Reviewerly/exHarmony huggingface dataset:
Research on Particle Swarm Algorithm Based on Adaptive Acceleration Mechanism

Aiming at the problems of the particle swarm optimization algorithm, such as poor parameter adjustment ability, low convergence accuracy and easy to fall into local minimum, this paper proposes a particle swarm optimization algorithm based on adaptive acceleration mechanism. According to the current particle position priority, the algorithm adjusts the particle flight acceleration in real time, so that the particles jump out of the local optimal position trap and avoid premature phenomenon. Take some tests about the adaptive acceleration mechanism, convergence accuracy, anti-interference ability and particle diversity of the proposed algorithm. The experimental results show that the particle swarm optimization algorithm with adaptive acceleration mechanism not only enhances the local and global search ability, but also improves the convergence accuracy, convergence speed and avoids the premature problem.
Camera-LiDAR Fusion Based Three-Stages Data Association Framework for 3D Multi-Object Tracking

3D multi-object tracking (MOT) ensures safe and efficient motion planning and vehicle navigation and plays an important role in perception systems in autonomous driving. Currently MOT is divided into tracking by detection and end-to-end, but most of them are tracking by detection using only single depth sensor such as LiDAR to detect and track objects. However, LiDAR has the limitation of not being able to obtain information about the appearance of the object due to the lack of pixel information, which can lead to obtaining inaccurate detection results thus leading to erratic tracking results. Therefore, in this paper, we propose a novel 3D MOT framework that combines the unique detection advantages of cameras and LiDAR. To avoid the IDs generated by the early death of the detection of the same object that produced low scores in successive frames, we design a 3D MOT framework with three-stages data association. And we also design a data association metric based on 3D IoU and Mahalanobis distance. The camera-LiDAR fusion-based 3D MOT framework we propose proves its superiority and flexibility by quantitative experiments and ablation experiments.
Research of Gesture Recognition Algorithm Based on Acceleration Trajectory Image

According to the characteristics of neural network computing, this paper designs a neural network based on acceleration for gesture detection. First, the acceleration information is collected by using the acceleration sensor to extract the key point information, and convert the effective data into the acceleration track image data. Two neural networks with depth distribution of 50 and 101 are built and trained by ResNet algorithm. Matching image data features to obtain models with accuracy rates of 85% and 91% respectively, so as to achieve higher gesture recognition accuracy with less computational time and storage space complexity.
A Novel Transformer-based Framework for Multi-View 3D Human Mesh Reconstruction

This paper addresses two key problems of multi-view 3D Human Mesh Reconstruction (HMR): the difficulty of fusing features from multiple images and the lack of training data. We design a novel Transformer-based framework called Multi-View Human Mesh Transformer (MV-HMT), which is comprised of parallel Tiny CNNs and Transformer Encoder. MV-HMT takes multi-view silhouette as inputs, regresses the parameters of human shape and pose, and is effective for multi-view feature fusion. Real-Time Data Synthetic (RT-DS) technique is proposed in this work to solve the second problem. RT -DS is a plug-and-play component that generates paired silhouettes-mesh on CUDA, and provides an inexhaustible supply of synthesis data for pre-training of the neural network. Our method outperforms existing methods for multi-view HMR on the four-view datasets MPI-INF-3DHP and Human3.6M. Another new three-view dataset, MoVi, with more subjects and more accurate annotation, was used to evaluate the generality of our method and showed remarkable results.
Improved Cryptanalysis of the Multi-Power RSA Cryptosystem Variant

The multi-power RSA cryptosystem is a variant of RSA where the modulus is in the form $$N=p^rq^s$$ with $$\max (r,s)\ge 2$$ . In the multi-power RSA variant, the decryption phase is much faster than the standard RSA. While RSA has been intensively studied, the security of the multi-power RSA variant needs to be deeply investigated. In this paper, we consider a multi-power RSA cryptosystem with a modulus $$N=p^rq^s$$ , and propose a method to solve the modular polynomial equations of the form $$F(x)\equiv 0~\left( \textrm{mod}~Wp^uq^v \right) $$ where F(x) is a polynomial with integer coefficients, W is a positive integer, and u, v are integers satisfying $$0\le u\le r$$ , $$0\le v\le s$$ , and $$su-rv\ne 0$$ . Our method is based on Coppersmith’s method and lattice reduction techniques. We show that the new results retrieve or supersede the former results. Moreover, we apply the new method to study various instances of the multi-power RSA cryptosystem, especially when the private exponent is small, when the prime factors have a specific form, and when the least significant or the most significant bits of the private exponent are known.
VGG16-based intelligent image analysis in the pathological diagnosis of IgA nephropathy

