Reviewerly/exHarmony huggingface dataset:
A new method for controlling the quality of technological products

Abstract The problem of conformity of products to the requirements of technical control or between products of different batches puts the methods of experimental determination of product properties high on the agenda. The measurement of the parameters of these properties is stochastic. Some product properties can only be determined in specially designed experiments. Thus, in the production of powders, their dispersion can be normalized, the specific charge of condenser powders can increase, the location of magnetic granules of nanoparticles on information carriers can be controlled, the content of physiologically active substances in drugs can be controlled, and the spectrum of mobilities of atmospheric particles can be analyzed. Possible approaches to solving the problem of checking a batch of products for compliance with the requirements of technical specifications (TS) are considered using the example of powder dispersion. For the first time, a method has been proposed for comparing product properties, which are continuous random variables, by determining the joint area under their differential curves. The decision on the conformity of the measured product properties to the requirements of technical specifications is based on a comparative assessment with the directive established joint area under the distribution density curves. The technique of determining the probability of correspondence of normal distribution laws to each other is described. A final analytical solution is obtained for two exponential distribution laws.
Self-dual codes over $\mathbb{F}_{q}+u\mathbb{F}_{q}+u^2\mathbb{F}_{q}$ and applications

Self-dual codes over finite fields and over some finite rings have been of interest and extensively studied due to their nice algebraic structures and wide applications. Recently, characterization and enumeration of Euclidean self-dual linear codes over the ring~$\mathbb{F}_{q}+u\mathbb{F}_{q}+u^2\mathbb{F}_{q}$ with $u^3=0$ have been established. In this paper, Hermitian self-dual linear codes over $\mathbb{F}_{q}+u\mathbb{F}_{q}+u^2\mathbb{F}_{q}$ are studied for all square prime powers~$q$. Complete characterization and enumeration of such codes are given. Subsequently, algebraic characterization of $H$-quasi-abelian codes in $\mathbb{F}_q[G]$ is studied, where $H\leq G$ are finite abelian groups and $\mathbb{F}_q[H]$ is a principal ideal group algebra. General characterization and enumeration of $H$-quasi-abelian codes and self-dual $H$-quasi-abelian codes in $\mathbb{F}_q[G]$ are given. For the special case where the field characteristic is $3$, an explicit formula for the number of self-dual $A\times \mathbb{Z}_3$-quasi-abelian codes in $\mathbb{F}_{3^m}[A\times \mathbb{Z}_3\times B]$ is determined for all finite abelian groups $A$ and $B$ such that $3\nmid |A|$ as well as their construction. Precisely, such codes can be represented in terms of linear codes and self-dual linear codes over $\mathbb{F}_{3^m}+u\mathbb{F}_{3^m}+u^2\mathbb{F}_{3^m}$. Some illustrative examples are provided as well.
Low-Complexity Chase Decoding of Reed-Solomon Codes through Basis Reduction

This paper proposes the low-complexity Chase (LCC) decoding using basis reduction (BR) interpolation for Reed-Solomon (RS) codes, namely the LCC-BR algorithm. With received soft information, a number of decoding test-vectors are formulated. The LCC-BR algorithm first constructs a common basis which will be utilized by the following individual basis constructions of all test-vectors. This eliminates the redundant computation in BR interpolation, resulting in a low decoding complexity. Moreover, the LCC-BR algorithm can decode each test-vector in parallel, lowering the decoding latency. This paper further proposes the progressive LCC-BR (PLCC-BR) algorithm that decodes the test-vectors sequentially and terminates once the intended message is found. This progressive decoding is realized without additional memory cost. Simulation results show the complexity and latency advantages of the proposed algorithms over the other benchmark algorithms.
Representation Learning of Logical Query for Knowledge Reasoning

This paper proposes a novel model for learning the embedding representation of the given first-order logic query and then leading the task of Question Answering (QA) on knowledge graph, especially handling the complex logical queries and mining the multi-hop paths contained in the knowledge graph. In the proposed model, the node (i.e., entity) embeddings and the query embeddings are trained jointly in the same latent semantic feature-space, so that we could measure the matching degree among query (i.e., question) and candidate entities (i.e., answers) by using the semantic distances among them. Recent years have witnessed great advance of QA models based on knowledge graph, however, the main difference between these previous work and ours, is that traditional QA focuses on understanding the natural language, while we are more attentive to detail in modeling and understanding the logical form. The experiments on several knowledge graph reasoning tasks with the real-world datasets demonstrate that, the proposed logical query learning model is more effective than the state-of-the-art models.
Self-service Behavior Recognition Algorithm Based on Improved Motion History Image Network

