Reviewerly/exHarmony huggingface dataset:
Multi-relation Identification for Few-Shot Document-Level Relation Extraction

Document-level relation extraction aims to extract relations between entities mentioned in the given text. Existing approaches characterize relations by concatenating the representation of entities from numerous instances for each relation. However, it fails to identify multiple relations that may be expressed by the same entity pair in few-shot scenarios, since there may be only one instance for some relations. In this paper, we propose a Context-aware Hybrid Attention Network (CHAN) for few-shot document-level relation extraction to identify multi-relation. Specifically, we design instance-specific attention to localize the relevant context for each entity pair and capture keywords associated with different relations. In addition, we introduce a contrastive prototypical network to further distinguish the subtle difference between multiple relations. Experimental results show that CHAN achieved the best performance compared to previous methods, especially the F1 of the multi-relation identification is improved by 17.94% under 1-doc setting in FREDo benchmark.
Accelerate SVM Training with OHD-SVM on GPU

The SVM (Support Vector Machine) faces a huge challenge on reducing training time in the presence of large data sets due to its high computational cost. The SMO (Sequential Minimal Optimization) algorithm is used to solve the quadratic programming problem that arises during the training of SVM. OHD-SVM leverages the power of GPU (Graphics Processing Unit) to accelerate SVM training and it is the fastest GPU implementation so far. OHD-SVM adopts two-level heuristics to select SMO working set. In this paper, we propose a method that narrows the search domain for selection of the working pair in each SMO iteration. Six datasets from LibSVM are used for performance evaluation. Experimental results demonstrate that in most cases our method outperforms OHD-SVM with similar accuracy. At best, the training can be about seven times faster. For several cases, both the training time and the accuracy are improved. In the best case, the accuracy is improved about ten times with 1.46 speedup of training time.
Multi-task Learning for Mongolian Morphological Analysis

Mongolian morphological analysis (MMA) includes two subtasks: morphological segmentation and morphological tagging. It is a crucial preprocessing step in many Mongolian NLP applications. Recently, end-to-end neural approaches have achieved excellent results in the MMA task. However, these approaches handle morphological segmentation and morphological tagging independently, and ignore the relationship between the two subtasks. In this paper, we propose a multi-task sequence-to-sequence model for the MMA task that learns Mongolian morphological segmentation and tagging jointly. The proposed neural model introduces a shared morphological feature encoder to learn character-level and context-level word information. Besides, we design a flat joint attention decoder and a hierarchical joint attention decoder to generate Mongolian segmentation and tagging results, respectively. We employ the dynamic weight scheme to optimize and balance the weights between the two subtasks in MMA. We compare the proposed model with the baselines and evaluate the effectiveness of the sub-modules in the experiment. The result suggests that the proposed MMA model outperformed the state-of-the-art baselines.
Multi-task Pre-training for Lhasa-Tibetan Speech Recognition

Compared to mainstream languages such as Chinese and English, Tibetan speech corpus is limited. Pre-training technology can improve the speech recognition performance for low-resource language by using multiple languages corpus, which involves initially training a neural network on the multi-language dataset, followed by fine-tuning the trained model on low-resource language. In this paper, a multi-task serial pre-training method is proposed to address the limited resources in Tibetan speech recognition. By designing the number and order of tasks in the pre-training process, better recognition performance can be achieved. The experiments on the Lhasa-Tibetan speech recognition task show that our proposed method is significantly superior to the baseline model, achieving a Tibetan word error rate of 4.12%, which is a 9.34% reduction compared to the baseline model and 1.06% lower compared to the existing pre-training model.
Using blockchain technology to combat counterfeiting

Although the globalization of trade entails multiple benefits for both traders and consumers, such as the expansion of markets and the possibility of accessing a wide range of products, it also entails the risk of the spread of counterfeit goods. One of the tools that could be used to combat this trend is blockchain technology. The aforementioned technology presupposes the existence of a single ledger that is shared and synchronized across many sites, organizations, or computers, with participants having the ability to register any type of information. Thus, in the near future, a single ledger could be created in which relevant information about the specific traded good, such as its origin or sanitary certificate, would be recorded. By doing so, the quantity of counterfeit goods on the market will decrease, while consumer confidence will increase. The World Intellectual Property Organization is currently analyzing the possibility of incorporating blockchain technology into several areas specific to intellectual property law, with one of the goals being to combat counterfeiting. Therefore, through this article, we propose to conduct an analysis of the legal provisions of the European Union that aim at fighting against counterfeit goods. Simultaneously, we will investigate the methods used by the private sector in this field, particularly blockchain technology. In this regard, some practical examples that have been used for several years by large retailers such as Walmart and Carrefour will be presented.
MEA-TransUNet: A Multiple External Attention Network for Multi-Organ Segmentation

