Reviewerly/exHarmony huggingface dataset:
STVP: A Spatiotemporal Visual Perception Method for User-generated Content Video Quality Assessment

With the popularity and development of short video applications, the behavior of using mobile devices to shoot and share user-generated content (UGC) videos has become increasingly common. Video quality assessment (VQA) is critical in guaranteeing end-user viewing experiences. UGC-VQA is a challenging problem due to the complexity and variety of distortion types of UGC videos and the absence of reference videos. To improve the consistency of UGC-VQA results and human subjective ratings, in this paper, we propose a UGC-VQA method based on spatiotemporal visual perception (STVP). Firstly, a hierarchical feature fusion module was added to the feature extraction network to realize the fusion of low-level visual features and high-level semantic features, and obtain the quality perception features with rich visual information. Then, we use the self-attention to weight different frames to distinguish their importance. The long short-term memory (LSTM) network and the time pool are used to model long-term dependencies and temporal memory effects. Experimental results on UGC-VQA datasets show that the proposed method achieves a performance improvement of nearly 2%, and its evaluation results are more consistent with human visual perception.
Enhancing 3D Human Skeleton Key-point Detection through Weakly Supervised Learning and Multi-level Attention Mechanisms

With the soaring interest in understanding the dynamics of human body skeletons for applications such as action recognition and video understanding, the significance of precise 3D key-point detection has become increasingly prominent. Despite the advancements, existing approaches struggle to address the issues of occlusions and limited annotated data. This paper proposes a novel framework integrating a multilevel attention mechanism and weakly supervised 3D key-point generation to tackle these prevalent issues, enhancing both the accuracy and efficiency of human pose estimation.
Feature Selection Using Evolutionary Techniques

Data clustering has many applications in machine learning, data mining and image processing. K-means is the most popular clustering algorithm due to its efficiency and simplicity of implementation. However, K-means has limitations, such as large feature spaces, which may affect its effectiveness. To improve K-means accuracy, we adopt the Biogeography-Based Optimization (BBO) evolutionary technique to select the most relevant features of datasets. We conducted several experiments to compare our approach with other methods, such as PCA and Particle Swarm Optimization (PSO). The results demonstrate the effectiveness of BBO for feature selection.
Multi-scale and Multi-view Feature Blending for Free-viewpoint Rendering of Dynamic Humans

With the great success in neural radiance fields (NeRF), human-specific NeRF has been actively introduced in recent years. However, such human rendering techniques often have difficulties to recover subtle details of textural surfaces such as wrinkles on clothes, which are mostly generated by complicated human motions. To solve this problem, we propose a new method for human-specific NeRF based on multi-scale and multi-view feature blending. Specifically, multi-scale image features, which are sampled from different views, are combined into the transformer architecture. This blended feature successfully guides the NeRF network to render photo-realistic results by enhancing local details of the human performer. Experimental results on the ZJU-MoCap dataset show that the proposed method outperforms previous methods both in qualitative and quantitative evaluations.
CSANet: Cross-Semantic Attention Network for Open-Set Object Recognition

With the increase of real-world scenarios such as robotics, urban rescue and autonomous driving, deep learning models are increasingly exposed to open-set scenarios where established methods should separate the known and unknown categories in the real world. However, most existing open-set recognition methods treat all features equally and focus on learning features that facilitate the discrimination of categories during the training, which is detrimental to the performance of models in the open world. In response to this challenge, we propose a novel framework based on a Cross-Semantic Attention Network (i.e., CSANet) to guide the model to explore more comprehensive features. In detail, we apply cross-semantic attention to guide both the high-level semantic features and a set of learnable category prototypes, which encourages the model to better characterise known categories and facilitates its ability to discriminate unknown categories in the open world. In addition, we develop a combined loss that widens the inter-category distance and narrows the intra-category distance, thus reserving the unknown categories a larger position in the feature space. Experimental results on several popular open-set recognition datasets demonstrate the effectiveness and efficiency of our method.
Cooperative path planning for USVs assembly task

In recent years, with the gradual increase of human exploration and development of marine resources, unmanned surface vessel (USV) has shown great application prospects. In this paper, a distributed cooperative path planning method for USVs assembly task is proposed. First, a guidance vector field is constructed for local path planning based on the global path to guide USVs to avoid obstacles, and a reinforcement learning strategy is introduced for adaptive coefficient adjustment of the vector field. Then, the speed of each USV is regulated using the average consensus theory to satisfy the constraint of simultaneous arrival. Finally, simulation experiments are conducted to verify the feasibility and effectiveness of the proposed approach.
A Multimodal Target Speech Extraction Algorithm Based on Long Short Term Attention Mechanism

