Reviewerly/exHarmony huggingface dataset:
Solving Math Word Problems Following Logically Consistent Template

Solving math word problems (MWPs) is a challenging task. Some existing solvers retrieve textually similar problems and draw on their solutions to solve the given problem. However, textually similar questions are not guaranteed to have similar solutions, and vice versa. Therefore, this work investigates the logical consistency among different problems and proposes a novel framework to solve math word problems following logically consistent templates. Experimental results show that our method outperforms many strong baselines, including some pre-trained language model-based methods. Further analysis shows that our retrieval method does learn the logical similarity between Questions and plays a crucial role in our model's nerformance.
Continuous Variational Quantum Algorithms for Time Series

Variational quantum algorithms (VQAs) are the leading algorithm for achieving quantum advantage using near-term quantum computers. VQAs use parameterized quantum circuits for inference, and the variational parameters in quantum circuits can be trained using a classical optimizer. The parameters are trained to guide how the quantum bits evolve and make the final measurements closely match the ground truth. However, this way of learning from raw data makes it difficult to capture the underlying dynamic information in the data, especially for time series data. To address this limitation, we proposed continuous variational quantum algorithms (CVQAs) for time series in this paper. CVQAs use quantum variational circuits to parameterize the dynamics of time series, thus they can learn the dynamic information behind the data. After the dynamics are trained, the prediction results will be obtained by a differential equation solver working on the dynamics. Since we aim to model the dynamics of data instead of the data itself, the quantum circuit in our approach will need fewer qubits and variational gates. To evaluate our proposed approach, we compare our model with baseline models on several weather time series. Experimental results prove that our approach has better or equivalent results but with fewer qubits and variational gates compared to baseline models.
Addressing Performance Inconsistency in Domain Generalization for Image Classification

Domain Generalization (DG) in computer vision aims to replicate the human ability to generalize well under a shift of data distribution, or domain. In recent years, the field of domain generalization has seen a steady increase in average left-out test accuracy, measured as the average test accuracy achieved when each domain (in turn) is left out of training and used only for testing. To date, average left-out test accuracy is the only metric used for evaluating and comparing different techniques in DG. We observe that despite the steady increase in average left-out test accuracy, there remains a vast inconsistency between the left-out test accuracy scores measured for individual domains. To the best of our knowledge, this domain inconsistency persists across all published DG methods to date. In this work, we argue that domain generalization cannot be said to be successful without substantially reducing this performance inconsistency between domains. We propose a formal metric for measuring domain inconsistency and apply it to results in the literature. We run experiments to explore how alternative choices of pretraining affects domain inconsistency, finding that, in some settings, careful choice of pretraining can improve consistency with minimal negative (and sometimes positive) impact on average left-out test accuracy. Finally we discuss other potential sources of domain inconsistency and limitations of our work.
Event Extraction Based on the Fusion of Dynamic Prompt Information and Multi-Dimensional Features

Event Extraction (EE) is an important part of Information Extraction (IE), whose primary mission is to automatically extract specific events and event-related arguments from the original text. This is helpful to classify, extract and reconstruct massive content. At present, most EE methods aim at single event extraction based on neural networks. However, some sentences contain multiple events, and a single argument may play different roles in different events, which is easy to cause the problem of argument role overlap. Furthermore, the latest proposed prompt learning requires too much template annotation cost. Therefore, this paper proposes a new EE method based on the self-attention mechanism, which obtains global sentence semantics by capturing the Dynamic Prompt Information (DPI) without additional manual annotation. In addition, we integrate the local sentence semantics of Multi-dimensional Event Feature (MEF) information with DPI to learn the internal correlation information between different events and different entities, so as to realize the extraction of argument multiple roles. Experiments on the widely used Automatic Content Extraction (ACE) English corpora show that our method achieves a substantial improvement over baselines. Additionally, further analysis of the extraction procedure is presented to illustrate the effectiveness of the proposed DPI and MEF.
Constructing Cloze Questions Generatively

We present a generative method called CQG for constructing cloze questions from a given article using neural networks and WordNet, with an emphasis on generating multigram distractors. Built on sense disambiguation, text-to-text transformation, WordNet's synset taxonomies and lexical labels, CQG selects an answer key for a given sentence, segments it into a sequence of instances, generates instance-level distractor candidates (IDCs) using a transformer and sibling synsets. It then removes inappropriate IDCs, ranks the remaining IDCs based on contextual embedding similarities, as well as synset and lexical relatedness, forms distractor candidates by combinatorially replacing instances with the corresponding top-ranked IDCs, and checks if they are legitimate phrases. Finally, it selects top-ranked distractor candidates based on contextual semantic similarities to the answer key. Experiments show that this method significantly outperforms SOTA results. Human judges also confirm the high qualities of the generated distractors.
Attention-Capsule Network for Low-Light Image Recognition

