Reviewerly/exHarmony huggingface dataset:
Textual entailment as an evaluation metric for abstractive text summarization

Automated text summarization systems require to be heedful of the reader and the communication goals since it may be the determining component of whether the original textual content is actually worth reading in full. The summary can also assist enhance document indexing for information retrieval, and it is generally much less biased than a human-written summary. A crucial part while building intelligent systems is evaluating them. Consequently, the choice of evaluation metric(s) is of utmost importance. Current standard evaluation metrics like BLEU and ROUGE, although fairly effective for evaluation of extractive text summarization systems, become futile when it comes to comparing semantic information between two texts, i.e in abstractive summarization. We propose textual entailment as a potential metric to evaluate abstractive summaries. The results show the contribution of text entailment as a strong automated evaluation model for such summaries. The textual entailment scores between the text and generated summaries, and between the reference and predicted summaries were calculated, and an overall summarizer score was generated to give a fair idea of how efficient the generated summaries are. We put forward some novel methods that use the entailment scores and the final summarizer scores for a reasonable evaluation of the same across various scenarios. A Final Entailment Metric Score (FEMS) was generated to get an insightful idea in order to compare both the generated summaries.
A monocular view pose processing method based on line vector direction and length object-image matching

In order to solve the problem of precise pose measurement by monocular view caused by weather or equipment failure in medium and long-term optical pose measurement in the shooting range, a measurement method of monocular 3 pose angle based on target image matching in linear vector direction for multi linear aircraft targets is proposed in this paper; For missile targets with only single feature of axis vector, the yaw and pitch angles are obtained by optical monocular view pose processing method based on axis vector direction, image length matching; This method not only makes use of the reliability and flexibility of linear feature extraction, but also avoids unnecessary error factors. This method can be applied to the pose processing of axisymmetric and non-axisymmetric rotating targets at the same time, unifies the processing approach for monocular pose processing, and provides a theoretical support for the medium and long-term optical pose measurement in range.
Rotation-equivariant transformer for oriented person detection of overhead fisheye images

Abstract Overhead fisheye images can be used for person detection in intelligent monitoring systems. Unlike horizontal images, people in fisheye cameras are generally distributed in any orientation. When the object is rotated, the feature maps from convolutional neural networks have nonlinear variations and lose many orientation features. Transformer can learn the orientation relationship between features. However, a transformer cannot directly extract orientation features and the effectiveness of detecting small objects needs to be improved. In this paper, We propose a novel rotation-equivariant transformer backbone network, which combines group-equivariant convolution with swin transformer to solve these problems. In our proposed model, the rotation-equivariant feature map extracted by group-equivariant convolution contains a large number of orientation features in multiple directions. Aggregates feature in different directions to enhance the communication of orientation features before computing window self-attention. We propose the equivariant-group relation module for evaluating the similarity of the equivariant-group and calculating the aggregation weights. Our network architecture for multi-level receptive field structure can expand the local receptive field to enhance the detection of small objects. The experiments validate that our model achieves state-of-the-art performance on fisheye image datasets MW-R, HABBOF, and CEPDOF. Compared with the swin transformer, the accuracy of our model is improved by 0.3 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> , 0.5 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> , and 1.3 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> , and the accuracy of small object detection in the CEPDOF dataset is improved by 0.73 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> .
A method for the 3D identification of the center of gravity of an aircraft

The aircraft weight and balance are a consequence of the own mass and the payload distribution, resulting in a position of the Centre of Gravity (CoG) that affect the behavior of the aircraft, its stability and the control effectiveness. The aircraft weight is normally measured statically using ground scales or sensors in the landing gear axles or struts, driving to 2D measurements of the CoG in longitudinal and lateral directions. Sometimes the 3D measurement is relevant because the aircraft architecture can involve weight shifts, i.e., the tiltrotor case. This paper studies the fast 3D measurement of the CoG in a tiltrotor type aircraft using photogrammetry and scales without previous levelling the aircraft. The system is modelled using multibody simulations to obtain data for the validation of the proposed methods. Finally, the expected uncertainty of the measurement is evaluated using Montecarlo method, analyzing the results statistically.
Anchor-based discriminative dual distribution calibration for transductive zero-shot learning

