Reviewerly/exHarmony huggingface dataset:
UNREADABLE OFFLINE HANDWRITING SIGNATURE VERIFICATION BASED ON GENERATIVE ADVERSARIAL NETWORK USING LIGHTWEIGHT DEEP LEARNING ARCHITECTURES

Today, it is known that there are great difficulties and problems in signature and signature examinations, which have a very important place in both our private life and business and commercial life. The major issue arises when the manuscript’s signature is so illegible and unclear that it is difficult, if not impossible, to authenticate it with the human eye. Researchers have proposed traditional deep learning techniques to solve or improve this challenge. However, the results are not satisfactory. In this study, a new use of Generative Adversarial Network (GAN) model is proposed as a high-quality data synthesis method to address the unreadable data problem on signature verification. A unique signature verification method based on Lightweight deep learning architecture is also proposed. The suggested data synthesizing approach is evaluated using three frequently used Convolutional Neural Network (CNN) methods: MobileNet, SqueezeNet, and ShuffleNet. In addition, in preprocessing phase, we added three different types of high-intensity noise, including Salt &amp; Pepper (S&amp;P), Gaussian, and Gaussian Blur, to the images to make the signature unreadable. We utilized Indic scripts dataset to train GAN and CNN models in our approach. The great quality of images generated by GAN model, as well as the signature verification of the generated images, point to the suggested model’s strong performance.
Sleep Action Recognition Based on Segmentation Strategy

In order to solve the problem of long video dependence and the difficulty of fine-grained feature extraction in the video behavior recognition of personnel sleeping at a security-monitored scene, this paper proposes a time-series convolution-network-based sleeping behavior recognition algorithm suitable for monitoring data. ResNet50 is selected as the backbone network, and the self-attention coding layer is used to extract rich contextual semantic information; then, a segment-level feature fusion module is constructed to enhance the effective transmission of important information in the segment feature sequence on the network, and the long-term memory network is used to model the entire video in the time dimension to improve behavior detection ability. This paper constructs a data set of sleeping behavior under security monitoring, and the two behaviors contain about 2800 single-person target videos. The experimental results show that the detection accuracy of the network model in this paper is significantly improved on the sleeping post data set, up to 6.69% higher than the benchmark network. Compared with other network models, the performance of the algorithm in this paper has improved to different degrees and has good application value.
Robust Control Allocation for Space Inertial Sensor under Test Mass Release Phase with Overcritical Conditions

This paper proposes a robust control allocation for the capture control of the space inertial sensor's test mass under overcritical conditions. Uncertainty factors of the test mass control system under the overcritical condition are analyzed first, and a 6-DOF test mass dynamics model with system uncertainty is established. Subsequently, a time-varying weight function is designed to coordinate the allocation of 6-DOF generalized forces. Moreover, a robust control allocation method is proposed to distribute the commanded forces and torques into individual electrodes in an optimal manner, which takes into account the system uncertainties. This method transforms the robust control allocation problem into a second-order cone optimization problem, and its dual problem is introduced to simplify the computational complexity and improve the solving efficiency. Numerical simulation results are presented to illustrate and highlight the fine performance benefits obtained using the proposed robust control allocation method, which improves capture efficiency, increases the security margin and reduces allocation errors.
PointPainting: 3D Object Detection Aided by Semantic Image Information

A multi-modal 3D object-detection method, based on data from cameras and LiDAR, has become a subject of research interest. PointPainting proposes a method for improving point-cloud-based 3D object detectors using semantic information from RGB images. However, this method still needs to improve on the following two complications: first, there are faulty parts in the image semantic segmentation results, leading to false detections. Second, the commonly used anchor assigner only considers the intersection over union (IoU) between the anchors and ground truth boxes, meaning that some anchors contain few target LiDAR points assigned as positive anchors. In this paper, three improvements are suggested to address these complications. Specifically, a novel weighting strategy is proposed for each anchor in the classification loss. This enables the detector to pay more attention to anchors containing inaccurate semantic information. Then, SegIoU, which incorporates semantic information, instead of IoU, is proposed for the anchor assignment. SegIoU measures the similarity of the semantic information between each anchor and ground truth box, avoiding the defective anchor assignments mentioned above. In addition, a dual-attention module is introduced to enhance the voxelized point cloud. The experiments demonstrate that the proposed modules obtained significant improvements in various methods, consisting of single-stage PointPillars, two-stage SECOND-IoU, anchor-base SECOND, and an anchor-free CenterPoint on the KITTI dataset.
Teaching with Chunking in Synchronous Classes: The Influence on University Students’ Intrinsic Motivation