Deep learning models have made extraordinary progress in recent years, but image recognition in low-light conditions has not been studied much. Due to its application prospects, low-light image recognition has still received focuses. Low-light image recognition is still challenging because of the crucial feature information that is hard to mine and learned by a deep learning model. Compared with enhancing the image's brightness before recognition, recognizing low-light images by end-to-end mode straightly has a more practical sense. In this paper, we propose a novel end-to-end model named Attention-Capsule Network (ACNet) for low-light image recognition tasks. The proposed model is extend from capsule structure, and its key component is the Global-Local Attention (GLA) module. The GLA module is designed to effectively and comprehensively combine important information. It mines global perception information by global block and gets local detail information from the particular local union. This strategy can significantly improve the performance of the proposed model. In addition, this paper proposes a directional learning loss, which guides the model to extract key features of images by optimizing the error between reconstructed images and normal-light images. Our experimental results demonstrate the effectiveness and robustness of our proposed attention-capsule Network model for low-light image recognition.
HGKT: Hypergraph-based Knowledge Tracing for Learner Performance Prediction

Knowledge tracing focuses on modeling learners' past answer sequences to trace the evolving knowledge state and predict their performance in the future. Most of the existing GNN-based knowledge tracing model only considers the static pairwise relationship between concepts and exercises, but ignores the mining of edge features. Also, the dynamic and complex higher-order relationships hidden in the learners' answer sequence have not been fully exploited. In this paper, a novel hypergraph-based knowledge tracing model (HGKT) is proposed to address these limitations. Firstly, we exploit edge feature that indicates the frequency of exercise-concept's occurrence to extend the common bipartite graph. Then we use Node and Edge features based graph Neural Networks (NENN) to obtain the embedding representation of exercises and concepts. Secondly, a hypergraph with different weights on vertices is constructed during the learners' exercise-answering process and then it is transformed to a simple graph based on its similarity between hyperedges. Thereafter, we use the hypergraph neural networks (HGNN) and line hypergraph convolution network (LHCN) to obtain the learners' embedding and discover the higher-order relationships formed during this process. Thirdly, the difficulty of exercises and the average response time are utilized to improve the learning of LSTM's hidden states. Finally, all the embeddings are jointly added to the generalized interaction module of GIKT to draw attention to the useful information for prediction. Experiments demonstrate that the proposed HGKT outperforms previous classical methods in terms of AUC on the three widely used datasets.
Backdoor Attack on Deep Neural Networks in Perception Domain

As deep neural networks (DNNs) are widely deployed in various applications, the security of pretrained DNNs is crucial since backdoors can be introduced through poisoned training. A backdoored DNN model works properly when benign inputs are provided, but it produces targeted misclassification on the inputs with an intended pattern known as a trojan trigger. Current technologies for trigger generation mainly focus on the physical and model domains. In this work, we investigate trojan triggers from the perception domain, especially the physical process of collecting light rays when they pass through the lens and hit the optical sensors. A new type of backdoor attack, Lens Flare attack, is introduced. It concentrates on the perception domain and is more physically plausible and stealthy. Experiments show that the DNNs with Lens Flare backdoor can achieve accuracy comparable to their original counterpart on benign input while misclassifying the input with high certainty if the Lens Flare trigger is present. It is also demonstrated that the Lens Flare backdoor is resistant to state-of-the-art backdoor defenses.
Multi-Scale Non-Local Sparse Attention for Single Image Super-Resolution

The non-local attention (NLA) has demonstrated its success in deep learning to solve various image processing and computer vision tasks. However, the NLA over the entire input image requires extremely high computational complexity and inevitably introduces irrelevant information since all the feature points are involved in calculating attention map. To address these problems, a novel attention module, called the multi-scale non-local sparse attention (MNSA), is proposed in this paper. In our MNSA, attention calculation is constrained within non-overlapping windows, and then only the most relevant feature points are selected to compute an attention map. The resulting sparse attention prevents the model from attending to irrelevant information and noise while reducing the computational complex-ity from quadratic to linear with respect to the input image size. To obtain receptive fields at different scales, our MNSA is further performed by exploiting different sizes of windows. Moreover, a novel local feature extraction (LFE) is proposed to extract the local structural information of natural images. To verify the effectiveness of our proposed attention module, a MNSA network is finally developed for conducting single image super-resolution (SISR). Extensive experimental results have clearly shown that our MNSA network can deliver superior performance over a number of state-of-the-art SISR methods.
TransGNN: A Transductive Graph Neural Network with Graph Dynamic Embedding