Zero-shot learning (ZSL) is machine learning task to recognize samples from classes that are not observed during training. Transductive ZSL (TZSL) is a more realistic and effective paradigm that leverages unlabeled unseen data during training to reduce the bias towards seen classes. However, most existing TZSL methods neglect the information gap between visual and semantic spaces, and thus fail to generate distribution-consistent unseen features. To address this issue, we propose a novel TZSL approach named Anchor-based Discriminative Dual Distribution Calibrated Feature Generative Network (AD3C-FGN), which performs anchor-based distribution calibration in both visual and semantic spaces for improving generalization ability of the model. In AD3C-FGN, we adopt conditional generative adversarial network with an unseen discriminator to construct a Y-shape generation model that mitigates the domain shift problem. Moreover, an AD3C module is elaborated for calibrating the distribution of generated and real samples in both visual and semantic spaces with real sample anchors, and also enhance the discriminability of the generated samples. AD3C enforces the generated sample to be closer to its homogenous anchor but farther away from inhomogeneous anchors in both spaces. Extensive experimental results on six popular ZSL benchmarks demonstrate that our method achieves promising performances in different settings. The source codes of our model have been released onhttps://github.com/ZYi-CQU/AD3C-FGN.
Improved MDCFT-based method for estimating physical parameters from Newton's rings

Newton's ring pattern is an typical interference fringe often encountered in optical measurements. The physical parameters, such as curvature radius, the ring's center can be estimated by analyzing it. Newton ring formed by spherical interference is two-dimensional Chirp signal, and its chirp parameters are almost impossible to be integers. However, the strict constraints of discrete Chirp Fourier transform (DCFT) and the existing discrete Modified Chirp Fourier transform (MDCFT) can only estimate the integer value part of parameter, which has a large recognition error. An improved MDCFT algorithm is proposed to further estimate the non-integer value part by using the object offset principle, thereby improving the estimation accuracy of Newton ring parameters. Experimental results verify the effectiveness and correctness of the proposed method.
Enhancing non-profiled side-channel attacks by time-frequency analysis

Abstract Side-channel analysis (SCA) has become an increasing important method to assess the physical security of cryptographic systems. In the process of SCA, the number of attack data directly determines the performance of SCA. With sufficient attack data, the adversary can achieve a successful SCA. However, in reality, the cryptographic device may be protected with some countermeasures to limit the number of encryptions using the same key. In this case, the adversary cannot use casual numbers of data to perform SCA. The performance of SCA will be severely dropped if the attack traces are insufficient. In this paper, we introduce wavelet scatter transform (WST) and short-time fourier transform (STFT) to non-profiled side-channel analysis domains, to improve the performance of side-channel attacks in the context of insufficient data. We design a practical framework to provide suitable parameters for WST/STFT-based SCA. Using the proposed method, the WST/STFT-based SCA method can significantly enhance the performance and robustness of non-profiled SCA. The practical attacks against four public datasets show that the proposed method is able to achieve more robust performance. Compared with the original correlation power analysis (CPA), the number of attack data can be reduced by 50–95%.
Learning multi‐organ and tumor segmentation from partially labeled datasets by a conditional dynamic attention network

Summary Multi‐organ segmentation is a critical prerequisite for many clinical applications. Deep learning‐based approaches have recently achieved promising results on this task. However, they heavily rely on massive data with multi‐organ annotated, which is labor‐ and expert‐intensive and thus difficult to obtain. In contrast, single‐organ datasets are easier to acquire, and many well‐annotated ones are publicly available. It leads to the partially labeled issue: How to learn a unified multi‐organ segmentation model from several single‐organ datasets? Pseudo‐label‐based methods and conditional information‐based methods make up the majority of existing solutions, where the former largely depends on the accuracy of pseudo‐labels, and the latter has a limited capacity for task‐related features. In this paper, we propose the Conditional Dynamic Attention Network (CDANet). Our approach is designed with two key components: (1) multisource parameter generator, fusing the conditional and multiscale information to better distinguish among different tasks, and (2) dynamic attention module, promoting more attention to task‐related features. We have conducted extensive experiments on seven partially labeled challenging datasets. The results show that our method achieved competitive results compared with the advanced approaches, with an average Dice score of 75.08%. Additionally, the Hausdorff Distance is 26.31, which is a competitive result.
UNet++ for estimating physical parameters from Newton’s rings