Recently, pioneering work has improved segmentation performance by combining the self-attention (SA) mechanism with UNet. However, since SA can only model its own features in a single sample, it ignores the potential relevance of the whole dataset. Additionally, medical image datasets are typically small, making it crucial to obtain as many features as possible within a limited dataset. To address these problems, we propose the Multiple External Attention (MEA) module, which characterizes the overall dataset by mining correlations between different samples based on external concerns. Furthermore, our method applies the Squeeze-and-Excitation (SE) module for the first time to low-level feature extraction of medical images. By using MEA and SE, we construct MEA-TransUNet for accurate segmentation of medical images. We test our method on two datasets and the experimental results demonstrate its superior performance compared to other existing methods. Code and pre-trained models are coming soon.
Multi-grained Aspect Fusion for Review Response Generation

Review response generation (RRG) aims to automatically generate responses to customer reviews. Responding to reviews in a right manner is important to online customer experience. However, most previous research on RRG focused on exploring coarse review information and ignored fine-grain aspects within reviews, especially those with negative sentiment. As a result, the generated responses are usually not targeted to users’ real concerns in their reviews. To this end, we proposed a multi-grained aspect fusion model (MGAF) model to improve the targeting of generated responses. In particular, we first enhance the targeting ability by performing sentence-level aspect selection and response script learning. Then we integrate aspect-level keywords with sentiment information to further improve the diversity of generated responses. Experimental results on both Chinese and English datasets show that our proposed model outperforms the state-of-the-art models available, demonstrating the importance of fusing multi-grained aspect information for targeted response generation.
Spatio-Temporal Attention Model with Prior Knowledge for Solar Wind Speed Prediction

Solar wind prediction is a critical aspect of space weather forecasting, and current research has primarily focused on feature extraction from historical wind speed or individual solar images. To enhance the quality of data and improve prediction accuracy, we propose a novel approach that leverages multi-modality, combining both temporal and spatial dimensions. Additionally, we utilize prior knowledge to guide model training, specifically in the image preprocessing and matrix multiplication stages, where prior knowledge constraints are applied. Our study introduces the spatio-temporal attention model (STA) for solar wind prediction, which comprises an image branch and a solar wind speed data branch. The image branch uses a shared-weight feature extraction network to extract features from EUV images, while the solar wind speed data branch models temporal dynamics with sequence networks. Furthermore, we incorporated an attention-based feature extraction module and a feature fusion module to enhance the model’s performance. Our experimental results demonstrate that the proposed STA model outperforms existing state-of-the-art models.
Spatially Invariant and Frequency-Aware CycleGAN for Unsupervised MR-to-CT Synthesis

Synthesis of computed tomography (CT) images from magnetic resonance (MR) images plays an important role in radiotherapy treatment planning. CycleGANs have achieved promising performance in unsupervised MR-to-CT synthesis. However, the inter-modality gap between the two modalities and the loss of high-frequency information in the synthetic CT images are still not well addressed. In this paper, we propose a spatially invariant and frequency-aware CycleGAN (SF-CycleGAN) to improve the performance of unsupervised MR-to-CT synthesis. Specifically, we introduce a translation-invariant generator to generate CT from MR images, while maintaining the invariance of spatial feature during translation for those positions having similar characteristics. Furthermore, we define a frequency-consistent loss to promote the consistency of the frequency between real and synthesized images and adaptively guide the model to pay more attention to synthesizing the harder-frequency (e.g., higher-frequency) parts. Intensive results in unpaired brain MR-to-CT image synthesis demonstrate that our method provides both quantitatively and qualitatively superior performance as compared to the baseline (CycleGAN) and other state-of-the-art approaches.
Image Semantic Segmentation: Scene-Aware System for Autonomous Vehicle

Understanding urban street scenes is an essential part of the perceptual job involved in the use of autonomous vehicles. The interpretation of scenes through semantic segmentation has been employed widely, and this helps with the task of autonomous driving including planning of path, object identification, and motion control. However, precise semantic image segmentation in computer vision is a difficult job. The development and study of image segmentation are the main topics of this paper. On the Cityscapes dataset, we analyzed our methodology. In the proposed work, the U-Net model is utilized since it is a well-known semantic segmentation network for image semantic segmentation, and it is a compact, elegant architecture and has much fewer convolutions. For recognizing the object, YOLOv3 algorithm is used. The result of the proposed work is the image segmented along with bounding boxes, labels, and detecting accuracy.
Selecting Distinctive-Variant Training Samples Base on Intra-class Similarity