Graph Neural Networks (GNNs) have become a rapidly growing field, due to their ability to capture the relationship among data, instead of only learning from the attribute of the data. The core of any GNN is the graph embedding generation by message passing mechanisms. In this work we propose a new message passing technique based on the Particle Competition and Cooperation (PCC) model, originally developed for community detection in graphs. The proposed framework performs a transductive learning in the network and passes the learned information to the nodes, prior to the inductive learning performed by traditional GNN schemes. The new GNN presents attractive features which overcomes the over-smoothing problem of traditional GNNs and shows promising results in terms of classification accuracy, computational cost and learning with very small quantity of labeled data.
T3S: Improving Multi-Task Reinforcement Learning with Task-Specific Feature Selector and Scheduler

Multi-task reinforcement learning (MTRL) is a technique to train multiple tasks simultaneously, where previous works usually train a single model to solve different tasks by sharing parameters across various tasks. However, these methods are faced with inter-task interference since what parameters should be shared across tasks is not addressed, dramatically reducing learning efficiency. To solve these problems, we propose a novel MTRL framework called Task-Specific feature Selector and Scheduler (T3S), which consists of two components: a feature selector and a task scheduler. Specifically, the feature selectors employ hypernetworks to construct task-specific soft masks, which can be applied by globally shared representation to construct task-specific features. The task scheduler selects tasks for learning through two metrics, where the selection probability is inversely proportional to task progress (e.g., success rate) and task learning speed. Experimental results show that T3S consistently outperforms the state-of-the-art MTRL algorithms on various robotics manipulation tasks.
A Novel Clinical Trial Prediction-Based Factual Inconsistency Detection Approach for Medical Text Summarization

Most existing works of factual inconsistency detection focus on text summarization of generic articles. In this paper, a clinical trial prediction-based factual inconsistency detection approach is proposed for medical text summarization. Inspired by the fact that medical articles related to a clinical trial can give some evidence to predict the outcome of this trial, we believe that a factual consistent summary of the medical articles can also accurately predict the outcome of the corresponding clinical trial. Therefore, we first gather a novel Clinical Trial Prediction-based summarization (CTPSum) dataset, which is a collection of the outcomes of the clinical trials together with the related medical articles and summaries. We then propose a keyword-aware classification model to predict the outcome (successful/failed) of a clinical trial. If the predicted outcome is correct, the generated summary is considered to be factually consistent with the source document, otherwise, it is labeled as factually inconsistent. To evaluate the proposed approach, we further collect a Fact Inconsistency Detection dataset in the Medical Domain (FIDMD), which includes summaries, medical articles, and binary labels indicating factual consistency or inconsistency. Experimental results demonstrate the effectiveness of the proposed approach, specifically, the clinical trial prediction-based factual inconsistency detection approach outperforms several NLI-based factual inconsistency detection methods on the FIDMD dataset.
Improved Keyword Recognition Based on Aho-Corasick Automaton

The recognition of out-of-vocabulary (OOV) words in many state-of-art automatic speech recognition (ASR) systems, which need the to recognize a word that has never been seen before during training (named entities mostly), is very challenging. Current keyword boosted beam search method solves this problem to some extent, However, underperformed on rare long-tail words and ignore the mismatch rate in the trie search. To reduce the mismatch, we propose a effective and fast method based on Aho-Corasick algorithm. So that the ASR can reduce the matching failure and be more efficient and fast in the decoding. We also assign different reward sizes to different lengths of OOV words in order to alleviate the insertion error of rare long-tail words that ASR can identify more easily in decoding. And we also improve the original Contextual Biasing method in Wenet, reduced the number of matching failures in the context graph, and increase the efficiency of matching. We also combine our proposed method with the internal language model estimation method, and experimentally demonstrate that our proposed method not only has a significant improvement in the performance of intra-domain OOV words recognition, but also improves the accuracy of OOV words in cross-domain speech recognition.
Embedding the Self-Organisation of Deep Feature Maps in the Hamburger Framework can Yield Better and Interpretable Results

