Reviewerly/exHarmony huggingface dataset:
A novel variational Bayesian adaptive Kalman filter with mismatched process noise covariance matrix

Abstract This paper proposes a novel variational Bayesian (VB) adaptive Kalman filter with mismatched process noise covariance matrix (PNCM). Firstly, this paper explains the reason why the predicted error covariance matrix (PECM) is chosen for variational inference. Secondly, compared with the earlier VB adaptive Kalman filter (VB‐AKF‐Q), the proposed filter calculate the dynamic model of the PECM with its historical estimation information. Therefore, the proposed filter can overcome the influence of mismatched PNCM on the initial value setting of PECM in the VB‐AKF‐Q. Finally, we use the evidence lower bound for the proposed filter and give the convergence criterion on this basis. Some examples with a target tracking simulation are carried out to demonstrate the superiority of the proposed filter.
A Study on the Super Resolution Combining Spatial Attention and Channel Attention

Existing CNN-based super resolution methods have low emphasis on high-frequency features, resulting in poor performance for contours and textures. To solve this problem, this paper proposes single image super resolution using an attention mechanism that emphasizes high-frequency features and a feature extraction process with different depths. In order to emphasize the high-frequency features of the channel and space, it is composed of CSBlock that combines channel attention and spatial attention. Attention block using 10 CSBlocks was used for high-frequency feature extraction. In order to extract various features with different degrees of feature emphasis from insufficient low-resolution features, features were extracted from structures connected with different numbers of attention blocks. The extracted features were expanded through sub-pixel convolution to create super resolution images, and learning was performed through L1 loss. Compared to the existing deep learning method, it showed improved results in several high-frequency features such as small object outlines and line patterns. In PSNR and SSIM, it showed about 11% to 26% improvement over the existing Bicubic interpolation and about 1 to 2% improvement over VDSR and EDSR.
A Comprehensive MCDM-Based Approach for Object-Oriented Metrics Selection Problems

Object-oriented programming (OOP) is prone to defects that negatively impact software quality. Detecting defects early in the development process is crucial for ensuring high-quality software, reducing maintenance costs, and increasing customer satisfaction. Several studies use the object-oriented metrics to identify design flaws both at the model level and at the code level. Metrics provide a quantitative measure of code quality by analyzing specific aspects of the software, such as complexity, cohesion, coupling, and inheritance. By examining these metrics, developers can identify potential defects in OOP, such as design defects and code smells. Unfortunately, we cannot assess the quality of an object-oriented program by using a single metric. Identifying design-defect-metric-based rules in an object-oriented program can be challenging due to the number of metrics. In fact, it is difficult to determine which metrics are the most relevant for identifying design defects. Additionally, multiple thresholds for each metric indicates different levels of quality and increases the difficulty to set clear and consistent rules. Hence, the problem of object-oriented metrics selection can be ascribed to a multi-criteria decision-making (MCDM) problem. Based on the experts’ judgement, we can identify the most appropriate metric for the detection of a specific defect. This paper presents our approach to reduce the number of metrics using one of the MCDM methods. Therefore, to identify the most important detection rules, we apply the fuzzy decision-making trial and evaluation laboratory (Fuzzy DEMATEL) method. We also classify the metrics into cause-and-effect groups. The results of our proposed approach, applied on four open-source projects, compared to our previous published results, confirm the efficiency of the MCDM and especially the Fuzzy DEMATEL method in selecting the best rules to identify design flaws. We increased the defect detection accuracy by the selection of rules containing important and interrelated metrics.
Models for Generation of Proof Forest in zk-SNARK Based Sidechains

Sidechains are among the most promising scalability and extended functionality solutions for blockchains. Application of zero knowledge techniques (Latus, Mina) allows for reaching high level security and general throughput, though it brings new challenges on keeping decentralization where significant effort is required for robust computation of zk-proofs. We consider a simultaneous decentralized creation of various zk-proof trees that form proof-trees sequences in sidechains in the model that combines behavior of provers, both deterministic (mutually consistent) or stochastic (independent) and types of proof trees. We define the concept of efficiency of such process, introduce its quantity measure and recommend parameters for tree creation. In deterministic cases, the sequences of published trees are ultimately periodic and ensure the highest possible efficiency (no collisions in proof creation). In stochastic cases, we obtain a universal measure of prover efficiencies given by the explicit formula in one case or calculated by a simulation model in another case. The optimal number of allowed provers’ positions for a step can be set for various sidechain parameters, such as number of provers, number of time steps within one block, etc. Benefits and restrictions for utilization of non-perfect binary proof trees are also explicitly presented.
Domain transfer and difference-aware band weighting for object tracking in hyperspectral videos