Most of the existing audio-video fusion mechanisms are fused by directly splicing or summing audio-visual cues, which cannot make full use of both kinds of information to work together. In order to solve this problem, a multimodal target speech extraction algorithm based on long short term attention mechanism is proposed in this paper. In this algorithm, firstly, the features of audio and lip are extracted by convolutional neural network and chunked with an overlap factor of 50% on the time axis. Secondly, the short term correlation and the long term correlation between the sequences are calculated by the long short term attention mechanism (LSTA). Finally, the obtained target speech mask sequence is multiplied with the target speech sequence and passed through the decoder to obtain the estimated target speaker speech. Experimental results show that compared with contrast algorithms, the proposed method performs better in the scale-invariant signal-to-noise ratio improvement (SI-SNRi) and perceptual evaluation of speech quality (PESQ), and achieves consistent improvement in cross-dataset evaluation.
Speech Separation for Multi-speaker Scenarios Based on Tensor Factorization Initialization

Speech separation is considered a complicated problem because the observed signals are made up of latent components and their delayed counterparts. However, speech separation is regarded as a critical pre-processing step for speech recognition. There are two flaws with the previous separation approaches. First, most of them are sensitive to initial values due to the large number of parameters involved. Second, speech separation in the frequency domain can result in permutation ambiguity. In this paper, an efficient system that combines tensor decomposition with Independent Low-rank Matrix Analysis (ILRMA) under the premise that the spatial covariance matrix may be jointly diagonalized is proposed. The higher-order characteristics of tensors can extract the spatial features of the observed signals and provide excellent initial values for the system. Furthermore, ILRMA can effectively avoid the permutation problem. Experimental findings express that the proposed algorithm improves separation accuracy while also achieving quicker convergence.
Add-if-Silent Rule-Based Growing Neural Gas with Amount of Movement for High-Density Topological Structure Generation of Dynamic Object

In order to realize a super-smart society (Society 5.0) where humans and robots coexist, there is a need for a perceptual system that can recognize the environment quickly and flexibly in an environment that changes from moment to moment. In an unknown environment, the characteristics of objects cannot be known in advance, and thus prior learning-based recognition methods such as deep reinforcement learning may not be able to cope with this situation. In this study, we construct a 3D topological map of the environment in real-time using Growing Neural Gas (GNG), which can learn 3D topological structures even for unlearned objects. However conventional GNG have the problem that they cannot generate nodes with high-density for distant objects and cannot identify whether an unknown object is static or dynamic. Therefore, by directly adding useful input data as a new node (reference vector) based on the object category labels of the winner nodes (nearest nodes) to the input vector (3D point cloud), it is possible to generate high-density topological structures even for distant objects. We proposed the Add-if-Silent rule-based GNG with Amount of Movement (AiS-GNG-AM), which can identify between static and dynamic objects based on the past amount of movement of a node. The effectiveness of the proposed method is verified through experiments using a 3D dynamics simulator.
Self-Attention-Enhanced Fine-Grained Information Fusion for Multi-View Clustering

Traditional multi-view methods often employ neural networks to extract features for clustering tasks. However, the obtained features are typically coarse-grained descriptions of multi-view data, and their discriminating power is limited. Fine-grained information is capable of providing more comprehensive data descriptions and detailed information. As a result, exploiting fine-grained features of multi-view data is of great significance for multi-view clustering. In this paper, we propose a self-attention-enhanced fine-grained information fusion method for multi-view clustering. Specifically, a linear layer is used to map raw multi-view data from different dimensions to a common dimension. Then, a fine-grained information extraction layer consisting of two convolution layers is employed to extract fine-grained information, which allows detailed information to be represented sufficiently. The self-attention learning module is utilized to determine which information the model should focus on and to fuse important information into a new feature representation. We adopt deep divergence-based clustering to maintain the compactness within a cluster and separation between clusters. Contrastive learning is leveraged to learn consistent clustering results between different views. We conduct experiments on multiple datasets, and the results demonstrate the effectiveness of our proposed self-attention-enhanced fine-grained information fusion method for multi-view clustering.
Geometric Contrastive Learning for Heterogeneous Graphs Encoding

Heterogeneous graphs can represent many network structures in the real world, and research on heterogeneous graph data has attracted more attention. Most existing approaches require additional label information to obtain meaningful node representations. However, labeling heterogeneous graphs is tedious and time-consuming, and low-quality labels will harm the efficiency of models. In addition, the number of nodes increases exponentially with the distance from the root node, but the linearly expanding Euclidean space is difficult to match this growth rate. In this paper, we propose a unified framework that leverages the label irrelevance of contrastive learning and the unique expressive ability of hyperbolic space to encode heterogeneous graphs. Specifically, we use the contrast mechanism to obtain semantic information in an unsupervised way. Meanwhile, we design hyperbolic encoders that are more suitable for graph structure to learn the latent information from heterogeneous graphs efficiently. Experiments on four real-world heterogeneous graph data sets demonstrate the competitive efficacy of the proposed method.
A Pairwise Surrogate Model using GNN for Evolutionary Optimization

