Reviewerly/exHarmony huggingface dataset:
Cross-Modal Image-Text Matching via Coupled Projection Learning Hashing

Hashing, which aims to explore the correlations between modalities for multimedia data, shows numerous ad-vantages in cross-modal image-text matching tasks. However, most studies simply consider inter-modal correlations and ignore the semantic information contained within individual modalities, which produces suboptimal correlation representations between modalities. Based on this, we propose a Coupled Projection Learning Hashing method (CPLH). It directly maps the image-text data pairs into the different Hamming spaces to preserve as much of the original feature information as possible. Next, the CPLH joins the heterogeneous data over the Hamming spaces to maximize the correlation between two distinct modalities. In this way, the discriminative hash functions applied to the image-text matching process have semantic information from all modalities, which improves the matching accuracy. Besides, to further reduce the computational complexity of the proposed method, we propose a variant called CPLH-ts. It separates the learning process of the hash functions from the objective function by exploiting a two-step hashing strategy. Adequate experiments on three public datasets illustrate the superiority of our method and its variant compared to several state-of-the-art methods in matching accuracy and training efficiency.
Low-altitude Wind Shear Detection Method Based on Dual Tunable Q-factor Wavelet Transform for Airborne Weather Radar

Airborne weather radar is an important means of detecting low-altitude wind shear for modern civil aviation airliners. The accuracy of wind speed estimation is the key factor affecting the detection effect of low-altitude wind shear. In this paper, a novel method of wind shear detection based on dual tunable Q-factor wavelet transform(D-TQWT)is proposed. According to the different oscillation characteristics of ground clutter and low-altitude wind shear, the morphological component analysis method is used to sparsely represent the wind shear and the ground clutter signals on the wavelet basis functions with different quality Q factors, and the noise signal is eliminated by solving the BPD optimization problem. Then, referring to the frequency energy distribution characteristics of signals under different Q factors, the effective separation of wind shear and ground clutter signals is realized. Finally, the sparse wavelet coefficients representing the wind shear signal are reconstructed to obtain more accurate wind speed estimation results. The experimental results show that the difference of signal oscillation characteristics is used in this method for dual tunable Q-factor wavelet transform, which can effectively distinguish wind shear signal and ground clutter signal without designing the clutter suppression filter, especially in the close interval of frequency domain, so as to obtain more accurate anemometer results.
A Scene-Aware Data Augmentation Method for SAR Ship Detection

This article presents a scene-aware data augmentation method, known as SADA, for ship detection in synthetic aperture radar (SAR) images. SADA can be aware of ship scenes, and conduct data augmentation selectively. It mainly adopts a classification convolutional neural network (CNN) to discriminate inshore ship scenes and offshore ones. Then, the hard-detected inshore scene samples are augmented, rather than the whole training samples. It is rather useful to improve the detection accuracy of inshore ships. We conduct experiments on the open SSDD dataset, and the results show SADA's effectiveness. SADA is a simple but rather high-beneficial method.
Research on Mini-Batch Affinity Propagation Clustering Algorithm

Clustering is a task of unsupervised learning, aiming to group a set of data so that data in the same group are more similar to each other than to those in other groups. Affinity propagation (AP) is a clustering algorithm which finds the exemplars (representative points) for data points by spreading messages among them. AP algorithm has several drawbacks. First, it is time-consuming and memory-consuming for clustering on large-scale dataset, due to its N square time and space complexity. Second, AP may produce too many small clusters. Third, AP may have difficulty in converging which leads to a higher cost of time for fine turning. To achieve better effectiveness and efficiency, in this paper we propose Mini-Batch Affinity Propagation (MBAP). MBAP processes small batches of data serially and obtains clustering results gradually. We also proposes MBAP with early stopping (MBAP_ES), which integrates MBAP with stopping strategy so that it can stop clustering early when the model is nearly unchanged. The experiments show the effectiveness and efficiency of MBAP and MBAP_ES in comparison to other AP-based algorithms.
Fast Mining RFM Patterns for Behavioral Analytics

