Reviewerly/exHarmony huggingface dataset:
Discwise Active Learning for LiDAR Semantic Segmentation

While LiDAR data acquisition is easy, labeling for semantic segmentation remains highly time consuming and must therefore be done selectively. Active learning (AL) provides a solution that can iteratively and intelligently label a dataset while retaining high performance and a low budget. In this work we explore AL for LiDAR semantic segmentation. As a human expert is a component of the pipeline, a practical framework must consider common labeling techniques such as sequential labeling that drastically improve annotation times. We therefore propose a discwise approach (DiAL), where in each iteration, we query the region a single frame covers on global coordinates, labeling all frames simultaneously. We then tackle the two major challenges that emerge with discwise AL. Firstly, we devise a new acquisition function that takes 3D point density changes into consideration which arise due to location changes or ego-vehicle motion. Next, we solve a mixed-integer linear program that provides a general solution to the selection of multiple frames while taking into consideration the possibilities of disc intersections. Finally we propose a semi-supervised learning approach to utilize all frames within our dataset and improve performance.
Verifying Agile Black-Box Test Case Quality Measurements: Expert Review

Tests on software are performed to ensure it has no defects. Software testing companies and organizations claimed that the testing problem increased their time and budget for testing by over 50%. As a result, the product’s release is delayed, which attracts customer complaints. Testing involves test cases, which are a vital part of the process. Therefore, quality test cases likely to identify defects and meet users’ requirements must be chosen. This paper aims to identify and verify the characteristics, sub-characteristics, and metrics that construct the proposed model for producing high-quality black-box test cases in an Agile Software Development environment. An expert review approach was used to verify the proposed model. Ten academic and six industry experts contributed to this verification. The results showed that six characteristics, 22 sub-characteristics, and 56 metrics are accepted to be included in the proposed model. Furthermore, findings revealed that the proposed model is comprehensive, consistent, relevant, and well-organized for Agile projects.
Frequency Regularization: Reducing Information Redundancy in Convolutional Neural Networks

Convolutional neural networks have demonstrated impressive results in many computer vision tasks. However, the increasing size of these networks raises concerns about the information overload resulting from the large number of network parameters. In this paper, we propose Frequency Regularization to restrict the non-zero elements of the network parameters in the frequency domain. The proposed approach operates at the tensor level, and can be applied to almost all network architectures. Specifically, the tensors of parameters are maintained in the frequency domain, where high-frequency components can be eliminated by zigzag setting tensor elements to zero. Then, the inverse discrete cosine transform (IDCT) is used to reconstruct the spatial tensors for matrix operations during network training. Since high-frequency components of images are known to be less critical, a large proportion of these parameters can be set to zero when networks are trained with the proposed frequency regularization. Comprehensive evaluations on various state-of-the-art network architectures, including LeNet, Alexnet, VGG, Resnet, ViT, UNet, GAN, and VAE, demonstrate the effectiveness of the proposed frequency regularization. For a very small accuracy decrease (less than 2%), a LeNet5 with 0.4M parameters can be represented by only 776 float16 numbers (over 1100× reduction), and a UNet with 34M parameters can be represented by only 759 float16 numbers (over 80000× reduction). In particular, the original size of the UNet model is reduced from 366 Mb to 4.5 Kb.
Contrastive Distillation With Regularized Knowledge for Deep Model Compression on Sensor-Based Human Activity Recognition

Deep learning (DL) approaches have been widely applied to sensor-based human activity recognition (HAR). However, existing approaches neglect to distinguish human activities which have similar sensory reading patterns, resulting in poor recognition accuracy on those activities. Moreover, these deep models often come with complex network architectures for performance improvement. Expensive computational cost and memory requirement hinder their feasibility to be deployed on resource-limited environments such as smartphones. As one of the most popular model compress techniques, knowledge distillation (KD) can be leveraged to boost the performance of a compact student with the knowledge from a cumbersome teacher. However, most of the existing KD works ignore the bias introduced by teacher's logits during distillation, leading to sub-optimal issue for student training. To address the above issues, we propose a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Con</b> trastive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> istillation framework with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> egularized <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</b> nowledge (ConDRK) for sensor-based human activity recognition. Particularly, we first utilize multiple intra-class samples to formulate a novel unbiased soft target as regularized knowledge which can significantly eliminate the bias introduced by teacher. Then, we propose a contrastive distillation scheme which employs the proposed unbiased soft target as positive pair and samples from other different classes as negative pairs to transfer the knowledge. Our proposed approach can not only reduce the intra-class variance but also maximize the inter-class distances, thus further enhancing the recognition performance of the compact student on similar activities. Extensive experimental results on two sensor-based HAR datasets clearly show that our proposed method can achieve superior and consistent performance over other state-of-the-art methods. The PyTorch implementation of our proposed method is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xuqing88/ConDRK</uri>
Application of Convolution Neural Network Algorithm in Online Education Emotion Recognition

