Reviewerly/exHarmony huggingface dataset:
A Non-linear Synapse Neural Network with O(N) Neurons for Graph Colouring

A feedback artificial neural network with nonlinear synaptic connections is proposed for the classical graph colouring problem. The network employs O(N) Neurons with O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) non-linear synapses for a graph with N nodes. The network has a transcendental energy function that is fundamentally different from Hopfield Network. The proposed network always converges to a feasible solution, with the number of assigned colours being nearly equal to the Chromatic number of the corresponding graph. HSPICE simulation confirms the functioning of the proposed circuit. The results are compared with the existing circuits to highlight the significant reduction in the number of colours required for colouring a given graph.
E-Module Learning Media Ignition System Material Based on Flipbook

Background:The flipbook-based ignition system e-module is an electronic ignition system module that includes ignition system material whose material is equipped with pictures, video explanations of material and interactive quizzes that can be applied on line using a smartphone or computer without having to download it first.&#x0D; Methodology:This type of research is Research &amp; Development (R&amp;D) with the adaptation of the Dick and Carey model using data collection instruments in the form of questionnaires and expert reviews, while the data analysis technique uses qualitative descriptive analysis techniques and descriptive statistical analysis.&#x0D; Findings:The results showed the feasibility of the product with a percentage figure above 85% (very feasible).&#x0D; Conclusion:The developer concludes that the ignition system e-module development product based on flipbooks can be used as a learning medium in the subject of Light Vehicle Electrical Maintenance in class XI TKRO ignition system material, as evidenced by the results of questionnaires and comments from experts that are very good/appropriate with the percentage figures above. 85%&#x0D; Originality:This flipbook-based ignition system e-module development product has been validated by material experts, media experts, design experts, and colleagues who all meet the minimum educational criteria for a master's degree in their respective fields, and have gone through individual trials, small group trials, and revised field trials.
Evaluation of Feature Engineering Methods for the Prediction of Sheet Metal Properties from Punching Force Curves by an Artificial Neural Network

The part quality that can be achieved in forming and stamping processes strongly depends on the properties of the sheet metal material to be processed. However, since these material properties may fluctuate considerably and thus lead to the production of scrap, it is important to monitor such material fluctuations during part production. For this, the ongoing digitization of production processes provides new possibilities for part or quality monitoring. In this context, a novel AI-based method for the direct determination of material parameters from punching forcePunching force curves measured in production was presented in a past study by the authors. This paper deals with the investigation of three further methods for extracting features from these recorded measuring data. In addition to domain knowledge-based feature engineering, statistical feature extraction (PCAPrincipal component analysis (PCA)) as well as a derivative-based method are analyzed and compared with each other and with the previously used AI (ANN) regarding their prediction accuracy of sheet metal properties.
Comparison of Forensic Value of Biometric Analysis in Face &amp; Ear Recognition in the Punjabi Population, Pakistan

Background: The use of face biometrics is very prevalent in forensic investigations for the identification of the perpetrators of crime due to the excessive use of CCTV footage that is usually available at the scene of a crime in urban settings. Ear biometric analysis of ear prints is also in vogue as a result of research advancements in the fields of biometrics. Keeping in view this scenario, it is the need of the hour to analyze the forensic value of this type of forensic evidence and compare these two tools of forensic biometrics. Purpose: To analyze and compare the forensic value of biometric analysis of face and ear print recognitions in the Punjabi population, in Punjab, Pakistan. Study design: This study is conducted by collecting the data from 100 samples of different people belonging to different backgrounds from different cities in Punjab, Pakistan after their informed consent and ethical approval. Their facial photographs and ear prints were collected for proceeding biometric analysis to form a database for comparison and recognition. Method and materials: After collecting data, the comparison is done to see whether we can recognize a person by only using ear print analysis or face biometrics after running a search in our own created database. Moreover, we also calculated the forensic values of this biometric analysis separately on its own and combined these two i.e., face and ear biometrics. . Furthermore, standard deviation, F-statistics, and Chitest p-value were also applied to see the power of discrimination of these two biometric methods of identification Results: In 100 samples, face recognition was proved to be 80 % recognition of identity as compared with only ear prints which showed 56 % accuracy in identifying the individuals who participated in this research. Furthermore, the combined result of both face recognition and ear biometrics showed 90 % recognition of the identity of the individuals. Statistical analysis proved that biometric analysis of the face for recognition of the identity of individuals was more valued as compared with ear print recognition. Also, it was found that if we combine these two methods of biometrics, the forensic value of recognition of individuals has increased and showed good results. Conclusion: The forensic value of biometric evidence of face and ear recognition is a very important tool for the forensic identification of individuals in crime scene investigations. Biometric facial recognition is better as compared to only ear print biometric analysis. Furthermore, using face and ear biometrics enhances the forensic value of biometric analysis. Keywords: Biometric analysis, Forensic value, face recognition, ear print recognition,
Deep Neural Network Compression for Image Inpainting

