Reviewerly/exHarmony huggingface dataset:
KD-INR: Time-Varying Volumetric Data Compression via Knowledge Distillation-based Implicit Neural Representation

Traditional deep learning algorithms assume that all data is available during training, which presents challenges when handling large-scale time-varying data. To address this issue, we propose a data reduction pipeline called knowledge distillation-based implicit neural representation (KD-INR) for compressing large-scale time-varying data. The approach consists of two stages: spatial compression and model aggregation. In the first stage, each time step is compressed using an implicit neural representation with bottleneck layers and features of interest preservation-based sampling. In the second stage, we utilize an offline knowledge distillation algorithm to extract knowledge from the trained models and aggregate it into a single model. We evaluated our approach on a variety of time-varying volumetric data sets. Both quantitative and qualitative results, such as PSNR, LPIPS, and rendered images, demonstrate that KD-INR surpasses the state-of-the-art approaches, including learning-based (i.e., CoordNet, NeurComp, and SIREN) and lossy compression (i.e., SZ3, ZFP, and TTHRESH) methods, at various compression ratios ranging from hundreds to ten thousand.
PointVST: Self-Supervised Pre-Training for 3D Point Clouds Via View-Specific Point-to-Image Translation

The past few years have witnessed the great success and prevalence of self-supervised representation learning within the language and 2D vision communities. However, such advancements have not been fully migrated to the field of 3D point cloud learning. Different from existing pre-training paradigms designed for deep point cloud feature extractors that fall into the scope of generative modeling or contrastive learning, this paper proposes a translative pre-training framework, namely PointVST, driven by a novel self-supervised pretext task of cross-modal translation from 3D point clouds to their corresponding diverse forms of 2D rendered images. More specifically, we begin with deducing view-conditioned point- wise embeddings through the insertion of the viewpoint indicator, and then adaptively aggregate a view-specific global codeword, which can be further fed into subsequent 2D convolutional translation heads for image generation. Extensive experimental evaluations on various downstream task scenarios demonstrate that our PointVST shows consistent and prominent performance superiority over current state-of-the-art approaches as well as satisfactory domain transfer capability. Our code will be publicly available at https://github.com/keeganhk/PointVST.
Feature Selection Graph Neural Network for Optimized Node Categorization

Graph Neural Networks (GNNs) is one of the most essential tools for learning from graph-structured data. A wide range of tasks has demonstrated their usefulness in graph-structured data. Engineering configuration has progressed fundamentally, further developing execution on different forecast errands. Utilizing learnable weight matrices, these neural networks typically incorporate feature transformation and node feature aggregation in the same layer. The articulateness of the layers of neural network and the significance of node information gathered from various hops are complicated to analyze. Because diverse graph datasets exhibit changeable degrees of heterophily and homophily in class label dissemination, it is necessary to recognize which features are crucial for the forecast tasks without preceding knowledge. The proposed work focus on demonstrating that the GNN model’s efficiency can be harmed by frequently employing less informative features and that not all aggregation process features are beneficial. The experimental outcomes validated that the performance can be enhanced on a wide range of datasets by learning specific subsets of these features. Based on the observations, several significant design concepts for neural graph networks are being introduce. More specifically, L2-Normalization is employed over GNN layers using SoftMax as a regularize and a “soft-selector” of characteristics gathered from neighbors at various hop distances. The Feature Selection Graph Neural Network (FSGNN), a straightforward and shallow model, is framed by combining these approaches. Nine standard datasets for the node classification task empirically exhibit that the proposed model outperforms current GNN models, with significant improvements of up to 50.80%.
First-Order Approximation of the Random Set Cluster Process for Extended Target Tracking

This work extends the extended target cluster process tracker [1] on learned spatial distribution by a first-order approximation in its associations. The first-order approximation provides a uni-modal posterior of the closed-form Bayesian recursion and thus, significant improvements in computational complexity and runtime at relatively little cost in estimation quality. We compare the relative performance of the proposed model with previously proposed approaches in a large-scale evaluation based on the Nuscenes data set.
Automatic Hand Recognition Using Deep Learning and YOLOv8