The setting of teaching environment is the key factor of teaching emotion recognition, and its superiority directly determines the teaching and learning effect between teachers and students. During online education, the changes of students' emotions are not paid attention to and addressed by teachers. Especially for young students, their self-study ability and self-discipline are poor, which further affects the learning. This paper proposes an improved convolutional neural network algorithm to create a decision tree model for managing students' scores. The experimental results show that the improved convolutional neural network algorithm improves the construction speed of the decision tree and reduces the calculation and execution time of the algorithm. The improved algorithm proposed in this paper has a good classification effect. The model provides a reference for the expansion and application of emotion recognition big data in education and teaching, and a feasible practical model for personalized teaching in online schools.
The Impact of Mobile Resources on Enhancing Lifelong Learning Among Chinese Undergraduate EFL Students

The COVID-19 pandemic has accelerated the adoption of online education, including mobile-assisted language learning (MALL). Extensive research has examined various aspects of MALL; there remains a gap in understanding the preferred mobile resources that promote lifelong learning ability, particularly in relation to gender differences. This study focuses on Chinese EFL learners' preferences for mobile resources in promoting lifelong learning. Employing a quantitative questionnaire, the researchers examine the relationship between mobile learning efficacy and lifelong learning ability. The findings indicate no significant differences in mobile resource preferences among male and female students. However, a moderately positive relationship between mobile learning efficacy and lifelong learning ability was discovered. Notably, this study identifies that Chinese EFL students preferred mobile resources that may act as a platform to enhance mobile learning efficacy and support lifelong learning.
On the dynamic reconfigurable implementations of MISTY1 and KASUMI block ciphers

Novel hardware architectures for dynamic reconfigurable implementation of 64-bit MISTY1 and KASUMI block ciphers are proposed to enhance the performance of cryptographic chips for secure IoT applications. The SRL32 primitive (Reconfigurable Look up Tables-RLUTs) and DPR (Dynamic Partial Reconfiguration) are employed to reconfigure single round MISTY1 / KASUMI algorithms on the run-time. The RLUT based architecture attains dynamic logic functionality without extra hardware resources by internally modifying the LUT contents. The proposed adaptive reconfiguration can be adopted as a productive countermeasure against malicious attacks with the added advantage of less reconfiguration time (RT). On the other hand, the block architecture reconfigures the core hardware by externally uploading the partial bit stream and has significant advantages in terms of low area implementation and power reduction. Implementation was carried out on FPGA, Xilinx Virtex 7. The results showed remarkable results with very low area of 668 / 514 CLB slices consuming 460 / 354 mW for RLUT and DPR architectures respectively. Moreover, the throughput obtained for RLUT architecture was found as 364 Mbps with very less RT of 445 nsec while DPR architecture achieved speed of 176 Mbps with RT of 1.1 msec. The novel architectures outperform the stand-alone existing hardware designs of MISTY1 and KASUMI implementations by adding the dynamic reconfigurability while at the same achieving high performance in terms of area and throughput. Design details of proposed unified architectures and comprehensive analysis is described.
Marker-based registration error estimation in see-through AR applications

The precise overlay in see-through AR applications and the development of methods to calculate the registration error for such applications using an AR head-mounted display (HMD) present significant challenges. This difficulty arises primarily due to the absence of ground truth data (GTD) as the scene is partially hidden by view restrictions. Traditional approaches may require expensive setups, like cameras or laser scanners, to capture the hidden area and generate GTD. We propose an approach to calculate the registration error by using a marker-based pose estimation method. We use synthetic data to show the suitability of our method. The synthetic data is created in Unity, where we replicated a see-through application. Therefore, we employ image augmentation technologies for simulating a real see-through forklift application. The utilization of the simulation environment enables the generation of GT). This data forms the basis for evaluating the accuracy of our proposed method. Our primary contributions include a simulated see-through AR application in Unity, a labeled, application-specific synthetic dataset, and a validated method for determining the 3D registration error in world units (mm). This work demonstrates the suitability of the development of a marker-based registration error method to determine a 3D registration error in AR HMD see-through applications, providing an alternative to traditional, more costly approaches.
Bridging Informal Reasoning and Formal Proving: The Role of Argumentation in Proof-Events

