Reviewerly/exHarmony huggingface dataset:
Unsupervised Style Transfer in News Headlines via Discrete Style Space

The goal of headline style transfer in this paper is to make a headline more attractive while maintaining its meaning. The absence of parallel training data is one of the main problems in this field. In this work, we design a discrete style space for unsupervised headline style transfer, short for D-HST. This model decomposes the style-dependent text generation into content-feature extraction and style modelling. Then, generation decoder receives input from content, style, and their mixing components. In particular, it is considered that textual style signal is more abstract than the text itself. Therefore, we propose to model the style representation space as a discrete space, and each discrete point corresponds to a particular category of the styles that can be elicited by syntactic structure. Finally, we provide a new style-transfer dataset, named as TechST, which focuses on transferring news headline into those that are more eye-catching in technical social media. In the experiments, we develop two automatic evaluation metrics — style transfer rate (STR) and style-content trade-off (SCT) — along with a few traditional criteria to assess the overall effectiveness of the style transfer. In addition, the human evaluation is thoroughly conducted in terms of assessing the generation quality and creatively mimicking a scenario in which a user clicks on appealing headlines to determine the click-through rate. Our results indicate the D-HST achieves state-of-the-art results in these comprehensive evaluations.
Overcoming Language Priors with Counterfactual Inference for Visual Question Answering

Recent years have seen a lot of efforts in attacking the issue of language priors in the field of Visual Question Answering (VQA). Among the extensive efforts, causal inference is regarded as a promising direction to mitigate language bias by weakening the direct causal effect of questions on answers. In this paper, we follow the same direction and attack the issue of language priors by incorporating counterfactual data. Moreover, we propose a two-stage training strategy which is deemed to make better use of counterfactual data. Experiments on the widely used benchmark VQA-CP v2 demonstrate the effectiveness of the proposed approach, which improves the baseline by $$21.21\%$$ and outperforms most of the previous systems.
P-MNER: Cross Modal Correction Fusion Network with Prompt Learning for Multimodal Named Entity Recognition

Multimodal Named Entity Recognition (MNER) is a challenging task in social media due to the combination of text and image features. Previous MNER work has focused on predicting entity information after fusing visual and text features. However, pre-training language models have already acquired vast amounts of knowledge during their pre-training process. To leverage this knowledge, we propose a prompt network for MNER tasks (P-MNER). To minimize the noise generated by irrelevant areas in the image, we design a visual feature extraction model (FRR) based on FasterRCNN and ResNet, which uses fine-grained visual features to assist MNER tasks. Moreover, we introduce a text correction fusion module (TCFM) into the model to address visual bias during modal fusion. We employ the idea of a residual network to modify the fused features using the original text features. Our experiments on two benchmark datasets demonstrate that our proposed model outperforms existing MNER methods. P-MNER’s ability to leverage pre-training knowledge from language models, incorporate fine-grained visual features, and correct for visual bias, makes it a promising approach for multimodal named entity recognition in social media posts.
FACT: A Dynamic Framework for Adaptive Context-Aware Translation

Document-level neural machine translation (NMT) has garnered considerable attention since the emergence of various context-aware NMT models. However, these static NMT models are trained on fixed parallel datasets, thus lacking awareness of the target document during inference. In order to alleviate this limitation, we propose a dynamic adapter-translator framework for context-aware NMT, which adapts the trained NMT model to the input document prior to translation. Specifically, the document adapter reconstructs the scrambled portion of the original document from a deliberately corrupted version, thereby reducing the performance disparity between training and inference. To achieve this, we employ an adaptation process in both the training and inference stages. Our experimental results on document-level translation benchmarks demonstrate significant enhancements in translation performance, underscoring the necessity of dynamic adaptation for context-aware translation and the efficacy of our methodologies.
Improving Cascade Decoding with Syntax-Aware Aggregator and Contrastive Learning for Event Extraction

Cascade decoding framework has shown superior performance on event extraction tasks. However, it treats a sentence as a sequence and neglects the potential benefits of the syntactic structure of sentences. In this paper, we improve cascade decoding with a novel module and a self-supervised task. Specifically, we propose a syntax-aware aggregator module to model the syntax of a sentence based on cascade decoding framework such that it captures event dependencies as well as syntactic information. Moreover, we design a type discrimination task to learn better syntactic representations of different event types, which could further boost the performance of event extraction. Experimental results on two widely used event extraction datasets demonstrate that our method could improve the original cascade decoding framework by up to 2.2 percentage points of F1 score and outperform a number of competitive baseline methods.
Enhancing Ontology Knowledge for Domain-Specific Joint Entity and Relation Extraction

