Reviewerly/exHarmony huggingface dataset:
A 2layer-BiLSTM-Attention-Based Method for Modulation Recognition of Communication Signals

Aiming at the problems that feature extraction accuracy of feature-based modulation recognition methods is difficult to guarantee, the recognition accuracy of one-dimensional-feature-based deep learning methods is low, and the time and storage overhead of high-dimensional-feature-based deep learning methods is large, a recognition method based on the two-layer BiLSTM followed by the attention layer is proposed. The experiment results show that, based on the RadioML2016.10a dataset, the method achieves high recognition accuracy without increasing the training burden and extracting features from original signals. The recognition reaches 86.7% at 0 dB and 91.5% at 18 dB, outperforming other deep learning methods.
SepFormer: Selective Patch Transformer for Image Classification

Although vision transformers have achieved great success in computer vision, the lack of ability to extract multi-scale features hinders their applications to downstream tasks such as image classification. Using the embedding of equal scales as input to the model is the main reason. Such a constraint inevitably limits the ability of each self-attention layer in capturing multi-scale features, thereby leading to performance degradation in handling images with multiple objects of different scales. In this paper, we propose a general vision backbone that can effectively extract and selectively fuse multiscale features, named Selective Patch Transformer(SepFormer). It is composed of Patch Pyramid Module and Selective Scale Module. Patch Pyramid Module takes patches from different scales as input, allowing multi-head self attention to model attention on different scales. Then, the feature is selectively fused by assigning different weights to the multi-scale branches in Selective Scale Module. This noval strategy goes beyond the model that using a single scale embedding and enables attention to learn the relationship between objects of different scales. It obtains more powerful feature expression ability with a small to moderate increase in FLOPs and model parameters. Extensive experiments the superiority of SepFormer. On the ImageNet-1k dataset, SepFormer-Base achieves top-1 accuracy of 83.8%, which is 0.5%and 2% more accurate than Swin transformer-Base and DeiT-Base for a similar parameters and computational costs.
ABMLP: Attention-Based Multi-Layer Perceptron Prefetcher

Cache prefetching is a traditional way to reduce memory access latency. Machine learning algorithms have shown the potential to accurately predict the future addresses of memory accesses. The existing approach is rule-based prefetchers. Rule-based prefetchers are effective at predicting common access patterns, but they are inefficient in complex cross-page access patterns. Therefore, we propose ABMLP prefetcher, which uses the attention method to learn the mapping relationship between network input and output, and assigns the corresponding weight. We used two prefetchers, MLP prefetcher and BO prefetcher, to cope with different access patterns. Overall, ABMLP prefetcher improved 19.85% overall coverage accuracy and 18.5% IPC improvement over baseline.
Attention-based Multi-scale ViT Fine-grained Visual Classification

Fine-grained visual classification (FGVC) is a challenging task in image classification due to the small differences between classes and the large differences within subclasses. In the early works, some methods mainly rely on constructing bounding box annotations and integrating attention mechanisms based on CNN methods for fine-grained visual classification. In recent years, the Vision Transformer (ViT) has begun to show better performance in image classification, object detection, and object tracking. To further investigate the performance of ViT in FGVC, this paper proposes to combine the CNN method with ViT and introduce a dual-path hierarchy into the pyramid structure - top-down feature path and bottom-up channel-spatial attention path; DropBlock is used to accurately localize discriminative regions; SENet and global covariance pooling (GCP) measures are used to further enhance the ability of the network model to extract feature maps information. The Attention-based Multi-scale ViT Fine-grained Visual Classification (AMViT-CNN) proposed in this work has achieved good classification results on public fine-grained datasets (CUB-200-2011, Stanford-Cars).
An Integrated Recommendation Model based on SMOTE, Denoising Auto Encoder and Multi-label Deep Forest

Aiming at the problem about the low recommendation performance, low training efficiency and too much parameter adjustment processes in the current recommendation models for multi-label long-tail distribution data, this paper proposed a recommendation model based on Synthetic Minority Over-Sampling Technique(SMOTE), Denoising Auto Encoder(DAE) and Multi-label Deep Forest(MLDF). Firstly, we used the SMOTE model for data oversampling or undersampling to generate new samples for minority data, and then used DAE model for feature transformation and feature reduction. Secondly, we introduced a multi-label learning method to solve the problem of data imbalance, and transformed the recommendation model into a multi-label classification problem by data-based learning and prediction, and established a multi-label deep forest recommendation model. Finally, through case analysis, it was proved that the integrated recommendation model established in this paper had better precision and applicability.
Development and Application of Multi-parameter Landslide Monitoring System based on Weak-reflection Fiber Grating Sensing Array

