Reviewerly/exHarmony huggingface dataset:
Loop closure detection with patch-level local features and visual saliency prediction

Loop closure detection (LCD) is essential in the field of visual Simultaneous Localization and Mapping (vSLAM). In the LCD system, geometrical verification based on image matching plays a crucial role in avoiding erroneous detections. This paper focuses on adopting patch-level local features for image matching to compute the similarity score between the current query image and the candidate images. However, an important factor that may reduce the robustness is that some distracting and dynamic regions in a scene (e.g., the sky, cars, pedestrians, the ground, etc.) are not helpful and may seriously harm the performance. To address this challenge, we first use a newly designed patch descriptor loss to optimize the distance relationship between the patch-level local features. In this way, the patch-level local features extracted from the query/candidate images are more suitable for performing image matching. Moreover, we mimic the visual attention mechanism and propose a patch matching with saliency strategy, which enables local patches in salient regions to play crucial roles in image matching by assigning suitable weights to them. Finally, experiments on several public datasets demonstrate that the proposed LCD system can achieve encouraging improvements over the state-of-the-art approaches regarding recall rates under 100% precision.
Silicon energy bulk material cargo ship detection and tracking method combining YOLOv5 and DeepSort

Silicon widely contained in sand is expected to become a new energy material with environmental protection, safety and low cost. The intelligent management of the exploitation and transportation of such energy resources has become an urgent demand. Aiming at solving the problem of low detection rate of silicon energy bulk material cargo ship targets in river monitoring videos, this paper proposes a multi-ship detection and tracking method based on YOLOv5x combined with DeepSort algorithm. In order to improve the detector recognition efficiency, CIoU Loss is used as the target bounding box regression loss function instead of GIoU Loss to speed up the bounding box regression rate while improving the localization accuracy; NMS is replaced by DIoU-NMS to tackle the problem of missed detection when the targets are dense. The structure of DeepSort appearance feature extraction network is adjusted and trained on a self-built ship dataset to reduce the identity switching caused by target occlusion. The experimental results show that YOLOv5x trained on Pytorch framework makes the model converge fast and accurate with mAP reaching 95.6%, and then combined with DeepSort tracking can achieve the detection and tracking of multiple types of ships. This can provide effective technical support for the supervision of illegal mining and transportation of silicon energy bulk material.
A Bagged Ensemble Convolutional Neural Networks Approach to Recognize Insurance Claim Frauds

Fighting fraudulent insurance claims is a vital task for insurance companies as it costs them billions of dollars each year. Fraudulent insurance claims happen in all areas of insurance, with auto insurance claims being the most widely reported and prominent type of fraud. Traditional methods for identifying fraudulent claims, such as statistical techniques for predictive modeling, can be both costly and inaccurate. In this research, we propose a new way to detect fraudulent insurance claims using a data-driven approach. We clean and augment the data using analysis-based techniques to deal with an imbalanced dataset. Three pre-trained Convolutional Neural Network (CNN) models, AlexNet, InceptionV3 and Resnet101, are selected and minimized by reducing the redundant blocks of layers. These CNN models are stacked in parallel with a proposed 1D CNN model using Bagged Ensemble Learning, where an SVM classifier is used to extract the results separately for the CNN models, which is later combined using the majority polling technique. The proposed method was tested on a public dataset and produced an accuracy of 98%, with a 2% Brier score loss. The numerical experiments demonstrate that the proposed approach achieves promising results for detecting fake accident claims.
Single-Shot Multi-Frequency 3D Shape Measurement for Discontinuous Surface Object Based on Deep Learning

It is challenging to stably and rapidly achieve accurate absolute phase retrieval for isolated objects with a single-shot pattern in fringe projection profilometry (FPP). In this context, a single-shot multi-frequency absolute phase retrieval (SAPR-DL) method based on deep learning is proposed, which only needs to capture one fringe image to obtain the full-field precise absolute phase. Specifically, a low-frequency deformed fringe image is loaded into the trained one-to-two deep learning framework (DLFT) to predict unit-frequency and high-frequency deformed fringe images. Then, three fringe images with different frequencies are loaded into the trained deep learning phase retrieval framework (DLPR) to calculate the corresponding absolute phase. The experimental results prove that the proposed SAPR-DL method can obtain the three-dimensional (3D) shape measurement of multiple complex objects by collecting a single-shot fringe image, showing great prospects in advancing scientific and engineering applications.
Intercorporeal Biofeedback for Movement Learning