Virtual education has allowed for the formation of new forms of teaching during the synchronous classes, thereby replacing traditional expository teaching. The aim of this study was to analyse the influence of chunking synchronous classes on the intrinsic motivation of university students enrolled in different academic programs in a public university in Peru. A total of 114 students and three professors participated in the study. The classes were systematised to consider the beginning, development, and closing of the educational process. In the stages of chunking, strategies for the recovery and consolidation of learning were used through the implementation of technological tools. This study focuses on the methodology of participant-active research under the systematisation of experiences. Three categories emerged in the results: active participation in interactive spaces, active listening in virtual spaces, and the use of technological tools allowing for the managing of a new way of directing the online teaching-learning process through teaching via chunking. The opportunities for students to interact were thereby increased, avoiding dissatisfaction, fatigue, and distractions, as well as the incorporation of activities that served to motivate, provide feedback, consolidate, and reinforce the capacities of attention, concentration, and the assimilation of the lesson content. This study concludes that teaching through chunking in synchronous classes that influence the intrinsic motivation of university students, as well as consolidating their learning and improving teachers’ pedagogical practices.
Nearest Neighbours Graph Variational AutoEncoder

Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results. However, both the generation of graphs and the handling of large graphs still remain open challenges. This work aims to introduce techniques for generating large graphs and test the approach on a complex problem such as the calculation of dose distribution in oncological radiotherapy applications. To this end, we introduced a pooling technique (ReNN-Pool) capable of sampling nodes that are spatially uniform without computational requirements in both model training and inference. By construction, the ReNN-Pool also allows the definition of a symmetric un-pooling operation to recover the original dimensionality of the graphs. We also present a Variational AutoEncoder (VAE) for generating graphs, based on the defined pooling and un-pooling operations, which employs convolutional graph layers in both encoding and decoding phases. The performance of the model was tested on both the realistic use case of a cylindrical graph dataset for a radiotherapy application and the standard benchmark dataset sprite. Compared to other graph pooling techniques, ReNN-Pool proved to improve both performance and computational requirements.
Parameters separation and calibration method for infinity imaging camera based on three-axis rotary table

Precision calibration is the key for infinity imaging camera to achieve accurate measurement, and calibration methods based on multi-axis rotary table are widely used for high accuracy and strong operability. However, conventional methods optimizing an integrated model lead to a coupling between intrinsic and extrinsic parameters, which may affect the accuracy of each parameter when applied separately. Moreover, the existing methods with parameters separation need intricate adjustment manually. Aiming at above problems, a novel calibration method realizing parameters separation by automatic calculation is proposed. By pure rotation motions based on three-axis rotary table, the correspondence between two groups of virtual homonymic points is constructed to solve the initial estimates of parameters, during which the vector of the calibration point coupled with intrinsic parameters can be eliminated. The simulation and experimental results verify the validity of the proposed method, whose measurement accuracy increased by 10.72% compared with that of traditional method.
The use of Web 2.0 services in organizing the individual educational trajectory of students

The article reveals the features of the use of Web 2.0 services in the educational process in order to organize the individual educational trajectory of students. This learning path contributes to the disclosure of students' individual abilities and opportunities in the field of mastering educational programs, taking into account their psychophysical characteristics. As a result of the use of Web 2.0 services, students form a certain range of cognitive actions that reveal their individual characteristics of studying a particular subject.
Evaluating Visual Analytics for Relevant Information Retrieval in Document Collections

