Reviewerly/exHarmony huggingface dataset:
Cascaded Bilinear Mapping Collaborative Hybrid Attention Modality Fusion Model

LiDAR and Camera feature fusion detection is widely used in the field of 3D object detection. A fusion method that integrates the two modalities into a unified representation has been proposed and proven effective. However, there is still a challenge in fusing these two modalities in the same space. To address this issue, we propose a fusion module that leverages bilinear mapping technology and a hybrid attention mechanism. Our method preserves the original independent branches for LiDAR and Camera feature extraction. We then employ a cascade bilinear mapping hybrid attention fusion module to combine the features of these two modalities in the Bird’s Eye View (BEV) space. We utilize a hybrid structure of Convolutional Neural Networks (CNN) and Efficient-Attention to process the fused features. Furthermore, Multilayer Perceptron (MLP) is used to extract information from the features and achieve an asymmetric deep fusion of the two modalities in BEV space through the cascaded bilinear mapping method. This approach helps to complement the information provided by both features and achieve improved detection results. On the nuScenes validation set, our model improves the accuracy by 1.2 $$\%$$ compared to the current Sota method of feature fusion in BEV space. It is important to note that this improvement is achieved without any data augmentation or Test-Time Augmentation (TTA) techniques.
Lexical Functional Grammar as a Construction Grammar

Lexical Functional Grammar (LFG) is a lexicalist, constraint-based grammatical theory that shares a lot of the basic assumptions of Construction Grammar (CxG), such as a commitment to surface-oriented descriptions (no transformations), and the simultaneous representation of form, meaning, and other grammatical information (no derivations). Nevertheless, LFG is not standardly viewed as a kind of CxG, in particular since its adherence to the principle of Lexical Integrity means that it insists on a strict morphology-syntax distinction where CxG canonically rejects such a divide. However, such a distinction is in fact entirely compatible with CxG assumptions; the actual problem with viewing LFG as a CxG is the difficulty it has in describing the more substantive end of the schematic-substantive spectrum of constructions. I suggest that by replacing the limited context-free grammar base of LFG responsible for this shortcoming with a more expressive formalism (in this case a description-based tree-adjoining grammar), we can obtain a fully constructional LFG, suitable as a formal framework for CxG.
Enhancing Active Visual Tracking Under Distractor Environments

Active Visual Tracking (AVT) faces significant challenges in distracting environments characterized by occlusions and confusion. Current methodologies address this challenge through the integration of a mixed multi-agent game and Imitation Learning(IL). However, during the IL phase, if the training data of students generated by the teacher lacks diversity, it can lead to a noticeable degradation in the performance of the student visual tracker. Furthermore, existing works neglect visual occlusion issues from distractors beyond the collision distance. To enhance AVT performance, we introduce a novel method. Firstly, to tackle the limited diversity issue, we propose an intrinsic reward mechanism known as Asymmetric Random Network Distillation (AS-RND). This mechanism fosters target exploration, augmenting the variety of states among trackers and distractors, thereby enriching the heterogeneity of the visual tracker’s training data. Secondly, to address visual occlusion, we present a distractor-occlusion avoidance reward predicated on the positional distribution of the distractors. Lastly, we integrate a classification score map prediction module to bolster the tracker’s discriminative abilities. Experiments show that our approach significantly outperforms previous AVT algorithms in a complex distractor environment.
The Influencing Factors of the Helpfulness of User-Generated Product Q&amp;As

User-generated product questions and answers (Q&amp;As) are a new way for consumers to exchange product knowledge that can provide important support for consumer decisions. Research on the influencing factors of consumers’ judgment of the helpfulness of user-generated product Q&amp;As can help optimize Q&amp;A systems. Although research has extensively examined the impacts of answers and answerers on user-generated Q&amp;As, the underlying mechanism of how the characteristics of questions and Q&amp;As affect the helpfulness of user-generated Q&amp;As has rarely been explored. Based on the elaboration likelihood model (ELM), a research model reflecting the impacts of the characteristics of questions and Q&amp;As on the helpfulness of user-generated product Q&amp;As is developed and empirically examined by using data collected from 4,814 product questions and 10,573 corresponding answers on Amazon.com. Specifically, the moderating effects of the question type and product type on the relationship between Q&amp;A cues and the helpfulness of user-generated product Q&amp;As are investigated. The results show that the helpfulness of user-generated product Q&amp;As is positively affected by answer professionalism, Q&amp;A topic consistency, answer opinion consistency, and answer valence, while it is negatively influenced by answer knowledge stickiness. As expected, the relationship between answer knowledge stickiness, answer opinion consistency, and the helpfulness of user-generated product Q&amp;As is weakened when products are experiential. The relationship between answer knowledge stickiness, Q&amp;A topic consistency, and the helpfulness of user-generated product Q&amp;As is weakened when consumers’ questions are correlated with product attributes.
Feature Enhancement with Text-Specific Region Contrast for Scene Text Detection

