Reviewerly/exHarmony huggingface dataset:
Balancing Localization Accuracy and Location Privacy in Mobile Cooperative Localization

Location privacy leakage in cooperative localization is receiving increasing attention. Geo-indistinguishability (GI) protects location privacy by adding random noise to an actual location, which inevitably degrades localization accuracy. This work considers a cooperative localization scenario with mobile nodes, where the cooperative nodes (CNs) share their locations under GI. We investigate the tradeoff between target localization accuracy and CNs’ location privacy. Different from the static scenario, the temporal correlations among the target states and CN locations in the mobile scenario are considered in evaluating the localization accuracy and location privacy. From the aspect of the target, we derive the posterior Cramér-Rao lower bound (PCRLB) about the target state and provide the theoretical localization accuracy. From the aspect of the CNs, their location privacy is evaluated by the squared position error bound (SPEB) inferred by adversaries. To balance the target localization accuracy and CNs’ location privacy, we propose a game-based incentive mechanism by formulating a bilevel joint optimization problem, which meets the objectives of the CNs and the target. First, we derive the closed-form solution for CNs’ privacy preservation strategies by maximizing their utilities. Second, with the privacy preservation information from CNs, the target decides its payment allocation strategy by minimizing its localization error under a finite budget. Extensive numerical results validate our theoretical analysis and the effectiveness of the proposed incentive mechanism in terms of localization accuracy and CNs’ utilities.
Distributed Fusion With Unknown Inputs Under Bandwidth-Aware Event-Triggered Mechanisms: Monotonicity and Boundedness

This paper addresses the distributed fusion estimation problem for a class of nonlinear systems subject to both unknown inputs and sensor failures, where a bandwidth-aware dynamic event-triggered strategy is proposed to reduce communication burden and energy consumption. First, a novel distributed filter is constructed with the aid of an intermediate variable resolving unknown inputs. In light of such a structure combined with Kalman filtering theory, an upper bound of the filtering error covariance is derived and subsequently minimized by designing appropriate gains. Furthermore, the distributed fusion depends on an optimization obtained under the covariance intersection fusion strategy. The ultimate boundedness and the monotonicity with respect to triggered thresholds are discussed for the minimized upper bound of the fusing error covariance. Finally, the effectiveness of the proposed method is vivid through illustrative simulation examples.
Blockchain: A Background for the Sake of Understanding

Technology is always present later to answer the problems faced by humans. One of them is about security and ease of transaction. The concept is as conflicting affairs in both their answers and their resolutions. An answer provides insight into getting the appropriate technology. Technology has the characteristics of connecting, which is enough to help special human activities and avoid various behaviors from human interests. From an ever-evolving concept, blockchain is the answer, but it requires understanding to solve related issues, where it requires a background from a different perspective. This paper reveals the essence of human interest in interacting and transacting based on several conditions of human attitude towards this technology.
The Use of Digital Educational Resources in the Educational Process

To date, the use of digital educational resources in various fields of modern education is large-scale and complex. The relevance of the topic is that today’s changing world requires new approaches to education, improving its quality and accessibility. The paper describes the experience of digital resources getLocus, Yandex.Maps, Yandex.Constructor. An example of effective use of digital resources to improve educational outcomes of students is given. #CSOC1120.
Gamification of E-Learning Based on Information Technology

E-learning has become an integral part of the educational environment. It makes it possible to significantly expand the methods and techniques used in traditional education. One of the elements that are actively used in the modern educational process is gamification. Gamification allows to increase students' motivation and involvement. It allows to improve the learning of theoretical material, gives an opportunity to improve practical skills in the study of a discipline. The paper presents the process of creating a project-game when reconstructing mountainous terrain, using 3D computer graphics game engine Unreal Engine. Creation of the mountain terrain simulator by students allows students to get acquainted with computer modeling in a game form, as well as to master practical skills and abilities in the field of information technology.
Dialectical Counterpoints: Luigi Nono and Nanni Balestrini's Collaboration

Abstract This article discusses the relationship between two seminal figures of the 1960s Italian experimental art scene: composer Luigi Nono and neo-avant-garde poet Nanni Balestrini. In particular, it focuses on their collaboration, in 1968, on Contrappunto dialettico alla mente (Dialectical Counterpoint for the Mind), a composition for two-channel magnetic tape based on Adriano Banchieri's 1608 madrigal Contrappunto bestiale alla mente (Bestial Counterpoint for the Mind). On the one hand, this essay provides a contextual analysis of Balestrini's four original poetic texts, which Nono elaborated electronically at the RAI's Studio di Fonologia in Milan. On the other, it demonstrates how the compositional technique used by Nono for Contrappunto dialettico alla mente inspired the later creation of Balestrini's eponymous poem published in 1969. Finally, this article argues for a novel reading of Balestrini's poem as the linguistic attempt at transposing to writing Nono's electronic montage and sound manipulation of the human voice.
SCWOMP Recovery Algorithm for 5G MIMO Communication Symbol Detection

