Problem Of Error Research Articles

Carry Your Fault: A Fault Propagation Attack on Side-Channel Protected LWE-based KEM

Post-quantum cryptographic (PQC) algorithms, especially those based on the learning with errors (LWE) problem, have been subjected to several physical attacks in the recent past. Although the attacks broadly belong to two classes – passive side-channel attacks and active fault attacks, the attack strategies vary significantly due to the inherent complexities of such algorithms. Exploring further attack surfaces is, therefore, an important step for eventually securing the deployment of these algorithms. Also, it is mportant to test the robustness of the already proposed countermeasures in this regard. In this work, we propose a new fault attack on side-channel secure masked implementation of LWE-based key-encapsulation mechanisms (KEMs) exploiting fault propagation. The attack typically originates due to an algorithmic modification widely used to enable masking, namely the Arithmetic-to-Boolean (A2B) conversion. We exploit the data dependency of the adder carry chain in A2B and extract sensitive information, albeit masking (of arbitrary order) being present. As a practical demonstration of the exploitability of this information leakage, we show key recovery attacks of Kyber, although the leakage also exists for other schemes like Saber. The attack on Kyber targets the decapsulation module and utilizes Belief Propagation (BP) for key recovery. To the best of our knowledge, it is the first attack exploiting an algorithmic component introduced to ease masking rather than only exploiting the randomness introduced by masking to obtain desired faults (as done by Delvaux [Del22]). Finally, we performed both simulated and electromagnetic (EM) fault-based practical validation of the attack for an open-source first-order secure Kyber implementation running on an STM32 platform.

Post-stack seismic inversion through probabilistic neural networks and deep forward neural networks

Seismic inversion, a nonlinear, non-unique-solution, and human-prone error problem, seeks the optimal solution in multivariate space, a task that AI algorithms perform better. So, inversion with AI becomes appropriate to extract information from post-stack data. A probabilistic neural network (PNN) and deep feedforward neural network (DFNN) inverted seismic data using multiattributes, and their results were compared with those provided by a model-based inversion (MBI). Applied to a post-stack volume of the Frontier Formation at the Teapot-Dome field, Wyoming-USA, the three methods provided porosity volumes (ϕ), gamma rays (GR), and Lamé impedances (µρ, λρ). The comparison between well-logs and the provided volumes revealed a reliable PNN prediction, while DFNN achieved the best geologically correlated parameter sections and a less reliable MBI prediction. The geomorphological time-slice analysis discriminated high ϕ-µρ areas and low GR–λρ areas, geologically interpreted as a deltaic environment, with distributary channels and beachfront lobes subject to the action of the tides, which agrees with the well-log interpretation and the basin geological evolution. The results enhanced the stacked section’s importance by the geological information contained, which would contribute to reducing the prospecting risk. Finally, PNN and DFNN encouraging results reaffirm their best searching-in-multivariate space ability.

Study on NOx emission estimation method of chain furnace based on EDMS model

In order to overcome the problems of low estimation accuracy and large relative error of traditional NOx emission estimation methods, a new NOx emission estimation method of chain furnace based on EDMS model is proposed in this paper. Firstly, the overall structure of the chain furnace is analyzed, and the EDMS model is used to count the NOx emission data of the chain furnace. Secondly, SVM model is used to train the emission sample data obtained from the above statistics. Finally, the NOx emission estimation objective function of chain furnace is constructed to complete the emission estimation. The experimental results show that compared with the traditional emission estimation methods, the estimation accuracy of this method is higher, and the relative error of estimation can be reduced.

Половые различия в склонности к когнитивным ошибкам у студенческой молодежи

The article delves into the problem of cognitive errors among students. It emphasizes the relevance of studying this problem in the aspect of adolescence, which is the period of higher education. The article presents the re-sults of the study of sex differences among student youth, which can be used by university psychologists in preventive and corrective work with boys and girls prone to cognitive errors. The study involved 485 students aged 17–25 years, including 433 boys and 52 girls. The Cognitive Mistakes Questionnaire (CMQ – OKO) (A. Freeman, R. Devulf) was used to identify the tendency to cognitive errors. The analysis of the obtained data allows us to conclude that the hypothesis of the study is partially confirmed: girls are more prone to various types of cognitive errors, while the hypothesis that students have the most expressed propensity to such cogni-tive errors as personalization, stubbornness and maximalism is not confirmed.

