Reviewerly/exHarmony huggingface dataset:
Hybrid-ensemble-based interpretable TSK fuzzy classifier for imbalanced data

Owing to its distinguished nonlinear mapping capability and interpretability, a Takagi–Sugeno–Kang (TSK) fuzzy classifier is always employed to achieve both enhanced generalization and better interpretability on imbalanced datasets. In this study, inspired by ensemble learning, a novel hybrid-ensemble-based imbalanced interpretable TSK fuzzy classifier (HI-TSK-FC) is proposed to integrate an imbalanced global linear regression sub-classifier (IGLRc) and several imbalanced TSK fuzzy sub-classifiers (I-TSK-FCs). According to both human's “wholly coarse to locally fine” cognitive behavior and the stacked generalization principle, the training method of HI-TSK-FC, called imbalanced residual sketch learning (IRSL), is further devised to share the virtues of both deep and wide learning. Concretely, IRSL firstly generates IGLRc by calling a newly proposed imbalanced global-sparse-representation-based regression method (IGSR) on all training samples to achieve a wholly coarse result and identifies the nonlinearly distributed training samples in imbalanced datasets. Subsequently, the nonlinearly distributed training samples can be partitioned into several imbalanced residual sketches with much imbalanced likely by calling the proposed residual-based partition method (RPM). After that, several I-TSK-FCs are generated to achieve locally fine results in a parallel way on the corresponding imbalanced residual sketches. Finally, a new minimal-distance-based voting strategy is taken on the stacked outputs of both IGLRc and all I-TSK-FCs to obtain the final output of HI-TSK-FC. Besides, the proposed minimal-distance-based voting strategy also guarantees an interpretable and clear prediction route of HI-TSK-FC for each testing sample. Consequently, HI-TSK-FC owns both feature-importance-based and linguistic-based interpretabilities. Both experimental and statistical results confirm that except for feature-importance-based interpretability, HI-TSK-FC achieves at least comparable generalization capability, better linguistic interpretability (fewer adopted fuzzy rules and smaller model complexity) and faster running speed on all adopted imbalanced datasets.
A framework for cognitive chatbots based on abductive–deductive inference

This paper presents a framework based on natural language processing and first-order logic aiming at instantiating cognitive chatbots. The proposed framework leverages two types of knowledge bases interacting with each other in a meta-reasoning process. The first one is devoted to the reactive interactions within the environment, while the second one to conceptual reasoning. The latter exploits a combination of axioms represented with rich semantics and abduction as pre-stage of deduction, dealing also with some of the state-of-the-art issues in the natural language ontology domain. As a case study, a Telegram chatbot system has been implemented, supported by a module which automatically transforms polar and wh-questions into one or more likely assertions, so as to infer Boolean values or snippets with variable length as factoid answer. The conceptual knowledge base is organized in two layers, representing both long- and short-term memory. The knowledge transition between the two layers is achieved by leveraging both a greedy algorithm and the engine's features of a NoSQL database, with promising timing performance if compared with the adoption of a single layer. Furthermore, the implemented chatbot only requires the knowledge base in natural language sentences, avoiding any script updates or code refactoring when new knowledge has to income. The framework has been also evaluated as cognitive system by taking into account the state-of-the art criteria: the results show that AD-Caspar is an interesting starting point for the design of psychologically inspired cognitive systems, endowed of functional features and integrating different types of perception.
Channel-overcomplete convolutional architectures for RGB-D salient object detection

It is a challenging task to detect small salient objects by fusing color information and spatial information from RGB-D images. Most RGB-D salient object detection (SOD) methods use U-Net or its variants because they tend to extract high-level features that can quickly determine the location of salient objects. However, it is also indispensable to effectively learn low-level features to detect fine boundaries and small structures. To this end, we propose a novel two-branch SOD network (COSU), which includes channel-overcomplete (CO) and spatial-undercomplete (SU) branches. First of all, the CO branch pays attention to low-level features by transform the spatial distribution into the channel connection and restricting the receptive field of filters. Secondly, SU branch uses increased receptive field to capture the high-level features. Finally, a multi-scale modal correction strategy is designed to fully fuse the multi-level features from the two branches and mitigate the modal divergence from RGB-D features. Experimental results on seven widely used datasets show that the proposed COSU outperforms ten state-of-the-art RGB-D SOD methods.
Dynamic quantum secret sharing between multiparty and multiparty based on single photons

