Reviewerly/exHarmony huggingface dataset:
Skew t Distribution-Based Nonlinear Filter with Asymmetric Measurement Noise Using Variational Bayesian Inference

This paper is focused on the state estimation problem for nonlinear systems with unknown statistics of measurement noise. Based on the cubature Kalman filter, we propose a new nonlinear filtering algorithm that employs a skew t distribution to characterize the asymmetry of the measurement noise. The system states and the statistics of skew t noise distribution, including the shape matrix, the scale matrix, and the degree of freedom (DOF) are estimated jointly by employing variational Bayesian (VB) inference. The proposed method is validated in a target tracking example. Results of the simulation indicate that the proposed nonlinear filter can perform satisfactorily in the presence of unknown statistics of measurement noise and outperform than the existing state-of-the-art nonlinear filters.
Dense three-dimensional point continuous measurement based on triple line-scan cameras

Dynamic three-dimensional surface measurement based on line-scan cameras has been developing gradually. Line-scan cameras have the advantage of high resolution and a high line rate. In this article, a measurement system based on triple line-scan cameras is presented. The measurement model is established considering the asynchronous acquisition of the same point. The point cloud optimization algorithm helps to minimize the error caused by six-degree-of-freedom vibration. Theoretical analysis reveals that the resolution of the method is approximately 0.07 mm, and the measurement uncertainty is about 0.025 mm. Experiments demonstrated the feasibility of the method.
Research on Application of Computer Recognition Technology in C Language Programming Modeling System

Abstract C language programming is more and more favoured by the majority of technical personnel in embedded systems. The application of C language technology in computer software programming can effectively avoid unnecessary language logic problems, ensure the smooth progress of programming work and effectively improve the quality and efficiency of programming. For the development of C language embedded system, the programming ideas of system software are explained, the functional module division based on hierarchical design is given, and the realization methods of project organization, program framework design, module reuse design, etc. in the software development process are clarified. To solve the contradiction between C language flexibility and application development engineering. Although it is introduced for the ARM platform, the basic experience and algorithms are also suitable for software design on other embedded platforms.
SG-CIM Mapping Entity Relationship Inference and Verification Techniques Based on Graph Representation Learning Technology

According to the problems of entity intelligent matching and relationship prediction of power grid model, this paper adopts the entity-relation triplet automatic reasoning method to predict and correct the relation between the given entities. Firstly, the relationship in the SG-CIM model is analyzed, aggregated, normalized, and the data set of triples is constructed. Then, association rule mining and knowledge reasoning model TRANSE are adopted to model the relationship on the low-dimensional entity representation space, which is used as the translation between embedded Spaces. Thus, it is easy to capture the semantic relationship between entity pairs and improve the intelligent reasoning ability of the model.
Using SQL to query the difficulty imposed by spaced retrieval in engineering mathematics

This WIP Research Paper investigates the temporal nature of the difficulty imposed by spacing. Spaced retrieval practice is an evidence-based strategy for improving memory and consists of asking multiple questions on a topic with intermittent delays. Spacing is often thought to impose difficulty by making questions harder to answer. However, this difficulty may be desirable, since spacing ultimately improves memory. In this paper, we (a) outline an implementation of spaced retrieval practice in an engineering mathematics classroom, (b) describe the development of an SQL database to organize and manage the large and complex dataset, and (c) discuss a brief but interesting dive into the rich data we have collected. Statistical analyses revealed that, when three questions targeting the same topic are spaced over multiple quizzes, versus being massed on a single quiz, students are less likely to answer the first and second questions correctly. Spacing does not affect students' ability to answer the third questions. This suggests that spacing may impose difficulty when students are first learning to perform mathematical operations, rather than when they are trying to retrieve memories of how to perform those operations.
Efficient Computation of SHAP Values for Piecewise-Linear Decision Trees

The interpretability of machine learning models is important in many applied data analysis problems. In recent years, a universal SHAP interpretation paradigm based on Shapley values has become popular. However, to interpret the values using SHAP directly, it is necessary to calculate the model&#x2019;s prediction the number of times that is exponential to the size of the feature space, which leads to the prohibitive complexity of this calculation method for complex models over complex feature spaces, for example, for neural networks or gradient boosting ensembles. It was shown that there are efficient algorithms for calculating SHAP values for decision trees and additive ensembles based on them, using structure of trees for an optimized calculation. One of interesting new classes of machine learning models is piecewise linear decision trees and gradient boosting ensembles based on them. In this paper, an efficient algorithm for computing SHAP values for piecewise linear decision trees and additive ensembles based on them was proposed.
ADANOISE: Training neural networks with adaptive noise for imbalanced data classification