Research on the Error Calibration Method of Rheological Parameters of Rectangular Tubular Viscometer

In order to solve the problem of high error in the process of measuring the rheology of drilling fluids by the common pipe flow method, an online measurement device for the rheology of drilling fluids with rectangular tubular drilling fluids was fabricated, and a calibration method for the measurement error of rheological parameters was proposed and corresponding experiments were carried out. By measuring the differential pressure value and flow, flow rate and other parameters when the drilling fluid flows through the measuring tube, upload them to the host computer for processing, use VMD to decompose the measured differential pressure data, remove the residual term, and then perform Hibert transform on each modal component to obtain its analytical signal, modulate the amplitude and phase of each analytical signal, and then reconstruct the signal to obtain the optimized differential data, and finally bring it into the constructed calibration model of the rheological parameter measurement error of drilling fluid to obtain the rheological parameters. The experimental results show that the proposed method can obtain more accurate rheological parameters of drilling fluid, improve the measurement accuracy of drilling fluid rheology parameters, so as to judge the downhole situation more accurately and ensure the personal safety of on-site staff.

Multi-Object Trajectory Prediction Based on Lane Information and Generative Adversarial Network.

Nowadays, most trajectory prediction algorithms have difficulty simulating actual traffic behavior, and there is still a problem of large prediction errors. Therefore, this paper proposes a multi-object trajectory prediction algorithm based on lane information and foresight information. A Hybrid Dilated Convolution module based on the Channel Attention mechanism (CA-HDC) is developed to extract features, which improves the lane feature extraction in complicated environments and solves the problem of poor robustness of the traditional PINet. A lane information fusion module and a trajectory adjustment module based on the foresight information are developed. A socially acceptable trajectory with Generative Adversarial Networks (S-GAN) is developed to reduce the error of the trajectory prediction algorithm. The lane detection accuracy in special scenarios such as crowded, shadow, arrow, crossroad, and night are improved on the CULane dataset. The average F1-measure of the proposed lane detection has been increased by 4.1% compared to the original PINet. The trajectory prediction test based on D2-City indicates that the average displacement error of the proposed trajectory prediction algorithm is reduced by 4.27%, and the final displacement error is reduced by 7.53%. The proposed algorithm can achieve good results in lane detection and multi-object trajectory prediction tasks.

Efficient secure channel free identity-based searchable encryption schemes with privacy preserving for cloud storage service

Cloud storage is a basic service model of the cloud computing. Since the cloud data is stored in the ciphertext for the data confidentiality, how to search the cloud data again in ciphertext state for users is a natural requirement. The searchable encryption (SE) gives an efficient solution to achieve both the data confidentiality and the searchable requirement simultaneously. In SE schemes, the user can transfer a search trapdoor to the cloud server from a secure channel and the cloud server can use the received trapdoor to search the corresponding keyword ciphertext. Since the keywords are with low entropy, the traditional SE is vulnerable to keyword guessing attack. In order to remove the need of the secure channel and also to achieve the security against the keyword guessing attack, we build a single-user identity-based secure channel free SE scheme in the random oracle model by using lattice-based tools. Only the designated cloud server can finish the ciphertext search which means the secure channel between the user and the cloud server is not needed in the proposed scheme. Under the hardness of the learning with errors problem, we prove that the proposed scheme is ciphertext indistinguishable under the selective identity and chosen keyword attacks. Moreover, the proposed scheme also satisfies the trapdoor indistinguishability which directly leads to the security against the outside offline keyword guessing attack. Furthermore, we extend the proposed single-user scheme into the multi-user scenario. The proposed multi-user scheme inherits all functions achieved in the single-user scheme, such as secure channel free. Meanwhile, ciphertext length is fixed with the number of users increasing, which is more suitable for the large group users. In addition, we analyze the theoretical evaluation and experimental evaluation between the proposed schemes and some known literatures respectively. The results demonstrate that our proposed algorithms have several advantages in the both single-user and multi-user schemes, for example, the ciphertext lengths in the single/multi-user schemes are only 5.632KB and 6.016KB respectively and the running time of Test algorithm for any user groups is fixed to be 0.65ms in our simulated experiments.