As a fundamental step in most visual text-related tasks, scene text detection has been widely studied for a long time. However, due to the diversity in the foreground, such as aspect ratios, colors, shapes, etc., as well as the complexity of the background, scene text detection still faces many challenges. It is often difficult to obtain discriminative text-level features when dealing with overlapping text regions or ambiguous regions of adjacency, resulting in suboptimal detection performance. In this paper, we propose Text-specific Region Contrast (TRC) based on contrastive learning to enhance the features of text regions. Specifically, to formulate positive and negative sample pairs for contrast-based training, we divide regions in scene text images into three categories, i.e., text regions, backgrounds, and text-adjacent regions. Furthermore, we design a Text Multi-scale Strip Convolutional Attention module, called TextMSCA, to refine embedding features for precise contrast. We find that the learned features can focus on complete text regions and effectively tackle the ambiguity problem. Additionally, our method is lightweight and can be implemented in a plug-and-play manner while maintaining a high inference speed. Extensive experiments conducted on multiple benchmarks verify that the proposed method consistently improves the baseline with significant margins.
A Complex-Valued Neural Network Based Robust Image Compression

Recent works on learned image compression (LIC) based on convolutional neural networks (CNNs) have achieved great improvement with superior rate-distortion performance. However, the robustness of LIC has received little investigation. In this paper, we proposes a complex-valued learned image compression model based on complex-valued convolutional neural networks (CVCNNs) to enhance its robustness. Firstly, we design a complex-valued neural image compression framework, which realizes compression with complex-valued feature maps. Secondly, we build a module named modSigmoid to implement a complex-valued nonlinear transform and a split-complex entropy model to compress complex-valued latent. The experiment results show that the proposed model performs comparable compression performance with a large parameter drop. Moreover, we adopt the adversarial attack method to examine robustness, and the proposed model shows better robustness to adversarial input compared with its real-valued counterpart.
Repdistiller: Knowledge Distillation Scaled by Re-parameterization for Crowd Counting

Knowledge distillation (KD) is an important method to compress a large teacher model into a much smaller student model. However, the large capacity gap between the teacher and student models hinders the performance of KD in various tasks. In this paper, we propose Repdistiller, a knowledge distillation framework combined with structural re-parameterization to alleviate the capacity gap problem. Repdistiller makes the student model search for parallel branches during training, thus the capacity gap between the teacher and student models is decreased. After knowledge distillation, the searched branches are merged into the student network without causing any computation overhead for inference. Taking the crowd counting task as an example, Repdistiller achieves state-of-the-art performance on the ShanghaiTech and UCF-QNRF datasets, outperforming many well-established knowledge distillation methods.
Pairwise Negative Sample Mining for Human-Object Interaction Detection

In recent years, Human-Object Interaction (HOI) detection has been riding the wave of the development of object detectors. Typically, one-stage methods exploit them by instantiating HOI and detecting them in a coarse end-to-end learning scheme. With the invention detection transformer (DETR), more studies followed and addressed HOI detection in this novel set-prediction manner and achieved decent performance. However, the scarcity of positive samples in the dataset, especially among object-dense images, hinders learning and leads to lower detection quality. To alleviate this issue, we propose a sample mining technique that utilizes non-interactive human-object pairs to generate negative samples containing HOI features, enriching the sample sets to help the learning of queries. We also introduce interactivity priors, namely filtering out insignificant background objects to inhibit their disturbance. Our technique can be seamlessly integrated into an end-to-end training scheme. Additionally, we propose an unorthodox two-stage transformer-based method that separates pairwise detection and interaction inference to be handled by two cascade decoders, further exploiting this technique. Experimental results on mainstream datasets demonstrate that our approach achieves new state-of-the-art performance, surpassing both one-stage and traditional two-stage methods. Our study reveals the potential to convert between the two method types by adjusting data utilization.
Possibilities of forming a creative space on the basis of a modern university

The article discusses the concept, functions and basic principles of the formation of a creative space, raises issues of managing the activities of creative spaces, and shows the possibility of both positive and negative influence of the «atmosphere» of a creative space on economic, political, social and cultural processes in the region. Particular attention is paid to the analysis of the capabilities of modern universities to create and maintain a creative space on the student campus.
Face type recognition method for beauty industry