With the rapid development of information technology, fiber optic sensing technology has been widely used in human life and various fields. However, the application of high-capacity distributed sensing technology based on the weak reflection grating array is still in the initial stage. This paper introduces the principle of weak reflection grating sensing array monitoring technology, builds a multi-parameter landslide monitoring system based on the weak reflection grating sensing array by combining it with the Internet of things technology, and then describes the structure designs of external fixed-point weak reflection grating sensing optic cable, weak reflection grating displacement sensor, and weak reflection grating inclination sensor. This paper also carries out an indoor experiment and the experiment can conclude that the sensing fiber optic cable and sensors have good linearity. Finally, this paper presents a field application experiment using the weak reflection grating sensing optic cable and the monitoring system, and the monitoring data can accurately locate the position of landslide displacement change and the displacement size.
Thangka Image Inpainting Algorithm Based on Edge Constraint

Thangka images have the characteristics of rich content, complex textures and bright colors. The existing deep learning inpainting algorithms have some problems in inpainting incomplete Thangka images, such as blurred boundary, distorted structure and unclear texture. To solve the above problems, this paper proposes a two-stage Thangka image inpainting model based on edge constraint, which consists of edge prediction model and texture generation model. First, the edge of incomplete Thangka is reconstructed by the generative adversarial network based on U-Net structure. In order to increase the ability of feature extraction, a hierarchical residual structure is constructed in U-NET. Second, with the constraint of filling edge, a texture generation model combining gated convolution and multi-scale attention is used to reconstruct Thangka texture. Here, multi-scale attention can effectively capture multiscale features to generate images consistent with the texture details of the ground truth images. Experiments demonstrate that the proposed model can achieve better results, and its subjective and objective evaluation indicators are superior than the current image inpainting algorithms.
Selective Context Network With Neighbourhood Consensus for Aerial Image Registration

Image registration for aerial images is an essential task with several applications, including change detection, unmanned aerial vehicle (UAV) -specific localization, etc. Considering aerial images’ high resolution, significant variations, and repetitive patterns, specific methods for aerial image registration are high on the agenda. In this paper, firstly, we propose a novel neural network structure named selective context network (SC-Net) which preserves context and neighborhood information to estimate the affine transformation between aerial image pairs. Secondly, we develop a selective fusion strategy based on an attention-based mechanism to fuse local and spatial features selectively, thus improving registration accuracy for images with repetitive patterns. Thirdly, we introduce a lightweight neighborhood consensus module for enforcing neighborhood consensus to enhance the accuracy of aerial image registration at a low time cost. The experimental results demonstrate that our approach achieves superior performance compared to conventional methods for aerial image registration on the google earth dataset, especially when dealing with scenarios with repetitive patterns. The accuracy is increased by 14 % under 0.1 PCK and 50.4% under 0.05 PCK compared to the efficient neighborhood consensus network (SNCNET).
High Quality and Similarity One-Shot Voice Conversion Using End-to-End Model

Voice Conversion (VC) is becoming increasingly popular in speech synthesis applications. Most methods focus on many-to-many VC which can not be used for unseen speakers. One-shot (any-to-any) VC allows the source and the target speakers can be both unseen in the inference phase. This relies on an additional model to disengage linguistic information and speaker information. Most previous works were based on two-stage VC, which can lead to mismatches between the acoustic model and the vocoder, and the generated speech has poor quality or similarity. In this work, we proposed a novel method trained end-to-end for one-shot voice conversion. Unlike other one-shot methods, we use a combination of multiple ASV models to obtain more accurate and robust speaker embedding that can achieve high quality and similarity conversion. Experiment results demonstrate that our proposed method outperforms all considered baselines in different gender setups.
Family Financial Planning based on Probabilistic Planners

Financial planning is a step-by-step guiding approach to meet financial goals in life, such as controlling income, expenses, and investments to manage money and estate, which is beneficial for both organizations and families. The functionality of family financial planning is reflected in its charges, which motivated automated family financial planning both from scholars and finance practitioners. However, previous papers and tools rely excessively on linear programming, are hard to explain and interpret, and are incompatible with probabilistic and paralleled natures of financial actions. In the meantime, a recently developed subarea of planning based on the Relational Dynamic Influence Diagram Language (RDDL) happened to model probabilistic and paralleled natures of actions. I.e., probabilistic planning, whose models are explainable and transparent but with few applications. Therefore, this paper proposed a simulation-based approach called the F2P3 (Family Financial Planning based on Probabilistic Planner), whose first action is collecting family information through interactions. Then, the F2P3 models the family financial planning as a probabilistic planning problem and translates it into an RDDL file. At last, it runs probabilistic planners with the RDDL file as a new paradigm of the planning application. Experiments showed that the F2P3 could model the probabilistic and paralleled natures of financial actions and be more comprehensive than existing academic methods and cost-effective than business approaches.
AquaMZ: New Lightweight Authenticated Encryption with Generalized Feistel Network Based Primitive for IoT Protocols