Highly Efficient Self-checking Matrix Multiplication on Tiled AMX Accelerators

General Matrix Multiplication (GEMM) is a computationally expensive operation that is used in many applications such as machine learning. Hardware accelerators are increasingly popular for speeding up GEMM computation, with Tiled Matrix Multiplication (TMUL) in recent Intel processors being an example. Unfortunately, the TMUL hardware is susceptible to errors, necessitating online error detection. The Algorithm-based Error Detection (ABED) technique is a powerful technique to detect errors in matrix multiplications. In this article, we consider implementation of an ABED technique that integrates seamlessly with the TMUL hardware to minimize performance overhead. Unfortunately, rounding errors introduced by floating-point operations do not allow a straightforward implementation of ABED in TMUL. Previously an error bound was considered for addressing rounding errors in ABED. If the error detection threshold is set too low, it will a trigger false alarm, while a loose bound will allow errors to escape detection. In this article, we propose an adaptive error threshold that takes into account the TMUL input values to address the problem of false triggers and error escapes and provide a taxonomy of various error classes. This threshold is obtained from theoretical error analysis but is not easy to implement in hardware. Consequently, we relax the threshold such that it can be easily computed in hardware. While ABED ensures error-free computation, it does not guarantee full coverage of all hardware faults. To address this problem, we propose an algorithmic pattern generation technique to ensure full coverage for all hardware faults. To evaluate the benefits of our proposed solution, we conducted fault injection experiments and show that our approach does not produce any false alarms or detection escapes for observable errors. We conducted additional fault injection experiments on a Deep Neural Network (DNN) model and find that if a fault is not detected, it does not cause any misclassification.

A New Sparse Bayesian Learning-Based Direction of Arrival Estimation Method with Array Position Errors

In practical applications, the hydrophone array has element position errors, which seriously degrade the performance of the direction of arrival estimation. We propose a direction of arrival (DOA) estimation method based on sparse Bayesian learning using existing array position errors to solve this problem. The array position error and angle grid error parameters are introduced, and the prior distribution of these two errors is determined. The joint probability density distribution function is established by means of a sparse Bayesian learning model. At the same time, the unknown parameters are optimized and iterated using the expectation maximum algorithm and the corresponding parameters are solved to obtain the spatial spectrum. The results of the simulation and the lake experiments show that the proposed method effectively overcomes the problem of array element position errors and has strong robustness. It shows a good performance in terms of its estimation accuracy, meaning that the resolution ability can be greatly improved in the case of a low signal-to-noise ratio or small number of snapshots.

Research on intelligent identification algorithm of coal and rock strata based on Hilbert transform and amplitude stacking

AbstractThe high precision identification of coal–rock layers is a significant challenge in intelligent mining. There is a large amount of electromagnetic noise and metal reflector signals in the full space detection environment of mining roadway, which makes it hard to distinguish the reflected waves at interface from a set of echo signals generated by the interface due to the similar amplitudes among them. So the method of identifying layers solely based on amplitude characteristics has poor stability and accuracy in coal mining environments. This paper proposes a method for identifying coal–rock layers based on Hilbert transform and tracking–scanning–stacking technology. There are two steps to achieve the recognition of air–coal–rock interfaces. First, by analysing the instantaneous amplitude spectrum obtained from the Hilbert transform, the first extreme point that is always the maximum value within a wavelength range is determined as the rough position of the air–coal interface. To solve the problem of recognition errors caused by noise and energy dispersion, the density difference method is used to remove discrete points. Second, the precise position of the air–coal interface is determined by tracking the extreme points within the 1.5 wavelength range around the rough position, and using the amplitude stacking method to quantitatively analyse and compare the degree of energy concentration. The data between zero time and the reflected waves at the air–coal interface is removed to avoid the impact of them on the recognition of the coal–rock interface. Results of physical model experiments and actual coal mine experiments show that this method yields better results and has high stability compared to conventional recognition method. Moreover, the average relative thickness errors are 4.5% for air layer and 4.2% for coal layer.

Efficient Identification of Error-in-Variables Switched Systems Using a Riemannian Embedding

This paper considers the problem of error in variables identification for switched affine models. Since it is well known that this problem is generically NP hard, several relaxations have been proposed in the literature. However, while these approaches work well for low dimensional systems with few subsystems, they scale poorly with both the number of subsystems and their memory. To address this difficulty, we propose a computationally efficient alternative, based on embedding the data in the manifold of positive semidefinite matrices, and using a manifold metric there to perform the identification. Our main result shows that, under dwell-time assumptions, the proposed algorithm is convergent, in the sense that it is guaranteed to identify the system for suitably low noise. In scenarios with larger noise levels, we provide experimental results showing that the proposed method outperforms existing ones. The paper concludes by illustrating these results with academic examples and a non-trivial application: action video segmentation.