Newton’s rings pattern is frequently encountered in optical interferometry, and by extracting the phase contained in it, the measured physical parameter information can be obtained. According to the purpose of point-to-point mapping of the image to be analyzed, a method based on UNet++ to extract the phase of Newton’s rings is proposed. Once the network training is completed, the continuous phase including the curvature radius and ring’s center coordinate can be directly predicted from a single Newton’s rings pattern immediately. The relative error of the curvature radius obtained by parameter fitting the phase is less than 0.83%, and the error of ring’s center coordinate is close to 0 pixel. In order to further improve the results of curvature radius estimation, the parameter estimation results obtained by UNet++ are taken as the initial value and corrected by the least-squares fitting method. Experimental results show that for the Newton’s rings pattern containing -5 dB Gaussian noise, the relative error of the corrected curvature radius is no higher than 0.31%.
Spatial extrapolation of downscaled geochemical data using conditional GAN

Multi-scale geochemical data play an important guiding role in mineral exploration and environmental assessment. However, due to the influence of certain factors such as terrain, sampling cost and time, and instrument detection limit, geochemical data obtained from a study area are often incomplete or sparse. In this study, a conditional generative adversarial network (GAN) model was developed as a stochastic simulation technique for spatial extrapolation of downscaled geochemical data by learning nonlinear relationships between two soil geochemical datasets with different sampling densities, one set called fine data and the other coarse data. Taking Qianjiang City, Hubei Province, China as an example, first the fine data and coarse data were divided separately into training area and verification area for simulation in the GAN model. Then, exploratory data analysis, error analysis and variogram analysis were used to evaluate the realizations. Finally, Ni anomalies related to soil heavy metal pollution were extracted by combined concentration–area (C–A) fractal model and uncertainty analysis, in the verification area. Our studies indicate that the proposed conditional GAN model is efficient in extrapolating downscaled geochemical data, since both the realizations and fine data maintain similar spatial autocorrelation and statistical distribution.
Meter image enhancement method in high light substation based on improved CycleGAN

When the patrol robot recognizes the instrument under strong light, the collected instrument image has serious exposure, details loss and other phenomena. Aiming at the above problems, an improved CycleGAN method for enhancing the instrument image in the substation under strong light is proposed. First of all, for the generator, the CSD network and the self-attention mechanism ACmix module are improved to improve the lighting processing effect. Secondly, the idea of Patch-GAN is adopted in the discriminator, and the feature graph is mapped to an N×N matrix at the end to improve the processing ability of the details. The experimental results show that the peak signal-to-noise ratio of the image is increased from 15dB to 20dB after the above improvement; The index of structural similarity increased from 0.5 to 0.8; The recognition accuracy after enhancement has also been improved by 6%. In conclusion, the method in this paper can eliminate the influence of illumination on instrument recognition, and has strong robustness.
Toward a quantum-enabled workforce: curriculum design, project based learning, and institutional partnerships as integrated methodologies

In this paper, we discuss a set of novel initiatives aimed at educating and training the next generation of quantum engineers via a combination of conventional coursework, project based learning and institutional partnerships. Toward this end, we discuss the role of our newly – designed laboratory experiments, together with our recently installed commercial quantum key distribution (QKD) test bed as the quintessential tool to provide “hands on” training and skills to our Master of Engineering (MEng) degree students. Of particular importance is the emphasis on the participation of experts from industry, especially companies looking to incorporate quantum technologies into their product/service portfolios, as well as companies developing such technologies. Next, the potential implications of these initiatives in the context of cutting-edge graduate education and research are discussed. Finally, we offer insight into our efforts at developing robust quantum education curricula and experiential learning approaches at the grassroots level on a wider scale, by partnering with institutions such as Historically Black Colleges and Universities (HBCUs), both locally within the state of Virginia, as well as nationwide.
Research on super resolution algorithm based on microstep in vibration environment of aviation equipment

Aerial imaging equipment has gradually become a crucial information detection equipment in modern war, so the accuracy of information collection has become the main research direction of imaging resolution algorithm. There are many limitations in the process of making and using aerial imaging equipment, which makes it impossible to implement the super resolution algorithm under ideal conditions. Therefore, it is necessary to study the difference resolution algorithm based on the influence of these realistic factors. In this paper, a mathematical model is established for the impact of airborne photoelectric load vibration on the imaging system, and a preliminary analysis is carried out. According to the vibration environment during aviation flight, a super-resolution algorithm based on micro-step is proposed to extract the non-redundant information contained in the multi-frame low-resolution image sequence for fusion processing, and to reconstruct the high-resolution clear image.
Quality Enhancement of Conventional Compression with a Learned Side Bitstream