Object tracking plays an important role in computer vision. In recent years, hyperspectral object tracking has gained increasing attention because the material information contained in a large number of spectral bands of hyperspectral images (HSIs), which is critical in distinguishing the target from the background. However, owing to the high-dimensional characteristics of HSIs and complex real-world scenarios, hyperspectral object tracking remains a challenging task. In this paper, we propose a domain transfer and difference-aware band weighting (DT-DBW) tracker for hyperspectral object tracking. Firstly, a domain transfer module is designed to adjust the feature distribution of HSIs, so that the deep learning object tracker can be effectively applied to hyperspectral videos. To further improve the performance and accuracy of the tracker, a difference-aware band weighting module is implemented to exploit the spectral difference features between the target and the background to generate individual band weights for the hyperspectral videos. Through the band weighting operation, the spectral response value of HSIs is recalibrated to enhance the value of spectral information and suppress the background spectral information. Experimental results on hyperspectral datasets demonstrate that the Area-Under-Curve (AUC) and tracking speed of DT-DBW tracker are up to 0.647 and 48.6 FPS, outperforming existing hyperspectral object trackers.
Temporal Extraction of Complex Medicine by Combining Probabilistic Soft Logic and Textual Feature Feedback

In medical texts, temporal information describes events and changes in status, such as medical visits and discharges. According to the semantic features, it is classified into simple time and complex time. The current research on time recognition usually focuses on coarse-grained simple time recognition while ignoring fine-grained complex time. To address this problem, based on the semantic concept of complex time in Clinical Time Ontology, we define seven basic features and eleven extraction rules and propose a complex medical time-extraction method. It combines probabilistic soft logic and textual feature feedback. The framework consists of two parts: (a) text feature recognition based on probabilistic soft logic, which is based on probabilistic soft logic for negative feedback adjustment; (b) complex medical time entity recognition based on text feature feedback, which is based on the text feature recognition model in (a) for positive feedback adjustment. Finally, the effectiveness of our approach is verified in text feature recognition and complex temporal entity recognition experimentally. In the text feature recognition task, our method shows the best F1 improvement of 18.09% on the Irregular Instant Collection type corresponding to utterance l17. In the complex medical temporal entity recognition task, the F1 metric improves the most significantly, by 10.42%, on the Irregular Instant Collection type.
Application of Optimized ORB Algorithm in Design AR Augmented Reality Technology Based on Visualization

The current media digitization and artistic strength are more powerful than the previous application. Using its advanced information display methods and technologies, this paper proposed a digital museum built by integrating digital media art with AR technology, which was helpful to analyze and solve the objective problems of current museums’ ecological imbalance and single-system function. Based on the principles and laws of augmented reality technology, the museum guide system is optimized. In the system evaluation experiment, firstly, the cultural relics of six kinds of materials are used as the target image to extract and identify the features of the image. In experiments, the recognition performance of three feature algorithms, Binary Robust Invariant Scalable Keypoints (BRISK), organizational retaliatory behavior (ORB), and Accelerated-KAZE (AKAZE), is compared. Among them, the ORB algorithm is superior to other algorithms in feature richness and recognition speed but is inferior to the other two algorithms in recognition accuracy. Therefore, this paper optimized the ORB algorithm based on the characteristics of the ORB algorithm. The ORB algorithm must calculate the orientation of the feature points before constructing the feature descriptor. After optimizing the parameters, the improved ORB algorithm not only has advantages in feature richness and recognition time but also improves the recognition accuracy up to 98.3%, which is 16% higher than the traditional ORB algorithm. Therefore, the application prospects of AR technology in digital media design are very important.
Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN). Part III: Enhancement of Scanco micro-CT images of granodiorite rocks using a 3D convolutional neural network super-resolution algorithm