Technology-supported movement learning has received increased attention in HCI. Previous design research has mostly focused on individual experiences, even though the social and situated context is essential to movement learning practices. Based on the experiences from two design projects in the fitness domain featuring open-ended biofeedback artefacts, we propose Intercorporeal Biofeedback as a strong concept to support the design and use of biofeedback in such practices. We ground the concept in situated movement learning theory, phenomenology of social cognition, and HCI work on biofeedback. We articulate four key characteristics of intercorporeal biofeedback: it provides participants with a shared frame of reference , upon which they engage in fluid meaning allocation and use it to guide attention and action , becoming an interactional resource . Intercorporeal biofeedback can serve to guide future design work for situated, social movement practices.
A Mirror to Human Question Asking: Analyzing the Akinator Online Question Game

Question-asking is a critical aspect of human communications. Yet, little is known about the reasons that lead people to ask questions, which questions are considered better than others, or what cognitive mechanisms allow the ability to ask informative questions. Here, we take a first step towards investigating human question-asking. We do so by an exploratory data-driven analysis of the questions asked by Akinator, a popular online game of a genie who asks questions to guess the character that the user is thinking of. We propose that the Akinator’s question-asking process may be viewed as a reflection of how humans ask questions. We conduct an exploratory data analysis to examine different strategies for the Akinator’s question-asking process, ranging from mathematical algorithms to gamification-based considerations, by analyzing complete games and individual questions. Furthermore, we use topic-modelling techniques to explore the topics of the Akinator’s inquiries and map similar questions into clusters. Overall, we find surprising aspects of the specificity and types of questions generated by the Akinator game, that may be driven by the gamification characteristics of the game. In addition, we find coherent topics that the Akinator retrieves from when generating questions. Our results highlight commonalities in the strategies for question-asking used by people and by the Akinator.
Robotic Path Planning for Rice Seeding in Hilly Terraced Fields

To realize the autonomous operation of a terraced rice sowing robot, a set of sowing robot operation path planning algorithms with universal significance for small, irregular terraced plots is proposed. According to the characteristics of terraces and the agronomic requirements of sowing seeding, the operation path mainly includes parallel operation and boundaries surrounding the operation path. The boundary pre-collision detection method is expounded, and the cyclic detection method judges the U-turn area. The Bézier curve fitting algorithm was used to smooth the boundary wrapping path. To verify the feasibility of the algorithm, four typical irregular small fields located in 666.7-hectare terraces of Sama in Hong He Prefecture, Yunnan Province, were randomly selected, and a field map was obtained through Google Earth. An existing seeding robot was used as a model, and the simulation and comparison tests were carried out with the mainstream EHNS algorithm and boundary polyline algorithm under the ROS-kinetic platform in the Cen Village Scientific Research Base of South China Agricultural University. The actual boundaries of the four fields with the same simulation test were used as the map to verify the field experiment. The simulation test results show that the area coverage of the sowing operation is greater than 93.53% and the replay rate is less than 3.46%, and the field test results show that the area coverage of the sowing operation is greater than 94.33% and the replay rate is less than 3.03%. The simulation test is in good agreement with the field test results, indicating that the algorithm has good adaptability, which meets the requirements of a sowing robot for sowing operation path planning and can provide a certain reference for the path planning of irregular field operation robots in hilly and mountainous areas.
Quality-Driven Dual-Branch Feature Integration Network for Video Salient Object Detection

Video salient object detection has attracted growing interest in recent years. However, some existing video saliency models often suffer from the inappropriate utilization of spatial and temporal cues and the insufficient aggregation of different level features, leading to remarkable performance degradation. Therefore, we propose a quality-driven dual-branch feature integration network majoring in the adaptive fusion of multi-modal cues and sufficient aggregation of multi-level spatiotemporal features. Firstly, we employ the quality-driven multi-modal feature fusion (QMFF) module to combine the spatial and temporal features. Particularly, the quality scores estimated from each level’s spatial and temporal cues are not only used to weigh the two modal features but also to adaptively integrate the coarse spatial and temporal saliency predictions into the guidance map, which further enhances the two modal features. Secondly, we deploy the dual-branch-based multi-level feature aggregation (DMFA) module to integrate multi-level spatiotemporal features, where the two branches including the progressive decoder branch and the direct concatenation branch sufficiently explore the cooperation of multi-level spatiotemporal features. In particular, in order to provide an adaptive fusion for the outputs of the two branches, we design the dual-branch fusion (DF) unit, where the channel weight of each output can be learned jointly from the two outputs. The experiments conducted on four video datasets clearly demonstrate the effectiveness and superiority of our model against the state-of-the-art video saliency models.
An Efficient and Robust Hybrid SfM Method for Large-Scale Scenes