Aiming at the problem of automatic detection of normal operation behavior in self-service business management, with improved motion history image as input, a recognition method of convolutional neural network is proposed to timely judge the occurrence of anomie behavior. Firstly, the key frame sequence was extracted from the self-service operation video based on the method of uniform energy down-sampling. Secondly, combined with the timing information of key frames to adaptively estimate the decay parameters of the motion history image, adding information contrast to generating a logic matrix can improve the calculation speed of the improved motion history image. Finally, the formed motion history image was input into the established convolutional neural network to obtain the class of self-service behavior and distinguish anomie behavior. In real scenarios of self-service baggage check-in for civil aviation passengers, the typical check-in behavior data set is established and tested in actual self-service baggage check-in system of the airport. The results show that the method proposed can effectively identify typical anomie behaviors and has high practical value.
Optimal Range and Beamwidth for Radar Tracking of Maneuvering Targets Using Nearly Constant Velocity Filters

For a given radar system on an unmanned air vehicle, this work proposes a method to find the optimal tracking range and the optimal beamwidth for tracking a maneuvering target. An inappropriate optimal range or beamwidth is indicative of the need for a redesign of the radar system. An extended Kalman filter (EKF) is employed to estimate the state of the target using measurements of the range and bearing from the sensor to the target. The proposed method makes use of an alpha-beta filter to predict the expected tracking performance of the EKF. Using an assumption of the maximum acceleration of the target, the optimal tracking range (or beamwidth) is determined as the one that minimizes the maximum mean squared error (MMSE) of the position estimates while satisfying a user-defined constraint on the probability of losing track of the target. The applicability of the design method is verified using Monte Carlo simulations.
Artificial Intelligence-based E-commerce Platform based on SaaS and Neural Networks

Artificial intelligence-based E-commerce platform based on SaaS and neural networks is designed in this paper. With the rapid development of the internet and the rapid development of e-commerce, corporate information systems have become more and more complex, and new requirements have been imposed on corporate management and business models. Inspired by this, the paper proposes the novel perspectives. We have the following novelties. (1) Characteristics of multi-tenancy in SaaS software making it difficult to describe SaaS software, we define the optimal solution. (2) The maximum entropy model does not rely on independence assumption, and can perform parameter smoothing, we find the optimal using methodology. (3) BI's core technology is the basic data warehouse, database construction period and it should be analyzed, we test on the different databases.
Evaluation and Analysis of Landscape Painting Aesthetic Image Quality Assessment Based on Visual Saliency Model

Evaluation and analysis of the landscape painting aesthetic image quality assessment based on visual saliency model is presented in this manuscript. Through simulation of the HVS visual perception of the visual scene to extract the image features, including features and spatial features transform domain, we designed and implemented the novel scenario for image feature extraction. The literati landscape painting mostly expresses interest of living in the mountains and the general forests, we extracted the visual saliency map to process the image data and use the image quality assessment model to construct the novel scenario for checking the image quality. Compared with the neural network and PCA based model, the detection accuracy of the proposed model is higher.
Tunable phase shifting algorithm insensitive to phase-step miscalibration

Phase-shifting (PS) is a well-established technique for phase retrieval in interferometry that requires a series of intensity measurements with known or unknown phase-steps. The objective of this work is determinate a tunable PS algorithm that minimize the error in the reconstructed phase when the images have additive random noise and the phase-step was determined with certain error. Simulations to show the performance of the algorithm will be presented.
Learning Temporal Structure of Videos for Action Recognition Using Pattern Theory

Aiming at the problem that a large amount of background information in the videos cause low judgment of actions, this paper proposed a graph model based on pattern theory for human complex action recognition. Firstly, a video is divided into video units and each video unit corresponds to an atomic action. The atomic action labels of videos are initialized by k-Means. Secondly, the key generator proposal module and the interpretative operation module are proposed to select important foreground information and obtain a reasonable representation of atomic action sequences. In the inference stage, the atomic action sequences of test videos are matched with template sequences by the Dynamic Time Warping algorithm (DTW) to obtain the action categories. The experimental results show that compared with the most existing human action recognition models, our model can explain the temporal process of action occurrence and obtain a more discriminatory sequence representation, which can effectively improve the accuracy of action recognition.
Multi-Target Blended Track Fusion in the Presence of Sensor Biases

