Reviewerly/exHarmony huggingface dataset:
Cross-Camera Prototype Learning for Intra-camera Supervised Person Re-identification

Person Re-Identification (ReID) aims at retrieving images of the specific pedestrian across disjoint cameras. However, the annotations are extremely costly as the number of cameras increases, which derives a new setting named Intra-Camera Supervision (ICS) ReID. ICS assumes that identity labels are independently annotated within each camera, while no cross-camera identity association is available. Previous ICS methods focus on connecting the inter-camera instances that are likely to be the same pedestrian, whereas fails to exploit the so far untapped yet informative supervision, i.e., ‘the cross-camera prototype relations’. In this paper, we propose the novel Cross-Camera Prototype Learning (CCPL) method to tackle this issue. Firstly, we ensure identities to be discriminative and associated with corresponding intra-camera prototypes, which can be considered as the semantic representations for each local identity. Besides, we claim that the distance between the same inter-camera prototypes is inevitably large, due to the variances of different cameras in views, lights, backgrounds etc. To that end, we propose the Camera-invariant Prototype Alignment (CPA) module, which preserves the cross-camera prototype relations by explicitly pulling together the same inter-camera prototypes and pushing away the different ones. Last but not least, we also introduce the inter-camera prototype pulling loss to constrain the same prototypes as close as possible. Extensive experiments on three benchmarks show the superiority of our method.
Object Detection in Foggy Images with Transmission Map Guidance

The performance of object detection methods is adversely affected by the low-quality images caused by fog. The main reasons are as follows: (i) object detection methods are difficult to recognize and locate the objects due to weak discriminative features extracted from low-quality images; (ii) existing methods are hard to adapt to variable fog densities. The transmission map, one important component from the atmospheric scattering model, containing depth and fog density information, is the key to addressing the above two problems. In this paper, we propose a novel network using the transmission map guidance, termed TGNet, which mines depth information to infer the existence and locations of objects and mines fog density information to help adapt to various fog densities. Experiments conducted on the two real-world object detection datasets in foggy conditions (i.e., RTTS and FoggyDriving) demonstrate that our TGNet outperforms the state-of-the-art methods. Additionally, our TGNet provides consistent improvements on various detection paradigms and backbones.
Correction while Recognition: Combining Pretrained Language Model for Taiwan-Accented Speech Recognition

Taiwan-accented speech bears similarities to the Mandarin Min dialect, but with substantial differences in vocabulary, which significantly impacts spoken language recognition outcomes. This paper concentrates on integrating pre-trained language models (PLMs) with state-of-the-art self-supervised learning (SSL)-based speech recognition systems for Taiwan-accented speech recognition tasks. We propose a progressive error correction process in tandem with recognition to fully exploit the autoregressive nature of PLM models. Experimental results demonstrate that our method effectively addresses recognition errors stemming from misspelled vocabulary in accented speech. Our proposed progressive approach achieves roughly a 0.5% improvement compared to the conventional method. Furthermore, we demonstrate that fine-tuning PLMs solely with the text from the accented dataset can enhance recognition performance, despite the limitations of accented speech resources.
A Shallow Information Enhanced Efficient Small Object Detector Based on YOLOv5

Shallow information is crucial in small object detection. Based on YOLOv5, an efficient small object detection algorithm (ES-YOLO) is proposed to improve identification accuracy using novel shallow feature extraction strategies. First of all, a detection head corresponding to shallow features is used to replace the original detection head corresponding to the deepest features in YOLOv5. Secondly, an attention module is directly added to the output layers of the backbone to filter redundant information and select representative original shallow features. Next, half of the inputs to the SPPF module are processed by the cross-stage partial connection method to reduce model parameters. Finally, the SIoU (SCYLLA-IoU) loss is used during the training stage to ensure fast convergence. Ablation studies are performed on two publicly available small object datasets. Results show that all the proposed models increase the model detection accuracy. Compared with the YOLOv5, the proposed model increases the identification accuracy by 2.4% and 3.5% on the BDD100K and VisDrone datasets, respectively. In addition, compared with the other 8 commonly used or up-to-date one-stage models, the proposed model achieves the best performance in identification accuracy. Source code is released in https://gitee.com/bai-hexiang/es-yolo .
Region Feature Disentanglement for Domain Adaptive Object Detection

