Reviewerly/exHarmony huggingface dataset:
Use Of Social Media For Marketing Of Library And Information Services In Academic Libraries

Academic libraries play a crucial role in facilitating the acquisition of knowledge and information for students in higher education.This study examines the utilization of social media platforms as a means of promoting academic libraries and enhancing their marketing effectiveness.Through the collection of primary quantitative data via a survey administered to 165 participants, the study explores the relationship between social media usage and library marketing strategies.Utilizing statistical analysis tools such as SPSS, the study interprets data pertaining to key variables including social media engagement and marketing approaches.The findings underscore the significance of leveraging social media platforms to amplify the visibility and utility of academic libraries, particularly in higher education settings.This research contributes to a deeper understanding of the evolving role of social media in modern library marketing practices and highlights opportunities for enhancing engagement and outreach within the academic community.
LEGAL-NORMAL BASIS OF FORMING SOCIAL-PEDAGOGICAL COMPETENCE OF A TEACHER IN INCLUSIVE EDUCATIONAL ENVIRONMENT

The article covers the issue of the development of socio-pedagogical competence of educators in the environment of inclusive education. Also, the state has analyzed the conditions created by students with disabilities in the educational environment and the implementation of developed laws and decisions.
ACTIVATION OF PEDAGOGICAL AND PSYCHOLOGICAL OPPORTUNITIES IN THE DEVELOPMENT OF THE INDEPENDENT EDUCATIONAL ACTIVITY OF THE FUTURE MUSIC TEACHERS

This article presents ideas on the pedagogical importance of developing the independent learning activity of future music teachers, the technologies and didactic games used in the development of independent learning activity of future music teachers.
THE ESSENCE OF CREATIVE COMPETENCE IN PEDAGOGY AND MODERN APPROACHES

This article talks about creative competence in pedagogy, its place and essence in the educational process, competence approaches to it, specific features of formation and development of creative competence in students.
3D orientation field transform

Abstract Vascular structure enhancement is very useful in image processing and computer vision. The enhancement of the presence of the structures like tubular networks in given images can improve image-dependent diagnostics and can also facilitate tasks like segmentation. The two-dimensional (2D) orientation field transform has been proved to be effective at enhancing 2D contours and curves in images by means of top-down processing. It, however, has no counterpart in 3D images due to the extremely complicated orientation in 3D against 2D. Given the rising demand and interest in handling 3D images, we experiment with modularising the concept and generalise the algorithm to 3D curves. In this work, we propose a 3D orientation field transform. It is a vascular structure enhancement algorithm that can cleanly enhance images having very low signal-to-noise ratio, and push the limits of 3D image quality that can be enhanced computationally. This work also utilises the benefits of modularity and offers several combinative options that each yield moderately better enhancement results in different scenarios. In principle, the proposed 3D orientation field transform can naturally tackle any number of dimensions. As a special case, it is also ideal for 2D images, owning a simpler methodology compared to the previous 2D orientation field transform. The concise structure of the proposed 3D orientation field transform also allows it to be mixed with other enhancement algorithms, and as a preliminary filter to other tasks like segmentation and detection. The effectiveness of the proposed method is demonstrated with synthetic 3D images and real-world transmission electron microscopy tomograms ranging from 2D curve enhancement to, the more important and interesting, 3D ones. Extensive experiments and comparisons with existing related methods also demonstrate the excellent performance of the proposed 3D orientation field transform.
SPACE-TIME INTEGRATION APPROACH TO TRACKING WEAK TARGETS USING SPATIALLY DISTRIBUTED SENSORS

This paper investigates the performance and robustness of the space-time integration technique by applying it to two different scenarios; a target moving in a straight line through a sonobuoy field; and a target undertaking a major course change. Although most techniques will track a target moving in a straight line quite successfully many have difficulty Packing the target as it manoeuvres through a slow rum. The performance of this approach is compared with the more traditional extended Kalman Filter algorithm where the advantages and dis-advantages of each technique is highlighted. Results are presented for scenarios using both simulated and sea trial data.
A novel multi-scale loss function for classification problems in machine learning

We introduce two-scale loss functions for use in various gradient descent algorithms applied to classification problems via deep neural networks. This new method is generic in the sense that it can be applied to a wide range of machine learning architectures, from deep neural networks to support vector machines for example. These two-scale loss functions allow to focus the training onto objects in the training set which are not well classified. This leads to an increase in several measures of performance for appropriately-defined two-scale loss functions with respect to the more classical cross-entropy when tested on traditional deep neural networks on the MNIST, CIFAR10, and CIFAR100 data-sets.
Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring

Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviors of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this article, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning for dietary intake assessment in real-life settings.
Temporal segment dropout for human action video recognition