IgA nephropathy (IgAN) is the most common primary glomerular disease worldwide, with heterogeneous clinical and pathological manifestations, and is a common cause of end-stage renal disease. Early detection and effective intervention measures are essential for improving the outcome of IgAN. Machine learning methods can make the pathological analysis, early detection and diagnosis, and prognosis prediction of IgAN more automated and accurate. This article discusses the application of machine learning methods in IgAN, from optimizing pathological diagnosis to discovering non-invasive specific biomarkers, predicting disease progression, and evaluating prognosis. It is the key to reducing the incidence rate and mortality of end-stage renal disease by relying on intelligent image analysis of VGG16 for accurate detection and diagnosis of IgA nephropathy to enable clinical to take effective prevention and treatment measures. A total of 452 cases of kidney disease admitted to the nephrology department of our hospital from January 2018 to February 2023 were selected, and it was ruled out that pathological diagnosis could not be made due to the small number of samples submitted for renal puncture; After excluding suspected cases of renal biopsy pathology diagnosis and patients who did not undergo immunofluorescence examination, a total of 135 confirmed IgA nephropathy patients were subjected to image analysis. The internationally recognized 5-level semi-quantitative method was used for evaluation, and traditional image processing methods were selected to segment and extract fluorescence deposition areas. Transform the input image into color space and generate a binary image using the adaptive threshold method in the two feature dimensions of color and brightness. Then, VGG16 regions were separated and merged to obtain independent sedimentary regions. VGG16 was used to add BN layers and SE visual attention to fully extract sensitive features with high inter-class similarity and significant intra-class differences in the IgA nephropathy image classification task. The contour, area, and average brightness of each sedimentary region were calculated, and automatic computer recognition of fluorescence deposition intensity and shape was obtained to improve the accuracy of IgA nephropathy image classification. The artificial intelligence image analysis based on VGG16 can achieve the interpretation of IgA nephropathy immunofluorescence results with a higher coincidence rate compared to the results of pathological diagnostic doctors. IgA reaches 88.9%, IgG reaches 85.8%, IgM reaches 83.8%, and C3 reaches 88.6%. Therefore, it can assist pathological diagnostic doctors in interpreting IgA nephropathy immunofluorescence. By fully utilizing computer and network technologies to change the pathological workflow, improve the work efficiency of pathological diagnostic doctors, reduce the misdiagnosis rate caused by fatigue during film reading, and make pathological diagnosis more accurate and objective.
The Old Church Slavonic Corpora and Their Use in Language Studies at the University

The paper focuses on the issues that surround the research of Old Church Slavonic (OCS). The most important point is that, despite the number of existing OCS corpora, none of them actually fully represents the language. Some of them contain texts that are not OCS ones, they are merely Old and Slavic. And each of these corpora is highly centralized, which makes them extremely vulnerable. The paper discusses corpora preservation issues that are becoming increasingly important for OCS studies. Another problem is that the OCS canon is extremely heterogeneous, and it is hard to discriminate the OCS texts from other Slavic texts of the approximately same time. The possible formal criteria are discussed in the article, as well as a set of texts that correspond to these criteria and should form the OCS corpus. The process of corpus construction is also addressed in the paper. The emphasis is on the three modules. The first helps to tag the data or edit it after automatic tagging. The second helps to visualize the relationships within the data, and the third is a corpus search module. Finally, the ways in which the corpus created can be used in the educational process are given.
A task-oriented neural dialogue system capable of knowledge accessing

As one of the most important AI applications, task-oriented dialogue systems have been a hot topic in this field. These systems are primarily designed to fulfill tasks and provide answers to queries, such as checking the weather or booking flights. However, there are two major challenges in implementing task-oriented dialogue systems: selecting the right knowledge from the relevant knowledge base and combining the knowledge to produce grammatically correct and fluent replies. In this paper, we present a task-oriented dialogue system using an end-to-end trainable neural model. This dialogue system consists of four components: an encoder for encoding the dialogue history, a three-hop memory network for storing the relevant knowledge entries and dialogue history words, a knowledge screener for selecting the knowledge that is really related to the dialogue history, and a decoder for generating the replies. Finally, we validate the proposed dialogue system on a benchmark dataset (KVRET). The experimental results show that the proposed model generates more accurate replies than baselines in automatic and human evaluations. Also, we verify the superiorities of the two improvements by ablation experiments.
On Adopting Software Analytics for Managerial Decision-Making: A Practitioner’s Perspective