Image inpainting techniques have recently been developed leveraging deep neural networks and have seen many real-world applications. However, image inpainting networks, which are typically based on generative adversarial network (GAN), suffer from high parameter complexities and long inference time. While there are some efforts to compress image-to-image translation GAN, compressing image inpainting networks has rarely been explored. In this paper, we aim to create a small and efficient GAN-based inpainting model by compressing the generator of the inpainting model without sacrificing the quality of reconstructed images. We propose novel channel pruning and knowledge distillation techniques that are specialized for image inpainting models with mask information. Experimental results demonstrate that our compressed inpainting model with only one-tenth of the model size achieves similar performance to the full model.
Uncertainty Quantification Using Query-Based Object Detectors

Recently, a new paradigm of query-based object detection has gained popularity. In this paper, we study the problem of quantifying the uncertainty in the predictions of these models that derive from model uncertainty. Such uncertainty quantification is vital for many high-stakes applications that need to avoid making overconfident errors. We focus on quantifying multiple aspects of detection uncertainty based on a deep ensembles representation. We perform extensive experiments on two representative models in this space: DETR and AdaMixer. We show that deep ensembles of these query-based detectors result in improved performance with respect to three types of uncertainty: location uncertainty, class uncertainty, and objectness uncertainty (Code available at: https://github.com/colinski/uq-query-object-detectors ).
Learning to Estimate Multi-view Pose from Object Silhouettes

While Structure-from-Motion pipelines certainly have their success cases in the task of 3D object reconstruction from multiple images, they still fail on many common objects that lack distinctive texture or have complex appearance qualities. The central problem lies in 6DOF camera pose estimation for the source images: without the ability to obtain a good estimate of the epipolar geometries, all state-of-the-art methods will fail. Although alternative solutions exist for specific objects, general solutions have proved elusive. In this work, we revisit the notion that silhouette cues can provide reasonable constraints on multi-view pose configurations when texture and priors are unavailable. Specifically, we train a neural network to holistically predict camera poses and pose confidences for a given set of input silhouette images, with the hypothesis that the network will be able to learn cues for multi-view relationships in a data-driven way. We show that our network generalizes to unseen synthetic and real object instances under reasonable assumptions about the input pose distribution of the images, and that the estimates are suitable to initialize state-of-the-art 3D reconstruction methods.
Survey on Blockchain Technology and Security Facilities in Online Education

Securing digital records is the crucial paradigm for the intelligent environment. Hence, several authentication mechanisms were implemented to improve the trustworthy of the online application in data sharing. In addition, blockchain models have been implemented that include the cryptographic function to offer security for the online education academic system. However, in some cases, the security efficiency against unknown attacks has degraded the digital application environment and security range. As a result, in order to determine the most appropriate security architecture for the online education environment, the benefits and cons must be thoroughly examined. Considering this, the present chapter has aimed to design the literature survey studies incorporating online education and blockchain security. In addition, the performance of the blockchain has been validated with different online education features are user verification, certificate sharing, and online teaching facilities. Finally, the performance has been measured based on the execution time, and the merits and limitations have been described separately.
Thai Word Disambiguation: An Experiment on Thai Language Dataset with Various Deep Learning Models

Recent advances in Natural Language Processing (NLP), the transformer and BERT models, have brought us more performance. However, one still challenging task in NLP is the word disambiguation task. Even in English, this task requires complex language model to achieve. Thai is one of the most difficult languages to learn and understand, even for Thai people. To understand the true meaning of an ambiguous word, the context of the sentence is needed. Attention mechanism, which is the core of both transformer and BERT, can be used to understand underlying context of sentences and hence can be used for disambiguation. In this article, we evaluate both unsupervised and supervised BERT-based models for disambiguating the Thai word “เขา” (Khao). Zero-shot classification models (unsupervised) can achieve an accuracy of around 60–70%, either Thai language fine-tuning models or other cross languages BERT-based models. On the other hand, fine-tuning supervised BERT-based models can achieve an accuracy of up to 95% from the best model we fine-tuned.
Applying BERT on the Classification of Chinese Legal Documents