The identification of human hands is a critical step in the pre-processing stage of most computer vision tasks. This is because human hands are involved in the action. Estimating the hand posture, recognizing hand gestures, monitoring human activities, and other similar jobs are some examples of the kinds of work that fall under this category. Human hands are capable of a broad range of mobility and can alter their outward appearance in a great number of distinct ways. Because of this, it might be difficult to distinguish certain hands in a crowded area, and the movements of some hands can take a wide variety of forms. In this inquiry, we give a condensed examination of CNN-based object recognition algorithms; more specifically, we focus on the Yolov8n models with both 50 and 100 epochs. This study investigates a wide variety of object detectors, some of which are employed in the process of locating hand identification applications. We also use the Yolov8 models with the Oxford Hand Dataset for training and testing our suggested method. The amount of GFLOPS, the mean average precision (mAP), and the detection time are only a few examples of the metrics used to track and monitor performance. When compared to other approaches, the findings of our research suggest that Yolov8n's training phase, which consists of 50 epochs, is the strategy that offers the greatest degree of stability. During the training process, it was able to attain a precision rate of 77.6%, a recall rate of 68.7%, and a mAP of 75.8%. During the testing phase, Yolov8n achieved the maximum possible average score on the mAP, which was 75.8%.
Simulation of Improved GLR Algorithm in Abnormal Diagnosis Model of English Translation Robot

With the continuous development of artificial intelligence technology, machine translation has been widely used in cross-language communication, global communication and other fields. However, there are some abnormal problems in the practical application of English translation robot, such as inaccurate machine translation results and the model can’t identify uncommon words. Firstly, this paper proposes an improved generalized probabilistic grammar (GLR) algorithm for the abnormal diagnosis model of English translation robot, and verifies it with simulation experiments. Firstly, the shortcomings and improvements of the current anomaly diagnosis model of translation robot are introduced, including the problems of accuracy and efficiency. Then, the traditional GLR algorithm is improved, and the prediction method based on deep learning is adopted, and the introduction of language model is added to improve its accuracy. In the experiment, the real abnormal data set is used for simulation, and the performance of the traditional GLR algorithm and the improved algorithm is compared. The results show that the improved GLR algorithm has significantly improved the accuracy and efficiency. Compared with the traditional method, the average accuracy is improved by about 6%, and the average processing time is shortened by about 30%. Therefore, the improved GLR algorithm can achieve better results and performance in the abnormal diagnosis model of English translation robot.
Real-time Human Movement Recognition and Interaction in Virtual Fitness using Image Recognition and Motion Analysis

This research focuses on the application of image recognition, motion detection, and artificial intelligence techniques to achieve real-time identification and analysis of human movements in a dynamic manner. The primary goal is to establish a seamless association between captured human movements and corresponding actions performed by virtual avatars. The implementation involves the utilization of image processing methodologies from python's OpenCV and pygame libraries. MediaPipe technology is employed to capture users' images and facilitate accurate detection of specific movement patterns. By calculating the angles between users' limbs and torso, the correctness of certain movements can be assessed. The research is practically developed and applied to a simulated fitness system, where an interactive exercise game platform is devised. The system is characterized by its engaging user interface, enhancing the ease and enjoyment of fitness activities. The seamless integration of interactive virtual avatars and real users within physical activities serves to stimulate greater participation, particularly among the elderly. The amalgamation of image recognition and motion detection technologies presents opportunities for combining fitness and entertainment in diverse life scenarios, offering an enhanced experience of physical exercise and enjoyment.
Joint Multiple Extended Object Tracking by Optimizing the Global Coherence

Complex multi-object scenarios pose the problem of entangled measurements. Effects like occlusion often prevent filtering the single objects independently: it becomes challenging for the tracking algorithm to detect the occluded object or to interpret its measurement, i. e., radar detections. We propose a state update technique that considers the entirety of all objects and computes an expected global measurement using fast raycasting. The update is based on the minimization of the deviation between the expected and the actual measurement under consideration of the state and measurement uncertainties. Because of the high dimensionality of the problem, our approach actively seeks the optimum by providing probabilistic cost gradients to an optimization algorithm. Finally, we discuss the performance of the approach based on real-world data.
Generating Odometry Measurements from Automotive Radar Doppler Measurements