In recent years, deep learning based object detection has shown impressive results. However, applying an object detector learned from one data domain to another one often faces performance degradation due to the domain shift problem. To improve the generalization ability of object detectors, the majority of existing domain adaptation methods alleviate the domain bias either on the feature encoder or instance classifier by adversarial learning. Differently, we try to alleviate domain discrepancy in the region proposal network (RPN) by performing feature disentanglement. To this end, an extractor is devised to extract domain-specific foreground representations from both the source and target features, respectively. Then, domain-invariant representations are decomposed from the domain-specific features by the disentanglement module. Through the decoupling operation, the gap between the domain-specific and domain-invariant features is enlarged, which promotes RPN feature to contain more domain-invariant information. Furthermore, we propose dynamic weighted adversarial training to alleviate the unstable training caused by adversarial learning. We conduct extensive experiments on multiple domain adaptation scenarios, and our experiment results demonstrate the effectiveness of our proposed approach.
FFTRL: A Sparse Online Kernel Classification Algorithm for Large Scale Data

Online kernel learning is an efficient way when dealing with nonlinearly large-scale data. The training speed of online kernel learning is improved by Fourier online gradient descent (FOGD). However, FOGD has a high space complexity when the number of features is relatively high because FOGD lacks of sparsity. In this paper, we propose a new sparse online kernel classification algorithm for large-scale data, called Fourier follow-the-regularized-leader (FFTRL). Existing budget (sparse) online kernel learning methods attempt to bound the number of support vectors through some budget maintenance strategies; however, budget maintenance strategies are unsuitable for FOGD. By introducing the proximal algorithm, follow-the-regularized-leader, FFTRL achieves sparsity in a different way. By applying random Fourier features as the kernel approximation schemes, FFTRL finds the optimal sparse solution in a linear manner. The regret bound analysis shows the feasibility of FFTRL in theory. Comprehensive experiments were carried out on public datasets to compare the performance of FFTRL with related online kernel algorithms. Promising results show that our proposed method enjoys both high accuracy and time efficiency and still produces sparse models, opening a window for obtaining sparsity in online kernel learning.
Boosting Few-Shot Classification with Lie Group Contrastive Learning

Few-shot learning can alleviate the issue of sample scarcity, however, there remains a certain degree of overfitting. There have been solutions for this problem by combining contrastive learning with few-shot learning. In previous works, sample pairs are usually constructed with traditional data augmentation. The fitting of traditional data augmentation methods to real sample distributions poses difficulties. In this paper, our method employs Lie group transformations for data augmentation, resulting in the model learning more discriminative feature representations. Otherwise, we consider the congruence between contrastive learning and few-shot learning with respect to classification objectives. We also incorporate an attention mechanism into the model. Utilizing the attention module obtained through contrastive learning, the performance of few-shot learning can be improved. Inspired by the loss function of contrastive learning, we incorporate a penalty term into the loss function for few-shot classification. This penalty term serves to regulate the similarity between classes and non-classes. We conduct experiments with two different feature extraction networks on the standard few-shot image classification benchmark datasets, namely miniImageNet and tieredImageNet. The experimental results show that the proposed method effectively improves the performance of the few-shot classification.
Exploring Individuality in Human EEG Using Reservoir Computing

In the present study, we investigate the discernibility of individuality in brain dynamics at a macroscopic level, alongside fingerprints, facial features, and gait patterns, by employing a reservoir computer for time series prediction. Electroencephalograms (EEGs) are acquired from 100 participants during a resting state, and the reservoir computer is utilized to forecast these time series. The findings demonstrate that individuality manifests in the performance of time series prediction. Specifically, the predictive pattern, namely the prediction error as a function of the electrode’s position, exhibits similarities within trials of the same participant while differing between participants. Furthermore, we illustrate that applying manifold learning to the predictive patterns facilitates the visualization of the similarity or dissimilarity among a substantial number of participants in a low-dimensional space. These results suggest the potential utilization of EEG signals for biometric authentication and other practical engineering applications.
Componentwise Adversarial Attacks

We motivate and test a new adversarial attack algorithm that measures input perturbation size in a relative componentwise manner. The algorithm can be implemented by solving a sequence of linearly-constrained linear least-squares problems, for which high quality software is available. In the image classification context, as a special case the algorithm may be applied to artificial neural networks that classify printed or handwritten text—we show that it is possible to generate hard-to-spot perturbations that cause misclassification by perturbing only the “ink” and hence leaving the background intact. Such examples are relevant to application areas in defence, business, law and finance.
The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning

In this work, we assess the theoretical limitations of determining guaranteed stability and accuracy of neural networks in classification tasks. We consider classical distribution-agnostic framework and algorithms minimising empirical risks and potentially subjected to some weights regularisation. We show that there is a large family of tasks for which computing and verifying ideal stable and accurate neural networks in the above settings is extremely challenging, if at all possible, even when such ideal solutions exist within the given class of neural architectures.
Enhancing Cross-Lingual Few-Shot Named Entity Recognition by Prompt-Guiding