This paper displays AquaMZ, an authenticated encryption with associated data (AEAD) algorithm for IoT protocols. It mainly focusing on the new cross field of IoT and symmetric cryptography and we optimized the algorithm by the lightweight idea. Unlike other AEAD schemes using conventional generalized Feistel network, AquaMZ improved its underlying primitive with the diffusion switching mechanism (DSM) to upgrade its 336-bit internal state such that it can provide sufficient security range for IoT devices. The permutation of AquaMZ has a unique symmetrical structure, hence, the encryption and decryption of AquaMZ are completely consistent and only require a low cost. Furthermore, AquaMZ has a low footprint on hardware and can be implement easily on RFID, FGPA or other resource-constrained platforms. AquaMZ is a duplex-structure-based algorithm and is efficiency for software, suitable for real-time and online communications. All family numbers of AquaMZ can not only encrypt plaintext and protect data integrity, but also provide high robustness to resist vulnerable implementation, and has satisfactory flexibility. We listed the features of the AquaMZ family and substantiate its security.
Cross-Modal Relation and Sketch Prototype Learning for Zero-Shot Sketch-Based Image Retrieval

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is an innovative cross-modal task that utilizes a sketch to retrieve corresponding images in the zero-shot learning scene. At present, most algorithms treat ZS-SBIR as a typical image classification problem, using image-level features with triplet or cross entropy loss to achieve retrieval, while ignore the correspondence between sketches and images on local features. Therefore, we propose a new Local Feature Contrastive Network (LFCN) for ZS-SBIR from the perspective of contrastive learning. More specifically, a local feature contrastive method is proposed to establish the cross-modal relationships between images and sketches with creatively applying transformers to extract similarity representation from sketch-image pairs to narrow the domain gap. Furthermore, a feature prototype memory bank is designed to learn sketch prototypes to address the in-class diversity problem in sketch domain. A large number of experiments show that our method notably superior to the state-of-the-art algorithms in both TU-Berlin and Sketchy datasets.
A Low-light Image Enhancement Method Balancing Brightness Boosting and Noise Smoothing

Images captured in low-light or uneven lighting environments need to use low-light image enhancement techniques to boost brightness and contrast for high-quality improved results. Some existing low-light image enhancement methods over-enhance the image and ignore the noise processing. While other methods do smooth the noise, the brightness and contrast of the result image are not significantly improved. Based on JED, we improve and introduce a low-light image enhancement method balancing brightness boosting and noise smoothing. We simplify and improve the optimization functions for estimating illumination and reflectance map. After that, a Gamma matrix is proposed for illumination correction, and we conduct multi-scale detail enhancement on the reflectance map. Finally, we get the enhanced result image by the Retinex model. Our method can boost the brightness and contrast of the image while effectively smoothing the noise and preserving the details; that is, a balance is achieved between brightness improvement and noise smoothing. The final comparative experiment results show that the proposed method has better performance on qualitative and quantitative evaluations than some classic low-light image enhancement algorithms.
Improving Chinese Named Entity Recognition Based on Lexical Information Adjustment

Lexical enhancement has been proven effective for Chinese Named Entity Recognition (NER), allowing for the use of word boundary information and the reduction of segmentation errors. Most present methods incorporate the lexical information by lattices but neglect the impact of candidate lexicon conflicts. The conflicts may misguide the model to make different predictions. In the work, a novel Long Short-Term Memory (LSTM) model based on the lexical information adjustment strategy (LIA-LSTM) is proposed. Firstly, all potential words are categorized into corresponding word sets. To lessen the influence of lexicon conflicts, the lexical features are introduced for the character sequence via elaborate weight adjustment. Then the LSTM and self-attention mechanism are applied to improve contextual awareness. Experiments on benchmark datasets demonstrate that LIA-LSTM outperforms the state-of-the-art methods compared. LIA-LSTM improves F1 score by 1.4% over baseline Lattice-LSTM on the MSRA dataset.
Learning a Good Representation for Metric-based Few-shot Classification