We propose a dynamic quantum secret sharing protocol between multiparty (m agents in group 1) and multiparty (n agents in group 2) based on single photons with the help of a third party. Compared with previous ones, our protocol has several improvements. Firstly, the quantum resources used in the protocol are merely single photons and local unitary operations, making it convenient to be implemented. Secondly, we do not need to verify the secret shares when adding agents, which simplifies the procedures. Finally, the security of the proposed protocol is improved. The attackers cannot get any effective information even if they have intercepted the sequences. At the same time, both two groups of agents could hold their own secret shares in privacy, which means they are not able to get each other’s secret shares.
A multi-channel approach for detecting tampering in colour filter images

Most devices capable of capturing photographs are provided with a Colour Filter Array (CFA). Images generated by these devices can be analysed to detect the so known CFA artefacts left by demosaicing methods. These artefacts are important in digital image forensics since they prove useful for determining the authenticity of an image. Previous works have focused exclusively on the analysis of the green band of the Bayer filter, neglecting the information in the remaining bands. In this work a process to estimate the pattern left by CFA artefacts regardless of their configuration is proposed, which can be used regardless of the size or colours used by the filter, or even in different colour spaces, obtaining in this way new sources of information for forensic analysis. A review and comparisons against state-of-the-art projects addressing the detection of manipulations using CFA artefacts are performed. The sensitivity of these proposals to post-processing methods such as compression and filtering will also be reviewed. We will also observe experimentally that the proposed technique can detect manipulations even in images affected by high levels of JPEG compression.
RDJCNN: A micro-convolutional neural network for radar active jamming signal classification

Timely awareness of jamming situations and classification of jamming categories are vital for radars to suppress jamming, ensure viability, and maintain functions in complex electromagnetic environments. To satisfy strict time requirements for radars on embedded devices, a micro-dynamic convolutional neural network for jamming signal classification is proposed in this paper. The proposed network takes the range-Doppler distribution obtained from built-in radar signal processing as input. The proposed data augmentation algorithm, together with the attention mechanism and the efficient convolutional architecture, improves the generalization capability and reduces the computational complexity. In addition, we propose a dynamic depth mechanism based on a task difficulty evaluator that enables the network to be adjusted automatically and further reduces the average computational complexity of classification. Simulation results verify the advantages of our approach in size, accuracy, and efficiency. The proposed network achieved 98.82% and 85.00% top-1 accuracy in two datasets with only 1.73 M multiply–accumulate operations.
A Fine-Grained Image Classification Approach for Dog Feces Using MC-SCMNet under Complex Backgrounds

In a natural environment, factors such as weathering and sun exposure will degrade the characteristics of dog feces; disturbances such as decaying wood and dirt are likely to make false detections; the recognition distinctions between different kinds of feces are slight. To address these issues, this paper proposes a fine-grained image classification approach for dog feces using MC-SCMNet under complex backgrounds. First, a multi-scale attention down-sampling module (MADM) is proposed. It carefully retrieves tiny feces feature information. Second, a coordinate location attention mechanism (CLAM) is proposed. It inhibits the entry of disturbance information into the network's feature layer. Then, an SCM-Block containing MADM and CLAM is proposed. We utilized the block to construct a new backbone network to increase the efficiency of fecal feature fusion in dogs. Throughout the network, we decrease the number of parameters using depthwise separable convolution (DSC). In conclusion, MC-SCMNet outperforms all other models in terms of accuracy. On our self-built DFML dataset, it achieves an average identification accuracy of 88.27% and an F1 value of 88.91%. The results of the experiments demonstrate that it is more appropriate for dog fecal identification and maintains stable results even in complex backgrounds, which may be applied to dog gastrointestinal health checks.
Freezing Sets Invariant-based Characteristics of Digital Images

Due to the widespread use of digital images of real-world objects as mathematical models, this research examines the freezing sets invariant-base properties of digital images. In contrast to earlier studies that only covered a discrete or limited collection of points, fixed points of digitally continuous functions are approved to deal with a variety of characteristics of digital images.
A 6G-Enabled Lightweight Framework for Person Re-Identification on Distributed Edges