The class imbalance problem, which has been recognized in many real-world applications, negatively affects the performance of neural networks. To address the class imbalance problem, resampling and cost-sensitive learning have been commonly applied. In this study, we propose a novel training method for neural networks based on adaptive noise, named ADANOISE. This method incorporates the ideas of both resampling and cost-sensitive learning to improve the training of a neural network under class imbalance. For the neural network, random noise is added to the input when it learns from the minority class. To make the learning objective cost-sensitive, each minority class instance is oversampled by adding different noise vectors randomly sampled from a noise distribution. The neural network and the parameter of the noise distribution are simultaneously trained. By doing so, the noise distribution adapts to the training data in a data-driven fashion toward improving the performance of the neural network. We demonstrate the effectiveness of the proposed method through experiments on benchmark datasets.
Characterization of p-ary functions in terms of association schemes and its applications

We obtain an explicit criterion for p-ary functions to produce association schemes in terms of their Walsh spectrum. Employing this characterization, we explicitly find a correlation between p-ary bent functions and association schemes; to be more exact, we prove that a p-ary bent function induces a p-class association scheme if and only if the function is weakly regular. As applications of our main criterion, we construct many infinite families of few-class association schemes arising from p-ary functions. Furthermore, we present four classes of p-ary two-weight linear codes, which are constructed from the association schemes produced in this paper.
Visual Studio Code Extension and Auto-completion for Mizar Language

Until now, the editor extension for the Mizar language has been developed as an Emacs plugin. However, since Emacs is an old editor with characteristic key bindings, the number of users has decreased in recent years. For this reason, there is a growing interest in Visual Studio Code as an IDE for interactive theorem provers, and extensions for Lean, Coq, PVS have already been developed. We have developed a new extension for the Mizar language on Visual Studio Code. In this paper, we outline the implementation status of each function and then propose an auto-completion algorithm using n-gram. We measured the reduction rate of keystrokes and the prediction accuracy when 0 to 3 characters are input. As a result, we confirmed that the number of keystrokes can be reduced by 21% and that the correct answer rate within the proposed three candidates exceeds 80% when the user enters two characters. The advantages of our proposed auto-completion algorithm are its ease of implementation and high accuracy.
Three-Pass Protocol Scheme for Securing Image Files Using the Hill Cipher 3x3 Algorithm

There will be a fatal risk when the submitted file is stolen or altered by someone else during the file submission process. To maintain the security of sending files from sender to recipient, it is necessary to secure files. The science of maintaining the security of messages is called cryptography. The authors were interested in examining the Three Pass Protocol scheme in this study because it eliminated the necessity for sender and receiver to exchange keys during the operation of the Hill Cipher 3x3 algorithm. The Hill Cipher algorithm was chosen because the key has an inverse and matrix-shaped value. Then the key used must be checked whether it has a GCD (Greatest Common Divisor) grade 1 or not and will be shaped like matrix. System implementation using the Java programming language using Android Studio software. System testing is done by encrypting and decrypting files. System testing results illustrate that the process encryption and decryption by the sender is faster than the recipient, so the encryption and decryption time needed directly proportional; the larger the pixel size of the image on the image file used, the longer it takes.
A Dual-branch Graph Convolutional Network on Imbalanced Node Classification

Graph convolutional neural networks (GCNs) have attracted much attention in dealing with various node classification tasks on graphs. Some real-world node classification tasks face the situation that the number of minority class nodes is significantly less than that of majority class nodes. This makes us more concerned about how to effectively solve the problem of imbalanced node classification based on GCNs. To solve this problem, we propose a Dual-branch Graph Convolutional Network framework (D-GCN), which can reduce the dominant effect of majority class on topology aggregation and the negative impact of information differences caused by graph structure reconstruction. This framework achieves the goal of decreasing the possibility of misrecognizing the minority class nodes as majority class and improving the classification performance of minority class nodes. Experiments on several graph datasets demonstrate that D-GCN outperforms representative baselines in solving imbalanced node classification tasks.
A Cross-role and Bi-national Analysis on Security Efforts and Constraints of Software Development Projects

