Reviewerly/exHarmony huggingface dataset:
Using Transformer Towards Cross-Hops Question Answering

Recently, multi-hop question answering (QA) is becoming more and more popular in research fields, as well as the message-passing Graph Neural Networks (MP-GNNs) for interfacing in questions. MP-GNNs has advantages in local propagation, however, MP-GNNs will fail in the case that when the distance between nodes is large than the number of layers of deep learning model because in this case, model needs more diverse information to refer the final correct answer. In this work, we propose an approach to fix the challenge above the we name it as “using transformer towards cross-hops question answering” (CHQA). Inspired by the architecture of BERT, we propose attention mechanism and position encoding for fusing the questions and nodes by encoding structural information. The experimental results that our proposed CHQA outperforms the SOTAs on the metrics of F1 score.
Rethinking Voxelization and Classification for 3D Object Detection

The main challenge in 3D object detection from LiDAR point clouds is achieving real-time performance without affecting the reliability of the network. In other words, the detecting network must be confident enough about its predictions. In this paper, we present a solution to improve network inference speed and precision at the same time by implementing a fast dynamic voxelizer that works on fast pillar-based models in the same way a voxelizer works on slow voxel-based models. In addition, we propose a lightweight detection sub-head model for classifying predicted objects and filter out false detected objects that significantly improves model precision in a negligible time and computing cost. The developed code is publicly available at: https://github.com/YoushaaMurhij/RVCDet .
PPIR-Net: An Underwater Image Restoration Framework Using Physical Priors

In recent years, underwater image processing has been a hot topic in machine vision, especially for underwater robots. A key part of underwater image processing is underwater image restoration. However, underwater image restoration is an essential but challenging task in the field of image processing. In this article, we propose an underwater image restoration framework based on physical priors, called PPIR-Net. The PPIR-Net combines prior knowledge with deep learning to greatly improve the structural texture and color information of underwater images. The framework estimates underwater transmission maps and underwater scattering maps through the structure restoration network (SRN). Moreover, the color correction network (CCN) is used to achieve image color correction. Extensive experimental results show that our method exceeds state-of-the-art methods on underwater image evaluation metrics.
Cross-Domain Reinforcement Learning for Sentiment Analysis

By transferring knowledge from the source domain labeled data to the target domain, cross-domain sentiment analysis can predict sentiment polarity in the lacking of labeled data in the target domain. However, most existing cross-domain sentiment analysis methods establish the relationship of domains by extracting domain-invariant (pivot) features. These methods ignore domain-specific (non-pivot) features or introduce a lot of noisy features to make domain adaptability weak. Hence, we propose a cross-domain reinforcement learning framework for sentiment analysis. We extract pivot and non-pivot features separately to fully mine sentiment information. To avoid the Hughes phenomena caused by the feature redundancy, the proposed framework applies a multi-level policy to select appropriate features extracted from the data. And a sentiment predictor is applied to calculate delayed reward for policy improvement and predict the sentiment polarity. The decision-making capacity of reinforcement learning can effectively tackle the problem of noisy feature data and improve domain adaptability. Extensive experiments on the Amazon review datasets demonstrate that the proposed model for cross-domain sentiment analysis outperforms state-of-the-art methods.
A Deep Investigation of RNN and Self-attention for the Cyrillic-Traditional Mongolian Bidirectional Conversion

Cyrillic and Traditional Mongolian are the two main members of the Mongolian writing system. The Cyrillic-Traditional Mongolian Bidirectional Conversion (CTMBC) task includes two conversion processes, including Cyrillic Mongolian to Traditional Mongolian (C2T) and Traditional Mongolian to Cyrillic Mongolian conversions (T2C). Previous researchers adopted the traditional joint sequence model, since the CTMBC task is a natural Sequence-to-Sequence (Seq2Seq) modeling problem. Recent studies have shown that Recurrent Neural Network (RNN) and Self-attention (or Transformer) based encoder-decoder models have shown significant improvement in machine translation tasks between some major languages, such as Mandarin, English, French, etc. However, an open problem remains as to whether the CTMBC quality can be improved by utilizing the RNN and Transformer models. To answer this question, this paper investigates the utility of these two powerful techniques for CTMBC task combined with agglutinative characteristics of Mongolian language. We build the encoder-decoder based CTMBC model based on RNN and Transformer respectively and compare the different network configurations deeply. The experimental results show that both RNN and Transformer models outperform the traditional joint sequence model, where the Transformer achieves the best performance. Compared with the joint sequence baseline, the word error rate (WER) of the Transformer for C2T and T2C decreased by 5.72% and 5.06% respectively.
Cross-Modality Visible-Infrared Person Re-Identification with Multi-scale Attention and Part Aggregation