In the upcoming 6G era, edge artificial intelligence (AI), as a key technology, will be able to deliver AI processes anytime and anywhere by the deploying of AI models on edge devices. As a hot issue in public safety, person re-identification (Re-ID) also needs its models to be urgently deployed on edge devices to realize real-time and accurate recognition. However, due to complex scenarios and other practical reasons, the performance of the re-identification model is poor in practice. This is especially the case in public places, where most people have similar characteristics, and there are environmental differences, as well other such characteristics that cause problems for identification, and which make it difficult to search for suspicious persons. Therefore, a novel end-to-end suspicious person re-identification framework deployed on edge devices that focuses on real public scenarios is proposed in this paper. In our framework, the video data are cut images and are input into the You only look once (YOLOv5) detector to obtain the pedestrian position information. An omni-scale network (OSNet) is applied through which to conduct the pedestrian attribute recognition and re-identification. Broad learning systems (BLSs) and cycle-consistent adversarial networks (CycleGAN) are used to remove the noise data and unify the style of some of the data obtained under different shooting environments, thus improving the re-identification model performance. In addition, a real-world dataset of the railway station and actual problem requirements are provided as our experimental targets. The HUAWEI Atlas 500 was used as the edge equipment for the testing phase. The experimental results indicate that our framework is effective and lightweight, can be deployed on edge devices, and it can be applied for suspicious person re-identification in public places.
Visual Description Augmented Integration Network for Multimodal Entity and Relation Extraction

Multimodal Named Entity Recognition (MNER) and multimodal Relationship Extraction (MRE) play an important role in processing multimodal data and understanding entity relationships across textual and visual domains. However, irrelevant image information may introduce noise that misleads the recognition of information. Additionally, visual and semantic features originate from different modalities, and modal disparity hinders semantic alignment. Therefore, this paper proposes the Visual Description Augmentation Integration Network (VDAIN), which introduces an image description generation technique that allows semantic features generated from image descriptions to be presented in the same modality as the semantic features of textual information. This not only reduces the modal gap but also captures more accurately the high-level semantic information and underlying visual structure in the images. To filter out the modal noise, we use VDAIN to adaptively fuse visual features, semantic features of image descriptions, and textual information, thus eliminating irrelevant modal noise. The F1 score of the proposed model in this paper reaches 75.8% and 87.78% for the MNER task and 82.54% for the MRE task on the three public data sets, respectively, which are significantly better than the baseline model. The experimental results demonstrate the effectiveness of the proposed method in solving the modal noise and modal gap problems.
Using Deep-Learned Vector Representations for Page Stream Segmentation by Agglomerative Clustering

Page stream segmentation (PSS) is the task of retrieving the boundaries that separate source documents given a consecutive stream of documents (for example, sequentially scanned PDF files). The task has recently gained more interest as a result of the digitization efforts of various companies and organizations, as they move towards having all their documents available online for improved searchability and accessibility for users. The current state-of-the-art approach is neural start of document page classification on representations of the text and/or images of pages using models such as Visual Geometry Group-16 (VGG-16) and BERT to classify individual pages. We view the task of PSS as a clustering task instead, hypothesizing that pages from one document are similar to each other and different to pages in other documents, something that is difficult to incorporate in the current approaches. We compare the segmentation performance of an agglomerative clustering method with a binary classification model based on images on a new publicly available dataset and experiment with using either pretrained or finetuned image vectors as inputs to the model. To adapt the clustering method to PSS, we propose the switch method to alleviate the effects of pages of the same class having a high similarity, and report an improvement in the scores using this method. Unfortunately, neither clustering with pretrained embeddings nor clustering with finetuned embeddings outperformed start of document page classification for PSS. However, clustering with either pretrained or finetuned representations is substantially more effective than the baseline, with finetuned embeddings outperforming pretrained embeddings. Finally, having the number of documents K as part of the input, in our use case a realistic assumption, has a surprisingly significant positive effect. In contrast to earlier papers, we evaluate PSS with the overlap weighted partial match F1 score, developed as a Panoptic Quality in the computer vision domain, a metric that is particularly well-suited to PSS as it can be used to measure document segmentation.
P-Raft: An Efficient and Robust Consensus Mechanism for Consortium Blockchains

