Reviewerly/exHarmony huggingface dataset:
Order Statistic Estimation With Application to Tracking in Autonomous Driving

In this work, we study the order statistic estimation and provide a simple solution to lidar bounding box (BB) centroid estimation for an autonomous vehicle. The estimated BB centroid and its uncertainty will be used in object tracking as measurement and measurement noise variance; the latter is commonly not available from the sensor manufacturer, and is needed for data association and target state estimation. The detected and clustered sensor observations for a single target, on one axis, are assumed to have 1) a triangular density or 2) a uniform (rectangular) density. These densities constitute the likelihood function (LF) of the corresponding support (the interval where they are nonzero) boundaries. Estimators are proposed for the LF support boundary parameter estimation and the centroid coordinates are given by the centers of the boundaries. Experiments using real data are carried out to show the performance of the proposed methods for autonomous driving applications. A comparison with the max–min average approach shows the superiority of the proposed algorithm.
Building and Comparing AI-Powered Algorithms in Road Sign Detection

In this paper we conducted research with AI-Powered algorithms in order to build a model for image classification with high accuracy and compare it to existing studies. The neural network used is Convolutional Neural Network (CNN), the model was built using German Traffic Sign Benchmark dataset where we added different characteristics of our own to improve the accuracy. We train our model for a limited number of epochs, all the while checking the values of accuracy and loss and comparing the performances with each epoch. During the training time our model is getting better through forward propagation and backpropagation. Our end-goal of our model is to be trained well-enough to detect features, we achieve our goal of an acceptable high-accuracy rate. We compare our results with another study where the road sign detection is done with a predictive filter solution using SVM with gaussian kernel.
Learning Multi-Level Consistency for Noisy Labels

Recent methods performing well on Learning with Noisy Label (LNL) problem generally are based on semi-supervised learning and consistency regularization. It usually consists of three stages: warm-up, noisy/clean data division, and semi-supervised learning. However, these methods trained purely with classification consistency suffer from the confirmation bias problem and tend to memorize the noisy labels, resulting in accumulated error and degraded performance. Leveraging the compositional and relational peculiarities of the noisy data, we propose a graph-based Multi-Level Consistency (MLC) framework that jointly exploits multi-level relation consistencies between graphs and classification consistency which can better correct wrong labels by continuing to learn the multi-level differences between clean data and noisy data. Moreover, we propose a Dynamic Filter Module (DFM) which effectively improves the reliability of divided data by re-filtering noisy data despite its simplicity. Our method achieves the state-of-the-art performance on multiple benchmark datasets. On Cifar-100 with 90% noisy labels, our method achieves a top-1 accuracy of 49.1%, outperforming DivideMix by 17.6%.
Time-varying Normalizing Flow for Generative Modeling of Dynamical Signals

We develop a time-varying normalizing flow (TVNF) for explicit generative modeling of dynamical signals. Being explicit, it can generate samples of dynamical signals, and compute the likelihood of a (given) dynamical signal sample. In the proposed model, signal flow in the layers of the normalizing flow is a function of time, which is realized using an encoded representation that is the output of a recurrent neural network (RNN). Given a set of dynamical signals, the parameters of TVNF are learned according to maximum-likelihood approach in conjunction with gradient descent (backpropagation). Use of the proposed model is illustrated for a toy application scenario - maximum-likelihood based speech-phone classification task.
Language Model for Earth Science: Exploring Potential Downstream Applications as well as Current Challenges

The use of deep learning techniques to build transformer language models such as SciBERT and GPT3 have transformed the natural language technology (NLT) landscape. These new NLTs are being used in speech to text and vice versa, automated text classification, sentiment analysis, topic modeling, text summarization, and cognitive assistants. While Earth science has no shortage of unstructured data such as journal and conference papers, little efforts have focused on harnessing NLTs for knowledge extraction and supporting the scientific process. This paper surveys the use of language models in different science. BERT-E, a new Earth science-specific language model, is presented. BERT-E is generated using a transfer learning solution. A language model that has already been trained for general Science (SciBERT) is fine-tuned using abstracts and full text extracted from various Earth science-related articles. A downstream keywords classification application is used for evaluation, and the use of BERT-E shows improved performance. The need to develop a robust set of benchmarks in evaluating the language model such as BERT-E is discussed. Finally, example applications are presented to inspire additional ideas for applications using domain-specific language models.
Egocentric Two-frame Pedestrian Trajectory Prediction Algorithm Based on a Panoramic Camera