X-ray micro-computed tomography (micro-CT) is a standard method to perform three-dimensional analysis of the internal structure of a rock sample. 3D X-ray microscopes, such as those from the XRadia Versa family, provide images of high resolution and contrast. Medical scanning machines can also be used for scanning rock samples to reduce operational cost and time, but they generally provide poorer spatial resolution and contrast compared to 3D X-ray microscopes. Recent success in implementing deep learning algorithms to enhance image quality demonstrated that, in some cases, the application of convolutional neural network (CNN) models might significantly enhance the resolution of the micro-CT images. In this research, a super-resolution technique employing the U-Net 3D CNN architecture is applied to enhance the resolution of granodiorite rock sample images obtained by two different 3D scanning machines. The high-resolution dataset was obtained using the XRadia Versa XRM-500 microscope. It contained images with nominal resolutions of 10.3 and 5μm. The low-resolution scanning was performed using a Scanco medical μ CT 50 machine, and the images from this dataset had a nominal resolution of 10.3μm. Several models were created to enhance the quality of the low-resolution images, and the results were analysed. It was observed that super-resolution processing could significantly improve the low-resolution micro-CT image quality and suppress noise that appeared on medical images. The results presented in this study are of particular interest and value to geoscientists that use medical scanners to study the structure of rock samples at large scale.
An efficient and provably secure certificateless protocol for the power internet of things

The Power Internet of Things (IoT) is a specific application of the IoT in the power grid. The threat existing in the Power IoT mainly comes from the terminal deployed in the open environment, which has the risk of being tampered with and impersonated. Thus, we design a certificateless anonymous key agreement protocol for the Power IoT to solve the problem of terminal access. The proposed protocol completes session key agreement in only one round exchange of key materials on the basis of certificatelessness, which saves the cost of certificate management. It also achieves anonymity by encrypting the authentication information to ensure the identity privacy of terminals. In addition, the protocol prevents malicious connections of internal systems by setting up software defined perimeter (SDP) controllers to complete pre-access authentication of terminals. Moreover, an improved eCK security model is designed and applied to demonstrate the proposed protocol. An experiment and analyses indicate that our scheme has advantages in terms of the communication and computation costs that are suitable for Power IoT.
What Does It Mean to Anthropomorphize Robots?

Anthropomorphism is a well-used but vague concept that demands further understanding and clarification to be effectively used in HRI research. Although most HRI research defines and uses anthropomorphism as a human-like attribution process, there is lack of distinction between its deployment in design versus its manifestation in user response. Furthermore, researchers need to separate mindless from mindful anthropomorphism and find ways to theorize and measure each. Researchers also need to consider the dynamic and contextual nature of anthropomorphism to generate relevant findings for research as well as practice.
FedFTHA: A Fine-Tuning and Head Aggregation Method in Federated Learning

Personalized federated learning (PFL) is a subfield of federated learning. Contrary to conventional federated learning that expects to find a general global model, PFL generates a personalized model adapted to its local data distribution for each client. Some existing PFL methods only consider improving the client-side personalization ability, discarding the server-side generalization capacity. To address this issue, we propose a fine-tuning and head aggregation method in federated learning (FedFTHA). It allows each client to maintain a personalized model head and fine-tune it after each local update to generate a local model containing the personalized head. Specifically, during FedFTHA training, these personalized heads are aggregated to generate a generalized head for the global model. FedFTHA meets the needs of both client-side model personalization and server-side model generalization. In addition, a universal optimization framework is employed to prove its convergence under convex and nonconvex conditions. We verify the personalization ability and generalization performance of FedFTHA under heterogeneous settings with benchmark data sets. The comparative analysis authenticates the significance of our proposal.
A Multimodal 3-D Detector With Attention From the Corresponding Modal

Robust 3-D detection based on camera-light detection and ranging (LiDAR) fusion has become a research focus for autonomous driving. Despite the extensive efforts invested, it remains an extremely challenging task to effectively establish fine-grained corresponding relations between different modalities and achieve complementary multimodal fusion free of interference. To this end, we propose a multimodal 3-D detector termed a corresponding feature-adaptive fusion network (CFFNet), mainly consisting of a multimodal feature extraction backbone network and a corresponding feature-based attention fusion (CFAF) module. Specifically, the multimodal feature extraction backbone is devised to build a simple, yet effective cross-modal correspondence by unifying multimodal features into a shared bird’s eye view (BEV) space. Expressive and noise-suppressed image BEV features are generated from pseudo-image voxels, whose depth information is estimated with range-wise attention drawn from LiDAR BEV features. With the prominent heterogeneity of multimodal features taken into consideration, the CFAF module performs a discriminative fusion targeted at informative and contributive features retained in different modalities. The fusion is accomplished by element-wise reweighting image BEV features with attention generated from differential features of LiDAR. The multimodal feature extraction backbone and CFAF module are both evaluated and proved to offer significant performance gains separately and together. Evaluations conducted on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) 3-D object detection dataset show that the proposed CFFNet achieves state-of-the-art (SOTA) performance.
UIE-FSMC: Underwater Image Enhancement Based on Few-Shot Learning and Multi-Color Space