Deep learning models often require a significant amount of data, which can be computationally intensive and architecturally complex. Efforts to address the challenge of handling large amounts of data in high-resolution scenarios have led to the development of techniques like data pruning and data diet approaches. We present a novel approach called Select Base on Intra-Class Similarity (SICS), distinguishes itself by measuring the similarity of samples within the same class and identifies the most informative samples that are most dissimilar from others, and introducing the novel concept of a distinctive-variant sample, vital for enhancing deep-learning classification tasks. We evaluated our method on several image classification benchmarks and compared it with existing techniques. Our results show that in high-resolution images and many class scenarios, SICS can achieve the same level of accuracy as the full data while using only about 80% of the training data, outperforming the ForgettingScore method by 20% to 90%. Additionally, our method maintains its robustness when switching to different training models. Our source code is publicly available at https://github.com/Gusicun/SICS .
A Multi-objective Path–Planning Based on Firefly Algorithm for Mobile Robots

Currently there are lots of Swarm Intelligence algorithms available that can be used in optimization problems. The modified Multi-objective Firefly algorithm proposed in the present work is used to find an optimal path for mobile robots in a static environment. The algorithm is based on the flashing behavior of the fireflies in nature, hence called the Firefly Algorithm (FA). The proposed approach takes into account three objectives to obtain efficient and optimal solutions and is used to plan a path for mobile robots in a static environment. The three objectives are as follows: path length, path safety and path smoothness. The results were obtained after optimizing for all three parameter values over several Iterations (generations) for different population sizes and different sets of obstacles. The performance of the proposed multi-objective Firefly algorithm was compared to the results obtained using the classical NSGA-II approach.
Multiple Object Tracking Based on Variable GIoU-Embedding Matrix and Kalman Filter Compensation

Despite tracking-by-detection having shown dramatically ra-pid improvement, most existing approaches are still scrabbling in dense pedestrian tracking. To address this problem, this work presents a new multiple object tracking approach, named VacoTrack. This method combines variable GIoU-Embedding matrix (VGE) and Kalman Filter compensation, which introduces motion compensation operation over trajectory parameters to construct virtual uniform linear trajectories for objects. This method combines GIoU distance and Embedding cosine distance of objects variably as a new association matrix VGE to adjustably calculate similarity matrix in facing different occlusion problem. After association, this method sends all tracked trajectories back to Kalman Filter, including constructed virtual trajectories for re-matched objects, and then operates Kalman Filter compensation to fine tune trajectory parameters. Thus, this approach regards the motion patterns of objects as uniform linear motion patterns to identify them across dense pedestrian, and improve robustness. Our proposed approach achieves 64.43 and 63.14 HOTA on MOT17 and MOT20 benchmarks respectively and outperforms state-of-the-art in most evaluation metrics.
Vulnerability Analysis of Continuous Prompts for Pre-trained Language Models

Prompt-based learning has recently emerged as a promising approach for handling the increasing complexity of downstream natural language processing (NLP) tasks, achieving state-of-the-art performance without using hundreds of billions of parameters. However, this paper investigates the general vulnerability of continuous prompt-based learning in NLP tasks, and uncovers an important problem: the predictions of continuous prompt-based models can be easily misled by noise perturbations. To address this issue, we propose a learnable attack approach that generates noise perturbations with the goal of minimizing their $$L_2$$ -norm in order to attack the primitive, harmless successive prompts in a way that researchers may not be aware of. Our approach introduces a new loss function that generates small and impactful perturbations for each different continuous prompt. Even more, our approach shows that learnable attack perturbations with an $$L_2$$ -norm close to zero can severely degrade the performance of continuous prompt-based models on downstream tasks. We evaluate the performance of our learnable attack approach against two continuous prompt-based models on three benchmark datasets and the results demonstrate that the noise and learnable attack methods can effectively attack continuous prompts, with some tasks exhibiting an F1-score close to 0.
PCDialogEval: Persona and Context Aware Emotional Dialogue Evaluation

Endowing dialogue systems with emotional intelligence is an essential strategy for machines to achieve deep social interaction with users, for which effective evaluation metrics for emotional dialogue are in urgent need. However, most existing evaluation methods ignore the impact of users’ individual differences and situational contexts on emotional expressions, which poses a significant challenge in assessing the emotional expression capabilities of dialogue models. To address these issues, we propose a novel evaluation model that incorporates personality into evaluation metrics for dialogue systems. Our model quantifies the influence of human personality on emotional expressions and simulates the emotional transfer during conversations to calculate the intensity of emotional expressions in candidate sentences. To accomplish this calculation, we first incorporates “Big Five Personality” traits for personality analysis, and subsequently modify the emotion vector in a Valence-Arousal-Dominance (VAD) space. Furthermore, we construct mood transfer equations to simulate the impact of the conversational context on emotional expressions. Additionally, we propose an additional assessment at both sentence and session-levels to evaluate the fluency and coherence of the generated dialogue. Experimental results on two datasets demonstrate the effectiveness of the proposed evaluation model in accurately assessing the emotional expression capabilities of dialogue systems.
Pure Physics-Informed Echo State Network of ODE Solution Replicator