Abstract Retrieving information from document collections is necessary in many contexts, e.g. researchers search for papers on a topic, physicians search for records of patients with a certain condition and police investigators seek relationships between different criminal reports. Finding relevant textual content in a corpus can be challenging in scenarios where the users expect a retrieval process with high recall. Visual Analytics (VA) systems that integrate interactive visualizations and machine learning algorithms are often advocated to support retrieval tasks in such complex scenarios. However, few studies report an end-user perspective on the utility of such systems. We present results from observational studies on VA-supported information retrieval conducted with graduate students and researchers using a system to explore collections of scientific papers. While users have, in general, positive views of the system’s potential to facilitate their retrieval tasks, some faced practical difficulties in using it effectively, and we found considerable variation in their assessment of specific functionalities. Our findings reinforce the potential of VA systems and also the importance of carefully informing users of the underlying conceptual models in such systems and their limitations.
Development of personal qualities of future specialists in physical education and sports favorable for a healthy lifestyle

As evidenced by the results of the conducted research, one of the priority tasks of educating future specialists in physical education and sports is the formation of a desire for a healthy lifestyle and the presence of relevant key characteristics of each of the components of health (physical, mental, spiritual, social).&#x0D; Such personal qualities define responsibility as a conscious attitude to duties, requirements and social tasks; communication as a necessary ability to find a common language with other people; emotional stability as the ability to effectively build behavioral strategies; self-regulation as the ability to monitor one's own emotional states and reactions, to resist negative factors; perseverance as readiness to overcome external and internal obstacles and difficulties, possessing the means to achieve the goal; discipline as compliance with social norms and requirements without reminders, control and external pressure, the ability to independently comply with defined rules; initiative as readiness for effective actions in a creative attitude to the surrounding reality; organization as planning and organizing one's own activities in accordance with defined principles and attitudes; purposefulness as a desire to achieve life goals, conscious and active direction of efforts to realize set tasks. The presence of such personal qualities of future specialists in physical education and sports is a guarantee for harmonious development and vitality. Prospects for further research. Work on the formation of personal qualities of future specialists in physical education and sports that are favorable for a healthy lifestyle should be scientifically based and aimed at making young people aware of the value of their own and public health as a resource for the life of an individual and the country as a whole. To implement the task, it is necessary to create conditions for the prevention of dangerous behavior, the development of skills and healthy lifestyle skills, etc.
Local saliency consistency‐based label inference for weakly supervised salient object detection using scribble annotations

Abstract Recently, weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling. However, there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information. Therefore, an intuitive idea is to infer annotations that cover more complete object and background regions for training. To this end, a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels. Specifically, k‐means clustering algorithm was first performed on both colours and coordinates of original annotations, and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres. Next, the same annotations for pixels with similar colours within each kernel neighbourhood was set further. Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state‐of‐the‐art results.
An empirical analysis of linguistic styles in new work services: The case of Fiverr.com

Abstract Current online marketplaces, characterized by a high number of sellers and the velocity of offerings, make service differentiation difficult for sellers. One particularly promising avenue for sellers (in this study: individuals) beyond classical demand‐side approaches (i.e., prices) is to employ linguistic descriptions of their offerings. Yet, it remains mostly unclear what constitutes “successful” linguistic strategies. To elaborate on this, the current paper mines more than 2000 unique service offerings on Fiverr.com , a leading online marketplace for freelance services. By distinguishing between different service categories (i.e., hedonic and utilitarian services) and other characteristics of individual sellers (e.g., the origin of a seller), the paper analyzes the linguistic service descriptions via the Linguistic Inquirer and Word Counts (LIWC) and provides an empirical taxonomy of linguistic styles among individuals. Although the paper is novel and explorative, a few interesting insights can be obtained. First, there are significant linguistic differences in how sellers describe their service offerings depending on the service category (hedonic/utilitarian). Second, linguistic proxies of complexity, namely, words per sentence, six‐letter words, and the overall word count (i.e., increasing informational content) as well as signals of analytical language, appear to be a beneficial strategy for sellers. Third, a linguistic strategy aimed at matching (congruence) of service categories (hedonic/utilitarian) and linguistic styles (analytical/emotional) appears to be beneficial. The results have important implications for creating linguistic strategies in online marketplaces focused on services on the supply side.
Style attention based global-local aware GAN for personalized facial caricature generation