Abstract This paper explores the relationship between informal reasoning, creativity in mathematics, and problem solving. It underscores the importance of environments that promote interaction, hypothesis generation, examination, refutation, derivation of new solutions, drawing conclusions, and reasoning with others, as key factors in enhancing mathematical creativity. Drawing on argumentation logic, the paper proposes a novel approach to uncover specific characteristics in the development of formalized proving using “proof-events.” Argumentation logic can offer reasoning mechanisms that facilitate these environments. This paper proposes how argumentation can be implemented to discover certain characteristics in the development of formalized proving with “proof-events”. The concept of a proof-event was introduced by Goguen who described mathematical proof as a multi-agent social event involving not only “classical” formal proofs, but also other informal proving actions such as deficient or alleged proofs. Argumentation is an integral component of the discovery process for a mathematical proof since a proof necessitates a dialogue between provers and interpreters to clarify and resolve gaps or assumptions. By formalizing proof-events through argumentation, this paper demonstrates how informal reasoning and conflicts arising during the proving process can be effectively simulated. The paper presents an extended version of the proof-events calculus, rooted in argumentation theories, and highlights the intricate relationships among proof, human reasoning, cognitive processes, creativity, and mathematical arguments.
“Write oneself into being”– <i>Ha</i> as an interpersonal pragmatic marker on WeChat

Abstract The pragmatic marker ha 哈 in Mandarin, has little meaning in itself; while some studies have looked into its usage in spoken discourse, we seek to address its usage and unpack its specific interpersonal pragmatic function in online, private, dyadic discourse, itself an underexplored area, and a setting in which participants are free from public scrutiny, unlike on mass social media. The results demonstrate that it can be attached to clauses and sentences to show the affect of the writer, from indicating a jocular and playful tone to softening a request. Therefore, ha is used as a way to express different types of rapport orientations and positive self-presentation. By drawing on personal WeChat messages, this paper bridges the gap by focusing on ha in technologically-mediated conversation. The expanding usage of ha also suggests a wish to create linguistic forms which can act as substitutes for non-verbal communication features.
An approach to PV fault defect detection based on computer vision

Solar panels are susceptible to defects such as hot patches and cracks due to environmental and human factors, which can directly affect energy management and power generation quality if not maintained in a timely manner. Computer vision technology provides a viable tool for PV fault defect detection by inputting defective images to train a model that learns the defective features and thus detects PV fault defects. This paper addresses the problem of uneven speed and accuracy of PV defect detection, and proposes an improved method based on YOLOv5, which uses the C2f module in YOLOv8 to replace the C3 module in the backbone network, enriching the gradient flow information while ensuring light weight, and embedding the SimAM attention mechanism in the Neck network to improve the detection capability of the network for low resolution as well as small targets The final experimental results show that our proposed network is not only lightweight but also rich in gradient flow information. The final experimental results show that our proposed method improves the accuracy by 1.2%, reduces the number of parameters and computation by 25.3% and 16.5% respectively, and has the same inference speed than the original algorithm. Thus, our proposed method achieves a balance between speed and accuracy.
Hierarchical Image Fine-Grained classification via Hierarchical Feature Mining and Filtering

Transformer network has been widely applied in the field of computer vision. Thanks to the application of self-attention mechanism, Transformer can extract and pay attention to features at each position in the image, capturing details more accurately. In this paper, in order to address the issue of obtaining different levels of classification results based on different semantic contexts, we propose a hierarchical image fine-grained classification network based on Swin Transformer. Specifically, multiple classification results of different classification granularity levels can be obtained for the same image. We also suggest a parallel multi-level feature mining module, establish a progressive training model, and use a patch generator to build training samples of different granularity and stages, so as to extract features of different classification granularity levels. We design a hierarchical feature fusion module to promote the classification results of the fine-grained branch on the coarse-grained branch. Furthermore, we present a weakly supervised feature selector to enhance the feature representation ability and improve the classification accuracy, which helps to select effective feature points from different stages of feature maps. Finally, we evaluate its performance on three benchmark datasets, and find that the proposed method achieves good performance in terms of weighted average accuracy.
Efficient Attention Fusion Feature Extraction Network for Image Super-Resolution