Inspired by recent arguments to the echo state neurons, a reservoir model of sequential data is polished to replicate the solution of given ODEs (Ordinary Differential Equations). By training with the differential equation itself, a pure physics-informed echo state network (ESN) is firstly introduced based on the loss function as consistent with the general invariance of differential equations, in which the actual training scheme is implemented as a regression in two stages. On such a physics-informed model of recurrent neurons, some dynamical problems are explored by means of sequential generation of the ODE solution as drawn from the nonlinear trajectories of hidden state in the reservoir model.
PCB Component Rotation Detection Based on Polarity Identifier Attention

Accurate detection of component rotation is a critical task in the production of printed circuit boards (PCBs). However, with the rapid development of integrated circuits, manually labeling large datasets of all kinds of components for training machine learning models is time-consuming and impractical. To address this challenge, we propose a novel one-shot conditional component rotation detection framework, PolarNet. Given a standard image of the target component, a query image, and a polar indicator image as input, PolarNet can detect the rotation angle of the component in the query image. PolarNet comprises a Siamese network, an Indicator Attention Generator, and a Rotation Detector. The Indicator Attention Generator takes the polar indicator image as heuristic knowledge to help the Rotation Detector. Once trained, PolarNet can detect component rotation in both seen and unseen classes without further training. Our experiments on our own dataset demonstrate that PolarNet achieves good performance for one-shot rotation detection in both accuracy and scalability.
Non-Outlier Pseudo-Labeling for Short Text Clustering

Instance-level correlation and cluster-level discrepancy of data are two crucial aspects of short text clustering. Current deep clustering methods, however, suffer from inaccurate estimation of either instance-level correlation or cluster-level discrepancy of data and strongly relay on the quality of the initial text representation. In this paper, we propose a Non-outlier Pseudo-labeling-based Short Text Clustering (NPLC) method, which consists of two parts. In the first part, we use Mask Language Model (MLM) to pre-train the feature model on a given dataset to enhance the initial text representation. The second part based on non-outlier pseudo-labeling is a joint training in which we first cluster the dataset and select cluster labels of outlier-free data in each cluster as pseudo labels for the next joint training based on a novel framework. The novel framework makes use of a contrastive loss to gain excellent inter-cluster separation by minimizing similarity between outlier-free and outlier data and a clustering loss to narrow intra-cluster distances by maximizing similarity among outlier-free data. Extensive experimental results demonstrate that NPLC achieves significant improvements over existing methods and advances the state-of-the-art results on most benchmark datasets with 1%–12% improvement on Accuracy and 1%–6% improvement on Normalized Mutual Information.
Forensic challenges regarding the Internet of Things

The Internet of Things brings many benefits to our society, but its advantages come with a price, namely with a huge opportunity for those who want to commit cybercrime. To fight against this kind of criminality, investigators must use appropriate methods. The Forensics of the Internet of Things (IoT) has evolved from “traditional” Digital Forensics, and includes, among other activities, the identification of the devices related to a certain crime, collecting the digital evidence in a form which can be used in front of the judicial bodies, the analysis, and the extraction of all possible information from the collected data, and the preservation of the evidence. The Forensics of IoT must face specific issues, mainly generated by the extraordinary large number of IoT devices and by the great diversity of these devices. An important problem is that data analysis is very difficult to perform, because of different data formats used by the components of IoT. In the forensic investigations related to the IoT, an important concern refers to privacy protection. Overall, IoT Forensics is a challenging field, but we must search for suitable solutions, because ensuring security for IoT is a very important aim.
Towards Better Dialogue Utterance Rewriting via a Gated Span-Copy Mechanism

Dialogue rewriting aims to reconstruct the incomplete utterance from dialogue history. It is a challenge task due to the frequent phenomena of coreference and ellipses in dialogue. Although the conventional encoder-decoder architecture has shown the effectiveness for dialogue rewriting, there are still two issues should be addressed. Firstly, the objects referred to or omitted are usually mentions, represented as spans. So the traditional word-by-word copy mechanism, which is widely used in current models, can lead to incompletion, repetition and disorder problems. Secondly, words in dialogue history and common vocabulary list have different effects on rewriting the current utterance. Intuitively, semantically and cohesively matched spans are more important. In this paper, we propose a novel Gated Span-level Copy Mechanism (GSCM) that aims to retrieve the omitted or co-referred spans contained in history dialogue and recover them for the incomplete utterance. The experimental results on the CamRest676 and RiSAWOZ corpora show that our GSCM can significantly improve the performance of dialogue rewriting.