With the development of service industry, service-oriented mobile robot navigation technology has received more and more attention. In order to improve the obstacle avoidance performance of service-oriented mobile robots, accurate pedestrian trajectory prediction is essential. Therefore, we propose an egocentric two-frame pedestrian trajectory prediction algorithm based on a panoramic camera, the Future Social Prediction (FSP) algorithm, to solve the pedestrian trajectory prediction problem in the human-robot coexistence environment. The input of FSP is the information in the two frames of a panoramic camera. First, the free pedestrian prediction network (FPPN) is used to predict the free movement of pedestrians. Then the future social pooling module (FSPM) is used to process the prediction results of FPPN. And finally, the social pedestrian prediction network (SPPN) combines the frame information with the output of FSPM to get the final prediction results. A panoramic camera is used as a sensor to reduce blind spots in the field of view. Only two frames are used for prediction to improve the real-time performance of FSP. We record the panoramic video and create a dataset. FSP is tested on our dataset with other two algorithms, and the results show that FSP has better performance than Spatio-Temporal Encoder-Decoder model (STED) and Future Person Localization (FPL).
Spatial Parsing and Dynamic Temporal Pooling networks for Human-Object Interaction detection

The key of Human-Object Interaction(HOI) recognition is to infer the relationship between human and objects. Recently, the image's Human-Object Interaction(HOI) detection has made significant progress. However, there is still room for improvement in video HOI detection performance. Existing one-stage methods use well-designed end-to-end networks to detect a video segment and directly predict an interaction. A side effect of these approaches is that we have no way of knowing the human-object pair of the interaction or the keyframes in which the interaction took place. It makes the model learning and further optimization of the network more complex. This paper introduces the Spatial Parsing and Dynamic Temporal Pooling (SPDTP) network, which takes the entire video as a spatio-temporal graph with human and object nodes as input. Unlike existing methods, our proposed network predicts the difference between interactive and non-interactive pairs through explicit spatial parsing, and then performs interaction recognition. Moreover, we propose a learnable and differentiable Dynamic Temporal Module(DTM) to emphasize the keyframes of the video and suppress the redundant frame. Furthermore, the experimental results show that SPDTP can pay more attention to active human-object pairs and valid keyframes. Overall, we achieve state-of-the-art performance on CAD-120 dataset and Something- Else dataset.
Part Based Face Stylization via Multiple Generative Adversarial Networks

In recent years, due to the improvement of scientific research methods and the wide-open source and acquisition of related data sets, face stylization has become a hot research field and application direction. There is a need to stylize face images in many applications, such as camera beauty, artistic photo processing, etc. However, most of the current schemes are not satisfactory, and the resultant image synthesis traces are obvious, and the effect is relatively monotonous. Based on the study of image features and style representation, this paper proposes a general-purpose face image style transfer whole process scheme. It can fill the gap in local style transfer of face images. Among the existing face stylization methods, the face stylization method is more complex, and the resulting obvious image synthesis trace along with the single effect. The project innovates the existing technology that can split the whole picture and implements the following six functions. Including the segmentation of specific portrait parts (hair), the skin buffing and whitening of the face, the defuzzification of the photos, the style transfer of the hair, the messy hair removal, and the implementation of the big eye effect. This study can realize the automatic style conversion of specific face images quickly and with high quality.
Automatic Parameter Adaptation for Quadrotor Trajectory Planning

Online trajectory planners enable quadrotors to safely and smoothly navigate in unknown cluttered environments. However, tuning parameters is challenging since modern planners have become too complex to mathematically model and predict their interaction with unstructured environments. This work takes humans out of the loop by proposing a planner parameter adaptation framework that formulates objectives into two complementary categories and optimizes them asynchronously. Objectives evaluated with and without trajectory execution are optimized using Bayesian Optimization (BayesOpt) and Particle Swarm Optimization (PSO), respectively. By combining two kinds of objectives, the total convergence rate of the black-box optimization is accelerated while the dimension of optimized parameters can be increased. Benchmark comparisons demonstrate its superior performance over other strategies. Tests with changing obstacle densities validate its real-time environment adaption, which is difficult for prior manual tuning. Real-world flights with different drone platforms, environments, and planners show the proposed framework's scalability and effectiveness.
Low-Rank Tensor Learning for Incomplete Multiview Clustering