Pursuing the perception of beauty is a natural instinct for human beings. Makeup, haircuts, and outfits can make people look more exquisite and attractive. Choosing a suitable fashion style is a form of art, and face types play an important role that is closely related to people's style choices. The importance of human appearance lies in giving people a favorable impression, enhancing positive feelings, and increasing charm. This study aims to meticulously classify people's face types based on their facial features and quickly identify a person's face type, enabling people to understand how to wear makeup and choose outfits to enhance their glamour value. The paper proposes a 16-face types classification method as the formula for achieving beauty. The study employs machine learning (ML), including data acquisition, pre-processing, feature extraction, and classification, to automatically identify face types among sixteen categories and assess accuracy using different samples. To design the supervised machine learning system, the study utilizes the vision transformer (ViT) model for feature extraction and the classical classifier random forest algorithm (RF) for classification. Additionally, the results are compared with other classifiers such as support vector machine (SVM), decision tree (DT), Adaboost, and k-nearest neighbor (k-NN).
ASL Classification using Deep learning

Abstract: In this study, the ever-evolving landscape of American Sign Language (ASL) classification is examined through the lens of cutting-edge deep learning techniques. The main objective of this research is to improve accuracy and overcome real-time constraints. For millions of individuals who are deaf or hard of hearing, sign language serves as a vital form of communication that bridges the gap between them and the rest of the world. Deep learning, specifically the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has shown incredible potential in numerous computer vision and natural language processing tasks. This paper offers a comprehensive exploration of the emerging field of sign language classification through deep learning, delving into the motivations behind this research.
MLP-YOLOv5: A Lightweight Multi-Scale Identification Model for Lotus Pods with Scale Variation

Lotus pods in unstructured environments often present multi-scale characteristics in the captured images. As a result, it makes their automatic identification difficult and prone to missed and false detections. This study proposed a lightweight multi-scale lotus pod identification model, MLP-YOLOv5, to deal with this difficulty. The model adjusted the multi-scale detection layer and optimized the anchor box parameters to enhance the small object detection accuracy. The C3 module with transformer encoder (C3-TR) and the shuffle attention (SA) mechanism were introduced to improve the feature extraction ability and detection quality of the model. GSConv and VoVGSCSP modules were adopted to build a lightweight neck, thereby reducing model parameters and size. In addition, SIoU was utilized as the loss function of bounding box regression to achieve better accuracy and faster convergence. The experimental results on the multi-scale lotus pod test set showed that MLP-YOLOv5 achieved a mAP of 94.9%, 3% higher than the baseline. In particular, the model’s precision and recall for small-scale objects were improved by 5.5% and 7.4%, respectively. Compared with other mainstream algorithms, MLP-YOLOv5 showed more significant advantages in detection accuracy, parameters, speed, and model size. The test results verified that MLP-YOLOv5 can quickly and accurately identify multi-scale lotus pod objects in complex environments. It could effectively support the harvesting robot by accurately and automatically picking lotus pods.
LASFormer: Light Transformer for Action Segmentation with Receptive Field-Guided Distillation and Action Relation Encoding

Transformer-based models for action segmentation have achieved high frame-wise accuracy against challenging benchmarks. However, they rely on multiple decoders and self-attention blocks for informative representations, whose huge computing and memory costs remain an obstacle to handling long video sequences and practical deployment. To address these issues, we design a light transformer model for the action segmentation task, named LASFormer, with a novel encoder–decoder structure based on three key designs. First, we propose a receptive field-guided distillation to realize mode reduction, which can overcome more generally the gap in semantic feature structure between the intermediate features by aggregated temporal dilation convolution (ATDC). Second, we propose a simplified implicit attention to replace self-attention to avoid its quadratic complexity. Third, we design an efficient action relation encoding module embedded after the decoder, where the temporal graph reasoning introduces an inductive bias that adjacent frames are more likely to belong to the same class of model global temporal relations, and the cross-model fusion structure integrates frame-level and segment-level temporal clues, which can avoid over-segmentation independent of multiple decoders, thus reducing further computational complexity. Extensive experiments have verified the effectiveness and efficiency of the framework. Against the challenging 50Salads, GTEA, and Breakfast benchmarks, LASFormer significantly outperforms the current state-of-the-art methods in accuracy, edit score, and F1 score.
Survey on Sequential Pattern Mining based on User - Constraints

Sequence data mining, also known as sequential pattern mining, is to find all frequent sub-sequences (called sequential patterns) in a sequence database, the threshold of frequency is specified by the user. In recent years, with the explosion growth of information and big data, this problem trends toward mining with constraints to overcome both effectiveness and efficiency challenges since that the constraints represent for the user’s interest. This paper presents a detailed survey of recent studies on mining sequential pattern and the categories of the constraints. Moreover, it also classifies the main methods and analyzes their advantages and limitations, thereby main approaches and strategies to solve sequential pattern mining problems in the future are presented.
WASAIUP: A Demand-driven Concolic Fuzzer for EOSIO Smart Contracts

