Reviewerly/exHarmony huggingface dataset:
FindVehicle and VehicleFinder: a NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system

Abstract Natural language (NL) based vehicle retrieval is a task aiming to retrieve a vehicle that is most consistent with a given NL query from among all candidate vehicles. Because NL query can be easily obtained, such a task has a promising prospect in building an interactive intelligent traffic system (ITS). Current solutions mainly focus on extracting both text and image features and mapping them to the same latent space to compare the similarity. However, existing methods usually use dependency analysis or semantic role-labelling techniques to find keywords related to vehicle attributes. These techniques may require a lot of pre-processing and post-processing work, and also suffer from extracting the wrong keyword when the NL query is complex. To tackle these problems and simplify, we borrow the idea from named entity recognition (NER) and construct FindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NL descriptions of vehicle tracks, containing information such as the location, orientation, type and colour of the vehicle. FindVehicle also adopts both overlapping entities and fine-grained entities to meet further requirements. To verify its effectiveness, we propose a baseline NL-based vehicle retrieval model called VehicleFinder. Our experiment shows that by using text encoders pre-trained by FindVehicle, VehicleFinder achieves 87.7% precision and 89.4% recall when retrieving a target vehicle by text command on our homemade dataset based on UA-DETRAC [1]. From loading the command into VehicleFinder to identifying whether the target vehicle is consistent with the command, the time cost is 279.35 ms on one ARM v8.2 CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than the Transformer-based system. The dataset is open-source via the link https://github.com/GuanRunwei/FindVehicle , and the implementation can be found via the link https://github.com/GuanRunwei/VehicleFinder-CTIM .
Verification of classification modernization solutions (the case study of Library Bibliographic Classification)

Efficiency of library classifications depends on their capacity to embrace current knowledge and social practices. Therefore, the verification (late Latin verificatio – evidence, confirmation, from the Latin verus – true and facio – to make) of upgrade solutions is an essential part of classification system operation and development. The problem of library classification verification is often out of researchers view. The verification mechanism (tools) for upgrade solutions has not been described yet. Therefore, the reliability and relevance of classification upgrade solutions is of scientific and practical value. The purpose of the paper is to summarize the related experience of Research LBC Center of the Russian State Library. Based on s tructured system analysis and the long practice of LBC development and application, the authors examine the mechanism for monitoring publications and identifying innovations, including new terms, concepts, social phenomena, etc., and for assessing and processing data for continuing update of the classification system. Using the example of LBC modernization solutions, the authors discuss advantages and disadvantages of individual methods. They conclude that the classification upgrade verification – essential to the nature of the library classification systems – has to become the key element of the classifying culture. The researchers review and suggest the ways to improve the verification mechanism adopted at the RSL LBC Research Center. The integrated approach to LBC verification is of applied relevance both for the theory of classifications and librarianship.
Research on the Image Background Base Algorithm Based on the Factor Space Theory

Image vision has wide application prospects in the field of intelligent manufacturing, and studying an effective background base extraction algorithm has become an urgent need for factor space applications in the fields of big data and image vision. Based on this, an image background base extraction algorithm for identifying image information is proposed. The algorithm analyzes the characteristics of the base points in the image and converts the brightness changes observed by the human eye into the data language form that the machine can read. Numerical experiments show the effectiveness and robustness of the proposed algorithm.
Bridging AI Paradigms with Cases and Networks

For many years, symbolic paradigms have dominated AI, but the impressive accomplishments of neural approaches have made them a new dominant paradigm. However, each of these paradigms has distinct advantages. This talk presents a hybrid paradigm that integrates case-based reasoning (CBR) with network methods to achieve the benefits of both. CBR is a knowledge-based reasoning and learning methodology inspired by human cognition that adapts prior cases—records of prior experiences, to solve new problems. The CBR process supports efficient knowledge-based reasoning and the reuse of structured or unstructured solutions; it is naturally interpretable, can learn from few examples, and provides inertia-free lazy learning. However, the success of CBR depends on similarity and case-adaptation knowledge, which may be hard to acquire. This talk presents opportunities for combining case-based reasoning with neural networks to leverage both paradigms.
Application of Improved Butterfly Optimization Algorithm in Mobile Robot Path Planning