In recent years, the problem of high-utility itemset mining (HUIM) has been extensively studied. However, HUIM algorithms only reveal profitable but generalized itemsets from transaction databases. In the market analysis domain, these mining results just reflect the sales trend of all customers and are not sufficient for making market strategies. In other words, it is hard to maintain specific customers for a long time due to the limitations of HUIM analysis of customer behaviors. In this paper, a novel data mining algorithm called RFM-Miner is proposed to discover RFM-patterns that are highly recent, frequent, and profitable in transaction databases. The novel algorithm relies on the array-bin structure to fast calculate adopted upper-bounds (i.e., transaction-weighted utilization, subtree and local utility) in linear time and space. In addition, RFM-Miner always searches for extension items of an itemset in a small projected database. And the merging technique is utilized to reduce the size of the search space. An extensive experimental study on four datasets (including real-life and synthetic) shows that RFM-Miner performs very well in terms of runtime and memory consumption. The novel algorithm also achieves better performance than the state-of-the-art benchmarks, especially on dense datasets.
SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP

Survival Analysis models usually output functions (survival or hazard functions) rather than point predictions like regression and classification models. This makes the explanations of such models a challenging task, especially using the Shapley values. We propose SurvSHAP, a new model-agnostic algorithm to explain survival models that predict survival curves. The algorithm is based on discovering patterns in the predicted survival curves that would identify significantly different survival behaviors, and utilizing a proxy model and SHAP method to explain these distinct survival behaviors. Experiments on synthetic and real datasets demonstrate that the SurvSHAP is able to capture the underlying factors of the survival patterns. Moreover, SurvSHAP results on the Cox Proportional Hazard model are compared with the weights of the model to show that we provide faithful overall explanations, with more fine-grained explanations of the sub-populations. We also illustrate the wrong model and explanations learned by a Cox model when applied to heterogeneous sub-populations. We show that a non-linear machine learning survival model with SurvSHAP can better model the data and provide better explanations than linear models.
Ground Clutter Suppression for Weather Radar Using An Improved Wavelet Method

For removing ground clutter signals in weather radar, we propose an improved wavelet method based on an adaptive threshold in this paper. In the wavelet domain, ground clutter signals are distributed in the low-frequency layer after wavelet decomposition and the meteorological signals usually exist in other high-frequency layers. We process wavelet coefficients using the threshold to suppress clutter signals, and reserve meteorological signals as many as possible. The effectiveness of the proposed method is verified using real weather radar data. It has a small computational load and is easy to implement.
Spectral Clustering Based EEND-vector Clustering: A Robust System Fine-tuned on Simulated Conversations

In this paper, we propose an EEND-vector clustering based speaker diarization system we implemented in ISCSLP 2022 CSSD Challenge. Recently proposed EEND-vector clustering integrated the advantages of EEND-based and clustering-based diarization. In contrast to typical clustering-based systems, it considerably simplifies operations while being able to handle overlapping speech. In order to get a competitive result, we investigated several aspects to improve the EEND-vector clustering pipeline. (1) We investigated methods to create natural simulated conversation speech data, which is proven has significant impact on diarization result; (2) In order to capture the fine-gained local features, we introduced Conformer as the encoder in place of Transformer; (3) We modified the PIT loss function by adding an additive margin penalty to it; (4) We do model average by choosing the top 5 ranked models; (5) We use Spectral Clustering replace of AHC to do the embedding cluster and speaker count estimation. On MagicData-RAMC dataset with the CDER metric, our system achieved 21.9% on the Dev set and 24.5% on the Test respectively. In the ISCSLP 2022 CSSD Challenge, our system came in second with 8.1% CDER.
Modulation Pattern Recognition of Underwater Acoustic Communication Signals

In this paper, the modulation pattern recognition of underwater acoustic communication signals is presented. The classification is evaluated. The experimental results show that the classification accuracy of the presented method is high.
A Recommendation System Framework to Generalize AutoRec and Neural Collaborative Filtering

AutoRec and neural collaborative filtering (NCF) are two widely used neural network-based frameworks in the recommendation system literature. In this paper, we show that these two apparently very different frameworks have a lot in common. We propose a general neural network-based frame-work, which gives us flexibility in choosing elements in the input sources, prediction functions, etc. Then, we show that AutoRec and NCF are special forms of our generalized framework. In our experimental results, first, we compare different variants of NCF and Autorec. Then, we indicate that it is necessary to use our general framework since there is no specific structure that performs well in all datasets. Finally, we show that by choosing the right elements, our framework outperforms the state-of-the-art methods with complicated structures.
HILvoice:Human-in-the-Loop Style Selection for Elder-Facing Speech Synthesis

