Reviewerly/exHarmony huggingface dataset:
Dual attentive graph convolutional networks for cross-domain recommendation

Cross-domain recommendation aims to alleviate the target domain’s data sparsity problem by leveraging source domain knowledge. Existing GCN-based approaches perform graph convolution operations in each domain separately. However, the direct effect of item feature and topological structure information in the source domain are neglected for user preference modeling in the target domain. In this paper, we propose a novel Dual Attentive Graph Convolutional Network for Cross-Domain Recommendation (DAG4CDR). Specifically, we integrate the source and target domain’s interaction data to construct a unified user-item bipartite graph and then perform GCN propagation on the graph to learn user and item embeddings. Over the unified graph, the interaction data from both domains can be leveraged to learn user and item embeddings via information propagation. In the embedding aggregation phase, the messages passed from different items of two domains to users are weighted by a designed dual attention mechanism, which considers the contributions of different items from both node- and domain-level. We conducted extensive experiments to validate the effectiveness of our method on several publicly available datasets, and the results demonstrate the superiority of our model on preference modeling for both common and non-common users.
Identifying Targeted and Generalized Offensive Speech from Anti-asian Social Media Conversations

During the Covid-19 pandemic Asian-Americans have been targets of prejudice and negative stereotyping. There has also been volumes of counter speech condemning this jaundiced attitude. Ironically, however, the dialogue on both sides is filled with offensive and abusive language. While abusive language directed at Asians encourages violence and hate crimes against this ethnic group, the use of derogatory language to insult alternative points of view showcases utter lack of respect and exploits people's fears to stir up social tensions. It is thus important to identify and demote both types of offensive content from anti-Asian social media conversations. The goal of this paper is to present a machine learning framework that can achieve the dual objective of detecting targeted anti-Asian bigotry as well as generalized offensive content. Tweets were collected using the hashtag #chinavirus. Each tweet was annotated in two ways; either it condemned or condoned anti-Asian bias, and whether it was offensive or non-offensive. A rich set of features both from the text and accompanying numerical data were extracted. These features were used to train conventional machine learning and deep learning models. Our results show that the Random Forest classifier can detect both generalized and targeted offensive content with around 0.88 accuracy and F1-score. Our results are promising from two perspectives. First, our approach outperforms contemporary efforts on detecting online abuse against Asian-Americans. Second, our unified approach detects both offensive speech targeted specifically at Asian-Americans and also identifies its generalized form which has the potential to mobilize a large number of people in socially challenging situations.
PairTraining: A method for training Convolutional Neural Networks with image pairs

In the field of image classification, the Convolutional Neural Networks (CNNs) are effective. Most of the work focuses on improving and innovating CNN’s network structure. However, using labeled data more effectively for training has also been an essential part of CNN’s research. Combining image disturbance and consistency regularization theory, this paper proposes a model training method (PairTraining) that takes image pairs as input and dynamically modify the training difficulty according to the accuracy of the model in the training set. According to the accuracy of the model in the training set, the training process will be divided into three stages: the qualitative stage, the fine learning stage and the strengthening learning stage. Contrastive learning images are formed using a progressively enhanced image disturbance strategy at different training stages. The input image and contrast learning image are combined into image pairs for model training. The experiments are tested on four public datasets using eleven CNN models. These models have different degrees of improvement in accuracy on the four datasets. PairTraining can adapt to a variety of CNN models for image classification training. This method can better improve the effectiveness of training and improve the degree of generalization of classification models after training. The classification model obtained by PairTraining has better performance in practical application.
Knowledge-of-Own-Factivity, the Definition of Surprise, and a Solution to the Surprise Examination Paradox

Fitch’s Paradox and the Paradox of the Knower both make use of the Factivity Principle. The latter also makes use of a second principle, namely the Knowledge-of-Factivity Principle. Both the principle of factivity and the knowledge thereof have been the subject of various discussions, often in conjunction with a third principle known as Closure. In this paper, we examine the well-known Surprise Examination paradox considering both the principles on which this paradox rests and some formal characterisations of the surprise notion, crucial in this paradox. Standard formalizations of the Surprise Examination paradox in modal logic do not seem, at first glance, to depend on either factivity or knowledge-of-factivity, but we will argue that both factivity and knowledge-of-factivity play a key implicit role in the paradox. Namely, they are implicitly, perhaps unintentionally, used in order to simplify the definition of surprise. We analyze modal logical formalizations of three versions of the paradox concluding that the Surprise Examination paradox is the result of two flaws: the assumption of knowledge-of-factivity, and the over-simplification of the definition of “surprise” accordingly. By fixing these two flaws, the Surprise Examination paradox vanishes.
SimDCL: dropout-based simple graph contrastive learning for recommendation