In the cross-modality visible-infrared person re-identification (VI-ReID) task, the cross-modality matching degree of visible-infrared images is low due to the large difference in cross-modality image features. Existing methods often impose constraints on the original pixels or extracted features to extract discriminative features, which are prone to introduce irrelevant background clutter and have a weak ability to extract cross-modality invariant features. This paper proposes an end-to-end neural network called multi-scale attention part aggregation network (MSAPANet). The framework consists of an intra-modality multi-scale attention (IMSA) module and a fine-grained part aggregation learning (FPAL). IMSA module is used to mine intra-modality attention-enhanced discriminative part features and suppress background feature extraction. FPAL fuses fine-grained local features and global semantic information through channel-spatial joint soft attention (CSA) to efficiently extract cross-modality shared features. Experiments were carried out on SYSU-MM01 and RegDB, two common datasets for VI-ReID, and the results show that under various settings, our method outperforms the reference current state-of-the-art methods. In this paper, by designing the network structure, the network mine the intra-modality and inter-modality salient information at the same time, improving the discriminative performance of fine-grained features in the channel and spatial dimensions, promoting modality-invariant and discriminative feature representation learning for VI-ReID tasks.
Accelerating Recommendation Inference via GPU Streams

Deep Learning based recommendation is common in various recommendation services and widely used in the industry. To predict user preferences accurately, state-of-the-art recommendation models contain an increasing number of features and various methods of feature interaction, which both lengthen inference time. We observe that the embedding lookup and feature interaction of different features in a recommendation model is independent of each other. However, current deep learning frameworks (e.g., TensorFlow, PyTorch) are oblivious to this independence, and schedule the operators to execute sequentially in a single computational stream. In this work, we exploit multiple CUDA streams to parallelize the execution of embedding lookup and feature interaction. To further overlap the processing of different sparse features and minimize synchronization overhead, we propose a topology-aware operator assignment algorithm to schedule operators to computational streams. We implement a prototype, namely StreamRec, based on TensorFlow XLA. Our experiments show that StreamRec is able to reduce latency by up to 27.8% and increase throughput by up to 52% in comparison to the original TensorFlow XLA.
NEURAL NETWORKS AND COMPOSTING PLANTS: AN INNOVATIVE Al METHOD FOR SYSTEM DESIGN AND MANAGEMENT

Neural Networks are powerful tools and can be easily used for modeling industrial plants. This study refers to a sewage sludge treatment installation where neural networks are implemented in plant design and management support development systems. The initial plant modeling phase compared neural networks with traditional systems based on regressive polynomial meta-models. The subsequent phase presents an innovative method for developing management support tools.
FedGR: Federated Learning with Gravitation Regulation for Double Imbalance Distribution

Federated Learning (FL) is a well-known framework for distributed machine learning that enables mobile phones and IoT devices to build a shared machine learning model via only transmitting model parameters to preserve sensitive data. However, existing Non-IID FL methods always assume data distribution of clients are under a single imbalance scenario, which is nearly impossible in the real world. In this work, we first investigate the performance of the existing FL methods under double imbalance distribution. Then, we present a novel FL framework, called Federated Learning with Gravitation Regulation (FedGR), that can efficiently deal with the double imbalance distribution scenario. Specifically, we design an unbalanced softmax to deal with the quantity imbalance in a client by adjusting the forces of positive and negative features adaptively. Furthermore, we propose a gravitation regularizer to effectively tackle the label imbalance among clients by facilitating collaborations between clients. At the last, extensive experimental results show that FedGR outperforms state-of-the-art methods on CIFAR-10, CIFAR-100, and Fashion-MNIST real-world datasets. Our code is available at https://github.com/Guosy-wxy/FedGR .
GIPA: A General Information Propagation Algorithm for Graph Learning

Graph neural networks (GNNs) have been widely used in graph-structured data computation, showing promising performance in various applications such as node classification, link prediction, and network recommendation. Existing works mainly focus on node-wise correlation when doing weighted aggregation of neighboring nodes based on attention, such as dot product by the dense vectors of two nodes. This may cause conflicting noise in nodes to be propagated when doing information propagation. To solve this problem, we propose a General Information Propagation Algorithm (GIPA), which exploits more fine-grained information fusion including bit-wise and feature-wise correlations based on edge features in their propagation. Specifically, the bit-wise correlation calculates the element-wise attention weights through a multi-layer perceptron (MLP) based on the dense representations of two nodes and their edge; The feature-wise correlation is based on the one-hot representations of node attribute features for feature selection. We evaluate the performance of GIPA on the Open Graph Benchmark proteins (OGBN-proteins) dataset and the Alipay dataset of Alibaba Group. Experimental results reveal that GIPA outperforms the state-of-the-art models in terms of prediction accuracy, e.g., GIPA achieves an average ROC-AUC of $$0.8917\,\pm \,0.0007$$ , which is better than that of all the existing methods listed in the OGBN-proteins leaderboard.
Factors Affecting Teaching Ability of Physical Education Teacher Trainees and Development Strategies