We propose Quality Enhancement via a Side bitstream Network (QESN) technique for lossy image compression. The proposed QESN utilizes the network architecture of deep image compression to produce a bitstream for enhancing the quality of conventional compression. We also present a loss function that directly optimizes the Bjontegaard delta bit rate (BD-BR) by using a differentiable model of a rate-distortion curve. Experimental results show that QESN improves the rate by 16.7% in the BD-BR compared to Better Portable Graphics.
Bins, Spans, and Tolerance: Three Theories of Microtiming Behavior

Abstract This study compares three recent theories of expressive microtiming in music. While each theory was originally designed to engage a particular musical genre—Anne Danielsen’s beat bins for funk, Neo-Soul, and other contemporary Black musical expressions, Chris Stover’s beat span for “timeline musics” from Africa and the African diaspora, and Mats Johansson’s rhythmic tolerance for Scandinavian fiddle music—we consider how they can productively coexist in a shared music-analytic space, each revealing aspects of musical structure and process in mutually reinforcing ways. In order to explore these possibilities, we bring all three theories to bear on a recording of Thelonious Monk’s “Monk’s Dream,” focusing on Monk’s piano gestures as well as the relationship between saxophonist Charlie Rouse’s improvised solo and Monk’s and bassist John Ore’s accompaniments.
Learning features from irrelevant domains through deep neural network

Abstract Features of data are much critical to the classification. However, when only small data are available, suitable features can not be easily obtained, easily leading to the bad classification performance. This paper propose a novel approach to automatically learns features from the irrelevant domain with much discriminative features for the given classification task. It first computes as the learning objectives the central vectors of each class in the irrelevant domain, and then uses machine learning method to automatically learn features for each sample in the target domain from these objectives. The merits of our method lie in that unlike the transfer learning, our method does not require the similarity between two domains. It can learn features from much discriminative domains. Its learned features are not limited to its original ones, unlike feature selection and feature extraction methods, so that the classification performance with the learned features can be better. Finally, our method is much general, simple, and efficient. Lots of experimental results validated the proposed method.
The influence of the main strategies overcoming stress on professional activity of transport police officers

The article is devoted to the study of methods, one of which is coping behavior, allowing traffic police officers to effectively cope with the impact of stressors and predict possible solutions to problems. The subject of our study is the leading coping strategies in the behavior of traffic police officers. Purpose: to identify typical coping strategies of employees that allow them to overcome difficult life situations. Methodological basis: provisions on coping behavior developed in humanistic psychology; about the inherent desire of the individual to overcome stressful situations, thereby ensuring safety in professional activities. In the course of the study, a set of methods was used: theoretical analysis of the literature; a number of psychodiagnostic methodics (the test “Methods of coping behavior” by R. Lazarus, the test “Youthful coping scale” adapted by T.L. Kryukova), evidence processing method (Microsoft Office Excel, Statistica10). 70 respondents took part from departments of the transport police. Results of the work: in most cases, employees prefer adaptive copings: self-control, acceptance of responsibility, work/achievements that contribute to overcoming existing stressors, it has also been found that one coping can strengthen or weaken the effect of another. The results of the study can be taken into account when organizing personnel and psychological work with personnel, as well as used during working hours for the constructive distribution of personal resources. Scientific novelty: based on the analysis of information sources and the study, significant stressors are identified that affect the activities of transport police officers, which are distinguished by their specifics and conditions for performing tasks. And the identified strategies for coping with stress made it possible to establish the degree of influence on the professional activities of transport police officers who had not previously been studied in this vein. Conclusion: knowledge of the leading stress coping strategies will allow employees to act consciously in a stressful situation and independently regulate their behavior, taking into account their existing abilities and conditions imposed by external circumstances.
A Crime Scene Reconstruction for Digital Forensic Analysis

The abundance of digital data within modern vehicles makes digital vehicle forensics (DVF) a promising subfield of digital forensics (DF), with significant potential for investigations. In this research, the authors apply DVF methodology to a SUV, simulating a real case by extracting and analyzing the data in the period leading up to an incident to evaluate the effectiveness of DVF in solving crime. The authors employ DVF approach to extract data to reveal evidential information for judicial evaluation and verdict. This data helped determine whether the incident represented an accident or an act of crime. This simulated case and the assumptions supported by the DVF evidence provides a compelling example of how law enforcement agencies can leverage DVF to collect and present evidence to relevant authorities. This form of forensics can assist government in planning for and regulating the deployment of DVF data, the judiciary in assessing the nature and admissibility of evidence, and vehicle manufacturers in complying with the regulations relating to the harvesting and retrieval of data.