Controllable speech synthesis has made great progresses over the last decades. State-of-the-art systems can provide flexible interfaces for configuring the styles of generated speech for target users. However, for a specific user group, e.g., the older adults, using the available configuration interfaces to select the styles that are favored by the group still needs to be investigated. Two main questions of such a style selection are (i) how to provide various options for the target users to pick; and (ii) how to effectively obtain the opinions from the target users. Since these two questions are highly correlated which makes it difficult to solve them separately, we propose a holistic framework to consider these two questions together by involving the target users in an iterative loop. We demonstrate by experimental results that the proposed framework can successfully select a speaking style preferred by the older adults than the default neutral setting. Analysis results show that the selected style has slower speaking rate, which coincides with previous studies on auditory perception of older adults.
STT: Soft Template Tuning for Few-Shot Adaptation

Prompt tuning has been an extremely effective tool to adapt a pre-trained model to downstream tasks. However, standard prompt-based methods mainly consider the case of sufficient data of downstream tasks. It is still unclear whether the advantage can be transferred to the few-shot regime, where only limited data are available for each downstream task. Although some works have demonstrated the potential of prompt-tuning under the few-shot setting, the main stream methods via searching discrete prompts or tuning soft prompts with limited data are still very challenging. Through extensive empirical studies, we find that there is still a gap between prompt tuning and fully fine-tuning for few-shot learning. To bridge the gap, we propose a new prompt-tuning framework, called Soft Template Tuning (STT) 1. STT combines manual and auto prompts, and treats down-stream classification tasks as a masked language modeling task. Comprehensive evaluation on different settings suggests STT can close the gap between fine-tuning and prompt-based methods without introducing additional parameters. Significantly, it can even outperform the time- and resource-consuming fine-tuning method on sentiment classification tasks.
Semi-supervised Regression with Data Partitioning and Feature Mapping

Semi-supervised regression attempts to utilize as much unlabeled data as possible and as little labeled data as possible to improve model performance. Methods based on data partitioning can improve regression performance from the perspective of data distribution. However, most partitioning methods only consider the correlation between the data, but not the relationship between the regressor and the data. In this study, a strategy of dividing the data and then regressing is proposed, where data partitioning is based on the relationship between the regression and data. Accordingly, an intuitive and effective algorithm named SRPF, i.e. Semi-supervised regression based on data partitioning and feature mapping, is proposed. First, we divide the labeled dataset into two disjoint subsets based on the relationship between the predicted and actual values of separation regressor. Second, we label these two subsets as distinct classes and use feature mapping to map data features to higher dimensions to better distinguish the data. Third, we construct a partitioner to determine which subset an unlabeled instance belongs to, and then use the regressor on the corresponding data set to make predictions. Finally, during the iterative training process, a self-training method is used to enrich labeled samples. Experiments are conducted on 15 well-known datasets compared to state-of-the-art algorithms. The results show that our method outperforms them in most datasets.
Towards ℓ<sub>1</sub> Regularization for Deep Neural Networks: Model Sparsity Versus Task Difficulty

Nowadays, numerous AI systems employ deep neural network models with excessive number of parameters to obtain superior performance on real-world applications. Such systems relies on high-performance GPUs to achieve real-time inference, especially for the ones that involve convolutional operations. This prohibits the practical deployment on resourcescarce edge devices. To reduce model size and inference time, model pruning for deep neural network models has been an active thread of research in the recent years. Among a significant amount of literature towards different model pruning strategies, ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> regularization is commonly considered as a simple solution for sparse models. However, a fundamental issue is seldom addressed: "why ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> regularization can be effective for model pruning". In this work, we provide a theoretical explanation showing that, for a specific type of neural network models with ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> regularization, tasks of higher accuracy result in higher pruning ratio. Hence, network pruning could be exceptionally effective on high accuracy tasks. Based on the theoretical analysis, we demonstrate the effectiveness of ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> regularization learning on two iconic computer vision tasks: (a) face detection and (b) image segmentation. The experiment results show that, with suitable ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -regularized optimizations, even for a compact model like MobileNetV2, the model size can be reduced by an order of magnitude without significant losses on the accuracy. Furthermore, we show that with proper implementation of sparse convolution, the obtained sparse neural network models can achieve multiple times speed-ups not only in FLOPs, but also in actual inference time.
Image Classification Based on Deep Graph Convolutional Networks