Using Machine-Learning Technology to Implement a Nonhardware and Inexpensive Posture Detection System for Analyzing Body Posture Angles in Front Crawl Swimming

<p>Swimming is a sport that relies heavily on motor skills. Inability to maintain adequate bodily balance in water prevents swimmers from remaining afloat and propelling themselves. Due to the difficulty of attaching reflective stickers or LED (light-emitting diode) emitters to the body while adjusting the swimming posture, it is not possible to capture the posture like during a bicycle fitting; the refraction of water also affects the detection of the body’s posture. Addressing the shortcomings, this study developed a low-cost nonhardware posture detection system based on machine-learning models in MediaPipe. The system provides real-time and post analyses of posture angles and posture lines during front crawl swimming, thereby facilitating observation of the relationship between angle at which the arm enters the water and the body horizon. Two participants practicing front crawl were invited to test the proposed system. The experimental results confirmed that the proposed system provides effective detection and analyses of posture lines and angles in swimmers. The study also proposed the algorithm for optimizing posture angle detection to solve the problems of posture line distortion and angle calculation errors that arise when MediaPipe was used to detect a human skeleton above a water line. The system does not require the installation of hardware and is inexpensive to deploy, and it can be widely applied in front crawl swimming lessons to help learners adjust their arm’s entry angle and body horizon to reduce forward drag and increase speed.</p> <p> </p>

Robust Positioning Estimation for Underwater Acoustics Targets with Use of Multi-Particle Swarm Optimization

With the extensive application of sensor technology in scientific ocean research, ocean resource exploration, underwater engineering construction, and other fields, underwater target positioning technology has become an important support for the ocean field. This paper proposes a robust positioning algorithm that combines the disadvantages of distributed estimation and particle swarm optimization, which can solve the large localization error problem caused by uncertainties in underwater acoustic communication and sampling processes. Considering the presence of ranging anomalies and sampling packet loss in underwater acoustic measurements, a weighted coupling filling method is used to correct the outliers in an underwater acoustic ranging signal. Based on the mapping model from the element array to the underwater acoustic responder, an unconstrained optimization algorithm for one-time localization estimation of underwater acoustic targets was established. Based on the one-time localization estimation results of underwater acoustic targets, an improved multi-particle swarm optimization estimation based on interactive search is proposed, which improves the accuracy of underwater target localization. The numerical results show that the positioning accuracy of the proposed algorithm can be effectively enhanced in cases of distance measurement errors and azimuth measurement errors. Compared with the positioning error before optimization, the positioning error can be reduced after optimization. Additionally, the experiment was carried out in the underwater environment of Hangzhou Qiandao Lake, which verified the positioning performance of the proposed algorithm.

LeGO-LOAM-FN: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM, Faster_GICP and NDT in Complex Orchard Environments.

To solve the problem of cumulative errors when robots build maps in complex orchard environments due to their large scene size, similar features, and unstable motion, this study proposes a loopback registration algorithm based on the fusion of Faster Generalized Iterative Closest Point (Faster_GICP) and Normal Distributions Transform (NDT). First, the algorithm creates a K-Dimensional tree (KD-Tree) structure to eliminate the dynamic obstacle point clouds. Then, the method uses a two-step point filter to reduce the number of feature points of the current frame used for matching and the number of data used for optimization. It also calculates the matching degree of normal distribution probability by meshing the point cloud, and optimizes the precision registration using the Hessian matrix method. In the complex orchard environment with multiple loopback events, the root mean square error and standard deviation of the trajectory of the LeGO-LOAM-FN algorithm are 0.45 m and 0.26 m which are 67% and 73% higher than those of the loopback registration algorithm in the Lightweight and Ground-Optimized LiDAR Odometry and Mapping on Variable Terrain (LeGO-LOAM), respectively. The study proves that this method effectively reduces the influence of the cumulative error, and provides technical support for intelligent operation in the orchard environment.