Pre-trained language models (PLMs) have been widely used in entity and relation extraction methods in recent years. However, due to the semantic gap between general-domain text used for pre-training and domain-specific text, these methods encounter semantic redundancy and domain semantics insufficiency when it comes to domain-specific tasks. To mitigate this issue, we propose a low-cost and effective knowledge-enhanced method to facilitate domain-specific semantics modeling in joint entity and relation extraction. Precisely, we use ontology and entity type descriptions as domain knowledge sources, which are encoded and incorporated into the downstream entity and relation extraction model to improve its understanding of domain-specific information. We construct a dataset called SSUIE-RE for Chinese entity and relation extraction in space science and utilization domain of China Manned Space Engineering, which contains a wealth of domain-specific knowledge. The experimental results on SSUIE-RE demonstrate the effectiveness of our method, achieving a 1.4% absolute improvement in relation F1 score over previous best approach.
TERL: Transformer Enhanced Reinforcement Learning for Relation Extraction

Relation Extraction (RE) task aims to discover the semantic relation that holds between two entities and contributes to many applications such as knowledge graph construction and completion. Reinforcement Learning (RL) has been widely used for RE task and achieved SOTA results, which are mainly designed with rewards to choose the optimal actions during the training procedure, to improve RE’s performance, especially for low-resource conditions. Recent work has shown that offline or online RL can be flexibly formulated as a sequence understanding problem and solved via approaches similar to large-scale pre-training language modeling. To strengthen the ability for understanding the semantic signals interactions among the given text sequence, this paper leverages Transformer architecture for RL-based RE methods, and proposes a generic framework called Transformer Enhanced RL (TERL) towards RE task. Unlike prior RL-based RE approaches that usually fit value functions or compute policy gradients, TERL only outputs the best actions by utilizing a masked Transformer. Experimental results show that the proposed TERL framework can improve many state-of-the-art RL-based RE methods.
Multiple-Reservoir Hierarchical Echo State Network

Leaky Integrator Echo State Network (Leaky-ESN) is a useful training method for handling time series prediction problems. However, the singular coupling of all neurons in the reservoir makes Leaky-ESN less effective for sophisticated learning tasks. In this paper, we propose a new improvement to the Leaky-ESN model called the Multiple-Reservoir Hierarchical Echo State Network (MH-ESN). By introducing a new mechanism for constructing the reservoir, the efficiency of the network in handling training tasks is improved. The hierarchical structure is used in the process of constructing the reservoir mechanism of MH-ESN. The MH-ESN consists of multiple layers, each comprising a multi-reservoir echo state network model. The sub-reservoirs within each layer are linked via principal neurons, which mimics the functioning of a biological neural network. As a result, the coupling among neurons in the reservoir is decreased, and the internal dynamics of the reservoir are improved. Based on the analysis results, the MH-ESN exhibits significantly better prediction accuracy than Leaky-ESN for complex time series prediction.
Temporal-Aware Neural Network for Dense Non-Rigid Structure from Motion

Modern neural networks addressing dense Non-Rigid Structure from Motion (NRSFM) dilemmas often grapple with intricate a priori constraints, deterring scalability, or overlook the imperative of consistent application of a priori knowledge throughout the entire input sequence. In this paper, an innovative neural network architecture is introduced. Initially, the complete 2D sequence image undergoes embedding into a low-dimensional space. Subsequently, multiple self-attention layers are employed to extract inter-frame features, with the objective of deriving a more continuous and temporally smooth low-dimensional structure closely resembling real data’s intrinsic structure. Moreover, it has been demonstrated by others that gradient descent during the training of multilayer linear networks yields minimum rank solutions, implicitly providing regularization that is equally applicable to this task. Benefiting from the excellence of the proposed network architecture, no additional a priori knowledge is mandated, barring the constraint of temporal smoothness. Extensive experimentation confirms the method’s exceptional performance in addressing dense NRSFM challenges, outperforming recent results across various dense benchmark datasets.
Performance Evaluation of Single-Carrier and Orthogonal Frequency Divison Multiplexing-Based Autoencoders in Comparison with Low-Density Parity-Check Encoder