In track to track correlation and fusion, the tracks are first correlated together, typically using one of the following approaches-greedy nearest neighbor, global nearest neighbor, or Murty's K-best hypothesis. Once the correlation relationships have been determined, then the tracks are fused together to improve the state estimate and covariance of the track representing each object. In the case of Murty's K-best approach, the correlation decisions from the best hypothesis are used to determine which tracks are then fused together. In this paper, we propose using a blending fusion approach using the correlation results from a set of the highest scoring hypotheses. This approach “hedges the bets” by using more than one hypothesis thus increasing the probability that the resulting covariance will contain the true target even in the case when the best hypothesis includes a miscorrelation. The result is that the blended fused tracks will in most cases have a larger covariance than the fused tracks from the best hypothesis with the tradeoff being a higher covariance containment probability. This paper builds upon the previous work by Ogle, et al. using a multiple level fusion system to investigate the effects of using blended fusion. Monte Carlo simulations were performed, and pattern metrics were used to assess the effectiveness.
Impact of Bitcoin's Distributed Structure on the Construction of the Central Bank's Digital Currency System

This paper analyzes the impact of Bitcoin's distributed structure on the construction of the central bank's digital currency system. In the sub-area chain system, anonymous collection namely all participation node composition set, like miner node, wallet node and so on. The anonymous centralism behavior probability distribution namely includes the inquiry, the transaction, the intelligent contract application and so on the node interactive behavior is been connected to the specific node the probability. Future research on blockchain anonymity can focus on two aspects. One is to seek solutions that can enhance the anonymity of the system without occupying too much system resources. The other is the application of the blockchain system and smart contracts based on the account model Research on anonymous solutions. The paper integrates the technology to design and implement the intelligent digital currency system. Network security is specially analyzed. The experiment shows satisfactory performance.
LDR Image Restoration using Non-Symmetry and Anti-Packing Model (NAM)

During the process of image acquisition, sometimes images are degraded by various reasons. Image restoration is a challenging task in the field of image processing. The process of recovering such a degraded or corrupted image is called image restoration. The restoration process improves the appearance of the image. The degraded image is the convolution of the original image, degraded function, and additive noise. The process of restoration is de-convolved in this degraded image to obtain noiselessly and de-blurred original image. Different techniques accessible for image rebuilding, for example, converse channel, weiner channel, compelled least square channel, daze de-convolution strategy and so on a portion of the strategies are either straight or non-direct strategy expels commotion and obscure from the image. In this portrayal and correlation of restoration, strategies are referenced. Presented work different spatial space channels are examined which are utilized to expel commotion from the images. This administrative work proposed another plan for changing over Low powerful range (LDR) image into a High unique range (HDR) image utilizing image restoration by non-evenness and Anti-pressing Model (NAM) technique. this work is finished with the assistance of MATLAB device and thinks about the techniques for various comparable work.
A Distributed Anomaly Filtering Algorithm for Heterogeneous Data Based on City Computing

In modern cities, numerous urban perception devices collect and release urban data all the time, but urban data may become abnormal due to environmental interference or artificial tampering. In view of the problem that urban data will face data anomalies, this paper designs a distributed gauss membership anomaly data filtering algorithm, and defines a set of extraction protocols suitable for heterogeneous data. Simulation results show that this algorithm can filter abnormal data in real time, improve the efficiency of urban computing and reduce the cost of network.
Automatic Localization of Elevation Values in a Poor Quality Topographic Map

A topographic sheet hosts various morphological features that effectively describe the terrain. This multi-faced information content not only elevates human perception but also provides ample direction for research initiatives. Out of all possible attributes based on utility, contours have wide set of application. A contour is characterized by its coordinate system and most importantly, its elevation detail. Upon, successful attainment of these two attributes, creating a fully automatic 3D projection system may be achieved with relative ease. In contrast to the traditional manual approach, this research initiative puts forward a novel mechanism for automatically localizing contour and its attributes including coordinate pattern and elevation value in a referenced map. To accomplish the aforementioned objectives, the proposed mechanism relies on various image processing techniques based on morphological operations. Further, the extracted details can be used to project the contours in a 3D space. This projection is also called Digital Elevation Model (DEM). DEM is crucial for various applications such as Terrain Modeling, Hydrological Modeling, Path Optimization, to name a few. Automatically and accurately created DEM from topographic sheet could contribute a lot in many Geographical Information System (GIS) applications. This paper focuses mainly on elevation value localization associated with specific contour.
Comparison of several calibration methods for fringe projection profilometry

In three-dimensional shape measurement by fringe projection, the calibration of the measurement system is an essential procedure to determine the relationship between the retrieval phase and the object height. In this work, we present an analysis of three calibration methods that had been proposed in the literature. These are based on three mathematical models: linear, quadratic and nonlinear. The linear and nonlinear mathematical models were deduced from the geometry of the measurement system, and parameters of the model were estimated by employing the ordinary least-squares method. The implementation of this calibration method was performed by translating a reference plane at different positions with known depths. The measurement accuracy for these three models was compared by numerical simulations, and the nonlinear model was employed to measure data.
Automatic Classification of Matching Rules in Pattern Matching