An improved butterfly optimization algorithm (IBOA) is proposed to overcome the disadvantages, including slow convergence, generation of local optimum solutions, and deadlock phenomenon, of the optimization algorithm in the path planning of mobile robots. A path-planning grid model is established based on an improved obstacle model. First, the population diversity is improved by introducing kent mapping during population position renewal in the normal butterfly optimization algorithm (BOA) to enhance the global search ability of the butterfly population. Second, an adaptive weight coefficient is introduced in the renewal process of each generation to increase the convergence speed and accuracy. An opposition-based learning strategy based on convex lens imaging is introduced to help the butterfly population jump out of the local optimum. Finally, a mutation strategy is introduced to solve the path planning problem. On this basis, two path simplification strategies are proposed to make up for the shortcomings of planning paths in grid maps. The shortest path lengths solved by IBOA, BOA, and GA in the 20 × 20 map are 30.97, 31.799, and 31.799, respectively. The numbers of iterations for the shortest paths searched by IBOA, BOA, and GA are 14, 24, and 38 in that order. The shortest path lengths solved by IBOA, BOA and GA in the 40 × 40 map are 63.84, 65.60, and 65.84, respectively. The number of iterations for the shortest paths searched by IBOA, BOA and GA are 32, 40, and 46, respectively. Simulation results show that IBOA has a strong ability to solve robot path planning problems and that the proposed path simplification strategy can effectively reduce the length of the optimal path in the grid map to solve the path planning problem of mobile robots. The shortest paths solved by IBOA in 20 × 20 and 40 × 40 maps are simplified to lengths of 30.2914 and 61.03, respectively.
A Low-Illumination Enhancement Method Based on Structural Layer and Detail Layer

Low-illumination image enhancement technology is a topic of interest in the field of image processing. However, while improving image brightness, it is difficult to effectively maintain the texture and details of the image, and the quality of the image cannot be guaranteed. In order to solve this problem, this paper proposed a low-illumination enhancement method based on structural and detail layers. Firstly, we designed an SRetinex-Net model. The network is mainly divided into two parts: a decomposition module and an enhancement module. Second, the decomposition module mainly adopts the SU-Net structure, which is an unsupervised network that decomposes the input image into a structural layer image and detail layer image. Afterward, the enhancement module mainly adopts the SDE-Net structure, which is divided into two branches: the SDE-S branch and the SDE-D branch. The SDE-S branch mainly enhances and adjusts the brightness of the structural layer image through Ehnet and Adnet to prevent insufficient or overexposed brightness enhancement in the image. The SDE-D branch is mainly denoised and enhanced with textural details through a denoising module. This network structure can greatly reduce computational costs. Moreover, we also improved the total variation optimization model as a mixed loss function and added structural metrics and textural metrics as variables on the basis of the original loss function, which can well separate the structure edge and texture edge. Numerous experiments have shown that our structure has a more significant impact on the brightness and detail preservation of image restoration.
Towards Efficient Federated Learning: Layer-Wise Pruning-Quantization Scheme and Coding Design

As a promising distributed learning paradigm, federated learning (FL) faces the challenge of communication–computation bottlenecks in practical deployments. In this work, we mainly focus on the pruning, quantization, and coding of FL. By adopting a layer-wise operation, we propose an explicit and universal scheme: FedLP-Q (federated learning with layer-wise pruning-quantization). Pruning strategies for homogeneity/heterogeneity scenarios, the stochastic quantization rule, and the corresponding coding scheme were developed. Both theoretical and experimental evaluations suggest that FedLP-Q improves the system efficiency of communication and computation with controllable performance degradation. The key novelty of FedLP-Q is that it serves as a joint pruning-quantization FL framework with layer-wise processing and can easily be applied in practical FL systems.
Temporal Estimation of Non-Rigid Dynamic Human Point Cloud Sequence Using 3D Skeleton-Based Deformation for Compression

This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud by temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCFs), and a key PCF is selected. The 3D skeleton is predicted through 3D pose estimation, and the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCFs by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured.
Image Encryption Algorithm Using Multi-Level Permutation and Improved Logistic–Chebyshev Coupled Map

To improve the randomness of the Chebyshev chaotic sequences by coupling the Logistic map and the Chebyshev map, a new one-dimensional Logistic–Chebyshev chaotic map (LCCM) is first presented in this paper. Several tests, including the bifurcation diagram, Lyapunov exponents, and information entropy, are employed to analyze the dynamics of the LCCM. The proposed LCCM has better ergodicity and unpredictability than the traditional Chebyshev map. Next, a new image encryption algorithm based on the LCCM and multi-level manipulation is proposed. The LCCM is used to control the pixel permutation, bit-level shuffling, and subsequent pixel diffusion based on the modulo and XOR operation. Extensive experiments, including histogram analysis, information entropy, adjacent pixel correlation, and key sensitivity, show that the image encryption algorithm has high security and can effectively resist malicious attacks.
Improved Traffic Small Object Detection via Cross-Layer Feature Fusion and Channel Attention