In order to obtain odometry measurements, we use off the shelf automotive radar sensors. To this end, we use Doppler measurements of static objects to estimate the yaw rate and the speed of a vehicle. We integrate those measurements with a nearly constant turn rate and velocity (CTRV) model in order to obtain dead reckoning position and yaw estimates. This can be performed in a single time frame, if at least two suitable measurements are available. However, it is crucial that only measurements from static objects are used. We leverage the information provided by previous time steps’ estimates in order to determine which measurements to use. We present results with simulated and real world radar data. In a sparse static object scenario, the proposed approach is more accurate than a baseline random-sample-consensus-based (RANSAC) selection of static objects. With dense static objects in the environment, the results from both approaches are equally good. In each case, our approach does require less computational resources.
Comparison of Federated Learning Algorithms for Image Classification

In the Traditional machine learning algorithms, users are required to transmit source data to the cloud server with huge computing power during private training. It leads to a lot of data out of control flow and sensitive data be leaked. Then federated learning (FL) is proposed. This a concept that leverages data spread across many devices, to learn classification tasks separately without recourse to data sharing. But federated learning also has many defects. To solve these problems, many improved algorithms based on traditional federated learning are proposed, they are designed to solve one aspect of the problems. This work introduces five algorithms of them, including the federated averaging algorithm (Fedavg), federated matched averaging algorithm (FedMA), FedProx, federated attentive message passing (FedAMP) algorithm, and model-contrastive learning (MOON) algorithm. The background of their birth and the problems they aspired to solve are introduced. In addition, their advantages and disadvantages are also mentioned for better analysis and comparison. Last, their performances are compared on Tiny-ImageNet, a dataset similar to the ImageNet project but is refined and smaller, to get more recognition of these algorithms.
Human Activity Recognition in Free Living Using a Wearable Sensor: A Two-Stage Approach

A novel two-stage approach for human activity recognition (HAR) using a wearable sensor is proposed in this study. The goal is to achieve both coarse-level and fine-level recognition of activities. The study focuses on five common activities: desk-working, driving, walking, downstairs, and upstairs. Three-axis acceleration signals were collected using a wrist-worn accelerometer sensor, and the activities were annotated during the participant’s daily routines. The acceleration data were preprocessed through segmentation, feature extraction, and normalization. In the first stage, a traditional machine learning model (decision tree) was used for coarse-level classification, distinguishing between activities with exercise and without exercise. In the second stage, deep learning models were employed for fine-level classification to recognize specific activities within the coarse classes. The performance of the models was evaluated using accuracy, sensitivity, and specificity. The proposed approach showed promising results with accuracies of 93.53%, 93.52%, 85.70%, 98.11%, and 85.91% respectively for desk-working, driving, walking, downstairs, and upstairs, demonstrating the effectiveness of the two-stage strategy for HAR. The proposed two-stage approach outperforms the one-stage method (decision tree classifier and deep learning model), particularly in imbalanced data, achieving a graceful equilibrium between sensitivity and specificity. The study highlights the importance of considering the complexity and variability of daily activities for accurate classification and resource optimization.
Using Large Language Models for Bug Localization and Fixing

As part of their learning journey, students frequently encounter challenges and make errors, especially with algorithmic programming questions. Regrettably, providing tailored solutions for these mistakes can impose a significant burden on instructors in terms of time and effort. To address this, automated program repair (APR) techniques have been explored to generate such fixes automatically. Previous research has investigated the use of symbolic and neural approaches for APR in the educational domain. However, both types of approaches necessitate substantial engineering endeavors or extensive data and training. In this study, we propose the utilization of a large language model trained on code to construct an APR system specifically designed for student programs. Our system has the capability to rectify semantic errors by employing a few-shot example generation pipeline solely based on the input code. We assess the performance of our system on one dataset of algorithm implementations, namely QuixBugs. The results demonstrate that the novel example generation pipeline not only enhances the overall system’s performance but also ensures its stability.
Improving object detection for truck-related classes by removing label inconsistencies

Accurate detection of a wide variety of objects is critically important for object detection in autonomous driving. However, many autonomous driving datasets are dominated by cars and pedestrians, while large vehicles such as trucks are both underrepresented and more diverse in size and appearance than the majority classes. One challenge is that trucks generally consist of multiple components (tractors and trailers), which can overlap with each other and may also be seen alone. Additionally, there is no consensus on the optimal class definitions for such objects: some datasets have separate classes for truck components or specific types of truck, and it is not uncommon for subordinate level (individual objects) and superior level (group of objects) definitions to be mixed in the same dataset. We believe that this inconsistency in labeling diminishes perception performance by introducing semantic confusion for object components with multiple valid class definitions. In this work, we test multiple labeling approaches for truck objects using a multiclass LiDAR-only object detection network. We consider labeling schemes including: both subordinate and superior class descriptions as a baseline, subordinate and superior class labels combined into one class, elimination of the superior class description (truck), and elimination of the subordinate class descriptions (truck tractor and truck trailer). We find that using only one descriptive level to categorize related objects is important: using only the subordinate classes or superior class yielded better results than mixing the two. Furthermore, we find that using the subordinate classes “Truck Front” and “Truck Trailer” yields the best overall performance and better captures trucks making wide turns, which is not well represented in many datasets.
Progressive Particle Filtering Using Projected Cumulative Distributions