Organizations have used software engineering data to support decision-making by applying data-driven approaches such as software analytics. However, adopting analytics tools depends on the information they provide and the real needs of practitioners. Significant research has addressed the needs of developers, whereas the needs of managers are not well understood. Moreover, few studies have focused on the practitioners’ view of data-driven decision-making. From a managerial viewpoint, this case study provides an in-depth analysis of the information needs and the perceptions of data-driven decision-making of practitioners from one software development organization. We interviewed personnel in leadership positions and used coding procedures (open and selective coding) to analyze the collected data. We identified 19 software analytics use cases and mapped them to the software life cycle processes from ISO/IEC/IEEE 12207:2017, of which organizational project-enabling and technical management processes were the most highlighted by the interviewees. We also provided a set of indicators to meet the identified use cases and shed light on critical aspects of the organization’s analytics scenario. Furthermore, we identified project-related, human-related, and context-specific factors that affect managerial decision-making and organizational aspects that influence the adoption of software analytics initiatives. Although our results are particularly relevant to organizations similar to the one described herein, they aim to serve as input for implementing new analytics solutions by practitioners and researchers in general and contribute to the body of knowledge on the topic from a practitioner’s perspective, helping organizations in their attempts to adopt data-driven approaches.
EMOTIVE-AXIOLOGICAL APPROACH IN MUSICOLOGY AND MODERN THEORY OF OPERA EXPERIENCE

In the context of the art of vocal and stage performance, the issues of the influence of the musical factor on the process of impersonation of the singer-actor, the existential nature of the art of impersonation, the psychological mechanisms of understanding and appropriation of the text-consciousness of the character by the performer, the processuality of experiencing as an activity of the opera performer and character, musical stage images and forms are touched upon, along with the issues of internal and external action of the character, modality factors of vocal intonation, including the expected circumstances of the role. The syncretism of the opera text as an integral interdeterministic system of stage action meaning, which has an emotional impact on the audience and performers through all three channels of communication - visual, auditory, and verbal, makes it possible to define the opera within the framework of emotionalology as a new branch of social psychology. An attempt is made to show that personal and collective emotions generated by an opera performance are decisive in a syncretic opera text.
MapFlow: latent transition via normalizing flow for unsupervised domain adaptation

Abstract Unsupervised domain adaptation (UDA) aims at enhancing the generalizability of the classification model learned from the labeled source domain to an unlabeled target domain. An established approach to UDA is to constrain the classifier on an intermediate representation that is distributionally invariant across domains. However, recent theoretical and empirical research has revealed that relying only on invariance fails to guarantee a small target error, thus making equality in the distribution of representations unnecessary. In this paper, we propose to relax invariant representation learning by finding a general relationship between the source and target representations, which allows an interchange of the more discriminative domain information. To this end, we formalize the MapFlow framework, which explicitly constructs an invertible mapping between the target encoded distribution and variationally induced source representation. Empirical results on public benchmark datasets show the desirable performance of our proposed algorithm compared to state-of-the-art methods.
On the Maximum Likelihood Estimation of Population and Domain Means

Abstract Estimating of population and domain means based on model-design approaches is considered in this paper. Population elements randomly belong to domains. A joint distribution of the variable under study and an auxiliary variable is assumed. Data are observed in a sample selected from a fixed population. The partition of the sample elements into domains of the population is also known. Outside of the sample, values of the auxiliary variable are known but their partition among the domains is not known. The domain means are estimated based on the likelihood function of the data observed in the sample and outside of it. The maximum likelihood estimation method provides regression-type estimators of domain means of the variable under study. They are dependent on posterior probabilities that observations of the auxiliary variable belong to particular domains. Moreover, the weighted means of the domain averages estimators are used to estimation of the population mean. The accuracy of the evaluated estimators and the ordinary estimator is compared using a simulation analysis. The results of this paper could be useful in economic, demographic and sociological surveys.
Underwater image clarifying based on human visual colour constancy using double‐opponency

Abstract Underwater images are often with biased colours and reduced contrast because of the absorption and scattering effects when light propagates in water. Such images with degradation cannot meet the needs of underwater operations. The main problem in classic underwater image restoration or enhancement methods is that they consume long calculation time, and often, the colour or contrast of the result images is still unsatisfied. Instead of using the complicated physical model of underwater imaging degradation, we propose a new method to deal with underwater images by imitating the colour constancy mechanism of human vision using double‐opponency. Firstly, the original image is converted to the LMS space. Then the signals are linearly combined, and Gaussian convolutions are performed to imitate the function of receptive fields (RFs). Next, two RFs with different sizes work together to constitute the double‐opponency response. Finally, the underwater light is estimated to correct the colours in the image. Further contrast stretching on the luminance is optional. Experiments show that the proposed method can obtain clarified underwater images with higher quality than before, and it spends significantly less time cost compared to other previously published typical methods.
Gender stereotypes and voice assistants: do users’ gender and conversation topic matter?