Small object detection has long been one of the most formidable challenges in computer vision due to the poor visual features and high noise of surroundings behind them. However, small targets in traffic scenes encompass a multitude of complex visual interfering factors, bearing crucial information such as traffic signs, traffic lights, and pedestrians. Given the inherent difficulties faced by generic models in addressing these issues, we conduct a comprehensive investigation on small target detection in this application scenario. In this work, we present a Cross-Layer Feature Fusion and Channel Attention algorithm based on a lightweight YOLOv5s design for traffic small target detection, named CFA-YOLO. To enhance the sensitivity of the model toward vital features, we embed the channel-guided Squeeze-and-Excitation (SE) block in the deep layer of the backbone. Moreover, the most excellent innovation of our work belongs to the effective cross-layer feature fusion method, which maintains robust feature fusion and information interaction capabilities; in addition, it simplifies redundant parameters compared with the baseline model. To align with the output features of the neck network, we adjusted the detection heads from three to two. Furthermore, we also applied the decoupled detection head for classification and bounding box regression tasks, respectively. This approach not only achieves real-time detection standards, but also improves the overall training results in parameter-friendly manner. The CFA-YOLO model significantly pays a lot of attention to the detail features of small targets, thereby it also has a great advantage in addressing the issue of poor performance in traffic small target detection results. Vast experiments have validated the efficiency and effectiveness of our proposed method in traffic small object detection. Compared with the latest lightweight detectors, such as YOLOv7-Tiny and YOLOv8s, our method consistently achieves superior performance both in terms of the model’s accuracy and complexity.
Ellipse Detection with Applications of Convolutional Neural Network in Industrial Images

Ellipse detection has a very wide range of applications in the field of industrial production, especially in the geometric detection of metallurgical hinge pins. However, the factors in industrial images, such as small object size and incomplete ellipse in the image boundary, bring challenges to ellipse detection, which cannot be solved by existing methods. This paper proposes a method for ellipse detection in industrial images, which utilizes the extended proposal operation to prevent the loss of ellipse rotation angle features during ellipse regression. Moreover, the Gaussian angle distance conforming to the ellipse axioms is adopted and combined with smooth L1 loss as the ellipse regression loss function to enhance the prediction accuracy of the ellipse rotation angle. The effectiveness of the proposed method is demonstrated on the hinge pins dataset, with experiment results showing an AP* of 80.93% and indicating superior detection performance compared to other methods. It is thus suitable for engineering applications and can provide visual guidance for the precise measurement of ellipse-like mechanical parts.
Robust Image Inpainting Forensics by Using an Attention-Based Feature Pyramid Network

Deep learning has injected a new sense of vitality into the field of image inpainting, allowing for the creation of more realistic inpainted images that are difficult to distinguish from the original ones. However, this also means that the malicious use of image inpainting technology to tamper with images could lead to more serious consequences. In this paper, we use an attention-based feature pyramid network (AFPN) to locate the inpainting traces left by deep learning. AFPN employs a feature pyramid to extract low- and high-level features of inpainted images. It further utilizes a multi-scale convolution attention (MSCA) module to optimize the high-level feature maps. The optimized high-level feature map is then fused with the low-level feature map to detect inpainted regions. Additionally, we introduce a fusion loss function to improve the training effectiveness. The experimental results show that AFPN exhibits remarkable precision in deep inpainting forensics and effectively resists JPEG compression and additive noise attacks.
Hadiths Classification Using a Novel Author-Based Hadith Classification Dataset (ABCD)