Attacks exploiting vulnerabilities in EOSIO smart contracts have caused serious economic losses. To detect these vulnerabilities, some approaches have been proposed, and concolic fuzzing is one of the most popular techniques among them. However, the existing concolic fuzzers have problems such as path explosion and adopting redundant constraint solving strategies, which reduce the detection efficiency. In order to alleviate these problems, we propose a demand-driven concolic fuzzing approach to discovering vulnerabilities in EOSIO smart contracts. In the approach, execution information is first collected to guide the execution of the system in a demand-driven manner. To improve the efficiency of vulnerability detection, we design a pruning strategy to eliminate the paths that are not relevant to the discovery of vulnerabilities and redundant paths to be explored. Meanwhile, an incremental constraint solving method is used to process only paths that can explore new branches. In addition, we also design a path prioritization method to preferentially explore paths which are more conducive to discovering vulnerabilities, so as to find vulnerabilities in smart contracts as early as possible. We have implemented our approach in a tool called WASAIUP and evaluated it on 3441 smart contracts. The experimental results show that WASAIUP improves the performance by 25.1% to 149.8% compared with the state-of-the-art tool WASAI in terms of efficiency, while maintaining high detection accuracy.
Similarity Measures Implementation on Face Authentication using Indonesian Citizen ID Card

The aim of this study is to demonstrate to computer science students the application of basic algebra concepts, particularly distance, in face authentication. Our simulation presents the results obtained from face authentication using five face images from 39 respondents and an Indonesian citizen ID card (KTP). We utilized MTCNN and RetinaFace for face detection and FaceNet512 for feature extraction. The KTP and images were then compared using Cosine, Euclidean, and Euclidean-L2 distances. The outcomes indicate that RetinaFace achieved a 100% success rate in image detection during this simulation, whereas MTCNN only reached 99.15%. The Cosine Distance method obtained the best Specificity and Accuracy results for both face detection techniques. In terms of Recall score, the RetinaFace-Euclidean Distance method shows a better performance than the other available scenarios.
Performance Optimization for Information Sharing Process of BlockIoV Based on Multi-Objective Particle Swarm

Blockchain effectively solves the security problem in the information sharing process of Internet of Vehicles (IoV). However, the additional information consensus process inevitably affects the information sharing performance of the Blockchain-based Internet of Vehicles (BlockIoV). In order to satisfies BlockIoV’s performance requirements such as transactions per second, latency, and block utilization, it is necessary to pay attention to the blockchain configuration. Since there exist conflicts among these performance requirements, increasing a single hand may cause other indicators to decrease. Therefore, the performance optimization problem for the information sharing process of BlockIoV can be regarded as a multiobjective optimization problem. In this regard, we propose a BlockIoV-oriented performance optimization method, namely BlockIoVOpt, which utilizes the multi-objective particle swarm optimization algorithm to find the Pareto optimal blockchain configuration. Specifically, we construct the objective functions for each of the performance indicators and design the iterative evolution rules for particle swarms. To obtain the performance results of the objective function under a given blockchain configuration, we also designed a BlockIoV-oriented information sharing process simulator, BlockIoVPref. The experimental results show that: BlockIoVOpt can effectively find the Pareto optimal configuration of the blockchain in a given test scenario and effectively optimize the transactions per second, latency of transaction, and block utilization rate of BlockIoV. This study provides an essential reference for the design of blockchain configuration schemes in information sharing of BlockIoV.
Distance Detection on an Autonomous Vehicle Using the Triangle Similarity Approach

Currently, autonomous vehicle advancement is very rapid, especially for developing algorithms or methods to support autonomous systems. Autonomous vehicles have become a hot topic in intelligent transportation and will improve safety on the road. This research aims to propose an autonomous vehicle safety system by detecting the distance between vehicles as initial research for the concept of autonomous vehicles regarding safe driving. For the car detection process, the Histogram of Oriented Gradients (HOG) method is used to obtain feature extraction from car objects. In the classification process, Support Vector Machine (SVM) linear classification is used to classify the car object. The main contribution of this research is to propose vehicle distance detection in autonomous vehicles using a triangle similarity approach. The triangle similarity is used to detect and calculate the distance (focal length) between the camera and the detected object. The decision boundary is obtained regarding the safe distance to the vehicle by marking the box in red for unsafe distances and green for safe distances. The result showed that accuracy reaches 75%, the MSE for bounding box has a value of 0.2, and the MSE for distance status has a value of 0.125.