We propose a progressive particle filter that inherently avoids sample degeneracy by splitting the likelihood into a product of wider functions applied step by step. Between steps, the particle distribution is resampled using projected cumulative distributions (PCDs). To be able to handle weighted Dirac mixture distributions, their corresponding one-dimensional densities are interpolated with a piecewise constant function. Both Cramér-von-Mises and 2-Wasserstein distance are used as base objective functions for PCDs to deterministically and optimally resample such distributions. The proposed filter is compared with a standard SIR particle filter on a simulated tracking problem.
Design and Implementation of an Intelligent Machine Learning System Based on Artificial Intelligence Computing

In the field of artificial intelligence, its applications have penetrated into daily life, such as smart home appliances, navigation, and medical treatment. This article introduces the design and implementation of an intelligent machine learning system based on artificial intelligence computing. This system utilizes artificial intelligence algorithms to analyze and process a large amount of data, thereby achieving automated learning and prediction of data. At the same time, the system also has good scalability and maintainability, which can adapt to the needs of different fields and application scenarios. The experimental results show that among the four algorithms, their accuracy has reached over 80%, and the time taken is within 3 seconds. The design and implementation of this system provides an effective solution for intelligent data analysis and decision-making.
Design and Research of Intelligent Operation and Maintenance Monitoring Platform for Database System

With the continuous enrichment and development of power business types and quantities, as well as the pilot application of domestic databases and self-developed databases, the database type of State Grid Corporation of China has changed from the traditional single database to the open source, multi-type and domestic database. It is urgent to monitor the database in a unified way, and it is becoming more and more important to realize the intelligence and automation of database monitoring. In view of the above problems, State Grid Henan electric power company has developed and deployed the database intelligent operation and maintenance monitoring platform. By introducing the machine learning model and intelligent algorithm, a database monitoring platform with in-depth IT health inspection service is provided to comprehensively ensure the safe and stable operation of the database system.
Evaluation of the Application of Artificial Intelligence Technology in Computers

With the rapid development of network technology, people’s enthusiasm for creating audio and video is growing, and the content of audio and video is becoming increasingly rich. Therefore, how to effectively protect and protect audio and video information in the network environment has become an urgent problem to be solved. Efficient extraction of targeted audio and video data from a large amount of audio and video data is a major issue currently faced by audio and video surveillance. With the rapid improvement of Artificial Intelligence (AI) chip processing capabilities, AI technologies such as machine learning and deep learning have also developed rapidly. Among them, deep learning has been widely applied in research such as computer vision and speech analysis. On this basis, this paper constructed an audio content oriented automatic identification system combining machine vision and sound analysis technology in deep learning. Through system identification testing of this method, it was found that the identification degree of photo 6 was the best, with an identification degree of 98%. Among all the experiments, photo 3 had the highest recognition rate, reaching 88%. Among them, photo 7 had the best recognition effect on LFW, with a recognition effect of 97%. Therefore, using AI technology on computers was very meaningful.
Groups of heterogeneous autonomous systems in area reconnaissance

Parallel deployment of heterogeneous autonomous assets as swarms or cooperating teams has become a realistic operational scenario nowadays. Especially in time-critical tasks (e.g. the search for missing persons, disaster relief and modern warfare operations) mass deployment of unmanned vehicles can be useful for obvious reasons. An important basis for efficient and effective autonomous reconnaissance or surveillance with an increasing task complexity or number of mobile vehicles is movement optimization considering the boundary conditions like vehicles or sensor capabilities. Special algorithms for such complex reconnaissance missions have been developed at Fraunhofer IOSB that enable fast appropriate planning and dynamic processing in very different situations with minimal effort of the operator. In the following, two reconnaissance methods are described in more detail and their possibilities are discussed and compared.