Software security, which is often regarded as a non-functional requirement, tends to be less prioritized than other explicit requirements in development projects. For designing security measures that can be used in software development, we must understand the obstacles that prevent the adoption of secure software development practices. In this study, we quantitatively analyzed security efforts and constraints of software development projects through an online survey of software development professionals in the US and Japan (N=664). We revealed how certain characteristics of a development project, such as the project's contractual relationships or the software's target users, influence security efforts and constraints. In addition, by comparing the survey results of two groups (developers and managers), we revealed how the gap in their security efforts and constraints influences software security. We believe the results provide insights toward designing usable measures to assist security-related decision-making in software development and conducting appropriate surveys targeting software development professionals.
Collision-aware Multi-robot Motion Coordination Deep-RL with Dynamic Priority Strategy

The motion coordination of multi-robots is the basis of the multi-robot system (MRS). The motion coordination problem of multi-robot satisfies the Markov property, so deep reinforcement learning (DRL) can be used to solve this problem. There are two limitations in the existing researches which apply DRL to solve this problem: the success rate of training is low and the effect of coordination is poor. We believe that in the training process, the agent should learn the collision constraint relationship from the collision. Therefore, we propose a Partially Tolerant Collision (PTC) collision handling strategy. And we believe that the completion time of motion coordination is related to the remaining distance of robots. Therefore, we propose the Dynamic Priority Strategy (DPS), which sets the priority for the robot based on the remaining distance of robots. This strategy is integrated into the reward setting of DRL. We use Path Checkerboard Diagram (PCD) as the basis for training and simulation. By experimenting with algorithms such as DQN, DDQN, and MLDDQN, the results of our proposed model are better than previous studies.
ModelShield: A Generic and Portable Framework Extension for Defending Bit-Flip based Adversarial Weight Attacks

Bit-flip attack (BFA) has become one of the most serious threats to Deep Neural Network (DNN) security. By utilizing Rowhammer to flip the bits of DNN weights stored in memory, the attacker can turn a functional DNN into a random output generator. In this work, we propose ModelShield, a defense mechanism against BFA, based on protecting the integrity of weights using hash verification. ModelShield performs real-time integrity verification on DNN weights. Since this can slow down a DNN inference by up to 7×, we further propose two optimizations for ModelShield. We implement ModelShield as a lightweight software extension that can be easily installed into popular DNN frameworks. We test both the security and performance of ModelShield, and the results show that it can effectively defend BFA with less than 2% performance overhead.
High-Performance Hybrid-Global-Deflated-Local Optimization with Applications to Active Learning

Mathematical optimization lies at the core of many science and industry applications. One important issue with many current optimization strategies is a well-known trade-off between the number of function evaluations and the probability to find the global, or at least sufficiently high-quality local optima. In machine learning (ML), and by extension in active learning - for instance for autonomous experimentation - mathematical optimization is often used to find the underlying uncertain surrogate model from which subsequent decisions are made and therefore ML relies on high-quality optima to obtain the most accurate models. Active learning often has the added complexity of missing offline training data; therefore, the training has to be conducted during the data collection which can stall the acquisition if standard methods are used. In this work, we highlight recent efforts to create a high-performance hybrid optimization algorithm (HGDL), combining derivative-free global optimization strategies with local, derivative-based optimization, ultimately yielding an ordered list of unique local optima. Redundancies are avoided by deflating the objective function around earlier encountered optima. HGDL is designed to take full advantage of parallelism by having the most computationally expensive process, the local first and second-order-derivative-based optimizations, run in parallel on separate compute nodes in separate processes. In addition, the algorithm runs asynchronously; as soon as the first solution is found, it can be used while the algorithm continues to find more solutions. We apply the proposed optimization and training strategy to Gaussian-Process-driven stochastic function approximation and active learning.
SMartyModeling: an instance of VMTools-RA for Engineering UML-based Software Product Lines