The cross-lingual named entity recognition task has attracted significant attention from researchers. Previous work has demonstrated that incorporating unlabeled data with potential entities in the target language can enhance cross-lingual model performance. However, unlabeled data for the target language is not always available, and the entity types may not share the same label space as the source language. To address this issue, we introduce a new NER task called cross-lingual few-shot NER. Distance metric learning has emerged as a popular solution for low-resource scenarios without semantic information for the target language. Inspired by few-shot metric learning and prompt learning, we propose a novel method called Cross-lingual Prompt-guiding Named Entity Recognition (CroPoNER) for this task. We use prompts from different languages to serve as 1) supervisory guidance for conveying unseen entity type information to the language model; 2) metric referents for predicting target language entity types; 3) a bridge between different languages that mitigates the language gap. Our experiments on several widely-used cross-lingual NER datasets (CoNLL, WikiAnn) in the few-shot setting demonstrate that our method outperforms state-of-the-art models by a significant margin in most cases for cross-lingual few-shot NER.
FeatEMD: Better Patch Sampling and Distance Metric for Few-Shot Image Classification

Few-shot image classification (FSIC) studies the problem of classifying images when given only a few training samples, which presents a challenge for deep learning models to generalize well on unseen image categories. To learn FSIC tasks effectively, recent metric-based methods leverage the similarity measures of deep feature representations with minimum matching costs, introducing a new paradigm in addressing the FSIC challenge. Recent metric-learning techniques, e.g., DeepEMD, measure the distance between features with the earth mover’s distance (EMD), and it is currently the state-of-the-art (SOTA) approach for FSIC. In this paper, we however identify two fundamental limitations in DeepEMD. First, it brings high computational cost, as it randomly samples image patches to extract features. This process is often wasteful due to suboptimal sampling strategies. Second, its accuracy is also limited by the use of optimal-transport costs based on cosine similarity, which only measures directional discrepancies. To mitigate the above shortcomings, we propose an improved method, which we call FeatEMD. First, it introduces a feature saliency-based cropping (FeatCrop) to construct image patches that concentrates computations on object-salient regions. Second, it proposes a Direction-Distance Similarity ( $$\textrm{DDS}$$ ) a more effective distance criterion in capturing subtle differences in latent space features. We conduct comprehensive experiments and ablations to validate our method. Experimental results show FeatEMD establishes new SOTA on two mainstream benchmark datasets. Remarkably, when compared with DeepEMD, FeatEMD reduces up to $$ 36\% $$ computational costs. Our code is available at https://github.com/SethDeng/FeatEMD .
Reinforcement Learning with Memory Based Automatic Chunking for Complex Skill Acquisition

Transformers are rapidly gaining popularity in the field of reinforcement learning. This research work proposes a transformer model with enhanced stability and learning speed. Deliberate chunking is conscious in its nature, it is strategically intended to structure the material to memorize. Automatic chunking on the other hand, is unconscious, and continuous, this research work applies automatic chunking on transformers. Firstly, the memory buffer of the transformer is divided into chunks. A high level attention is then performed on summaries of these chunks to select the most relevant parts of memory during training. Thus, the transformer model learns to work on only the relevant parts of memory instead of performing self attention on the entire buffer. Gating connections which make use of gated recurrent units, layer normalization and positional encoding are also used for further improvement in performance. Training and testing for various visual navigation and robotic locomotion tasks is done.
Decorelated Weight Initialization by Backpropagation

A hybrid, trainable weight initialization method for neural networks has been proposed to address potential training issues caused by weight symmetry. By pre-optimizing randomly initialized weights, using backpropagation, this method enhances parameter diversity. This efficient approach, applicable to any neural network architecture, decreases symmetry and decorrelates weights, thus optimizing performance with fewer trainable parameters.
Improving Neural Network Verification Efficiency Through Perturbation Refinement

This paper presents a novel approach to efficient neural network verification through the use of adversarial attacks and symbolic interval propagation. The proposed method leverages low-cost adversarial attacks to quickly obtain a rough estimate of the first set of bounds, and then utilizes symbolic interval propagation to compute tighter bounds. We demonstrate the effectiveness of our proposed method on the popular MNIST dataset, which contains hand-written digit images. The results show that the proposed method achieves state-of-the-art verification accuracy with significantly reduced computational cost, making it a promising approach for practical neural network verification.
HaarStyle:Revision Style Transfer Based on Multiple Resolutions