CNN-based method for chromatic confocal microscopy

In view of the inevitable error problems caused by peak extraction, calibration curve fitting and other essential operations in the traditional data processing of chromatic confocal microscopy (CCM), a regression model based on convolutional neural network (CNN) is proposed so that the above necessary operations are no longer required. This CNN-based regression model draws on the core concepts of the AlexNet model and has been moderately customized and optimized to make it more suitable for CCM application scenarios. The proposed method has been validated using a completely homemade CCM apparatus The experimental results showed that the CNN-based method is feasible for the CCM measurement and exhibits better stability and higher axial resolution than traditional methods, indicating that deep learning has good application value in the data processing of CCM.

A meticulous covariance adaptive Kalman filter for satellite attitude estimation

Aiming at the problems of model errors, non-Gaussian noise and measurement anomaly in the spacecraft attitude estimation system, this article proposes an improved adaptive filtering method based on covariance matching, which solves the problems of simultaneous dynamics model error and measurement model error in the attitude estimation system, and at the same time, effectively reduces the effects of non-Gaussian noise and large outlier situations occurring in the vector measurement sensor. Firstly, an adaptive filtering algorithm based on the innovation sequence estimation covariance is investigated under the framework of multiplicative extended Kalman filter (MEKF), which is used to correct process noise covariance, then the Sage–Husa adaptive Kalman filtering (SHAKF) method is combined to correct the measurement noise covariance, and finally the meticulous covariance adaptive multiplicative extended Kalman filter is designed. The proposed algorithm uses both innovation and SHAKF methods to correct the two covariance matrices simultaneously. Several attitude estimation simulation scenarios are set up to simulate the proposed algorithm in the presence of model errors, non-Gaussian noise, and large outlier. The simulation results demonstrate that the proposed algorithm outperforms the conventional algorithms in terms of estimation accuracy and robustness.

Linear Congruence and Reduction on the Learning with Errors Problem

Linear Congruence and Reduction on the Learning with Errors Problem

Solubility of Nd2O3 in LiF and LiF–NdF3 molten salts

An experimental device was developed with a diaphragm and NdOF was used instead of Nd2O3 to solve the problem of analysis errors. A more accurate method for measuring the dissolution of Nd2O3 in the LiF–NdF3 molten salt system was reported.

Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection.

Video anomaly detection aims to find the events in a video that do not conform to the expected behavior. The prevalent methods mainly detect anomalies by snippet reconstruction or future frame prediction error. However, the error is highly dependent on the local context of the current snippet and lacks the understanding of normality. To address this issue, we propose to detect anomalous events not only by the local context, but also according to the consistency between the testing event and the knowledge about normality from the training data. Concretely, we propose a novel two-stream framework based on context recovery and knowledge retrieval, where the two streams can complement each other. For the context recovery stream, we propose a spatiotemporal U-Net which can fully utilize the motion information to predict the future frame. Furthermore, we propose a maximum local error mechanism to alleviate the problem of large recovery errors caused by complex foreground objects. For the knowledge retrieval stream, we propose an improved learnable locality-sensitive hashing, which optimizes hash functions via a Siamese network and a mutual difference loss. The knowledge about normality is encoded and stored in hash tables, and the distance between the testing event and the knowledge representation is used to reveal the probability of anomaly. Finally, we fuse the anomaly scores from the two streams to detect anomalies. Extensive experiments demonstrate the effectiveness and complementarity of the two streams, whereby the proposed two-stream framework achieves state-of-the-art performance on ShanghaiTech, Avenue and Corridor datasets among the methods without object detection. Even if compared with the methods using object detection, our method reaches competitive or better performance on the ShanghaiTech, Avenue, and Ped2 datasets.

Multidimensional data processing and tensor analysis for large-scale power grids in a parallel computing framework

Abstract Grid data is compressed and stored without processing. There are problems of large compression errors and long running time, which affect the compression and storage effect. Therefore, this paper proposes a method for storing grid data using parallel computing frameworks. While drawing on the task scheduling strategy of MapReduce distributed computing framework, combined with the current situation of centralized deployment of data in the information system of the State Grid Corporation, an efficient tensor-based parallel computing method is proposed. The massive multivariate and heterogeneous smart grid data is compressed using the Tucker decomposition method for big data compression of smart grids. This method’s line resistance and reactance dynamic parameters have average values of 0.033 and 0.520, respectively, which are very close to the actual values. The method used in this paper has a high degree of accuracy in estimating resistance dynamic parameters. It possesses certain practical application performance. By predicting the daily average load of the North China Power Grid, the accuracy of the prediction data of the gridded load analysis is close to the actual value, indicating the superiority of the grid multidimensional data processing method designed in this paper.