With the rise in blockchain technology, consortium blockchains have garnered increasing attention in practical applications due to their decentralization and immutability. However, the performance of current consortium blockchains remains a significant obstacle to large-scale commercial adoption. The consensus algorithm, as a fundamental component of blockchain technology, plays a critical role in ensuring both security and efficiency. Unfortunately, most existing consensus algorithms for consortium blockchains are vote-based consensus algorithms, and the performance of vote-based consensus algorithms is largely limited by the performance of the leader node. Therefore, we present P-Raft: a high-performance consensus algorithm that builds upon the Raft algorithm and leverages node server performance evaluations. The primary objectives of this article included enhancing the efficiency of Leader processing, promoting the utilization of the consortium blockchain, and ensuring the robustness of Leader election. Specifically designed to meet the service requirements of consortium blockchain’s consensus mechanism, the P-Raft incorporated the Yasa model, which evaluated the instant machine performance of each node. The performance of each node is then associated with the election timeout, ensuring that nodes with superior performance are more likely to be chosen as Leaders. Additionally, we implemented a leader verification mechanism based on the Bohen-Lynn-Shacham (BLS) signature, which prevented malicious Byzantine nodes from becoming Leaders without receiving enough votes. Empirical findings show that the P-Raft can swiftly designate high-performing nodes as Leaders, thereby greatly improving service efficiency in the consensus process and the overall performance of the consensus mechanism. Ultimately, P-Raft is better equipped to meet the demands of consortium blockchain applications for large-scale transactions.
Distillation Sparsity Training Algorithm for Accelerating Convolutional Neural Networks in Embedded Systems

The rapid development of neural networks has come at the cost of increased computational complexity. Neural networks are both computationally intensive and memory intensive; as such, the minimal energy and computing power of satellites pose a challenge for automatic target recognition (ATR). Knowledge distillation (KD) can distill knowledge from a cumbersome teacher network to a lightweight student network, transferring the essential information learned by the teacher network. Thus, the concept of KD can be used to improve the accuracy of student networks. Even when learning from a teacher network, there is still redundancy in the student network. Traditional networks fix the structure before training, such that training does not improve the situation. This paper proposes a distillation sparsity training (DST) algorithm based on KD and network pruning to address the above limitations. We first improve the accuracy of the student network through KD, and then through network pruning, allowing the student network to learn which connections are essential. DST allows the teacher network to teach the pruned student network directly. The proposed algorithm was tested on the CIFAR-100, MSTAR, and FUSAR-Ship data sets, with a 50% sparsity setting. First, a new loss function for the teacher-pruned student was proposed, and the pruned student network showed a performance close to that of the teacher network. Second, a new sparsity model (uniformity half-pruning UHP) was designed to solve the problem that unstructured pruning does not facilitate the implementation of general-purpose hardware acceleration and storage. Compared with traditional unstructured pruning, UHP can double the speed of neural networks.
Designing a Fuzzy Adaptive Neural Network Leveraging a Feature Self-Enhancement Unit and Statistical Selection Methods

In this study, we propose an advanced category of a fuzzy adaptive neural network (FANN) based on a feature self-enhancement unit (FSU) and statistical selection methods (SSMs). Undoubtedly, the raw data contain a large amount of information with varying importance. One of the most important tasks for regression model design is to avoid losing these important details. However, the raw data cannot participate in the whole training process due to the data fuzzification unit in the structure of conventional fuzzy neural networks (FNNs). Meanwhile, the polynomial-based neuron also has its limitations as a common node in FNNs. For example, in the polynomial neuron, the complexity of the neurons increases exponentially with the increase in network size. Consequently, overfitting and insufficient raw data information are two primary drawbacks in the structure of conventional FNNs. To address these limitations, we designed the FSU and the SSM as effective vehicles to reduce data dimensionality and select significant raw information. The proposed FANN also demonstrates the capability to improve modeling accuracy in neural networks. Moreover, this is the first instance of integrating statistical methods and feature self-enhancement techniques into a fuzzy model. To validate and showcase the superiority of the proposed FANN, the model is applied to 16 machine learning datasets, outperforming other comparative models in 81.25% of the datasets utilized. Additionally, the proposed FANN model outperformed the latest FNN models, achieving an average 5.1% increase in modeling accuracy. The comparison experiment section not only includes classical machine learning models but also references the experimental results from two recent related studies.
A Novel Image Similarity Measure Based on Greatest and Smallest Eigen Fuzzy Sets

A novel image similarity index based on the greatest and smallest fuzzy set solutions of the max–min and min–max compositions of fuzzy relations, respectively, is proposed. The greatest and smallest fuzzy sets are found symmetrically as the min–max and max–min solutions, respectively, to a fuzzy relation equation. The original image is partitioned into squared blocks and the pixels in each block are normalized to [0, 1] in order to have a fuzzy relation. The greatest and smallest fuzzy sets, found for each block, are used to measure the similarity between the original image and the image reconstructed by joining the squared blocks. Comparison tests with other well-known image metrics are then carried out where source images are noised by applying Gaussian filters. The results show that the proposed image similarity measure is more effective and robust to noise than the PSNR and SSIM-based measures.