Light propagates in water with certain attenuation, which causes quality problems, such as color cast, low contrast, and low illumination, in underwater images. Moreover, it is generally difficult to obtain a large number of real-world underwater images and the corresponding ground truth in convoluted underwater environments. To address these two problems, this article recommends an underwater image enhancement scheme based on few-shot learning and multi-color space, called UIE-FSMC. Specifically, for the first time, we propose a lightweight underwater image enhancement network based on few-shot learning. We design a new strategy to train the network. Specific training steps include the following: First, synthetic underwater images are used for large-scale pre-training, which can obtain the initial weight of the network. Then, according to the characteristics of the data, meta-learning based on supervised and unsupervised loss is suggested. It further enhances the feature expression aptitude of the network by learning the external and internal characteristics of the data. Finally, fine-tuning based on supervised and unsupervised loss is designed. It further improves the accuracy, robustness, and generalization of the network through an average strategy. In addition, we design a post-processing method founded on the RGB and LAB color spaces. In the RGB color space, we suggest a local region-based adaptive color correction method for underwater images. In the LAB color space, we design a multi-scale local adaptive contrast enhancement method for the L channel and a local region-based color balance strategy for the AB channels. Quantitative and qualitative experiments on five different underwater image datasets show that the outcomes of UIE-FSMC are superior to those of other techniques. Furthermore, application research further validates the excellent performance of UIE-FSMC.
Applications of Knowledge Graphs in Telecommunication Systems Management

Telecommunications systems management represents a technical challenge with profound economic implications. Many different sources of information must be integrated, and complex decisions must be automatically taken and executed. In this article, we review the usage of knowledge graphs in this domain since they are a powerful tool for dealing with data integration and complex knowledge representation. The main insights are that they have been applied in several areas of telecommunications management, such as resource/service discovery and monitoring, with a particular focus on software-defined networks. Their main advantages are their extensibility and interoperability. However, data quality and graph performance are still challenges, and work is yet to be done for its application to the rest of the telecommunication management areas.
Multi-Spectral Palmprints Joint Attack and Defense With Adversarial Examples Learning

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">As an emerging biometric technology, multi-spectral palmprint recognition has attracted increasing attention in security due to its high accuracy and ease of use. Compared to single spectral case, multi-spectral palmprint model is more susceptible to the attack of adversarial examples. However, the previous adversarial example attack approaches cannot generate the most aggressive adversarial examples for multi-spectral palmprint recognition. In addition, most of them are dependent on the explicit architecture or need time-consuming queries about the network to be attacked, which significantly limits their application in the field of security. To solve the above problems, in this paper, we proposed the multi-spectral palmprints joint attack and defense framework based on multi-view adversarial examples learning. First, we respectively capture the multi-view deep common feature space for the different spectra and the discriminative feature space across the different subjects. Second, we introduce perturbation in the deep common space to achieve adversarial multi-spectral palmprints with gradient propagation. In addition, we pursue the manifold of the difference space and use it to suppress the discriminability of the recognition model with adversarial region theory. Finally, the generated adversarial examples are fed into the training model to enhance the robustness of the recognition algorithm. The experimental results on multi-spectral palmprint dataset demonstrate that the proposed multi-view joint attack approach is superior to the state-of-the-art adversarial example attack methods in attack accuracy and transferability. Moreover, the defense strategy with the adversarial examples by our method can significantly promote the robustness of multi-spectral palmprint recognition methods</i> .
Comparison of Search Behaviors in Chess, Shogi, and the game of Go

Searching is important in games, and the relationship between depth and the best-changes has been investigated in chess programs using alpha-beta search. The combination of Deep Neural Network and Monte-Carlo Tree Search is a successful method in chess, shogi, and go, and it is important to investigate this method. Our purpose in this work is to find the differences in games and in search methods. If programs using the same method behaves differently in the different games, it may be possible to find the differences in the games. In this paper, we focus on an increase or decrease in evaluation value with increasing search depth reported in previous research in chess and the problem of fortress. We obtained the different results from the previous work when using Leela Chess Zero, a chess program with Deep Neural Network and Monte-Carlo Tree Search, although the results for other game programs with Deep Neural Network and Monte-Carlo Tree Search and other chess programs are the same from the previous work. The combination of a large number of draws and pUCT is a possible cause. We believe that there is room to allocate more resources to the best moves and that improvements can be made through ingenuity.