Abstract Representation learning of users and items is the core of recommendation, and benefited from the development of graph neural network (GNN), graph collaborative filtering (GCF) for capturing higher order connectivity has been successful in the recommendation domain. Nevertheless, the matrix sparsity problem in collaborative filtering and the tendency of higher order embeddings to smooth in GNN limit further performance improvements. Contrastive learning (CL) was introduced into GCF and alleviated these problems to some extent. However, existing methods usually require graph perturbation to construct augmented views or design complex CL tasks, which limits the further development of CL-based methods in the recommendation. We propose a simple CL framework that does not require graph augmentation, but is based on dropout techniques to generate contrastive views to address the aforementioned problem. Specifically, we first added dropout operation to the GNN computation, and then fed the same batch of samples twice into the network for computation. Using the randomness of dropout, a pair of views with random noise was obtained, and maximizing the similarity of the view pairs is set as an auxiliary task to complement the recommendation. In addition, we made a simple modification to the computation of the GNN to alleviate the information loss due to embedding smoothing by means of cross-layer connected graph convolution computation. We named our proposed method as Simple Contrastive Learning Graph Neural Network based on dropout (SimDCL). Extensive experiments on five public datasets demonstrate the effectiveness of the proposed SimDCL, especially on the Amazon Books and Ta-Feng datasets, where our approach achieves 44% and 43% performance gains compared to baseline.
Encryption/Decryption with Optical Transform

In this chapter, the methods of image encryption and decryption based on optical transformation are introduced. The extended fractional Fourier transform is constructed by designing an eccentric lens group, and is used in the image encryption system. By using the Fresnel diffraction model, an iterative phase recovery algorithm is constructed, and a color image hiding method and optical system are introduced. A parallel phase retreival technique in gyrator transform domain is constructed, and is applied to construct an image decryption system.
Block SMRT and knapsack optimization-based sequency selector for robust, imperceptible, and payload-efficient color image watermarking for binary watermark

Watermarking is a generic strategy for overcoming numerous issues associated with multimedia security. Performance of a watermarking system in terms of imperceptibility, robustness and payload is highly dependent on the positions used for embedding. The choice of sequency packets plays an important role in the performance when Sequency-based Mapped Real Transform (SMRT) is used for embedding. A novel sequency selector for robust, imperceptible and payload-efficient color image watermarking of binary watermark is proposed using SMRT and two-level optimization. SMRT of block partitioned Cb channel of cover image is input to sequency selector where results of first-level optimization, based on imperceptibility and robustness, along with payload is optimized using knapsack optimization in the second level. The binary watermark is LSB embedded in SMRT coefficients present in each packet of the optimal combination of sequencies chosen by the sequency selector. Simulation results show better performance compared to existing optimized and non-optimized embedding techniques.
Improving Face Recognition by Integrating Decision Forest into GAN

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Research on Named Entity Recognition for Spoken Language Understanding Using Adversarial Transfer Learning

In this paper, we propose an adversarial transfer learning method to solve the lack of data resources for named entity recognition (NER) tasks in spoken language understanding. In the framework, we use bi-directional long short-term memory with self-attention and conditional random field (BiLSTM-Attention-CRF) model which combines character and word information as the baseline model to train source domain and target domain corpus jointly. Shared features between domains are extracted by a shared feature extractor. This paper uses two different sharing patterns simultaneously: full sharing mode and private sharing mode. On this basis, an adversarial discriminator is added to the shared feature extractor to simulate generative adversarial networks (GAN) and eliminate domain-dependent features. This paper compares ordinary adversarial discriminator (OAD) and generalized resource-adversarial discriminator (GRAD) through experiments. The experimental results show that the transfer effect of GRAD is better than other methods. The F1 score reaches 92.99% at the highest, with a relative increase of 12.89%. It can effectively improve the performance of NER tasks in resource shortage fields and solve the problem of negative transfer.
Reduction of solar photovoltaic system output variability with geographical aggregation