Few-shot learning (FSL) has gradually become the most successful application of transfer learning. It focuses on classifying novel classes by only a few images, which do not appear in the training set. Among all kinds of few-shot learning methods, metric-based methods are the most widely used. It aims to learn representations of different categories and classify novel categories by measuring the distance between images and these representations. However, represented by prototypical networks, traditional metric-based methods usually calculate the category representation by overly simplistic approaches. To improve the accuracy of metric-based methods, we propose two models to learn a better representation for different categories: Normalized Prototypical Network (NPN) and Mixed Prototypical Classifier (MP). NPN is a complete network which performs better than many baselines of metric-based methods. MP is a classifier which can be combined with any metric-based model, it greatly improves the accuracy of the combined model on 1-shot FSL benchmarks. Experiments and ablation studies in MiniImageNet and CIFAR-FS prove the effectiveness of our methods.
DOSP: Distributed Computing-Based Operating Systems Labs Platform

Operating Systems (OS), as a core course in Computer Science, is given much attention by all universities all over the world. Labs are the most important part of the course, and universities apply different solutions for it. Mainstream OS labs can be divided into two categories: Algorithm simulation-based labs are not beneficial for students to understand the actual mechanism of OS systematically; Kernel development-based labs have a higher barrier to entry and require more hardware resource, which is difficult for low-level universities to carry out. To solve the drawbacks of current labs solutions, this paper proposes a distributed computing-based OS labs platform, DOSP. In DOSP, students can perform labs in the virtualization-based pre-built environment, and get real-time feedback from the judging system; Teachers can check students' score overview and real-time teaching progress via the scoring system. The judging system can utilize idle resources in the PC room to perform distributed judging efficiently, to reduce the consumption of computing resources on the central server. Evaluation results show that our solution outperforms others in load capacity and scalability. As the result, it can solve many pain points in current OS lab teaching, which has good application prospects.
Multi-view Discriminative Fusion on Canonical Correlation Analysis in Event Classification

Video event carries very rich and complex semantics, and video event analysis is intrinsically multi-view learning problem. In this paper, by revealing the relationship between canonical correlation analysis (CCA) and linear discriminant analysis (LDA), we propose a new convenient multi-view learning architecture, i.e., multi-view discriminative fusion on CCA (MvDF-CCA). Instead of pair-wise relation consideration among different views, MvDF-CCA separately aligns each view space to the target one comprised by labeled indicators, which is easy expanding to additional view data. MvDF-CCA leverages the discriminative ability in LDA and the strengths in CCA, and maps all view spaces to the same target labeled space, which can be concurrent execution on large scale video categorization. Competitive results are reported on the well-known UCF101 and Columbia Consumer Video (CCV) benchmarks.
Multi-stage Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

Skeleton-based action recognition has received a lot of attention because it is independent of the physical features of the human body and it conveys important information about human behavior recognition in simple and clear way. Most of the existing approaches only use spatial or temporal models to extract features. However, that cannot capture the complex regional spatio-temporal joint dependence, which limited its sensory field. To address this problem, a novel model of dynamic skeletons called Multi-Stage Temporal Graph Convolutional Networks (SMS-GCN) is proposed, which consists of the graph convolutional network (GCN) model as well as the multi-stage temporal convolutional network (MS-TCN) model. The MS-TCN model consists of multiple stages of single-stage TCNs and incorporates a bottleneck design to extract temporal information to achieve aggregation of temporal information. The SMS-GCN model achieved an accuracy of 36.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and 93.13% on the datasets Kinetics and NTU-RGB+D respectively. The experimental results indicate that it achieves substantial improvements over mainstream methods.
Research on End-to-End Continuous Sign Language Sentence Recognition Based on Transformer

In 2021, the World Health Organization estimates that there are approximately 70 million deaf mutes in the world. At present, the method that facilitates the communication between normal people and deaf mutes is still not widely available. In the era of rapid development in the field of artificial intelligence, sign language recognition technology based on deep learning and mining human visual and cognitive laws has become an effective tool. In this paper, a Transformer based end-to-end continuous sign language sentence recognition model (TrCLR) is established. The CLIP4Clip video retrieval method is used for feature extraction, and the overall model framework uses an end-to-end Transformer structure. The sign language data set (CSL data set) is used as the data of this experiment. Nine sign language recognition models are used for experimental comparison on this data set. The experimental results show that the accuracy of TrCLR reaches 96.3%, which is 13.9% improvement over the best results of other models. Our model promotes the communication between normal people and deaf-mute people, and contributes to the establishment of a barrier free society.
Noise-Based and Class-Based Curriculum Learning for Image Classifiers

Datasets often contain different difficulty samples and even noisy samples. This paper introduces two naive curriculum learning methods, one using an image dataset with noise and another one using an image dataset that contains samples from other datasets with presumed higher difficulty. The final goal is to improve the performance of the model by gradually introducing more difficult samples during the training process rather than using them from the very beginning. Experiments demonstrated that using the proposed curriculum learning methods, a classifier can achieve higher accuracy in less training epochs.