Religious studies are a rich land for Natural Language Processing (NLP). The reason is that all religions have their instructions as written texts. In this paper, we apply NLP to Islamic Hadiths, which are the written traditions, sayings, actions, approvals, and discussions of the Prophet Muhammad, his companions, or his followers. A Hadith is composed of two parts: the chain of narrators (Sanad) and the content of the Hadith (Matn). A Hadith is transmitted from its author to a Hadith book author using a chain of narrators. The problem we solve focuses on the classification of Hadiths based on their origin of narration. This is important for several reasons. First, it helps determine the authenticity and reliability of the Hadiths. Second, it helps trace the chain of narration and identify the narrators involved in transmitting Hadiths. Finally, it helps understand the historical and cultural contexts in which Hadiths were transmitted, and the different levels of authority attributed to the narrators. To the best of our knowledge, and based on our literature review, this problem is not solved before using machine/deep learning approaches. To solve this classification problem, we created a novel Author-Based Hadith Classification Dataset (ABCD) collected from classical Hadiths’ books. The ABCD size is 29 K Hadiths and it contains unique 18 K narrators, with all their information. We applied machine learning (ML), and deep learning (DL) approaches. ML was applied on Sanad and Matn separately; then, we did the same with DL. The results revealed that ML performs better than DL using the Matn input data, with a 77% F1-score. DL performed better than ML using the Sanad input data, with a 92% F1-score. We used precision and recall alongside the F1-score; details of the results are explained at the end of the paper. We claim that the ABCD and the reported results will motivate the community to work in this new area. Our dataset and results will represent a baseline for further research on the same problem.
A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks

Owing to the heterogeneity and incomplete information present in various domain knowledge graphs, the alignment of distinct source entities that represent an identical real-world entity becomes imperative. Existing methods focus on cross-lingual knowledge graph alignment, and assume that the entities of knowledge graphs in the same language are unique. However, due to the ambiguity of language, heterogeneous knowledge graphs in the same language are often duplicated, and relationship triples are far less than those of cross-lingual knowledge graphs. Moreover, existing methods rarely exclude noisy entities in the process of alignment. These make it impossible for existing methods to deal effectively with the entity alignment of domain knowledge graphs. In order to address these issues, we propose a novel entity alignment approach based on domain-oriented embedded representation (DomainEA). Firstly, a filtering mechanism employs the language model to extract the semantic features of entities and to exclude noisy entities for each entity. Secondly, a Structural Aggregator (SA) incorporates multiple hidden layers to generate high-order neighborhood-aware embeddings of entities that have few relationship connections. An Attribute Aggregator (AA) introduces self-attention to dynamically calculate weights that represent the importance of the attribute values of the entities. Finally, the approach calculates a transformation matrix to map the embeddings of distinct domain knowledge graphs onto a unified space, and matches entities via the joint embeddings of the SA and AA. Compared to six state-of-the-art methods, our experimental results on multiple food datasets show the following: (i) Our approach achieves an average improvement of 6.9% on MRR. (ii) The size of the dataset has a subtle influence on our approach; there is a positive correlation between the expansion of the dataset size and an improvement in most of the metrics. (iii) We can achieve a significant improvement in the level of recall by employing a filtering mechanism that is limited to the top-100 nearest entities as the candidate pairs.
Local Feature Enhancement for Nested Entity Recognition Using a Convolutional Block Attention Module

Named entity recognition involves two main types: nested named entity recognition and flat named entity recognition. The span-based approach treats nested entities and flat entities uniformly by classifying entities on a span representation. However, the span-based approach ignores the local features within the entities and the relative position features between the head and tail tokens, which affects the performance of entity recognition. To address these issues, we propose a nested entity recognition model using a convolutional block attention module and rotary position embedding for local features and relative position features enhancement. Specifically, we apply rotary position embedding to the sentence representation and capture the semantic information between the head and tail tokens using a biaffine attention mechanism. Meanwhile, the convolution module captures the local features within the entity to generate the span representation. Finally, the two parts of the representation are fused for entity classification. Extensive experiments were conducted on five widely used benchmark datasets to demonstrate the effectiveness of our proposed model.
Blockchain-Based Licensed Spectrum Fair Distribution Method towards 6G-Envisioned Communications

Spectrum distribution is a classical licensed spectrum accessing method in mobile communication networks. The licensed idle spectrum resources are authorized and distributed from spectrum owners to mobile users. However, the exponential growth of user capacity brings excessive load pressure on the traditional centralized network architecture. With a lack of sufficient supervision and penalty measures, dishonest behaviors of spectrum owners and spectrum users will lead to an unfair status in the distribution process. As a result, the honest participants’ interest will be harmed. As an important supporting infrastructure of Internet of Things technology, 6G cannot completely follow the existing spectrum distribution method. Towards 6G network spectrum distribution, a blockchain-based licensed spectrum fair distribution method is proposed. A lightweight consensus mechanism named proof of trust (PoT) is applied to reduce computational power consumption and consensus time overhead. We deploy the method on the Ethereum test chain; a theoretical analysis and experimental results demonstrate the fairness, effectiveness and security of the method.