Variable power outputs are one of the largest challenges facing the widespread adoption of renewable energy systems. The inherent variability of solar resources makes it challenging to integrate large amounts of solar energy into the electric grid. However, the weather factors that influence solar production are often local in nature. In this study, eleven solar photovoltaic systems with publicly available historical data were identified for analysis. The systems are located within a circle with a diameter of approximately 130 km. The historical power output data for each system were acquired, and quality control measures were applied. A comparison is made between the variability of the time-varying power output from individual systems compared to the variability of the aggregated output of the eleven systems combined. Next, the effect of increasing the geographical spread of the aggregated systems is investigated. This is done by comparing the variability of the aggregated time-varying power output from closely-spaced systems against the variability of the aggregated time-varying power output from systems spread out over a large geographical area. Next, the correlations between the outputs from each of the individual systems are explored. The data show that the correlation decreases by approximately 0.1 for each 80 km of separation distance. Finally, the historical solar output data is used to define the “expected output”, and the deviation from this expected output is compared for individual systems and various sets of aggregated systems. The four aggregated systems located far apart are 31% more likely to have a combined output that is close to their expected output, defined as having a normalized power output deviation less than or equal to 0.2 kW/kW. Furthermore, the four aggregated systems located far apart are 54% less likely to have a combined output that is significantly different from their expected output, defined as having a normalized power output deviation greater than or equal to 0.4 kW/kW.
Improved Graph Convolutional Network with Enriched Graph Topology Representation for Skeleton-Based Action Recognition

Lately, skeleton-based action recognition has drawn remarkable attention to graph convolutional networks (GCNs). Recent methods have focused on graph learning because graph topology is the key to GCNs. We propose to align graph learning on the channel level by introducing graph convolution with enriched topology based on careful channel-wise correlations, namely the attentive channel-wise correlation graph convolution (ACC-GC). For the model to learn channel-wise enriched topologies, ACC-GC learns a shared graph topology spanning many channels and enhances it with careful channel-wise correlations. Encoding the intra-correlation between various nodes within each channel, boosting informative channel-wise correlations, and suppressing trivial ones generates attentive channel-wise correlations. Our enhanced ACC-GCN is created by substituting our ACC-GC for the GC in a standard GCN. Extensive experiments on NTURGB60 and Northwestern-UCLA datasets demonstrate that our proposed ACC-GCN performs comparably to state-of-the-art methods while reducing the computational cost.
Improved Video Anomaly Detection with Dual Generators and Channel Attention

Video anomaly detection is a crucial aspect of understanding surveillance videos in real-world scenarios and has been gaining attention in the computer vision community. However, a significant challenge is that the training data only include normal events, making it difficult for models to learn abnormal patterns. To address this issue, we propose a novel dual-generator generative adversarial network method that improves the model’s ability to detect unknown anomalies by learning the anomaly distribution in advance. Our approach consists of a noise generator and a reconstruction generator, where the former focuses on generating pseudo-anomaly frames and the latter aims to comprehensively learn the distribution of normal video frames. Furthermore, the integration of a second-order channel attention module enhances the learning capacity of the model. Experiments on two popular datasets demonstrate the superiority of our proposed method and show that it can effectively detect abnormal frames after learning the pseudo-anomaly distribution in advance.
Modelling of Speech Parameters of Punjabi by Pre-trained Deep Neural Network Using Stacked Denoising Autoencoders

Statistical parametric speech synthesis techniques such as deep neural network (DNN) and hidden Markov model (HMM) have grown in popularity since last decade over the concatenative speech synthesis approaches by modelling excitation and spectral parameters of speech to synthesize the waveforms from the written text. Due to inappropriate acoustic modelling, speech synthesized using HMM-based speech synthesis sounds muffled. DNN tried to improve the acoustic model by replacing decision trees in HMM with powerful regression model. Further, the performance of a deep neural network is greatly enhanced using pre-learning either restricted Boltzmann machines (RBM) or autoencoders. RBMs are capable to map multi-modal property of speech but result in spectral distortion of synthesized speech waveforms as non-consideration of reconstruction error. This article proposed the model of deep neural network, which is pre-trained using stacked denoising autoencoders to map speech parameters of the Punjabi language. Denoising autoencoders work by adding noise in the training data and then reconstructing the original measurements to reduce the reconstruction error. The synthesized voice using the proposed model showed the VARN of 0.82, F0 RMSE (Hz) 9.03, and V/UV error rate of 4.04% have been observed.
Low-complexity JPEG quantization table requiring only bit-shift operations

