discharging rate of the battery, thereby reducing the total number of network requests and maintaining the required accuracy of energy status. The proposed framework identifies the BLE beacons with similar battery discharging rates, suggests a dynamic report interval, and leverages this information to reduce the number of energy status reports. We have experimented with real-life BLE beacon energy status data for 50 days to demonstrate that we could reduce the total number of network requests up to 70% while retaining 99% estimation accuracy.
Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks

Adversarial attacks can easily fool object recognition systems based on deep neural networks (DNNs). Although many defense methods have been proposed in recent years, most of them can still be adaptively evaded. One reason for the weak adversarial robustness may be that DNNs are only supervised by category labels and do not have part-based inductive bias like the recognition process of humans. Inspired by a well-known theory in cognitive psychology - recognition-by-components, we propose a novel object recognition model ROCK (Recognizing Object by Components with human prior Knowledge). It first segments parts of objects from images, then scores part segmentation results with predefined human prior knowledge, and finally outputs prediction based on the scores. The first stage of ROCK corresponds to the process of decomposing objects into parts in human vision. The second stage corresponds to the decision process of the human brain. ROCK shows better robustness than classical recognition models across various attack settings. These results encourage researchers to rethink the rationality of currently widely-used DNN-based object recognition models and explore the potential of part-based models, once important but recently ignored, for improving robustness.
What is the optimal position of low‐frequency words across line boundaries? An eye movement investigation

Abstract When displaying text on a page or a screen, only a finite number of characters can be presented on a single line. If the text exceeds that finite value, then text wrapping occurs. Often this process results in longer, more difficult to process words being positioned at the start of a line. We conducted an eye movement study to examine how this artefact of text wrapping affects passage reading. This allowed us to answer the question: should word difficulty be used when determining line breaks? Thirty‐nine participants read 20 passages where low‐frequency target words were either line‐initial or line‐final. There was no statistically reliable effect of our manipulation on passage reading time or comprehension despite several effects at a local level. Regarding our primary research question, the evidence suggests that word difficulty may not need to be accounted for when determining line breaks and assigning words to new lines.
The f-index of inclusion as optimal adjoint pair for fuzzy modus ponens

We continue studying the properties of the f-index of inclusion and show that, given a fixed pair of fuzzy sets, their f-index of inclusion can be linked to a fuzzy conjunction which is part of an adjoint pair. We also show that, when this pair is used as the underlying structure to provide a fuzzy interpretation of the modus ponens inference rule, it provides the maximum possible truth-value in the conclusion among all those values obtained by fuzzy modus ponens using any other possible adjoint pair.
Robust Sampling-Based Control of Mobile Manipulators for Interaction With Articulated Objects

In this article, we investigate and deploy sampling-based control techniques for the challenging task of the mobile manipulation of articulated objects. By their nature, manipulation tasks necessitate environment interactions, which require the handling of nondifferentiable switching contact dynamics. These dynamics represent a strong limitation for traditional gradient-based optimization methods, such as model-predictive control and differential dynamic programming, which often rely on heuristics for trajectory generation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sampling-based</i> techniques alleviate these constraints but do not ensure robots' stability and input/state constraints either. On the other hand, real-world applications in human environments require safety and robustness to unexpected events. For this reason, we propose a novel framework for safe robotic manipulation of movable articulated objects. The framework combines sampling-based control together with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">control barrier functions</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passivity theory</i> that, thanks to formal stability guarantees, enhance the safety and robustness of the method. We also provide the practical insights that enable robust deployment of stochastic control using a conventional central processing unit. We deploy the algorithm on a ten-degree-of-freedom mobile manipulator robot. Finally, we open source our generic and multithreaded implementation.
MGMASR: Multi-Graph and Multi-Aspect Neural Network for Service Recommendation in Internet of Services

With the flourishing development of Everything-as-a-Service (EaaS) and Internet of Everything (IoE), Internet of Services (IoS) has recently emerged as a new buzzword in the field of service computing. Providing accurate and personalized service recommendations to users is essential yet highly challenging in IoS, from a sea of services. Besides the severe sparsity of users’ historical behavior data on services, little study has been reported in the literature on fully exploiting multiple relationship networks embedded in IoS. To fill this gap, we propose a novel Multi-Graph and Multi-Aspect neural network-powered method for Service Recommendation in IoS. Graph neural networks (GNNs) and attention mechanism are jointly employed to simultaneously extract information from a collection of heterogeneous knowledge graphs, constructed from historical data recorded in IoS including the user-service interaction graph, the user-user social graph, and the service-mashup graph. Based on the knowledge learned, user-service interactions are scrutinized from multiple aspects to better learn the multiple preferences of users and the multiple characteristics of services, in order to refine their profiles for future recommendation. The results of extensive experiments over the real-world datasets have demonstrated that MGMASR outperforms the baseline methods and can provide service recommendations more accurately for users in IoS.
Larynx Ultrasound Image Stitching Based on Multiconstraint Super-Pixel Feature