Image style transfer aims to obtain content images with corresponding styles by migrating style information to content images, but transfer models in recent years have certain application limitations, for which good image quality and transfer speed of the model cannot be guaranteed at the same time, and it’s common that clear edges cannot be maintained when stylizing. This paper proposes a new feed-forward model Haar Based Network (HaarStyle), which uses different resolution modules to complement the edge information and content features of the image to improve its quality. It is experimentally demonstrated that HaarStyle can ensure a certain transmission speed with fewer artifacts, avoiding the problem of over stylization.
VDCNet: A Vulnerability Detection and Classification System in Cross-Project Scenarios

The existence of software vulnerabilities is the primary cause of most security incidents in cyberspace. Timely detection of potential vulnerabilities from source code during the software development stage is a critical issue for developers. With the increasing scale of open-source projects, traditional static analysis tools are becoming more and more unreliable and stagnant in their development. Meanwhile, approaches for vulnerability detection based on deep learning are being investigated. This paper introduces a novel deep learning-based vulnerability detection system, VDCNet, to identify and classify multiple vulnerabilities more effectively. We extract advanced semantic information from AST representations of source code and capture patterns of vulnerable functions by training neural networks. VDCNet constructs a BERT model for embedding and a Bi-LSTM network for prediction. The experimental results on a comprehensive dataset demonstrate that our method is more efficient in binary vulnerability detection than other deep learning-based methods, with outstanding multi-classification performance in cross-project scenarios.
APPLICATION OF FUZZY ANALYTICAL HIERARCHY PROCESS (FAHP) FOR TEACHING QUALITY EVALUATION AT DEFENCE FOUNDATION CENTRE

Academic staff is one of the backbones of any educational institution and is responsible for the quality of students produced. Assessment of quality teaching has always been an endless debate within institutions. Research towards better teaching assessment on various criteria has been proposed. In past studies, teaching performance of academic staff was mostly evaluated based on only single numerical value, i.e. rating. Various studies have suggested different criteria on how to assess teaching performance. Due to the subjective nature of the students’ evaluation, the single rating, i.e. 1 to 5 score (very low to very high, respectively) achieved by academic staff are very general and lack accuracy. This research paper will venture into the teaching evaluation using Fuzzy Analytical Hierarchy Process (Fuzzy AHP) from the perspective of subject experts. The study will incorporate three main criteria for evaluating teaching quality, i.e. staff personal traits, knowledge transfer, and knowledge evaluation. It is found that speech culture (0.149) is deemed as the priority among other teaching quality criteria assessed.
The Need Analysis of Learning Module Development Using Self Organized Learning Environment (Sole) Assisted by Augmented Reality on Rotational Dynamics and Rigid Body Equilibrium

This study aims to analyze the need for the development of learning module using the Self Organized Learning Environment (SOLE) model assisted by Augmented Reality on rotational dynamics and rigid body equilibrium. This type of research is a survey research and literature study which later can be the basis for further development research. The research instrument used was a needs analysis questionnaire with 20 questions given to 72 respondents who were students and 3 respondents who were teachers of class XI MIPA from SMA Negeri 5 Bengkulu City, SMA Negeri 4 Bengkulu City and SMA Negeri 9 Bengkulu City. In this research the data analysis uses descriptive statistics. Based on the results of the data obtained it can be concluded that currently students are in dire need of computer-based learning media, one of which is by using Augmented Reality. This is based on data obtained that 76,26 % of students answered Strongly Agree and 77,92 % of teachers answered Strongly Agree to the need for the development of the development of learning module using the Self Organized Learning Environment (SOLE) model assisted by Augmented Reality on rotational dynamics and rigid body equilibrium. We know that the development of digital technology in the current Industrial 4.0 era has brought changes and influenced various aspects of human life, including in the field of education. Augmented Reality are technologies that can be developed to provide alternative instructional media provided previously and to see the effectiveness of these media if used in learning activities.
A Major Upgrade of the V8 package

This week version 2.0 of the V8 package has been released to CRAN. Go get it now!install.packages("V8") The V8 package provides an embedded JavaScript engine that can be used inside of R. You can use it interactively as a JavaScript console, but it is mostly useful for wrapping JavaScript libraries in R packages. Some cool examples include jsonld, jsonvalidate, and daff.
Community Call - Security for R

“Security” can be a daunting, scary, and (frankly) quite often a very boring topic. BUT!, we promise that this Community Call on May 7th will be informative, engaging, and enlightening (or, at least not boring)! Applying security best practices is essential not only for developers or sensitive data storage but also for the everyday R user installing R packages, contributing to open source, working with APIs or remote servers.
Join, split, and compress PDF files with pdftools

Last month we released a new version of pdftools and a new companion package qpdf for working with pdf files in R. This release introduces the ability to perform pdf transformations, such as splitting and combining pages from multiple files.