Caricature is an exaggerated pictorial representation of a person, which is widely used in entertainment and political media. Recently, GAN-based methods achieved automatic caricature generation through transferring caricature style and performing shape exaggeration simultaneously. However, the caricature synthesized by these methods cannot perfectly reflect the characteristics of the subject, whose shape exaggeration are not reasonable and requires facial landmarks of caricature. In addition, the existing methods always produce the bad cases in caricature style due to the simpleness of their style transfer method.In this paper, we propose a Style Attention based Global-local Aware GAN to apply the characteristics of a subject to generate personalized caricature. To integrate the facial characteristics of a subject, we introduce a landmark-based warp controller for personalized shape exaggeration, which employs the facial landmarks as control points to warp image according to its facial features, without requirement of the facial landmarks of caricature. To fuse the facial feature with caricature style appropriately, we introduce a style-attention module, which adopts an attention mechanism, instead of the simple Adaptive Instance Normalization (AdaIN) for style transfer. To reduce the bad cases and increase the quality of generated caricatures, we propose a multi-scale discriminator to both globally and locally discriminate the synthesized and real caricature, which improves the whole structure and realistic details of the synthesized caricature.Experimental results on two publicly available datasets, the WebCaricature and the CaVINet datasets, validate the effectiveness of our proposed method and suggest that our proposed method achieves better performance than the existing methods.The caricatures generated by the proposed method can not only preserve the identity of input photo but also the characteristic shape exaggeration for each person, which are highly close to the real caricatures drawn by real artists. It indicates that our method can be adopted in the real application.
Automatic Generation of Literary Sentences in French

In this paper, we describe a model for the automatic generation of literary sentences in French. Although there has been much recent effort directed towards automatic text generation in general, the generation of creative, literary sentences that is not restricted to a specific genre, which we approached in this work, is a difficult task that is not commonly treated in the scientific literature. In particular, our present model has not been previously applied to the generation of sentences in the French language. Our model was based on algorithms that we previously used to generate sentences in Spanish and Portuguese and on a new corpus, which we constructed and present here, consisting of literary texts in French, called MegaLitefr. Our automatic text generation algorithm combines language models, shallow parsing, the canned text method, and deep learning artificial neural networks. We also present a manual evaluation protocol that we propose and implemented to assess the quality of the artificial sentences generated by our algorithm, by testing if they fulfil four simple criteria. We obtained encouraging results from the evaluators for most of the desired features of our artificially generated sentences.
Efficient graph edit distance computation using isomorphic vertices

In this paper, we study the problem of graph edit distance (GED) computation. We empirically observe that real graph data contain many isomorphic substructures, which incur redundant computation. Based on this observation, we aim at reducing the cost of GED computation by avoiding redundant computation caused by isomorphic substructures. To detect isomorphic substructures, we precisely define a notion of vertex isomorphism and propose a dynamic programming algorithm that identifies isomorphic vertices in a graph. By taking advantage of isomorphic vertices, we develop an efficient GED computation algorithm. In experiments, we show that isomorphic vertices are effectively used in reducing the search space of GED computation, and as a result our approach improves the performance of GED computation.
The experience of organizing the teaching of pedagogical university students in the discipline "Elementary Mathematics" using the resources of the technopark

The article presents the experience of building the process of teaching future teachers the discipline &amp;quot;Elementary Mathematics&amp;quot; on the basis of the technopark of universal pedagogical competencies of the Volgograd State Social and Pedagogical University. The technology of teaching the discipline &amp;quot;Elementary Mathematics&amp;quot; to students studying in the direction of Pedagogical education profile Mathematics is built, within which the integration of the model of station rotation and the use of an online course is carried out.
An efficient method for generating a discrete uniform distribution using a biased random source

Abstract We present an efficient algorithm to generate a discrete uniform distribution on a set of p elements using a biased random source for p prime. The algorithm generalizes Von Neumann’s method and improves the computational efficiency of Dijkstra’s method. In addition, the algorithm is extended to generate a discrete uniform distribution on any finite set based on the prime factorization of integers. The average running time of the proposed algorithm is overall sublinear: $\operatorname{O}\!(n/\log n)$ .