In order to obtain wide-field ultrasound images and comprehensively observe the larynx and its surrounding tissues, this article presents a larynx ultrasound image stitching algorithm based on multconstraint super-pixel features, which can achieve effective stitching of local images on both sides of the larynx. In this study, the 120 larynx ultrasound images from healthy volunteers of different ages were collected, including children, middle-aged, and old-aged. First, to perform the pre-aligning of local field images of larynx, it can be represented by homography matrix from Scale-invariant Feature Transform (SIFT) point and super-pixel region features. Then, a multiconstraint energy function was introduced to further adjust the registration, which include Gabor texture feature constraint and saliency perception. Finally, a linear weighting blending method was utilized to determine the optimal seam and finish stitching. The proposed method outperforms compared state-of-the-art methods in the visualization results in stitching larynx ultrasound images of three different age groups. Compared with the closest competitor, the peak signal-to-noise ratio (PSNR) improves 7.6%, 6.3%, and 5.8%, the structural similarity index (SSIM) improve 12.9%, 6.5%, and 14.3%. This demonstrated that the stitching image obtained by proposed method has the least distortion and the highest image quality. Moreover, the maximum absolute error does not exceed 0.251 cm, which can basically be consistent with the ground truth. These experiments results illustrate the effectiveness and robustness of the proposed method on larynx ultrasound image stitching, and provide an effective and feasibility solution for the ultrasound diagnosis of the larynx.
A Comparative Analysis of Apriori and FP-Growth Algorithms for Market Basket Analysis Using Multi-level Association Rule Mining

Nowadays, many companies have massive amounts of data. Data mining is essential to gain business insights from large amounts of data. Market basket analysis is a data mining technique that analyzes customer buying behavior by discovering relationships between pairs of products purchased together. This analysis helps companies to design a better strategy for business decisions such as marketing and campaign management, store layout optimization, and inventory control. FP-Growth and Apriori are two widely used algorithms for market basket analysis. In this study, Apriori and FP-Growth algorithms are applied for market basket analysis with real-life data from an FMCG retailer. Furthermore, the performance of these algorithms is compared using multilevel association rules mining. Numerical results showed that the FP-Growth has greater performance than Apriori in terms of run time and memory for all levels of product groups. On the other hand, compared to FP-Growth, Apriori is better in terms of generating good candidates. At this point, a hybrid approach can be developed that minimizes the disadvantages of both algorithms.
Classification and regression trees

At the end of this chapter, you will be able to: Understand the circumstances in which Classification and Regression Trees (CARTs) can be used. Establish differences between the estimation criteria via Shannon's Entropy, Gini Index and Variance. Measure the impurity of a dataset. Understand the problem of overfitting and how to mitigate it with prunning techniques. Estimate CARTs in R, understand their differences and interpret their results.
Support vector machines

At the end of this chapter, you will be able to: Understand the reasons for using support vector machine algorithms. Understand the concepts of separating hyperplanes and maximal margin classifiers. Understand the differences between support vector classifiers and support vector machine algorithms. Implement support vector machine algorithms in R-based language, and interpret their results.
Quantitative Movement Analysis Using Scaled Information Implied in Monocular Videos

Background: Quantitative movement analysis is significant in clinic decision-making for neurological diseases. However, current methods fail to be widely used due to expensive equipment and professional workers. Methods: To make movement analysis more accessible even in remote areas, this paper provides a quantitative movement analysis framework based monocular camera analyzing gait parameters automatically. A size-aligned method is formulated to recover scaled information uncertain in a single view under scaled restrictions from body size and geometric constraints among subject joints’ positions. The image key points are the subject joints’ positions in images and the scaling key points are processed by the size-aligned method. The attention mechanism fuses deep representations learned from image key points and scaling key points respectively. The fused representations are decoded to provide final analyses. Results: The suggested learning framework can analyze parameters with strong correlations to the ground truth according to experimental results on a dataset for cerebral palsy: cadence <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(r=0.81)$ </tex-math></inline-formula> , speed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(r=0.78)$ </tex-math></inline-formula> , step length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(r=0.78)$ </tex-math></inline-formula> , knee flexion at maximum extension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(r=0.85)$ </tex-math></inline-formula> , gait deviation indexes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(r=0.75)$ </tex-math></inline-formula> , and asymmetric in gait deviation indexes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(r=0.55)$ </tex-math></inline-formula> . Conclusion: The encouraging findings show that it is feasible to quantitatively assess patients’ lower limb movement ability using a monocular camera.