In this paper, a low-complexity quantization table is proposed for the baseline JPEG encoder. The proposed scheme does not require any multiplications or additions; only bit-shift operations are involved. The computational complexity should be drastically reduced without almost incurring any significant performance loss. The resulting approximate quantization table has extremely low arithmetic cost and is compared to state-of-the-art schemes in terms of computational complexity. Comparisons showed that our proposal exhibits better or similar results. The proposed low-complexity quantization table is also investigated in image compression context. It could outperforms other related schemes in low and high-compression scenarios. Comparisons with respect to average PSNR and SSIM are conducted. Experiment outcomes show up to 4.03 dB improvement in average PSNR over the state-of-the-art competitor. The proposed quantization table has a considerably lower computational cost while maintaining much higher image quality.
Deep Neural Network with Embedding Fusion for Chinese Named Entity Recognition

Chinese Named Entity Recognition (NER) is an essential task in natural language processing, and its performance directly impacts the downstream tasks. The main challenges in Chinese NER are the high dependence of named entities on context and the lack of word boundary information. Therefore, how to integrate relevant knowledge into the corresponding entity has become the primary task for Chinese NER. Both the lattice LSTM model and the WC-LSTM model did not make excellent use of contextual information. Additionally, the lattice LSTM model had a complex structure and did not exploit the word information well. To address the preceding problems, we propose a Chinese NER method based on the deep neural network with multiple ways of embedding fusion. First, we use a convolutional neural network to combine the contextual information of the input sequence and apply a self-attention mechanism to integrate lexicon knowledge, compensating for the lack of word boundaries. The word feature, context feature, bigram feature, and bigram context feature are obtained for each character. Second, four different features are used to fuse information at the embedding layer. As a result, four different word embeddings are obtained through cascading. Last, the fused feature information is input to the encoding and decoding layer. Experiments on three datasets show that our model can effectively improve the performance of Chinese NER.
A deep semi-dense compression network for reinforcement learning based on information theory

Although reinforcement learning (RL) can solve complex tasks after training, it’s difficult to extend trained agents to target environments with perturbations. This lack of generalization ability has caused the dilemma of large-scale application of RL. Rich neural networks have brought huge improvements to deep learning, but these aren’t adapted to RL and even bring negative effects. Therefore, RL algorithms have few choices of neural network, which greatly limits the representation and generalization ability of RL. To overcome these limitations, we propose a deep semi-dense compression network (DSCN) to improve RL generalization ability. First, we perform a structural extension on a general network model for RL. Then based on the information theory, we propose a semi-dense connection to enhance the neural information flow (NIF) of initial features. Finally, drawing on the ideas of course learning, we propose a channel compression approach to filter the redundant information of the deep network. In addition, we innovatively extend the experimental environment and evaluation metrics of the existing platform, which can fully evaluate the performance of DSCN. The experimental results show that our model achieves stable and significant generalization performance improvement.
Resolution enhancement of neutron radiography image using combined SRCNN-POCS method

In this work some post-acquisition image enhancement methods are applied to the case of under-sampled and low-resolution neutron radiography image. The digital neutron imaging system used in this study is a low resolved one; mainly based on an 8 bits CCD camera and a 250μm LiF-ZnS scintillator screen. In this paper, a software solution is proposed to improve the quality of digital neutron radiography images. The suggested method is a combination between the convolution neural network single-image and the projection onto convex sets multi-image super-resolution algorithms (SRCNN-POCS). The results obtained are compared to those by each algorithm separately. The proposed SRCNN-POCS method was found to be more reliable by producing higher quality image than the others tested methods in terms of modulation transfer function, peak-signal-to-noise ratio (PSNR), and structural similarity (SSIM). The visual analysis of the obtained image confirms too the obtained results.
Think More Ambiguity Less: A Novel Dual Interactive Model with Local and Global Semantics for Chinese Named Entity Recognition

Chinese is a representative East Asian language. Chinese Named Entity Recognition (CNER) aims to recognize various entities. It is significant for other NLP tasks to utilize CNER. Recent research to develop CNER systems has been dedicated to either considering word enhancement or capturing global information to strengthen local composition and alleviate word ambiguity in the meanings of words. However, information on words acquired from external lexicons is often confused, and this has led to incorrect judgments regarding the boundaries of words. Moreover, relevant studies typically use excessively complex models to capture the global semantics of sentences. To solve these two problems, we incorporate a global representation into the procedure of local word enhancement. We propose an intuitive and effective dual-module interactive network that can enhance the boundaries of words and extract the global semantics by using a rethinking mechanism to refine the importance of local composition and global information. The results of experiments on four CNER datasets showed that the proposed model can outperform other baselines in terms of the F1 score.