Recently, the growing demands for ultra-high speed applications require more advanced and optimal data transmission techniques. Wireless autoencoders have gained significant attention since they provide global optimization of the transceiver structure. This article explores the application of autoencoders to enhance the performance of wireless communication systems. It provides the performance evaluation of the systems using single-carrier and OFDM-based autoencoders under the conditions of AWGN and fading channels. Then, in terms of the BLER metric, the wireless systems with autoencoders are compared with conventional systems using LDPC coding and quadrature amplitude modulation for various configurations. Simulation results indicate that for high-modulation orders (QAM-64 or QAM-256), communication systems employing autoencoders provide superior performance compared to systems using LDPC channel encoding in regions with a low signal-to-noise (SNR) ratio. Specifically, a gain of 1–2 dB in signal power is obtained for single-carrier autoencoders and 0.3–2 dB is obtained for OFDM-based autoencoders. Therefore, wireless communication systems utilizing autoencoders can be considered as a promising candidate for future wireless communication systems.
Otomatik sürüş için derin öğrenme metodu kullanarak trafik işaretlerinin tespiti

Deep learning practices used in many fields, in particular, in health, military, economy, and production industries, are an important area of artificial intelligence in our age. The object classification and object recognition applications, which play a significant role in the development of autonomous vehicle technologies, constitute the focal point of the deep learning studies. It is clear that the recent studies based on the deep learning models show that they are useful and successful performances for safe driving not only for vehicles but also for pedestrians. It is very crucial and significant that the autonomous systems recognize the traffic signs with high accuracy for a safe driving. Especially, the pedestrian crossing, school district, urban speed limits can be regarded among the most critical traffic signs. In this study, we have used the data set including the traffic signs obtained by our own means to carry out trainings by using faster R-CNN which is regarded as one of the most important recognition architectures. Thanks to the hardware module produced as a result of the operation, we have developed a system that warns the driver of the vehicle with audible warning. The developed hardware module can detect not only the speed limits, traffic signs but also pedestrian crossings and school districts and alert the driver in reel-time. The developed hardware module is based on Arduino and because of the GPS sensor, it can also show the speed of the vehicle. Moreover, we have used Python for the developed software and the dataset trainings have been carried out by using the Tensorflow library. We think that the study will contribute a lot to the recognition of traffic signs for the autonomous vehicle applications.
Evaluation of Machine Learning Algorithms using Combined Feature Extraction Techniques for Speaker Identification

The aim of this study is to evaluate and compare machine learning algorithms when various feature extraction techniques are employed together and determine the optimal feature combinations for the models studied. The TIMIT online database was used where 5 male and 5 female non-native English speakers from five American locations were selected. Each speaker had ten 3-second utterances, totaling 500. Mel frequency cepstral coefficients (MFCC), linear predictive cepstral coefficients (LPCC), gammatone frequency cepstral coefficients (GFCC), discrete wavelet transforms (DWT) and pitch features were extracted using MATLAB and concatenated. The concatenated features were used to train and evaluate three classifier models—Random Forest (RF), Linear Discriminant Analysis (LDA), and Logistic Regression (LR)—using Python software. The results obtained showed that as the number of features combinations increased, the models’ performances improved as well. This improved performance was observed when all the cepstral features were part of the combinations. This implies that cepstral features are more robust and improve speaker identification systems. The best average score of accurate predictions of ≈ 76% for the LR model was obtained for the MGL (39 features) features combination and dropped to 70% for the highest number of feature combinations MGDLP (53 features). This indicates that more training data improves system performance, however, too much data does not translate to even better performance because the system will eventually achieve its peak performance. This information is useful for applications where limited data can present a problem.
BcsUST: universal style transformation network for balanced content styles

Composition, color, and brushstrokes are the primary factors in evaluating and appreciating artwork and are vital points to be considered and addressed in arbitrary style transfer tasks. However, existing methods do not balance these elements well, so we propose an approach that balances content structure and style patterns for universal style transformation (BcsUST). Specifically, two lightweight encoders based on residual networks pass the extracted style and content features into the multi-domain structure attention module, which applies a self-attention mechanism to align the input features point by point followed by manifold alignment. A secondary alignment strategy is used to integrate global styles harmoniously into the semantic structure of the content, while obtaining content details to keep the structure intact. Then, the texture modulator generates style convolution parameters to dynamically adjust the filter within the micro-decoder feature subspace, injecting complex and flexible style signals for stylization to enrich the colors and form exquisite strokes. In addition, this work proposes a two-stage training strategy that introduces an artistic appreciation loss to further balance content structure and stylistic signals. Numerous qualitative and quantitative studies demonstrate that the BcsUST framework produces images that resemble the artists’ paintings more closely.
Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives

Abstract. Knowledge of the spatial and temporal characteristics of solar surface irradiance (SSI) is critical in many domains. While meteorological ground stations can provide accurate measurements of SSI locally, they are sparsely distributed worldwide. SSI estimations derived from satellite imagery are thus crucial to gain a finer understanding of the solar resource. Inferring SSI from satellite images is, however, not straightforward, and it has been the focus of many researchers in the past 30 to 40 years. For long, the emphasis has been on models grounded in physical laws with, in some cases, simple statistical parametrizations. Recently, new satellite SSI retrieval methods have been emerging, which directly infer the SSI from the satellite images using machine learning. Although only a few such works have been published, their practical efficiency has already been questioned. The objective of this paper is to better understand the potential and the pitfalls of this new family of methods. To do so, simple multi-layer-perceptron (MLP) models are constructed with different training datasets of satellite-based radiance measurements from Meteosat Second Generation (MSG) with collocated SSI ground measurements from Météo-France. The performance of the models is evaluated on a test dataset independent from the training set in both space and time and compared to that of a state-of-the-art physical retrieval model from the Copernicus Atmosphere Monitoring Service (CAMS). We found that the data-driven model's performance is very dependent on the training set. Provided the training set is sufficiently large and similar enough to the test set, even a simple MLP has a root mean square error (RMSE) that is 19 % lower than CAMS and outperforms the physical retrieval model at 96 % of the test stations. On the other hand, in certain configurations, the data-driven model can dramatically underperform even in stations located close to the training set: when geographical separation was enforced between the training and test set, the MLP-based model exhibited an RMSE that was 50 % to 100 % higher than that of CAMS in several locations.
Observability-Constrained Resampling-Free Cubature Kalman Filter for GNSS/INS with Measurement Outliers

Integrating global navigation satellite systems (GNSSs) with inertial navigation systems (INSs) has been widely recognized as an ideal solution for autonomous vehicle navigation. However, GNSSs suffer from disturbances and signal blocking inevitably, making the performance of GNSS/INSs degraded in the occurrence of measurement outliers. It has been proven that the sigma points-based Kalman filter (KF) performs better than an extended KF in cases where large prior uncertainty is present in the state estimation of a GNSS/INS. By modifying the sigma points directly, the resampling-free sigma point update framework (SUF) propagates more information excepting Gaussian moments of prescribed accuracy, based on which the resampling-free cubature Kalman filter (RCKF) was developed in our previous work. In order to enhance the adaptivity and robustness of the RCKF, the resampling-free SUF depending on dynamic prediction residue should be improved by suppressing the time-varying measurement outlier. In this paper, a robust observability-constrained RCKF (ROCRCKF) is proposed based on adaptive measurement noise covariance estimation and outlier detection, where the occurrence of measurement outliers is modelled by the Bernoulli variable and estimated with the state simultaneously. Experiments based on car-mounted GNSS/INSs are performed to verify the effectiveness of the ROCRCKF, and result indicates that the proposed algorithm outperforms the RCKF in the presence of measurement outliers, where the heading error and average root mean square error of the position are reduced from 1.96° and 6.38 m to 0.27° and 5.95 m, respectively. The ROCRCKF is robust against the measurement outliers and time-varying model uncertainty, making it suitable for the real-time implementation of GNSS/INSs in GNSS-challenged environments.
The role of blockchain technology in advancing sustainable energy with security settlement: enhancing security and efficiency in China’s security market

Blockchain technology has the potential to revolutionize securities settlement systems, offering an efficient, reliable, and cost-effective alternative to traditional methods. Its features, including distributed data authenticity, programmability, and scalability, can enhance security and efficiency in China’s securities market, while promoting a sustainable energy future. However, to fully leverage the benefits of blockchain in securities registration and settlement, it is crucial to address algorithmic loopholes and operational risks associated with smart contracts. Establishing blockchain technical standards and rules is also necessary to ensure smooth system operation. Furthermore, given the uncertainty of the final settlement time point, adherence to decentralization principles and the incorporation of embedded technology for supervision are essential. Legislative measures are required to regulate smart contracts and mitigate systemic risk effectively. This will ensure a stable settlement time expectation and enable fair allocation of legal responsibility among the involved parties. Additionally, other regulatory approaches should be implemented to provide effective supervision and adapt to the rapid development of blockchain. By addressing challenges and risks, blockchain’s full potential can be realized, enabling a sustainable energy future, while enhancing security and efficiency in China’s securities market.