In order to solve the problem of small capacity and high energy consumption in China’s 5G communication technology system, the research proposes that based on the segmented weakly orthogonal matching pursuit (SWOMP) algorithm, it is combined with the compressed sensing matching pursuit algorithm to form a segmented backtracking weak selection positive algorithm and Cross Match Tracking (SCWOMP) algorithm. First, the sparseness of MIMO system technology and its transmission structure is analyzed. Then, the new model is built after comparing with other algorithms, and the problem of overestimating the low recovery probability in the calculation process is improved by the backtracking of the algorithm and the improvement of the angle of the atomic column selection, so as to reduce the number of iterations and improve the performance of the algorithm. The results show that, in the performance comparison of different sampling points under different compressed sensing recovery algorithms, the recovery probability of the SCWOMP algorithm is the best, and when the number of sampling points is 80, although the fixed step size of the SCWOMP algorithm is different, there is recovery. The probability has a maximum value, close to 1. Then, the improved compressed sensing recovery algorithm is simulated and analyzed. When the pruning coefficient is 0.5 and the number of sampling points is 80, the reconstruction rate has a maximum value, and when other algorithms reach the maximum reconstruction rate, the number of sampling points (M) is significantly greater than that of the SCWOMP algorithm. An increase in the rate of reduction of the reconstruction probability of the SCWOMP algorithm is significantly lower than that of other algorithms; when sparsity is equal to 70, the reconstruction probability becomes 0, indicating that SCWOMP has a wider reconfigurable range and has a significant performance effect. This shows that the proposed SCWOMP algorithm has the best detection performance for 5G communication symbol detection, which can effectively increase the capacity of the system and better promote technology.
CNN with Embedding Transformers for Person Reidentification

For person reidentification (ReID), most slicing methods (such as part-based convolutional baseline (PCB) and AlignedReID) introduce a lot of background devoid of pedestrian parts, resulting in the cross-aliasing of features in the deep network. Besides, the resulting component features are not perfectly aligned with each other, thus affecting model performance. We propose a convolutional neural network (CNN) with embedding transformers (CET) person ReID network architecture based on the respective advantages of CNN and transformer. In CET, first, the residual transformer (RT) structure is first embedded in the backbone network of CNN to obtain a feature extractor, named transformers in CNN. The feature aliasing phenomenon is improved by utilizing transformer’s advantage in grasping the relevance of global information. Second, a feature fuse with learnable vector structure for fusing the output vector is added to the output of the transformer at the end of the network. A two branches loss structure is designed to balance the two different fusion strategies. Finally, the self-attention mechanism in transformer is used for automatic part alignment of human body parts to solve the part alignment problem caused by inaccurate detection frames. The experimental results show that CET network architecture achieves better performance than PCB and some other block-slicing methods.
Data Mining and Machine Learning

This chapter introduces data mining and machine learning. It also introduces the concept of generalization, and shows methods to evaluate the performance of supervised machine learning.
Poetry4painting: Diversified poetry generation for large-size ancient paintings based on data augmentation

Chinese painting poetry is an extraordinary art form, which not only describes the painting contexts but also grasps the sentiment of the painters. In this paper, we propose an automatic poetry generation method Poetry4painting, which enhances the poetry diversity for large-size ancient paintings. The basic framework is based on multiple modern sentences, that are first captioned from the ancient painting and then used to generate a poem using CNN and LSTM. To solve the repeatability issue of this framework, four kinds of data augmentation are employed during online processing, including quantity, shape, surrounding, and object augmentation. In offline training, data augmentation is also used to create an image caption dataset with over 1500 painting images and 7500 captions. Through ablation studies, evaluations of poetry qualities and diversities, and comparisons with other methods, we demonstrate the validity of the proposed method.
Feature structure distillation with Centered Kernel Alignment in BERT transferring