Temporal information is important for human action video recognition. With the widely used spatio-temporal neural networks, researchers have found that the learned high-level features preserve overfitted spatial information and limited temporal information, leading to inferior performance. This is because existing networks lack efficient regularization for the temporal structure. To learn more robust temporal features, we propose a temporal regularization method named Temporal Segment Dropout (TSD). TSD drops the most salient spatial features in order to enhance the temporal features in a clip of temporal segments. Without learning from complex examples, TSD can be easily deployed in existing networks. In the experiment, TSD is extensively evaluated on benchmark action recognition datasets, which brings consistent improvements over the baselines, especially for the action-centric classes.
Bayesian Landmark-Based Shape Analysis of Tumor Pathology Images

Medical imaging is a form of technology that has revolutionized the medical field over the past decades. Digital pathology imaging, which captures histological details at the cellular level, is rapidly becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods have facilitated tumor region segmentation from pathology images. The traditional shape descriptors that characterize tumor boundary roughness at the anatomical level are no longer suitable. New statistical approaches to model tumor shapes are in urgent need. In this article, we consider the problem of modeling a tumor boundary as a closed polygonal chain. A Bayesian landmark-based shape analysis model is proposed. The model partitions the polygonal chain into mutually exclusive segments, accounting for boundary roughness. Our Bayesian inference framework provides uncertainty estimations on both the number and locations of landmarks, while outputting metrics that can be used to quantify boundary roughness. The performance of our model is comparable with that of a recently developed landmark detection model for planar elastic curves. In a case study of 143 consecutive patients with stage I to IV lung cancer, we demonstrated the heterogeneity of tumor boundary roughness derived from our model effectively predicted patient prognosis (p-value <0.001). Supplementary materials for this article are available online.
An unfolding method based on conditional invertible neural networks (cINN) using iterative training

The unfolding of detector effects is crucial for the comparison of data to theory predictions. While traditional methods are limited to representing the data in a low number of dimensions, machine learning has enabled new unfolding techniques while retaining the full dimensionality. Generative networks like invertible neural networks~(INN) enable a probabilistic unfolding, which map individual data events to their corresponding unfolded probability distribution. The accuracy of such methods is however limited by how well simulated training samples model the actual data that is unfolded. We introduce the iterative conditional INN (IcINN) for unfolding that adjusts for deviations between simulated training samples and data. The IcINN unfolding is first validated on toy data and then applied to pseudo-data for the pp \to Z \gamma \gamma <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mo>→</mml:mo><mml:mi>Z</mml:mi><mml:mi>γ</mml:mi><mml:mi>γ</mml:mi></mml:mrow></mml:math> process.
Joint Multi-View Unsupervised Feature Selection and Graph Learning

Despite significant progress, previous multi-view unsupervised feature selection methods mostly suffer from two limitations. First, they generally utilize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">either</i> cluster structure <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">or</i> similarity structure to guide the feature selection, which neglect the possibility of a joint formulation with mutual benefits. Second, they often learn the similarity structure by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">either</i> global structure learning <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">or</i> local structure learning, which lack the capability of graph learning with both global and local structural awareness. In light of this, this article presents a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</i> oint <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> ulti- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</i> iew unsupervised <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> eature selection and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</i> raph learning (JMVFG) approach. Particularly, we formulate the multi-view feature selection with orthogonal decomposition, where each target matrix is decomposed into a view-specific basis matrix and a view-consistent cluster indicator. The cross-space locality preservation is incorporated to bridge the cluster structure learning in the projected space and the similarity learning (i.e., graph learning) in the original space. Further, a unified objective function is presented to enable the simultaneous learning of the cluster structure, the global and local similarity structures, and the multi-view consistency and inconsistency, upon which an alternating optimization algorithm is developed with theoretically proved convergence. Extensive experiments on a variety of real-world multi-view datasets demonstrate the superiority of our approach for both the multi-view feature selection and graph learning tasks. The code is available at https://github.com/huangdonghere/JMVFG.
CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge’s improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.
Reducing redundancy in the bottleneck representation of autoencoders

Autoencoders (AEs) are a type of unsupervised neural networks, which can be used to solve various tasks, e.g., dimensionality reduction, image compression, and image denoising. An AE has two goals: (i) compress the original input to a low-dimensional space at the bottleneck of the network topology using an encoder, (ii) reconstruct the input from the representation at the bottleneck using a decoder. Both encoder and decoder are optimized jointly by minimizing a distortion-based loss which implicitly forces the model to keep only the information in input data required to reconstruct them and to reduce redundancies. In this paper, we propose a scheme to explicitly penalize feature redundancies in the bottleneck representation. To this end, we propose an additional loss term, based on the pairwise covariances of the network units, which complements the data reconstruction loss forcing the encoder to learn a more diverse and richer representation of the input. We tested our approach across different tasks, namely dimensionality reduction, image compression, and image denoising. Experimental results show that the proposed loss leads consistently to superior performance compared to using the standard AE loss.
The formation of innovative behavior values in new type high schools - national research universities