Incomplete multiview clustering (IMVC) is an effective way to identify the underlying structure of incomplete multiview data. Most existing algorithms based on matrix factorization, graph learning or subspace learning have at least one of the following limitations: (1) the global and local structures of high-dimensional data are not effectively explored simultaneously; (2) the high-order correlations among multiple views are ignored. In this article, we propose a low-rank tensor learning (LRTL) method that learns a consensus low-dimensional embedding matrix for IMVC. We first take advantage of the self-expressiveness property of high-dimensional data to construct sparse similarity matrices for individual views under low-rank and sparsity constraints. Individual low-dimensional embedding matrices can be obtained from the sparse similarity matrices using spectral embedding techniques. This approach simultaneously explores the global and local structures of incomplete multiview data. Then, we present a multiview embedding matrix fusion model that incorporates individual low-dimensional embedding matrices into a third-norm tensor to achieve a consensus low-dimensional embedding matrix. The fusion model exploits complementary information by finding the high-order correlations among multiple views. In addition, the computational cost of an improved fusion strategy is dramatically reduced. Extensive experimental results demonstrate that the proposed LRTL method outperforms several state-of-the-art approaches.
Research on Ship’s Officer Behavior Identification Based on Mask R-CNN

The negligence and inattention of the navigators on duty are the main causes of ship accidents. Based on the CCTV records of the bridge, the identification on human behaviors on duty has high value and significance. In this paper, a human behavior recognition method based on Mask R-CNN and a knowledge-based model is proposed. At the very beginning, the images of bridges collected by the camera are used as the input, the keypoints are extracted through a convolution neural network. Subsequently, the feature distribution of the keypoints of the k-nearest neighbor algorithm is then analyzed by clustering, which realizes the real-time online recognition of common behaviors such as walking, standing, sitting, steering and looking through the mobile phone, looking out and napping. At the final step, the proposed method was validated on the samples of the ferry ship from Zhoushan, Zhejiang, China. In the validation, the one-time recognition accuracy of this method was 82%, and the rolling recognition rate had reached almost 99%, which might significantly improve the safety of navigation.
Latent Variables Associated with Teachers’ Views About ICT and Integrating Them in Classroom Processes

The use of technologies in education gained widespread acceptance in recent years, but its prevalence and dominance accelerated due to the arrival of the COVID-19 pandemic. This study aims to describe the teaching experiences with ICT in secondary education in Paraguay. An exploratory qualitative and quantitative methodology was used with focus group interviews and open- and closed-ended questionnaires. Qualitative content analysis was first carried out, resulting in the following categories: virtual platforms, teaching materials, technological resources and infrastructure, and teacher training. Next, univariate and multivariate descriptive analysis was performed, and the Student’s T-test, Chi-square (95% confidence), and Exploratory Factor Analysis, using the IBM SPSS 21 statistical software. The results evidenced problems in implementing ICT in the classroom and the challenges teachers faced in technological training and acquiring technological tools. This study’s value is the expectation of affecting public policy about implementing and using ICT efficiently and equitably to attain superior educational quality.
Explainable AI for ML Ops

This chapter explains the significance of blending two emerging technologies in AI/ML—Explainable AI (XAI) and Machine Learning Operations (ML Ops) and demonstrates a focused use case that derives value from leveraging XAI to enhance ML Ops. The chapter starts by laying out the “growing pains” problems that enterprises are encountering to scale AI. We highlight the relatively low maturity of post-production ML processes thus exposing enterprises to reputation, compliance, and hence financial risk. We give a historical perspective on the rise of AI. Subsequent gain in mindshare of ML Ops is explained. XAI as a solution is introduced. After a brief explanation on XAI, we delve into the experimental setup and detail the results that demonstrate the potential of using XAI to enhance ML Ops. We end the white paper by reiterating the benefits, opportunities, and market potential and finally some recommendations.
Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network

Due to the rapid growth of earth observation (EO) data and the complexity of machine learning models, the high requirement on the computation power for EO data analysis becomes a bottleneck. Exploiting quantum computing might tackle this challenge in the future. In this paper, we present a hybrid quantum-classical convolutional neural network (QC-CNN) to classify EO data which can accelerate feature extraction compared with its classical counterpart and handle multi-category classification tasks with reduced quantum resources. The model's validity is verified with the Overhead-MNIST dataset through the TensorFlow Quantum platform.
Crosshair Optimizer

A novel local search based optimization algorithm named Crosshair Optimizer (CHO) is introduced in this paper. In most algorithms, much of the computational resources are used to explore around a solution space, but in reality, much of the time there are only parts of an optimal solution that can be improved. Using this information, if an algorithm explores the space around an optimal solution using a random number of its dimensions to dictate their placements, rather than all of them, the solution space is explored in a less random fashion. CHO employs a rapid neighbourhood generation on each iteration and selects a sub-sequences of best performing solutions. These selected solutions then randomly generate neighboring solutions only in certain search space dimensions. Exploration is done by randomly generating solutions in only two dimensional axis to the neighbourhood cluster. This speeds up the search process with fine grain sampling, and is quickly able to migrate the search space to another location without using drift migration. Experimentation was conducted on the standard unimodal and multimodal problems, with CHO performing extremely well against standard evolutionary algorithms.
The Role of BlockChain Adoption and Supply Chain Practices on Social Commerce