Physical education teacher trainees are the main source of physical education teachers in China, and the training mode and educational philosophy of physical education teacher trainees in colleges and universities affect the personal ability of future physical education teachers.Influenced by the "disembodied education" model, education and teaching are regarded as a "higher-level process" that promotes mental development and is not related to "lower-level processes" such as physical experience, bodily sensation and sensory-motor system.It is not related to the "lower processes" such as bodily experience, body sensation, and sensory-motor system.This concept of separation of mind and body is one of the important factors that affect the development of practical ability of physical education teacher trainees.The research suggests that: universities should improve the special training sites for physical education teaching skills and optimize the curriculum of teacher education; teachers in universities should establish the teaching concept of "students' body as the main body"; teachers in universities should enrich the content and form of teaching skills evaluation; physical education teacher-training students should pay attention to the development of their teaching skills and actively participate in the teaching skills competitions held by colleges; and the teachers of colleges and universities should enrich the content and form of teaching skills evaluation; physical education students should pay attention to the development of their teaching skills and actively participate in the teaching skills competitions held by colleges.
The current advances in the use of Virtual Reality technology in book publishing

Virtual Reality (VR) books are one of the most innovative publishing products for improving reading comprehension and lifelong reading skills for mass readers. As an industry perspective paper, we summarize the current advances in VR books including their themes, interface design, distribution channels, and the problems and difficulties they face. Readers will be able to gain an understanding of VR book publishing through this enriching introduction.
A Generic Enhancer for Backdoor Attacks on Deep Neural Networks

Backdoor attack, which attempts to manipulate model prediction on specific poisoned inputs, poses a serious threat to deep neural networks. It mainly utilizes poisoned datasets to inject backdoor(s) into a model through training or fine-tuning. The backdoor will be activated by attacker specified triggers that are included in the datasets and associated with the pre-defined target classes. To achieve a better trade-off between attack effectiveness and stealthiness, many studies focus on more complex designs like using natural-appearing poisoned samples with smaller poisoning rates. Effective as they are, the results of the heuristic studies can still be readily identified or invalidated by existing defenses. It is mainly because the backdoored model is often overconfident in predicting poisoned inputs, also its neurons exhibit significantly different behaviour on benign and poisoned inputs. In this paper, we propose a generic backdoor enhancer based on label smoothing and activation suppression to mitigate these two problems. The intuition behind our backdoor enhancer is two-fold: label smoothing reduces the confidence level of the backdoored model over poisoned inputs, while activation suppression entangles the behaviour of neurons on benign/poisoned samples. In this way, the model is backdoored gently. Extensive experiments are conducted to assess the proposed enhancer, including using three different network architectures and three different poisoning mechanisms on three common datasets. Results validate that the enhancer can enhance various backdoor attacks, even the most rudimentary ones, to the level of state-of-the-art attacks in terms of effectiveness and bypassing detection.
AAT: Non-local Networks for Sim-to-Real Adversarial Augmentation Transfer

In sim-to-real task, domain adaptation is one of the basic challenge topic as it can reduce the huge performance variation caused by domain shift. Domain adaptation can effectively transfer knowledge from a labeled source domain to an unlabeled target domain. Existing DA methods always consider to match cross-domain local features, however, only consider local features may lead to negative transfer. To alleviate this problem, in this paper, we propose a novel non-local networks for sim-to-real adversarial augmentation transfer (AAT). Our method leverages attention mechanism and semantic data augmentation to focus on global features and augmented features. Specifically, to focus on the global features, we leverage the non-local attention mechanism to weight the extracted features which can effectively eliminate the influence of untransferable features. Additionally, in order to enhance the ability of classifier adaptation, semantic data augmentation is leveraged to augment source features toward target features. We also give an upper bound of the divergence between the augmented features and the source features. Although our method is simple, it consistently improves the generalization performance of the popular domain adaptation and sim-to-real benchmarks, i.e., Office-31, Office-Home, ImageCLEF-DA and VisDA-2017.
L2QA: Long Legal Article Question Answering with Cascaded Key Segment Learning