Chinese Legal documents contain complex underlying facts, controversies and legal application issues that make most domestic legal document retrieval platforms perform poorly in terms of relevance and accuracy. In this paper, we try to evaluate the performance of BERT on Chinese legal document classification. The data set for this paper is obtained from the legal judgment documents of a single charge on China Judicial Documents Network, with a total of 8 accusations and 2454 legal cases. The experimental result shows that the BERT performs much better than FastText, TextCNN, and RNN on our data set, obtaining a classification accuracy of 0.89.
IPT-CFI: Control Flow Integrity Vulnerability Detection Based on Intel Processor Trace

Control flow integrity has always been the focus of security research, but how to achieve complete and accurate control flow graph construction and how to achieve transparent and efficient control flow detection has always been a current research difficulty. In this paper, we design a static construction and optimization method of control flow graph for programs, and propose a transparent and accurate extraction technology of program execution flow using Intel Processor Trace under Windows, and design a control flow integrity detection strategy based on IPT (IPT-CFI). The experimental results show that, in terms of security, the system can successfully detect the control flow hijacking attack. In terms of system performance, the static CFG code coverage of this system is over 99%, and the IPT running overhead is about 5%.
A Pedestrian Avoidance System for Visual Impaired People Based on Object Tracking Algorithm

Avoiding obstacles and pedestrians during the movement is a challenge for the visual impaired people. With the advancement of computer vision, some new technologies can be applied to improve this problem. In this paper, we proposed a new framework to realize such function based on the object tracking algorithm, FairMOT. Our framework detects the pedestrians around users and alarm users to avoid the collision between them. In our system, we use the 360-degree camera as the system input to record the users’ surrounding situations, and use the VR headset as the output device with a feedback application, that is realized using WebVR technologies. In this paper, we also discuss some experiments of the current system and some further improvement methods according to the results of experiments.
A Comparative Study of Several Spatial Domain Image Denoising Algorithm

With the development of science and technology, images have become an essential source for people to obtain external information, but they are easily disturbed by noise in the process of image transmission, which degrades image quality. In this paper, the advantages and disadvantages of the mean filter, median filter, Gaussian filter, and bilateral filter in removing pepper noise and Gaussian noise are investigated through qualitative and quantitative analyses using MATLAB software. The experimental results show that for images containing pepper noise, median filtering has the best denoising effect while retaining the original image information to the greatest extent; for images containing Gaussian noise, mean filtering and Gaussian filtering have better denoising effects; bilateral filtering, although not the best denoising effect, has better retention of edge information.
Blockchain Applications for Mobility-as-a-Service Ecosystem: A Survey

The emerging technologies on automatic driving and connected vehicles will eventually enable a vast amount of applications regarding Mobility-as-a-Service (MaaS). However, the design and management of proprietary MaaS operators and integrator will have a great impact in developing this new ecosystem. The complexity of MaaS ecosystem requires decentralized and scalable solutions in order to accommodate a wide range of transportation services. Permissionless blockchain can be leveraged to support MaaS in managing transactions, identity and smart contracts. In this paper, we analyze some of the pioneering works in the field and identify he potential contributions on how can MaaS benefit from blockchain technology.
A Depth Camera Based Soldering Motion Analysis System for Attention Posture Detection Considering Body Orientation

Recently, governments are promoting employment of persons with physical, intellectual, mental or other disabilities, but the employers should ensure safety in the workplace. However, these persons the technical training requires explanation of detailed work procedures and the process monitoring to prevent accidents. In this paper, in order to solve these problems, we present a depth camera based soldering motion analysis system for attention posture detection. We carried out many experiments using the implemented system. We present and discuss experimental results of the posture detection according to the orientation of the body during soldering. From experimental results, we found that the proposed system can detect the posture from the three-dimensional coordinates of the body and the accuracy for three patterns of body orientation is about 96 [ $$\%$$ ].
Group Object Trajectories Extraction in Drone-Based Videos with Dynamic Background