The structure from motion (SfM) method has achieved great success in 3D sparse reconstruction, but it still faces serious challenges in large-scale scenes. Existing hybrid SfM methods usually do not fully consider the compactness between images and the connectivity between subclusters, resulting in a loose spatial distribution of images within subclusters, unbalanced connectivity between subclusters, and poor robustness in the merging stage. In this paper, an efficient and robust hybrid SfM method is proposed. First, the multifactor joint scene partition measure and the preassignment balanced image expansion algorithm among subclusters are constructed, which effectively solves the loose spatial distribution of images in subclusters problem and improves the degree of connection among subclusters. Second, the global GlobalACSfM method is used to complete the local sparse reconstruction of the subclusters under the cluster parallel framework. Then, a decentralized dynamic merging rule considering the connectivity of subclusters is proposed to realize robust merging among subclusters. Finally, public datasets and oblique photography datasets are used for experimental verification. The results show that the method proposed in this paper is superior to the state-of-the-art methods in terms of accuracy and robustness and has good feasibility and advancement prospects.
Toward Building Smart Contract-Based Higher Education Systems Using Zero-Knowledge Ethereum Virtual Machine

The issuing and verification of higher education certificates, including all higher education documents, still functions in a costly and inappropriately bureaucratic manner. Blockchain technology provides a more secure and consistent way to revolutionize the widely used generalized mechanisms and system concepts. In this paper, the most necessary requirements are examined regarding a blockchain-based higher education system, based on the most well-known research papers. Moreover, the opportunities of working on an education system by maintaining a decentralized structure organization are recommended as well. This paper recommends the most suitable blockchain scaling solution for the architecture of an education system which uses the most state-of-the-art EVM (Ethereum virtual machine) compatible approach to implement the higher education system with all the predefined requirements. It is proven that the explained smart contract-based higher education system, which uses zkEVM (zero-knowledge Ethereum virtual machine), consists of all necessary functionalities and satisfies all predefined requirements. In fact, the recommended system, by using a modular blockchain structure, implements all the functionality and capability of the examined related works in one system, namely GDPR (General Data Protection Regulation), which is compatible and more secure.
Abnormal-Trajectory Detection Method Based on Variable Grid Partitioning

Abnormal-trajectory detection can be used to detect fraudulent behavior by taxi drivers when carrying passengers. Existing methods usually detect abnormal trajectories based on the characteristics of “few and different”, which require large data sets and, therefore, may identify “few and near” trajectories chosen by drivers according to their driving experience as abnormal situations. This study proposed an abnormal-trajectory detection method based on a variable grid to address this problem. First, the urban road network was divided into three regions: high-, medium-, and low-density road network regions using a kernel density analysis method. Second, grids with different sizes were set for different types of road network regions; trajectory tuples were obtained based on the grid division results, and the abnormality rate of the trajectory was calculated. Finally, a trajectory-abnormality probability function was developed to calculate the deviation of each trajectory from the benchmark trajectory to detect abnormal trajectories. Experimental results on a real taxi trajectory dataset demonstrated that the proposed method achieved a higher accuracy in detecting abnormal trajectories than similar methods.
A Method for Long-Term Target Anti-Interference Tracking Combining Deep Learning and CKF for LARS Tracking and Capturing

Autonomous underwater vehicles (AUV) recycling in an underwater environment is particularly challenging due to the continuous exploitation of marine resources. AUV recycling via visual technology is the primary method. However, the current visual technology is limited by harsh sea conditions and has problems, such as poor tracking and detection. To solve these problems, we propose a long-term target anti-interference tracking (LTAT) method, which integrates Siamese networks, You Only Look Once (YOLO) networks and online learning ideas. Meanwhile, we propose using the cubature Kalman filter (CKF) for optimization and prediction of the position. We constructed a launch and recovery system (LARS) tracking and capturing the AUV. The system consists of the following parts: First, images are acquired via binocular cameras. Next, the relative position between the AUV and the end of the LARS was estimated based on the pixel positions of the tracking AUV feature points and binocular camera data. Finally, using a discrete proportion integration differentiation (PID) method, the LARS is controlled to capture the moving AUV via a CKF-optimized position. To verify the feasibility of our proposed system, we used the robot operating system (ROS) platform and Gazebo software to simulate the system for experiments and visualization. The experiment demonstrates that in the tracking process when the AUV makes a sinusoidal motion with an amplitude of 0.2 m in the three-dimensional space and the relative distance between the AUV and LARS is no more than 1 m, the estimated position error of the AUV does not exceed 0.03 m. In the capturing process, the final capturing error is about 28 mm. Our results verify that our proposed system has high robustness and accuracy, providing the foundation for future AUV recycling research.
Educational Psychology Aspects of Learning with Chatbots without Artificial Intelligence: Suggestions for Designers