Software Product Line (SPL) life cycle comprises a set of essential activities. Variability Management (VM) is one of its most important activities to the success of an SPL, especially those based on UML, as the solution space encompasses different diagrams and perspectives on variability. However, the lack of tools to support UML-based SPLs reflects difficulties in adopting this approach. This scenario motivated the development of SMartyModeling, an environment for engineering UML-based SPL. The SMartyModeling architecture was instantiated based on VMTools-RA, an existing reference architecture for software variability tools. VMTools-RA describes architectural requirements, elements and views on software variability, which aid one to instantiate variability tool architectures. The instantiation process started from the identification of requirements, selection of elements, modules, and visions of VMTools-RA, planning and design of the architectural solutions, implementation of modules and organization of features. We then analyzed the feasibility of adopting VMTools-RA for instantiating an specific tool architecture. In this sense, such instantiation is part of the development process of SMartyModeling, which includes the main activities related to VM. We also empirically evaluated SMartyModeling in three ways: (i) a field study to analyze the instantiation process and the decisions taken; (ii) a comparative experiment analyzing efficiency and effectiveness of SMartyModeling in relation to a general purpose UML tool; and (iii) an evaluation of aspects related to perceived ease of use and perceived usability. The results of such evaluations provide initial evidence VMTools-RA is feasible to instantiate specific architectures and SMartyModeling is feasible to support to VM for UML-based SPLs.
Text Classification Using XLNet with Infomap Automatic Labeling Process

Text data is growing rapidly and used in various fields such as chatbots and question answering systems, which are currently popular, where the system identifies the question category and the possibility of an answer to help provide answers to the questions entered. Having good quality text data, especially in text classification, significantly affects the performance of the model. Manual labeling by humans, generally used in labeling training data in supervised learning, is expensive, prone to mistakes, and has a low quantity. Automatic labeling that providing high quality and high quantity of training data is necessary to improve text classification performance. This study attempts to conduct community detection with the Infomap algorithm for automatic labeling in text classification using XLNet. The accuracy of the model is compared to the baseline, which using data with manual labeling. While the accuracy has not outperformed the overall baseline yet, but the result shows that automatic labeling can improve data labeling quickly with high quantity.
Development of New Efficient Transposed Convolution Techniques for Flame Segmentation from UAV-captured Images

Although Fully Convolutional Networks (FCNs) have been proven to be a very powerful tool in deep learning-based image segmentation, they are still too computationally expensive to be incorporated into mobile platforms such as Unmanned Aerial Vehicles (UAVs) for real-time performance. While significant efforts have been made to make the encoder side of a FCN more efficient, the decoder side, which involves upsampling the feature maps, is still overlooked in comparison. This paper proposes two new efficient upsampling techniques, “Reversed Depthwise Separable Transposed Convolution (RDSTC)” and “Compression-Expansion Transposed Convolution (CETC)”. U-Net architecture and UAV-captured forest pile fire images have been used to evaluate the performance of these new efficient upsampling techniques. RDSTC and CETC achieve Dice scores of 0.8815 and 0.8832 respectively, outperforming commonly used bilinear interpolation and original transposed convolution, while significantly reducing the number of upsampling computations. The results of this paper demonstrate that upsampling operation in a deep learning architecture can be made more efficient without degradation in performance.
Multi-domain Feature Extraction for Human Activity Recognition Using Wearable Sensors

The extraction and use of features from the raw sensor data plays an extremely crucial role in determining the recognition performance of an activity recognizer. Existing studies aim to train an accurate prediction model by extracting different features, however, few of them systematically investigate the power of features from different domains when they are used separately or jointly. To this end, we conduct a comparative study on multi-domain feature extraction for human activity recognition. Specifically, we first extract features from the time-, frequency-, and wavelet-domains, and then use different combinations of the three domain features to build activity recognizers. Finally, comparative experiments are performed on two activity recognition datasets and four classification models are used to avoid selection bias. Results indicate the superiority of using time-domain or frequency-domain features over wavelet features in terms of prediction performance and also show that the simultaneous use of multi-domain features generally generalizes better across datasets and classifiers, indicating that they, to a certain extent, contain complementary feature information.
Classification of Malaria Parasitized and Uninfected Images Using Machine Learning Approach

Human health is an important concern in the current scenario. Different diseases have adverse effect on human society. Malaria parasite is one of them. So, it is very much essential for the classification of malaria parasitized and uninfected cases at the earliest so that preventive measures can be taken accordingly. Machine Learning (ML) plays an important role for the identification, classification as well as analysis of different medical images which can help in accelerating the diagnosis process. In this work, an attempt has been made for the classification of malaria parasitized and uninfected cases from the analysis of different images using ML based methods. In this paper, the ML based methods such as k-Nearest Neighbor (k-NN), Neural Network (NN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), AdaBoost (AB) and Naïve Bayes (NB) are used for such classification. The methods are evaluated using Classification Accuracy (CA) performance parameter. This work is carried out using Orange 3.26.0.