Knowledge distillation is an approach to transfer information on representations from a teacher to a student by reducing their difference. A challenge of this approach is to reduce the flexibility of the student’s representations inducing inaccurate learning of the teacher’s knowledge. To resolve the problems, we propose a novel method feature structure distillation that elaborates information on structures of features into three types for transferring, and implements them based on Centered Kernel Analysis. In particular, the global local-inter structure is proposed to transfer the structure beyond the mini-batch. In detail, the method first divides the feature information into three structures: intra-feature, local inter-feature, and global inter-feature structures to subdivide the structure and transfer the diversity of the structure. Then, we adopt CKA which shows a more accurate similarity metric compared to other metrics between two different models or representations on different spaces. In particular, a memory-augmented transfer method with clustering is implemented for the global structures. The methods are empirically analyzed on the nine tasks for language understanding of the GLUE dataset with Bidirectional Encoder Representations from Transformers (BERT), which is a representative neural language model. In the results, the proposed methods effectively transfer the three types of structures and improves performance compared to state-of-the-art distillation methods: (i.e.) ours achieve 66.61% accuracy compared to the baseline (65.55%) in the RTE dataset. Indeed, the code for the methods is available at https://github.com/maroo-sky/FSD.
Toward practical and plausible counterfactual explanation through latent adjustment in disentangled space

Extensive research into eXplainable AI (XAI) has raised interest in generating counterfactual (CF) explanations. In the past, minimizing the perturbation of input was considered a priority aspect of CF for the benefit of user practicality. However, closeness to the CF data manifold, indicating plausibility, is now emerging as another important property of CF. Thus, we propose a novel framework for generating practical and plausible CFs by minimally perturbing the semantic information of inputs in a disentangled latent space of a generative adversarial network (GAN). Considering the possibility of linear change of semantic information in a disentangled latent space, we obtain the desired CFs using proposed algorithms that adjust the input latents and reference CF latents derived using an optimization-based GAN inversion method. The results of qualitative and quantitative experiments on several datasets from different domains demonstrate the superiority and versatility of our framework. In comparative experiments, it not only achieves 1.0 Validity for test samples from all datasets but also achieves the minimum values of 0.07 Dissimilarity, 5.96 Rec. Error, 0.94 IM1, and 0.01 Infer. Time for the MNIST dataset.
Evidential Decision Theory via Partial Markov Categories

We introduce partial Markov categories. In the same way that Markov categories encode stochastic processes, partial Markov categories encode stochastic processes with constraints, observations and updates. In particular, we prove a synthetic Bayes theorem; we apply it to define a syntactic partial theory of observations on any Markov category whose normalisations can be computed in the original Markov category. Finally, we formalise Evidential Decision Theory in terms of partial Markov categories, and provide examples.
Computational expressivity of (circular) proofs with fixed points

We study the computational expressivity of proof systems with fixed point operators, within the ‘proofs-as-programs’ paradigm. We start with a calculus μLJ (due to Clairambault) that extends intuitionistic logic by least and greatest positive fixed points. Based in the sequent calculus, μLJ admits a standard extension to a ‘circular’ calculus CμLJ.Our main result is that, perhaps surprisingly, both μLJ and CμLJ represent the same first-order functions: those provably total in $\Pi _2^1 - {\text{C}}{{\text{A}}_0}$, a subsystem of second-order arithmetic beyond the ‘big five’ of reverse mathematics and one of the strongest theories for which we have an ordinal analysis (due to Rathjen). This solves various questions in the literature on the computational strength of (circular) proof systems with fixed points.For the lower bound we give a realisability interpretation from an extension of Peano Arithmetic by fixed points that has been shown to be arithmetically equivalent to $\Pi _2^1 - {\text{C}}{{\text{A}}_0}$ (due to Möllerfeld). For the upper bound we construct a novel computability model in order to give a totality argument for circular proofs with fixed points. In fact we formalise this argument itself within $\Pi _2^1 - {\text{C}}{{\text{A}}_0}$ in order to obtain the tight bounds we are after. Along the way we develop some novel reverse mathematics for the Knaster-Tarski fixed point theorem.
Novel Bilinear Fusion Network Based on Multimodal Data for Student Distracted Behavior Recognition

As governments, education departments, and academic accreditation bodies have begun to encourage schools to develop evidence-based decision-making and innovation systems, learning analysis techniques have shown great advantages in decision-making aid and teaching evaluation. After integrating relevant algorithms and technologies in artificial intelligence and machine learning, learning analysis has achieved higher analysis accuracy. In order to realize the recognition of students' classroom behaviors such as standing up, sitting up, and raising hands and improve the recognition accuracy and recall rate, multi-modal data such as human key point information and RGB images are used for experiments. To further improve the feature extraction capability of the model, features are extracted from the improved ResNet-50 and EfficientNet-B0 models, and bilinear fusion is performed to further improve the recognition accuracy of the models.
On Edge Detection Algorithms for Water-Repellent Images of Insulators Taking into Account Efficient Approaches