Many lightweight approaches for image super-resolution are currently unsatisfactory in their performance. To address this issue, we propose an efficient attention fusion feature extraction network (EAFFEN) for lightweight image super-resolution model. Our method is comprised of multiple efficient attention fusion feature extraction blocks (EAFFEB), each of which performs aggregation operations of deep feature extraction, enhancement, refinement, and redistribution of feature information. In each block, we employ the progressive residual feature extraction module (PRFEM) to extract deep information of image. Then the channel contrastive-aware attention module (CCAAM) is used to enhance the feature information. And finally, we use the channel-spatial joint attention module (CSJAM) to jointly redistribute the feature information from the previous layer and the enhanced deep feature information to collect various refined feature information. These three modules are concatenated in a cascade manner to form an efficient attention fusion feature extraction block. Our method can enable the performance while utilizing a small number of parameters. We evaluate our method on popular benchmark datasets and achieve superior performance compared to existing methods. For example, our EAFFEN achieves the PSNR of 30.50 dB and the SSIM of 0.9087 on Manga109 ×4 dataset.
STFGSM: Intelligent Image Classification Model Based on Swin Transformer and Fast Gradient Sign Method

The convolutional neural network is relied upon by the mainstream image classification model to be achieved, but the convolutional neural network itself has defects such as easy loss of data. At the same time, deep learning models are vulnerable to adversarial perturbations, resulting in a decline in model performance. In order to effectively solve the above problems, this paper presents STFGSM, an intelligent image classification model based on Swin Transformer and fast gradient sign method. The attention mechanism is utilized by the Swin Transformer to extract picture features, with the traditional convolution operation being replaced. The field of the image is enhanced and information loss is avoided by this. Furthermore, the anti-interference capability of the model is strengthened through adversarial training that uses adversarial samples generated via the fast gradient sign method algorithm. The experimental results show that the classification performance of STFGSM outperformed other mainstream image classification models, whose speed is faster and adaptability to adversarial samples is stronger. In the future, more complex adversarial training strategies can be introduced on the basis of the model or the model can be extended to tasks in other fields such as target detection and image generation.
Graph Neural Collaborative Filtering Algorithm Based on Self-Supervised Learning and Degree Centrality

In recent years, with the introduction of graph neural networks in recommendation systems, collaborative filtering has been significantly improved, especially in handling large-scale, high-dimensional, and sparse user behavior data. Graph neural networks can better capture the complex relationships between users and items. However, existing graph neural network methods still have limitations in dealing with collaborative filtering problems, particularly in addressing data sparsity and long-tail issues. To address these challenges, this paper proposes a novel graph neural collaborative filtering (SDGCF) algorithm, which combines self-supervised learning and degree centrality fusion strategy, as well as jointly optimizes multiple loss functions including BPR_Loss and contrastive loss, to further improve the performance of collaborative filtering algorithms. Through experiments on three public datasets, the SDGCF algorithm demonstrates superior performance in terms of recommendation accuracy, recall, hit rate, and NDCG, proving its effectiveness in alleviating data sparsity and long-tail issues.
Explainable Meta-Path Based Recommender Systems

Meta-paths have been popularly used to provide explainability in recommendations. Although long/complicated meta-paths could represent complex user-item connectivity, they are not easy to interpret. This work tackles this problem by introducing a meta-path translation task. The objective is to translate a meta-path to its comparable explainable meta-paths that perform similarly in terms of recommendation but have higher explainability compared to the given one. We propose a definition of meta-path explainability to determine comparable explainable meta-paths and a meta-path grammar that allows comparable explainable meta-paths to be formed in a similar way as sentences in human languages. Based on this grammar, we propose a meta-path translation model, a sequence-to-sequence (Seq2Seq) model to translate a long and complicated meta-path to its comparable explainable meta-paths. Two novel datasets for meta-path translation were generated based on two real-world recommendation datasets. The experiments were conducted on these generated datasets. The results show that our model outperformed state-of-the-art Seq2Seq baselines regarding meta-path translation and maintained a better trade-off between accuracy and diversity/readability in predicting comparable explainable meta-paths. These results indicate that our model can effectively generate a group of explainable meta-paths as alternative explanations for those recommendations based on any given long/complicated meta-path.