Optimization problems widely arise in various science and engineering fields and can be computationally expensive in many real-world applications. Evaluation of the fitness function to assess a candidate solution is the main operation in all optimization procedures which can be heavily compute-intensive. Machine learning-based surrogate models can contribute to learning the specific pattern among the decision variables and objective values to consequently reduce the computation time of fitness evaluation. In this study, we have proposed a novel pairwise surrogate model to identify the superiority between candidate solutions in a pairwise comparison despite the fact that most of the surrogate models try to predict the exact fitness value. The proposed idea can significantly help the optimizer to reach better results in a shorter period of time. It seems comparing two candidate solutions for a greedy selection is much easier than approximating fitness values for both. We demonstrated Graph Neural Network (GNN) for this purpose to be trained on a limited number of pairwise ranks and then utilized to compare a pair of candidate solutions. In order to examine the efficacy of our model, we utilized different well-known single-objective optimization benchmarks in dimensions 10,20, and 30. Moreover, the results of the learning-based evaluation are compared with the results from the real fitness evaluation. The results, assessed in terms of the number of fitness calls and the best-found solution, showed that the proposed method is able to decrease the computing cost of fitness evaluation significantly while we achieve a comparable solution. Our model can be tested with any optimization algorithm which employs a comparison-based mechanism among its candidate solutions.
Improving End-to-End Automatic Speech Recognition with Multi-Layer-Enriched Supervised Learning

To enhance speech recognition performance with limited network capacity, an end-to-end speech recognition algorithm based on multi-layer-enriched supervision (MES-ASR) is proposed in this paper. This approach allows the model to learn more speaking content information despite the capacity constraints. Specifically, the intermediate layer supervision loss is optimized by introducing an additional supervision loss at that layer. This forces the lower layers of the network to focus on acquiring more speaking content information, leading to improved recognition performance. Notably, this method adds the supervisory loss solely in the middle layer, ensuring simplicity during training and no additional memory or computational overhead during testing. The proposed approach is evaluated on the Aishell-1 dataset, employing WeNet as the baseline model. The experimental results demonstrate a reduction in character error rate and an enhancement in speech recognition performance.
Genetic Programming Lifelong Multitasking Evolution: LLGP-Tasking

We present a Lifelong Multi-Tasking learning algorithm based on Genetic Programming referred to as “LLGP-Tasking”. This paper extends previously published work “GP-Tasking” [7], evolving a population of GP trees using a multi-faceted strategy. In GP-Tasking, each individual is trained with multiple fitness functions (where each function represents one task and has different training/testing sets). Empirical evidence demonstrated that the quality of evolved solutions is comparable to standard GP achieving significantly faster computational time while maintaining smaller evolved population sizes. In this work, we improved GP-Tasking allowing the system to accumulate knowledge and use them not only in multitasking, but also with different problems to mimic lifelong learning. We further introduced a new crossover mechanism to transfer useful knowledge across different tasks. Moreover, we introduced new population initialisation approach to accumulate knowledge across different domains. Experimental results of the new LLGP-Tasking demonstrate superiority of evolved solutions over standard GP and it maintained same search speed produced by its predecessor (i.e., GP-Tasking).
Modulation Recognition of Communication Signal with Class-Imbalance Sample Based on CNN-LSTM Dual Channel Model

Modulation recognition of communication signals is a fundamental and crucial work in the field of space electromagnetic signal processing. With the development of emerging technologies such as Radio Frequency Machine Learning (RFML), data-driven approaches have made great progress in this area. However, the challenges of modulation recognition arise from obtaining communication signals, few-shot data, and class-imbalance samples. Based on our previously proposed method called the CNN-LSTM dual-channel model, the transformation of the signal data including rotation and cyclic time-shift is introduced to address the problem of class-imbalance sample. Compared with our previous results, the overall recognition rate is increased by 2.7%, and the number of parameters is reduced by 1 %. The experiment proves that this work is efficient and feasible.
Personalized Educational Video Evaluation Combining Student's Cognitive and Teaching Style

AI-powered technologies, like ChatGPT and learning analytic technologies, have encouraged the sharing of online teaching resources and the transformation of teaching methods and learning pathways. However, the mixed resources and the result-oriented video evaluation repeatedly let students fall into an information trap and only appeal to students' attention to unsuitable resources. Inspired by human-computer interaction, a novel online video assessment LPSA(Linguistic- Presentative-scientific-Artistic) is proposed, integrated by cognitive style and teaching style, to realize more precise learning detection and teaching quality assessment. The LPSA evaluation consists of a four-level classification and eight secondary indexes quantified by machine learning algorithms. By automatically searching for an appropriate threshold of all secondary indexes, a real-time video assessment system is developed to certify its technical feasibility and pedagogical availability. The results show that the proposed AI-assisted assessment could realize practical pedagogical recommendations and real-time supervision.
Multi-Constrained Symmetric Nonnegative Latent Factor Analysis for Accurately Representing Undirected Weighted Networks