Voice assistants (VAs), such as Alexa, Siri, or Google Now, are becoming increasingly ubiquitous. Consequently, the potential societal impacts of such systems are gaining relevance in public and academic discourses. Investigating the effects of VA voice features on user perception of VAs is one central aspect of research. Here, the potential effects of VAs on the perception of gender roles and the attribution of gender stereotypes stand out. Following the Computers as Social Actors paradigm, it is assumed that gendered voices have the potential to reproduce existing gender stereotypes. Yet, recently, the implementation of gender-ambiguous voices has been suggested to mitigate such effects and promote more diverse technology design. In this study, we set out to inquire about the relationship between gendered VA voice (male, female, gender-ambiguous) and gender stereotype activation. In two online surveys with an experimental manipulation (Study 1: N = 140, Study 2: N = 397), we test stereotype activation as a function of VA voice, user gender, and conversation topic. Empirical findings are mixed, with no effects for conversation topic but indications of small effects of VA voice. Implications for future research are discussed.
Graphs with the unique path property: Structure, cycles, factors, and constructions

Abstract In a directed graph with the unique path property , there is a unique directed path of a given length between any ordered pair of its vertices. Such graphs, their paths, cycles, factors, and other properties, were extensively studied during the years, mainly in the context of de Bruijn graphs, forming the most important family of such graphs. Such a graph is equivalent to a binary matrix for which , where is the all‐ones matrix. In this paper, some new results on the structural properties of such graphs, which are not necessarily de Bruijn graphs, are proved. We mainly concentrate on such graphs for which each vertex has in‐degree two and out‐degree two. The minimum number and the maximum number of vertices which are reached from a given vertex using all paths of length smaller than are considered. We provide an exact formula for the number of factors in these graphs and show that this number depends only on the number of alternating cycles in the graph, where in an alternating cycle every two consecutive edges have opposite directions in the cycle. Constructions of such graphs using several methods are presented. In particular, we present a new construction which provides a large set of such graphs with long alternating cycles. We define the integral graph and the derivative graph and implement these definitions on this family of graphs. Various properties from the point of view of the de Bruijn graph and the theory of nonsingular shift‐registers sequences are also considered. Finally, generalizations for graphs in which the in‐degree and the out‐degree of each vertex are greater than two are also discussed.
Boosting Few-shot Object Detection with Discriminative Representation and Class Margin

Classifying and accurately locating a visual category with few annotated training samples in computer vision has motivated the few-shot object detection technique, which exploits transfering the source-domain detection model to the target domain. Under this paradigm, however, such transferred source-domain detection model usually encounters difficulty in the classification of the target domain because of the low data diversity of novel training samples. To combat this, we present a simple yet effective few-shot detector, Transferable RCNN. To transfer general knowledge learned from data-abundant base classes to data-scarce novel classes, we propose a weight transfer strategy to promote model transferability and an attention-based feature enhancement mechanism to learn more robust object proposal feature representations. Further, we ensure strong discrimination by optimizing the contrastive objectives of feature maps via a supervised spatial contrastive loss. Meanwhile, we introduce an angle-guided additive margin classifier to augment instance-level inter-class difference and intra-class compactness, which is beneficial for improving the discriminative power of the few-shot classification head under a few supervisions. Our proposed framework outperforms the current works in various settings of PASCAL VOC and MSCOCO datasets; this demonstrates the effectiveness and generalization ability.
On Hitting Times for General Quantum Markov Processes

Random walks (or Markov chains) are models extensively used in theoretical computer science. Several tools, including analysis of quantities such as hitting and mixing times, are helpful for devising randomized algorithms. A notable example is Sch\"oning's algorithm for the satisfiability (SAT) problem. In this work, we use the density-matrix formalism to define a quantum Markov chain model which directly generalizes classical walks, and we show that a common tools such as hitting times can be computed with a similar formula as the one found in the classical theory, which we then apply to known quantum settings such as Grover's algorithm.
AI Intelligent Detection Technology of Metal Hose for Rocket Engine

Abstract Endoscope is an important method for defect detection of metal hose used in rocket engine. Due to the traditional endoscopic detection technology is dependent on manual and low detection efficiency, this paper introduces Ai Image recognition technology to detect inner surface defects of metal hose. Aiming at the problem that most of the defects on the inner wall of the bellows of metal hose are small in scale and easy to be misjudged, a method of post-processing and rechecking of the bellows defect detection model is proposed, it is used to strengthen the defect detection model of the inner wall of the bellows of the metal hose for rocket engine. In this paper, based on the block classification network, the defect detection of the captured image of bellows is carried out, and the defect detection results of n*n blocks are output, then the metal hose defects are classified by using the bi fine-grained classification network with small structure, and the final n*n multi-class detection results are obtained. The results show that the engine and model of Ai Algorithm reduce the rate of missed detection and false detection, and improve the reliability of endoscope detection.