Word embedding algorithm is usually used in recommendation system, relation mining, text similarity matching and other fields. In this paper, we apply Word2vec in the word embedding algorithm to automatic classification of matching rules. During pattern matching, a matching rule needs to be given to find all substrings that are identical to the matching rule in a string. In practical application scenarios, some matching rules can be divided into several categories. The same category of matching rules has the same meaning with different expressions. For homogeneous matching rules with consistent structure and strong regularity, regular expressions can be used to aggregate them together. However, in the actual scene, such homogeneous matching rules are rare, and most of them are random and disordered matching rules. For such matching rules, manually designing regular expressions to aggregate them becomes time consuming and laborious. In the field of natural language processing, there are many algorithms based on neural network that can embed words into low-dimensional space vectors, However, these algorithms take into account the relationship between semantic information and context, so a large amount of data is needed. If only the matching rules in pattern matching are considered, there is often not enough data to reflect the context relationship, which leads to the failure to get accurate results. In this paper, a method of automatic classification of matching rules is designed based on Word2vec.
Scientific research in the interests of digital transformation of society

Academy of Sciences in the interests of digital transformation in a number of areas (artificial intelligence, high-performance platform for research, modeling for the synthesis of new materials, etc.).
Pseudo Independent Conditional Approximation for Training the Mixtures of Gaussian Processes

The mixture of Gaussian processes (MGP) is a powerful probabilistic model for regression and classification. However, how to effectively infer the posteriors and learn the parameters in the model is still a very challenging problem due to the exponential complexity of computation. Although several approximation schemes have been utilized to reduce the computational cost, they usually do not provide reasonable interpretations. In this paper, we first propose a specific variational approximation mechanism for the MGP model in which the joint distribution of latent indicators and latent functions can be factorized as the product of two independent variational distributions. The resulted inference procedure can be well interpreted under the framework of information theory. Inspired by this perspective, we then propose a new approximation method called pseudo independent conditional approximation (PIC) for training the MGP model. It is demonstrated by the experimental results that our proposed training method is more effective than the other existing methods.
Attribute-based Granular Evaluation for Performance of Machine Learning Models

There is an increasing demand for quality assurance of machine learning (ML) models as more and more ML applications are investigated in various domains. This means that we need to explicitly take account of the requirements and environmental assumptions for quality evaluation and improvement of ML models. The traditional approach has been performance evaluation, typically in accuracy, for the target dataset. However, this approach only focuses on the global accuracy over the whole dataset and obfuscates performance for individual specific aspects in the requirements and environmental assumptions. We then lack insights necessary to deal with high-priority requirements and to detect risks or weaknesses in specific situations. In response to this problem, we investigate a testing method based on attributes that capture aspects in the requirements and environmental assumptions. We divide the input space into explicit and explainable sub-spaces, which allows the divide-and-conquer style in a granular manner as we have done for traditional software testing. We demonstrate our method with simple attributes for CIFAR-10 and BDD100K datasets.
Arduino and Remote based Gesture Keyboard

Different keyboards are available in the market, with variations in size, layouts, programs, and functionalities. Recently, virtual keyboards are also in use but for every single keyboard, the user should remember the arrangement of keys in order to enter different characters.}{Remote-based gesture keyboard is a system that serves on the Internet of Things [IoT] architecture and embedded with Arduino as well as the accelerometer and machine learning process that enforces the system to perform in the desired direction. The remote-based gesture keyboard is the model, where the user can enter text and number in a text editor by moving a hand to the specific motion of characters in the air using Arduino. Arduino operates as a remote where gestures relocate into the air, translating into letters, and exposing them on a computer screen.
Duplication with transposition distance to the root for q-ary strings

We study the duplication with transposition distance between strings of length n over a q-ary alphabet and their roots. In other words, we investigate the number of duplication operations of the form x = (abcd) →y = (abcbd), where x and y are strings and a, b, c and d are their substrings, needed to get a q-ary string of length n starting from the set of strings without duplications. For exact duplication, we prove that the maximal distance between a string of length at most n and its root has the asymptotic order n/logn. For approximate duplication, where a β-fraction of symbols may be duplicated incorrectly, we show that the maximal distance has a sharp transition from the order n/logn to logn at β = (q - 1)/q. The motivation for this problem comes from genomics, where such duplications represent a special kind of mutation and the distance between a given biological sequence and its root is the smallest number of transposition mutations required to generate the sequence.