Abstract An important task of the education system in modern market conditions is the preparation and retraining of personnel for innovative activity. Current methods of training specialists do not fully provide the solution of the problem, most of the management training given to the theoretical knowledge. It is necessary to organize context educational process with the participation of managers, owners of existing enterprises, startups and enterprises on the way of modernization of various sectors of the economy that will also help to practice the innovative and entrepreneurial skills. Strategic partnership of Kazan national research technological University and all-Russian public organization "Support of Russia" in the training of personnel for innovative activity in Russia creates a new resource - innovative knowledge, achievements and technologies of the future, creating conditions for the implementation of the national development strategy of the Russian Federation.
Fostering Interest and Knowledge in the Information Security Industry for K-12 Students Using Virtual Machines

Abstract The field of information security is growing rapidly as malicious attacks to information systems become more frequent and detrimental to individuals and organizations who have become increasingly dependent upon these systems. With such a dramatic growth in demand in a short timeframe, there exists a shortage in the supply of professionals entering the field. In order to assist in the remediation of this gap, the Penn State Berks Information Sciences and Technology (IST) and Security Risk Analysis (SRA) programs are hosting an annual NSA and NSF sponsored summer camp as part of the GenCyber program. This camp is focused on critical thinking, problem-solving, and igniting interest in information security and other STEM fields through the application of hands-on activities in the security of a virtual machine environment. By implementing this program, Penn State Berks aims to show students the value of information security as early as reasonably possible, share the knowledge of safe practices and principles for using the Internet safely, and instill an interest in cybersecurity, as well as the STEM field as a whole. The evaluation of the program shows that the participants make significant progress towards achieving the learning outcomes of the summer camps.
Findings of the Pilot Offering of an Application Oriented Course (ENGR101)

Students' inability to easily apply concepts of mathematics to engineering problems and applications is detrimental to their success in pursuing an engineering degree.It has a direct impact on the retention and graduation numbers in engineering colleges.In addition, high failure rate in first year mathematics courses is also hurting students' chances to make satisfactory progress towards their degree and ultimately graduate within even six years.In order to address these serious issues, in Fall of 2016, New Jersey Institute of Technology offered ENGR 101 -an application oriented course based on Wright State University model to engineering students placed in pre-calculus courses.Although only one year worth of data has been collected thus far and there is much to be studied and analyzed on the effectiveness of the course, we can already see that this experiment has produced encouraging results and students taking this course performed better in their pre-calculus courses compared to students who did not take ENGR 101.This paper will present the results of our analysis, including performance in the mathematics course while taking ENGR 101 simultaneously, and performance in the mathematics course in the subsequent semester.With the lessons learned from last year, a few additions and changes will be made for Fall 2017.
SO(2)-Equivariant Downwash Models for Close Proximity Flight

Multirotors flying in close proximity induce aerodynamic wake effects on each other through propeller downwash. Conventional methods have fallen short of providing adequate 3D force-based models that can be incorporated into robust control paradigms for deploying dense formations. Thus, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning</i> a model for these downwash patterns presents an attractive solution. In this paper, we present a novel learning-based approach for modelling the downwash forces that exploits the latent geometries (i.e. symmetries) present in the problem. We demonstrate that when trained with only 5 minutes of real-world flight data, our geometry-aware model outperforms state-of-theart baseline models trained with more than 15 minutes of data. In dense real-world flights with two vehicles, deploying our model online improves 3D trajectory tracking by nearly 36% on average (and vertical tracking by 56%).
A small object detection algorithm based on feature interaction and guided learning

At present, for the diverse target sizes, different angles, and complex environment in aerial images, how to detect small objects effectively in aerial images is still a challenge task. In order to further improve the detection accuracy and reduce the missing rate, in this paper, we proposes a small target detection algorithm based on feature interaction and guided learning. Firstly, the feature guided alignment module is designed to deal with the problem of information loss and overlap. Secondly, the misallocation of small object samples is optimized by introducing gaussian sample allocation strategy. Then the interactive parallel detection header is designed to solve the detection task conflict problem. Finally, the new regression loss function is designed to enhance the localization ability of the target. The experimental results on the Tinyperson dataset and Visdrone dataset show that this model can improve the detection accuracy and reduce the missing rate of small targets effectively.
Predicting Hypersonic Glide Vehicle Behavior With Stochastic Grammars

Hypersonic glide vehicles are a new class of vehicles that fly at hypersonic speeds and have high maneuverability. These fast-moving targets exhibit different flight characteristics compared to conventional vehicles, so traditional tracking and defense systems require new methods to contend with them. In this paper, we propose a machine learning method for predicting the behavior of hypersonic glide vehicles. Our method is based on a stochastic grammar, which is a mathematical framework that describes the possible transition patterns of sequences. We use the stochastic grammar to predict the transition patterns in hypersonic glide vehicle trajectories. Given a partial trajectory, our method uses the grammar to predict the hypersonic glide vehicle's future kinematics, such as its altitude, velocity, and acceleration. We evaluate our method on two datasets of simulated hypersonic glide vehicle trajectories and show that it can successfully predict hypersonic glide vehicle behavior, even in the presence of noise. We also show that our method can predict several minutes into the future and can accurately predict future hypersonic glide vehicle behavior based on shorter observation times. Our results suggest that our method has the potential to be a valuable tool for predicting behavior of hypersonic glide vehicles.