Computer vision has become an essential interdisciplinary field that aims to extract valuable information from digital images or videos. To develop novel concepts in this area, researchers have employed powerful tools from both pure and applied mathematics. Recently, the use of fractional differential equations has gained popularity in practical applications. Moreover, symmetry is a critical concept in digital image processing that can significantly improve edge detection. Investing in symmetry-based techniques, such as the Hough transform and Gabor filter, can enhance the accuracy and robustness of edge detection algorithms. Additionally, CNNs are incredibly useful in leveraging symmetry for image edge detection by identifying symmetrical patterns for improved accuracy. As a result, symmetry reveals promising applications in enhancing image analysis tasks and improving edge detection accuracy. This article focuses on one of the practical aspects of research in computer vision, namely, edge determination in image segmentation for water-repellent images of insulators. The article proposes two general structures for creating fractional masks, which are then calculated using the Atangana–Baleanu–Caputo fractional integral. Numerical simulations are utilized to showcase the performance and effectiveness of the suggested designs. The simulations’ outcomes reveal that the fractional masks proposed in the study exhibit superior accuracy and efficiency compared to various widely used masks documented in the literature. This is a significant achievement of this study, as it introduces new masks that have not been previously used in edge detection algorithms for water-repellent images of insulators. In addition, the computational cost of the suggested fractional masks is equivalent to that of traditional masks. The novel structures employed in this article can serve as suitable and efficient alternative masks for detecting image edges as opposed to the commonly used traditional kernels. Finally, this article sheds light on the potential of fractional differential equations in computer vision research and the benefits of developing new approaches to improve edge detection.
A convolutional neural network-based blind robust image watermarking approach exploiting the frequency domain

Image watermarking embeds information in the image that is visually imperceptible and can be recovered even if the image is modified or attacked during distribution, thus protecting the image copyright. Current image watermarking methods make the learned model resistant to attacks by simulating specific attacks but lack robustness to unspecified attacks. In this paper, we propose to hide the information in the frequency domain. To control the distribution and intensity of watermarking information, we introduce a channel weighting module based on modified Gaussian distribution. In the spatial domain, we design a spatial weighting module to improve the watermarking visual quality. Moreover, a channel attention enhancement module designed in the frequency domain senses the distribution of watermarking information and enhances the frequency domain channel signals to improve the watermarking robustness. Abundant experimental results show that our method guarantees high image visual quality and high watermarking capacity. The generated watermarking images can robustly resist unspecified attacks such as noise, crop, blur, color transform, JPEG compression, and screen-shooting.
QPDE: Quantum Neural Network Based Stabilization Parameter Prediction for Numerical Solvers for Partial Differential Equations

We propose a Quantum Neural Network (QNN) for predicting stabilization parameter for solving Singularly Perturbed Partial Differential Equations (SPDE) using the Streamline Upwind Petrov Galerkin (SUPG) stabilization technique. SPDE-Q-Net, a QNN, is proposed for approximating an optimal value of the stabilization parameter for SUPG for 2-dimensional convection-diffusion problems. Our motivation for this work stems from the recent progress made in quantum computing and the striking similarities observed between neural networks and quantum circuits. Just like how weight parameters are adjusted in traditional neural networks, the parameters of the quantum circuit, specifically the qubits’ degrees of freedom, can be fine-tuned to learn a nonlinear function. The performance of SPDE-Q-Net is found to be at par with SPDE-Net, a traditional neural network-based technique for stabilization parameter prediction in terms of the numerical error in the solution. Also, SPDE-Q-Net is found to be faster than SPDE-Net, which projects the future benefits which can be earned from the speed-up capabilities of quantum computing.
Sparse Subgraph Prediction Based on Adaptive Attention

Link prediction is a crucial problem in the analysis of graph-structured data, and graph neural networks (GNNs) have proven to be effective in addressing this problem. However, the computational and temporal costs associated with large-scale graphs remain a concern. This study introduces a novel method for link prediction called Sparse Subgraph Prediction Based on Adaptive Attention (SSP-AA). The method generates sparse subgraphs and utilizes Graph SAmple and aggreGatE (GraphSAGE) for prediction, aiming to reduce computation and time costs while providing a foundation for future exploration of large-scale graphs. Certain key issues in GraphSAGE are addressed by integrating an adaptive attention mechanism and a jumping knowledge module into the model. To address the issue of adaptive weight distribution in GraphSAGE, an aggregation function is employed, which is based on the attention mechanism. This modification enables the model to distribute weights adaptively among neighboring nodes, significantly improving its ability to capture node relationships. Furthermore, to tackle the common issue of over-smoothing in GNNs, a jumping knowledge module is integrated, enabling information sharing across different layers and providing the model with the flexibility to select the appropriate representation depth based on the specific situation. By enhancing the quality of node representations, SSP-AA further boosts the performance of GraphSAGE in various prediction tasks involving graph-structured data.