Chatbots without artificial intelligence can play the role of practical and easy-to-implement learning objects in e-learning environments, allowing a reduction in social or psychological isolation. This research, with a sample of 79 students, explores the principles that need to be followed in designing this kind of chatbot in education in order to ensure an acceptable outcome for students. Research has shown that students interacting with a chatbot without artificial intelligence expect similar psychological and communicative responses to those of a live human, project the characteristics of the chatbot from the dialogue, and are taken aback when the chatbot does not understand or cannot help them sufficiently. The study is based on a design through research approach, in which students in information studies and library science interacted with a specific chatbot focused on information retrieval, and recorded their experiences and feelings in an online questionnaire. The study intends to find principles for the design of chatbots without artificial intelligence so that students feel comfortable interacting with them.
Reduced Clustering Method Based on the Inversion Formula Density Estimation

Unsupervised learning is one type of machine learning with an exceptionally high number of applications in various fields. The most popular and best-known group of unsupervised machine learning methods is clustering methods. The main goal of clustering is to find hidden relationships between individual observations. There is great interest in different density estimation methods, especially when there are outliers in the data. Density estimation also can be applied to data clustering methods. This paper presents the extension to the clustering method based on the modified inversion formula density estimation to solve previous method limitations. This new method’s extension works within higher dimensions (d &gt; 15) cases, which was the limitation of the previous method. More than 20 data sets are used in comparative data analysis to prove the effectiveness of the developed method improvement. The results showed that the new method extension positively affects the data clustering results. The new reduced clustering method, based on the modified inversion formula density estimation, outperforms popular data clustering methods on test data sets. In cases when the accuracy is not the best, the data clustering accuracy is close to the best models’ obtained accuracies. Lower dimensionality data were used to compare the standard clustering based on the inversion formula density estimation method with the extended method. The new modification method has better results than the standard method in all cases, which confirmed the hypothesis about the new method’s positive impact on clustering results.
LiST: A Lightweight Framework for Continuous Indian Sign Language Translation

Sign language is a natural, structured, and complete form of communication to exchange information. Non-verbal communicators, also referred to as hearing impaired and hard of hearing (HI&amp;HH), consider sign language an elemental mode of communication to convey information. As this language is less familiar among a large percentage of the human population, an automatic sign language translator that can act as an interpreter and remove the language barrier is mandatory. The advent of deep learning has resulted in the availability of several sign language translation (SLT) models. However, SLT models are complex, resulting in increased latency in language translation. Furthermore, SLT models consider only hand gestures for further processing, which might lead to the misinterpretation of ambiguous sign language words. In this paper, we propose a lightweight SLT framework, LiST (Lightweight Sign language Translation), that simultaneously considers multiple modalities, such as hand gestures, facial expressions, and hand orientation, from an Indian sign video. The Inception V3 architecture handles the features associated with different signer modalities, resulting in the generation of a feature map, which is processed by a two-layered (long short-term memory) (LSTM) architecture. This sequence helps in sentence-by-sentence recognition and in the translation of sign language into text and audio. The model was tested with continuous Indian Sign Language (ISL) sentences taken from the INCLUDE dataset. The experimental results show that the LiST framework achieved a high translation accuracy of 91.2% and a prediction accuracy of 95.9% while maintaining a low word-level translation error compared to other existing models.
Polarity-Based Sentiment Analysis of Georeferenced Tweets Related to the 2022 Twitter Acquisition

Twitter, one of the most important social media platforms, has been in the headlines regularly since its acquisition by Elon Musk in October 2022. This acquisition has had a strong impact on the employees, functionality, and discourse on Twitter. So far, however, there has been no analysis that examines the perception of the acquisition by the users on the platform itself. For this purpose, in this paper, we use georeferenced Tweets from the US and classify them using a polarity-based sentiment analysis. We find that the number of Tweets about Twitter and Elon Musk has increased significantly, as have negative sentiments on the subject. Using a spatial hot spot analysis, we find distinct centres of discourse, but no clear evidence of their significant change over time. On the West Coast, however, we suspect the first signs of polarisation, which could be an important indication for the future development of discourse on Twitter.
Formal Algebraic Model of an Edge Data Center with a Redundant Ring Topology

Data center organization and optimization presents the opportunity to try and design systems with specific characteristics. In this sense, the combination of artificial intelligence methodology and sustainability may lead to achieve optimal topologies with enhanced feature, whilst taking care of the environment by lowering carbon emissions. In this paper, a model for a field monitoring system has been proposed, where an edge data center topology in the form of a redundant ring has been designed for redundancy purposes to join together nodes spread apart. Additionally, a formal algebraic model of such a design has been exposed and verified.