Evidences in Legal Question Answering (LQA) help infer accurate answers. Current sentence-level evidence extraction based methods may lose the discourse coherence of legal articles since they tend to make the extracted sentences scattered over an article. To this end, this paper proposes a cascaded key segment learning enhanced framework for $${\textbf {L}}$$ ong $${\textbf {L}}$$ egal article $${\textbf {Q}}$$ uestion $${\textbf {A}}$$ nswering, namely $${\textbf {L2QA}}$$ . The framework consists of three cascaded modules: Sifter, Reader, and Responder, which first transfers a long legal article into segments and each segment is inherent in the discourse coherence from consecutive sentences. And then, the Sifter is trained by automatically sifting out key segments in an iterative answer-guided coarse-to-fine way. The Reader utilizes a range of co-attention and self-attention mechanisms to obtain the semantic representations of the question and key segments. Finally, the Responder predicts final answers in a cascaded manner, identifying where the answer is located. Conducted on CAIL 2021 Law MRC dataset, our L2QA achieves 83.1 Macro-F1 and 65.8 EM and outperforms a state-of-the-art legal QA model by 4.1% and 9.1%.
Hierarchical All-Pairs SimRank Calculation

All-pairs SimRank calculation is a classic SimRank problem. However, all-pairs algorithms suffer from efficiency issues and accuracy issues. In this paper, we convert the non-linear simrank calculation into a new simple closed formulation of linear system. And we come up with a sequence of novel algorithms to efficiently solve the linear system with accuracy guarantees. To reduce the memory consumption and improve the computational efficiency, we build a hierarchical framework to calculate the all-pairs SimRank scores, which includes locally coarse calculation and globally refine calculation. We first solve the local linear systems generated from the subgraphs, then we refine the SimRank scores on the full graph from the residuals of the local structures. We also show that our algorithms outperform the state-of-the-art all-pairs SimRank computation algorithms on real graphs.
Augmented Reality-Based Smart Mobile Application to Make ASD Children Accustomed to Daily Challenges

Many times people say that an individual belongs to the autistic spectrum disorder. There are a variety of symptoms that people diagnosed with autism might experience, ranging from mild to severe. Autistic individuals share several features in common. To begin with, there were communication and social interaction impairments. Second, autistic individuals have a narrow range of interests. They had repetitive behaviour and might grow agitated if their routines were disrupted. Autism is a condition that can be managed rather than a disease that can be cured. As a result of the vast range of conditions, no single “technique” would work for all autistic children. In this paper, an Augmented Reality-based solution has been tried to explore how technology can help children with Autism Spectrum Disorder (ASD). An application backed by Augmented Reality (AR) and Artificial Intelligence (AI) has been proposed. It will help children reduce anxiety and fear of new places by letting them interact with different public places in an AR environment. The application uses the performance data to understand the level of autism and presents with appropriate difficulty levels.
Let Attackers Program Ideal Models: Modularity and Composability for Adaptive Compromise

We show that the adaptive compromise security definitions of Jaeger and Tyagi (Crypto ’20) cannot be applied in several natural use-cases. These include proving multi-user security from single-user security, the security of the cascade PRF, and the security of schemes sharing the same ideal primitive. We provide new variants of the definitions and show that they resolve these issues with composition. Extending these definitions to the asymmetric settings, we establish the security of the modular KEM/DEM and Fujisaki-Okamoto approaches to public key encryption in the full adaptive compromise setting. This allows instantiations which are more efficient and standard than prior constructions.
Cross-Modal Contrastive Learning for Event Extraction

Event extraction aims to extract information of triggers and arguments from texts. Recent advanced methods leverage information from other modalities (e.g., images and videos) besides the texts to enhance event extraction. However, the different modalities are often misaligned at the event level, negatively impacting model performance. To address this issue, we firstly constructed a new multi-modal event extraction benchmark Text Video Event Extraction (TVEE) dataset, containing 7,598 text-video pairs. The texts are automatically extracted from video captions, which are perfectly aligned to the video content in most cases. Secondly, we present a Cross-modal Contrastive Learning for Event Extraction (CoCoEE) model to extract events from multi-modal data by contrasting text-video and event-video representations. We conduct extensive experiments on our TVEE dataset and the current benchmark VM2E2 dataset. The results show that our proposed model outperforms baseline methods in terms of F-score. Furthermore, the proposed cross-modal contrastive learning method improves event extraction in each single modality. The dataset and code will be released once upon acceptance.