This study demonstrates the role of blockchain adoption in facilitating supply chain operations, as well as contributing to providing security in commercial operations that take place through the supply chain and supporting it in obtaining the necessary information about the exchange and transportation of products in a timely manner, which generally facilitates social trade operations. Providing the best that customers desire in commercial operations, which is the ease of conducting transactions and the speed of their completion, as well as eliminating the most important thing that keeps consumers away from adopting social commerce due to their fear of account penetration, data manipulation, and fraud resulting from the loss of the safety factor in digital accounts.
Estimate the Precision of Defects Based on Reports Duplication in Crowdsourced Testing

When analyzing the defects of crowdsourced testing, the testing reports need to be preprocessed, including removing duplicate and false positives. At present, most crowdsourced testing research focuses on duplication of the reports, which has achieved high accuracy. However, studies on reducing false positives of defects have rarely been conducted. Starting from the duplication of defects in the reports, this paper discusses the relationship between the duplication and the accuracy of defects and proposes an estimation approach based on the defect distribution in historical crowdsourced testing projects. Experiments have shown that our approach can provide a priori knowledge of defects and exhibits good stability. We applied this approach to the defect population estimation of crowdsourced testing. With better experimental results, our improved model is more accurate than the original model. We attempt to apply it to the estimation of the defect population of crowdsourced testing, which is more accurate than the original model.
Addressing Cloud Security Challenges through Blockchain Technology

Cloud computing is the collection of computing services available through the internet. ICloud virtual services are provided to the user through data centres around the world via the internet. It is currently the base of most Internet applications. Email, web engines, interpersonal organizations, web-based media, and different administrations are currently facilitated in “the cloud”. The accelerated growth in this field by both private and public sectors globally has heightened the cyberattacks on stored user data. While cost and usability are the primary advantages of cloud computing, trust and security are the two top worries of clients of cloud administrations. Blockchain is known as Distributed Ledger Technology (DLT). It is an unalterable and transparent digital ledger that uses decentralization and cryptography for security and authentication. It is the future of security and privacy improvements in the IT industry as it provides integrity, authenticity, and confidentiality to all stored data. This paper attempts to understand the root cause of cloud security attacks along with a proposed solution using blockchain technology and various cryptographic algorithms.
Design of Wushu Training Action Simulation System Based on Virtual Reality Technology

This paper studies the design of Wushu training action simulation system based on virtual reality technology. Among them, Chinese Wushu is an important means for people to enhance their physique and cheer up their spirit. In the process of Wushu fitness, we must follow the scientific training principles and methods, otherwise it will cause sports injury and certain injury to the human body. This paper summarizes and analyzes the basic methods and requirements of Wushu training through virtual reality technology and expert interviews, in order to provide reference for the scientific and reasonable fitness of Wushu lovers.
3D Textured Model Encryption Using 2D Logistic and 3D Lorenz Chaotic Map

The widespread of recent multimedia, including various 3D model applications in different domains of areas, may lead to 3D models being stolen and attacked by hackers. Moreover, 3D models must be protected from unauthorized users and when transmitting over the internet. Nowadays the 3D model protection is a very important issue. This paper proposed a scheme that provides high protection for the textured 3D model by implementing multiple levels of security. The first level of security is achieved by encrypting the texture map based on a key generated by a 2D Logistic chaotic map. The second level of security is implemented by modifying the vertices values of the 3D mesh based on keys generated by the 3D Lorenz chaotic map. The proposed scheme was implemented on various 3D textured models varying in the number of vertices and faces. The experimental results show that the proposed scheme has a good encryption and provides high security by completely deforms the whole texture and 3D mesh of the textured 3D model into the two levels. The encryption scheme has a large key space 10135 making the scheme resists violent attacks. The Hausdorff Distance (HD) and histogram metrics are adopted to calculate the matching degree between the original and extracted model. The results show that the original and extracted model are identical through the values of HD, which are approximate to zero, and the histogram visually is similar. Index Terms— 3D textured model, encryption, 2D Logistic map, 3D Lorenz map, chaotic map
AI Chatbot For Answering FAQ’s

FAQ section answers the frequently asked questions of the user. Generally, these are answered by humans but not a chat bot. Hence, this paper proposes a new chat bot which can also answer Frequently asked questions. In addition to answering textual queries, it also has the capability of answering the queries by observing images provided by the user. If user is not satisfied with the answers given by chat bot, then the control is given to agent and agent replies to the query and adds it to the database.