An Undirected Weighted Network (UWN) is frequently encountered in a big-data-related application concerning the complex interactions among numerous nodes. A Symmetric High-Dimensional and Incomplete (SHDI) matrix can smoothly illustrate such a UWN, which contains rich knowledge like node interaction behaviors and local complexes. To extract desired knowledge from an SHDI matrix, an analysis model should carefully consider its topology for describing a UWN's intrinsic symmetry precisely. Representation learning to a UWN borrows the success of a pyramid of symmetry-aware models like a Symmetric Nonnegative Matrix Factorization (SNMF) model whose objective function utilizes a sole Latent Factor (LF) matrix for representing SHDI's symmetry precisely. However, they suffer from the following drawbacks: 1) their computational complexity is high; and 2) their modeling strategy narrows their representation features, making them suffer from low learning ability. Aiming at addressing the above critical issues, this paper proposes a Multi-constrained Symmetric Nonnegative Latent-factor-analysis (MSNL) model with two-fold ideas: 1) introducing multi-constraints composed of multiple LF matrices, i.e., inequality and equality ones into a data-density-oriented objective function for precisely representing the intrinsic symmetry of an SHDI matrix with broadened feature space; and 2) implementing an alternating direction method of multipliers (ADMM)-incorporated learning scheme for efficiently solving such a multi-constrained model. Empirical studies on three SHDI matrices from a real bioinformatics or industrial application demonstrate that the proposed MSNL model achieves higher representation accuracy than state-of-the-art models do, as well as promising computational efficiency.
Redundant Voronoi Roadmap Graph Using Imaginary Obstacles for Multi-Robot Path Planning

Roadmap-based path planning is a well-established method for multi-robot systems, where the free space of the environment is represented as a roadmap graph. The Voronoi diagram is known for its efficiency in creating roadmaps for single-robot systems due to its ability to generate paths with high clearance from obstacles. However, the design of the Voronoi diagram does not allow for redundant paths, as only one path is obtained between each pair of obstacles. In contrast, multi-robot path planning requires multiple path options for efficient solutions. Therefore, we propose redundant Voronoi roadmap graph, which incorporates multiple paths computed from the Voronoi diagram. In this approach, we introduce imaginary obstacles to modify the costmap and obtain a roadmap with more paths. Our proposed roadmap retains the high clearance feature of the Voronoi diagram and is suitable for multi-robot systems due to the availability of alternative route options. We demonstrate that our method can generate roadmaps in various simulated environments with different levels of redundancy. Additionally, we verify the efficiency of our proposed roadmap graph through graph analysis and multi-robot path planning experiments. Comparative analysis shows that the use of the proposed roadmap increases the success rate and solution quality compared to the roadmap obtained directly from the conventional Voronoi diagram.
О состоянии грамотности в Калмыцком ханстве в XVIII в.

It would not be an exaggeration to say that there is still an opinion, at least among the masses, that in pre-revolutionary times literacy was poorly developed among the Kalmyks. It was believed that representatives of the clergy and noble classes ― Noyons and Zaisangs — were literate. The clergy accounted for the largest percentage of literates, which was due to their type of activity. Nevertheless, there was an opinion that even among them the bulk was mired in drunkenness and deception of the ignorant masses of the people. The purpose of the proposed work is, as far as possible, to answer some of the above questions, although the study and coverage of the content of the problem as a whole is not a matter of one article. Materials and methods. To solve these problems, archival material, which is being introduced into scientific circulation for the first time, is mainly involved. To achieve this goal, it is necessary to approach the resolution of certain emerging problems, abandoning the recent habit of approaching the analysis of a diverse historical panorama with a single pre-regulating theoretical attitude. Descriptive, historical-comparative, historical-genetic and analytical methods were used in the study. Results. In Kalmyk society, representatives of the Noyono-Zaisang environment were primarily literate. The Noyons were almost all literate, including their wives. The Zaisangs had a fairly high literacy rate; there were also women among the literate, but their number was small. There are no specific data on commoners, but it can be assumed that the number of literate among them was small. In the XVIII century. literacy education in general in the Kalmyk Khanate went in two directions. Russian Russian was taught to the representatives of the Kalmyks, and the Russians — Kalmyk. This training was initiated by the Russian government (translators, interpreters, etc. were needed) and spontaneously, in the process of everyday communication between representatives of neighboring peoples.