Due to their powerful modeling and reasoning capabilities, graph neural networks have not only achieved adequate performance in unstructured data but, in recent years, their research interests in Euclidean data such as images have also been on the rise. In this context, the most common task is image classification, whose method, based on graph neural networks, is roughly divided into two stages. The first is the graph construction stage, where the images are converted into graph structure data (composed of a node-edge-node form). The second is the graph classification stage, where the processed graph data is loaded into the graph classification network for graph classification. Subsequently, the images classification results are obtained. However, nearly two problems are faced in this setting. One is that the graph construction stage takes a long time due to the considerable computation that is required in order to convert images into graph structured data, the other problem is related to the graph classification used in the graph classification stage. The number of layers in the network tends to be limited, usually 4 layers or less. Hence, the graph’s classification accuracy is affected to some extent. Subsequently, this study proposes a deep graph neural image classification model based on gSLIC, combining the attention mechanism to conduct related experiments, in order to prove that while the speed of graph construction is greatly improved, the image classification accuracy exceeds that of most existing models based on graph neural networks for image classification.
Sentiment Analysis of Google Classroom Application Using Support Vector Machine

Technological advances are growing very rapidly and affect human life and also have an impact during the pandemic. During the pandemic, Google Classroom ranks first on the platform that is often used during distance learning. On the Google Play site, many reviews about the application. Due to the many reviews given by users, sentiment analysis is carried out so that public opinion on the application is known, thus users or application developers know the advantages and disadvantages of the application. Sentiment analysis was carried out using the SVM algorithm with the help of the python programming language on google colab. From the results of sentiment analysis, the sentiment of Google Classroom application users is 64% negative sentiment and 36% positive sentiment with an algorithm accuracy of 81%. In addition, data visualization is carried out, so that it can be seen what causes users to give positive reviews and negative reviews, from which priority improvements are obtained to improve Google Classroom performance.
SAR Image Segmentation Based on Fast Superpixel Clustering

Accurate segmentation on synthetic aperture radar (SAR) images is a challenging task. In this paper, we propose a SAR image segmentation method based on the fast superpixel clustering algorithm. First, our proposed multi-scale superpixel generation method is used to generate a superpixel map with high quality. Second, the edge-aware superpixel smoothing is performed on the superpixels to alleviate the effect of speckle noise, which facilitates improving segmentation accuracy. Finally, the fuzzy C-means (FCM) clustering is implemented on the superpixels to obtain the final segmentation result. Experimental results on two Sentinel-1 C band SAR images demonstrate that the proposed method exceeds the comparing methods both quantitatively and qualitatively, confirming its advantages in SAR image segmentation.
A Fast Block-Based Feature Method for Low Cost Dynamic Objects Detection

Realtime foreground/background segmentation based on sequence video was of great significance for autonomous vehicles perception, edge device application and higher level data analysis. A new fast background subtraction method for dynamic objects detection was proposed by using the digital features of the whole block of pixels. The algorithm considered that the change of the current pixel was closely related to the surrounding pixels, took the current pixel and its eight neighboring pixels as a whole block, and used the digital features - average and variance to reflect the pixel level of the block and establish the background model. At the same time, a local remodeling method was proposed, which made the algorithm can process and eliminate ghost quickly. The results based on CDnet2014 dataset showed that our algorithm could adapt to various dynamic objects detection scenarios and initialize fast under the condition of low hardware cost, and provided a good overall performance.
Named Entity Recognition for Smart Grid Operation and Inspection Domain using Attention Mechanism

With the rapid development of smart power grid systems, the field of power grid operation and maintenance is in urgent need of intelligent construction. In the daily operation of the power system, a large amount of operation and maintenance texts are accumulated. Using these data for named entity recognition to support the construction of knowledge graphs in the power grid domain and finally realize the decision intelligence in the power grid operation and inspection domain is the key of this paper. In this paper, we propose a named entity recognition algorithm based on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{BERT}+\text{BiLSTM}+\text{CRF}$</tex> , which combines attention mechanism and word-character joint embedding vector, for the text features in the field of grid operation and inspection. Experiments show that the algorithm proposed in this paper achieves 91.47%, 88.88% and 90.16% in accuracy, recall and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> values while the training convergence speed is smaller than that of the baseline model, which is 1.27% 4.06% better than the traditional model. In particular, the improvement in position of fault and type of fault entity recognition is obvious, with 0.25% 6.79% and 3.28% 9.21% respectively, which verifies the feasibility and effectiveness of the algorithm model proposed in this paper and thus effectively supports the subsequent construction of the knowledge graph and intelligent system in the field of power grid operation and maintenance.