Multiple object trajectory extraction from drone-based videos is a challenging problem due to the dynamic background. In this paper, we propose a novel method to extract small object trajectories and identify group movement from drone-based videos. A deep learning-based feature point extractor is used to extract features from video frames. The points that represent the features are tracked in frame sequence and joined to generate trajectories. To obtain the actual trajectories, we introduce a strategy to model the background movement and remove it from point-connected trajectories. Furthermore, we identify multiple objects into groups based on trajectory similarity and object class. Experiments on the public drone-based videos demonstrate that our method can effectively extract group object trajectories and avoid the distortion of background movement.
Strategy Optimization of Imperfect Information Games Based on NFSP with DDQN

The imperfect information machine games means that the agents participating in the games only know part of the information in the games process, and cannot have a clear grasp of the overall information, so there are more uncertainties and challenges. This paper proposes an optimization method of imperfect information games strategy based on the Neural Fictitious Self-Play (NFSP) with Double Deep Q-Learning (DDQN), where the agent uses the DDQN learning algorithm to train the optimal response strategy network by alternating sampling and optimizing. DDQN ameliorates the problem of over estimation through decoupling the action evaluation and selection of target Q-value, and is conducive to the convergence of the optimal response strategy network. In the training of the average response strategy network, different sampling weights are given to the empirical data according to the temporal difference error. This increases the probability of important empirical data being sampled, makes the network learning more efficient and improves the reliability of average response strategy. Taking Leduc Hold’em as the research object, the proposed method is compared with several state-of-the-art methods, and the experimental results show that the proposed method has a faster convergence speed and higher reliability.
Aggressive Collision Avoidance and Control Method for Quadrotor

To solve the problem of aggressive flight control, this paper designs the trajectory tracking controller and collision avoidance strategy for quadrotor. Firstly, a six degree of freedom model of quadrotor is established, and quaternion is used to represent the attitude error of a quadrotor, which is used to design the aggressive trajectory tracking controller. Secondly, the collision avoidance method based on artificial potential field is designed. The quadrotor can track the predetermined trajectory while avoiding high-speed moving obstacles under the control of virtual force. Finally, the simulation experiment of aggressive collision avoidance in typical environment is completed. Using the proposed method, the deviation from the desired position is half of the traditional method, and the safety distance from the moving obstacles is 1.5 times of traditional method while avoiding high-speed moving obstacles.
Adaptive Filtering Algorithm for Urban Traffic UAV Integrated Navigation Based on MEMS Devices

When UAVs fly in cities, the environment often interfered with GPS signals, reducing accuracy. The changes in statistical noise characteristics caused by abnormal measurement information can quickly decrease the accuracy of integrated navigation filtering or even divergence, affecting UAVs’ stability and safety. In this paper, a combined navigation model of GNSS/VG dual observation is constructed, combined with an adaptive filtering algorithm based on fading factor, to improve the navigation accuracy and attitude angle stability in the case of abnormal measurement information. The simulation and sports car experiments show that, compared with the ESKF(Error State Kalman Filter) algorithm, the SHAESKF(Sage-Husa Adaptive Error State Kalman Filter) algorithm improves navigation system performance and enhances stability.
Cooperative Collision Avoidance Route Planning Based on Improved RRT Algorithm

To address the issue of multi-UAVs cooperative combat and realize threat avoidance in 3D space, a cooperative collision avoidance route planning strategy based on improved RRT algorithm is proposed. Firstly, the planning space establishment, constraint analysis and threat modeling are carried out. Aiming at the shortcoming of lacks of preference in searching while using RRT algorithm, a biased principle to improve the selection strategy was proposed, which leaded random number close to the target. Meanwhile, the improved algorithm is applied to the planning process of multi-UAVs cooperative route planning. Finally, the feasibility of cooperative collision avoidance route planning strategy based on improved RRT algorithm is verified by simulation analysis. By comparison, the improved algorithm can close to optimum with less time, which proves the effect of the algorithm.
A Novel Multivariate Degradation Data Generation Method Based on Flow Model

With the growing popularity of multivariate degradation data in the field of Prognostics and Health Management (PHM), the ability in generating or imputating high-accuracy and high-reliability data becomes critical. This paper proposes a multivariate degradation data generation model based on a flow model. Firstly, the JS-NICE model is constructed based on the flow model and JS divergence. Then, the generative model is trained to learn the rules of the original data, and the data are generated by sampling. Finally, the experimental results validates the excellent quality and accuracy of the generated data by the model proposed, which indicates that the proposed method has learned the distribution law of the original data, and at the same time, it provides an important advance check information for tasks such as fault detection and remaining useful life prediction.