The popularity of neural networks that explicitly utilise the global correlation structure of their features have become vastly more popular ever since the Transformer architecture was developed. We propose to embed unsupervised Self-Organising Maps within neural networks as a means to model the global correlation structure. By enforcing topological preservation therein, such a neural network is able to represent more complex correlation structures and to produce interpretable visualisations as a byproduct. We validate this approach by comparing with the existing state-of-the-art attention substitute within its own ‘Hamburger’ framework and by illustrating maps learnt by the module. Overall, this paper serves as a proof of concept for integrating Self-Organising Maps within a supervised network.
Cross Range Quantization for Network Compression

Quantization is effective in reducing model memory and accelerating inference, and is an important way to deploy deep neural networks on mobile smart devices. However, current popular learnable quantization functions often take simple truncation operations for values beyond the quantization range. We find that the truncation operation cause information loss and and restricts the update of values out of quantization range. To address this problem, we propose a universal cross range quantization (CRQ) method to reduce the information loss caused by the conventional truncation operation. CRQ splits the values exceeding the quantization range into two parts for separate quantization, and thus retain the information efficiently for performance improvement. In addition, we define a new metric named performance improvement efficiency (PIE) to measure the relationship between increased computation and performance improvement. Experiments on public benchmark image classification datasets show that CRQ achieves a significant accuracy gain with only a small increase in computation compared to the original learnable quantization method, and also outperforms many sophisticated designed state-of-the-art quantization methods in terms of accuracy and PIE.
QCA-Net: Quantum-based Channel Attention for Deep Neural Networks

The channel attention mechanism, which adaptively recalibrates each channel's weight, can enhance the performance of deep neural networks. Most channel attention modules use simple pooling operations to aggregate spatial information. The drawback is the incapability to express complex global spatial information effectively. In this paper, we propose a Quantum-based Channel Attention (QCA), which only involves a handful of parameters but brings apparent performance gain. Using quantum mechanics analogy, we utilize wave functions describing microscopic particles to generate complex global spatial information. In addition, the QCA module has no convolutional layer, making it suitable for integration with various network architectures, including transformer and multilayer perceptron (MLP). We evaluate QCA through experiments on ImageNet-1K, and we also demonstrated the effect of QCA in combination with pre-training networks on small downstream transfer learning tasks.
L-Yolov5: A multi-scale channel attention-based method for real-time safety helmet detection of electrical construction workers

With the increasing maturity of smart grid and computer vision technologies, the use of mobile edge devices in collaboration with “cloud” to monitor the safety condition of construction workers wearing helmets has been widely used in power system construction scenarios. In this paper, based on YOLOv5, a new target detector-L-YOLOv5 is proposed to improve the accuracy and efficiency of the detector by optimizing key components. First, the traditional convolutional layer is replaced by using iconv in the backbone network; second, based on ShuffleNetV2, the MS attention mechanism module is used to improve the network structure, and a new backbone network structure isnet is proposed to reduce the computation and improve the accuracy of the detector while ensuring that the feature fusion performance is not affected. In the neck, we propose a GSPAN structure, firstly, we use 1*1 odconv to make the number of channels of features consistent with the minimum number of channels of backbone network output, which can effectively enhance the feature extraction ability of the network and reduce the network parameters; in addition, we down sample the GSPAN again and add a new feature scale to help the detector detect more targets and solve the problem of false detection due to occlusion and overlap caused by false detection, missed detection, and insufficient feature extraction ability. The experimental results show that the L-YOLOv5 model on RTX3090 is 44.3FPS, which is 82% less than the parameter amount of YOLOv5 and 3.3% more accurate on average, achieving excellent performance and satisfying the real-time detection of construction workers wearing helmets in construction scenarios of power systems.
Q-SAT: Value Factorization with Self-Attention for Deep Multi-Agent Reinforcement Learning

In many real-world tasks, a team of agents learn to cooperate with each other under the setting of partial observability and communication constraints, where value factorization has been demonstrated as an effective solution. In a multi-agent system, it's important to capture the inter-connection between agents and push agents to consider more of the relevant teammates. Motivated by the success of self-attention in natural language processing and computer vision, we propose a novel value factorization mechanism, called Q-function Self ATtention (Q-SAT). It models the pairwise action-value functions and connection coefficient between agent pairs explicitly, and pays more attention to the interrelated agents when making decisions. Satisfying the IGM principle, Q-SAT introduces the self-attention into value factorization network. This attention mechanism enables more effective and efficient learning in complex multi-agent environments. Q-SAT can be viewed as a basic building block and is ready to be applied to existing value factorization methods. The experimental results show that Q-SAT captures the connection relationship between agents and significantly improves the learning performance on the challenging StarCraft II micromanagement task and Google Research Football task.