Reviewerly/exHarmony huggingface dataset:
Dynamic ESPI Evaluation of Deformation and Fracture Mechanism of 7075 Aluminum Alloy

The deformation and fracture mechanism in 7075 aluminum alloy is discussed based on a field theoretical approach. A pair of peak-aged and overaged plate specimens are prepared under the respective precipitation conditions, and their plastic deformation behaviors are visualized with two-dimensional electronic speckle pattern interferometry (ESPI). The in-plane velocity field caused by monotonic tensile loading is monitored continuously via the contour analysis method of ESPI. In the plastic regime, the peak-aged specimen exhibits a macroscopically uniform deformation behavior, while the annealed specimen exhibits non-uniform deformation characterized by a localized shear band. The occurrence of the shear band is explained by the transition of the material’s elastic resistive mechanism from the longitudinal force dominant to shear force dominant mode. The shear force is interpreted as the frictional force that drives mobile dislocations along the shear band. The dynamic behavior of the shear band is explained as representing the motion of a solitary wave. The observed decrease in the solitary wave’s velocity is accounted for by the change in the acoustic impedance with the advancement of plastic deformation.
Footballer Action Tracking and Intervention Using Deep Learning Algorithm

Fédération Internationale de Football Association is the governing body of the football world cup. The international tournament of football requires extensive training of all football players and athletes. In the training process of footballers, players and coaches recognize the training actions completed by footballers. The training actions are compared with standard actions, calculate losses, and scientifically intervene in the training processes. This intervention is important for better results during the training sessions. Coaches must determine and confirm that every action performed by the footballers meets the minimum standards. It is because the actions of individual players are performed quickly; as a result, the coach’s eye may not produce accurate results as human activities are prone to errors. Therefore, this paper designs and develops a footballer’s motion and gesture recognition and intervention algorithm using a convolutional neural network (CNN). In this proposed algorithm, initially, texture features and HSV features of the footballer’s posture image are extracted and then a dual-channel CNN is constructed. Each characteristic is extracted separately, and the output of the dual-channel network is combined. Finally, the obtained results are passed from a fully connected CNN to estimate and construct the posture image of the footballer. This article performs experimental testing and comparative analysis on a wide range of data and also conducts ablation studies. The experimental work shows that the proposed algorithm achieves better performance results.
Distributed Fusion Estimation for Stochastic Uncertain Systems With Network-Induced Complexity and Multiple Noise

This article investigates an issue of distributed fusion estimation under network-induced complexity and stochastic parameter uncertainties. First, a novel signal selection method based on event trigger is developed to handle network-induced packet dropouts, as well as packet disorders resulting from random transmission delays, where the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H_{2}}/{H_{\infty } }$ </tex-math></inline-formula> performance of the system is analyzed in different noise environments. In addition, a linear delay compensation strategy is further employed for solving the complex network-induced problem, which may deteriorate system performance. Moreover, a weighted fusion scheme is used to integrate multiple resources through an error cross-covariance matrix. Several case studies validate the proposed algorithm and demonstrate satisfactory system performance in target tracking.
Use of Blockchain Technology in Implementing Information System Security On Education

Utilizing blockchain technology for information system security in the field of education is that all information data that has been entered cannot be faked, lost, or damaged easily, and also with blockchain, one can send data and information instantaneously and be decentralized without relying on third parties. Blockchain is a ledger that can efficiently and effectively record transactions between two parties that have been distributed and in a verifiable and permanent way. However, even though the application of blockchain technology in education has not been utilized globally However, researchers try to evaluate the security of information systems in education using fingerprints by implementing cryptographic hash security sensors so that the results of this study are achieved and are useful for the world of education. This research method uses the SWOT method and all these things will be discussed. Keyword : Blockchain Technology, Education, Swot Method
Deep Convolutional Neural Network for Breast Mass Classification from Mammogram

Breast cancer is the largest detection of cancer among women worldwide.Advancement in computer-aided diagnosis (CAD) makes it easy to detect and to classify benign and malignant images, henceforth to increase the life span of women.But finetuning of the accuracy of the existing CAD system comes to the limelight with the available resources.In recent study shows deep convolutional network provides greater accuracy.In this paper, we use deep CNN to extract the features with AlexNet.Then we Fine-tuned the various parameters to improve the accuracy with various optimizers and learning rates to classify the malignant and benign masses with CBIS-DDSM (Curated Breast Imaging Subset of DDSM) dataset.The two classifiers used the Support vector machine (SVM) and the Extreme Learning Machine (ELM) which provides an accuracy of 97.36% and 100% respectively.
A Practical Approach to Ontology-Based Data Modelling for Semantic Interoperability

Efforts to provide a standard representational framework based on current materials modelling and characterization knowledge facilitating collaboration, digital data representation, knowledge systems and semantic interoperability has been the main agenda for the European Materials Modelling Council (EMMC).One challenge in adopting the technology is related to the on-boarding process, particularly regarding the availability of ontologies and practical aspects to linking data to the ontologies, which requires deep insights in the current knowledge organization system.Latter challenges we address by constructing an interoperability framework based on data-models and explicit ontological mappings.Data models allow for building a representation of a computer system from different perspectives.The conceptual view identifies what real-world concepts the data represents.The logical perspective defines the rules and structures of how to implement the strategy.The physical perspective establishes the relationship between the data model and a specific database system.We show the ontology-based data modelling approach.It addresses the separation of the logical and the conceptual perspective, and allows for the development of the data-models.This approach also puts the data-models directly into production before applying the ontological mappings or developing the domain ontologies.Finally, we show a framework for information exchange that connects data models to physical storage and allows software applications to eliminate the need to support specific input-output operations, file conversion, file versioning, etc.
Comparative observer effects in 2D and 3D localization tasks

Purpose: Three-dimensional "volumetric" imaging methods are now a common component of medical imaging across many imaging modalities. Relatively little is known about how human observers localize targets masked by noise and clutter as they scroll through a 3D image and how it compares to a similar task confined to a single 2D slice. Approach: Gaussian random textures were used to represent noisy volumetric medical images. Subjects were able to freely inspect the images, including scrolling through 3D images as part of their search process. A total of eight experimental conditions were evaluated (2D versus 3D images, large versus small targets, power-law versus white noise). We analyze performance in these experiments using task efficiency and the classification image technique. Results: In 3D tasks, median response times were roughly nine times longer than 2D, with larger relative differences for incorrect trials. The efficiency data show a dissociation in which subjects perform with higher statistical efficiency in 2D tasks for large targets and higher efficiency in 3D tasks with small targets. The classification images suggest that a critical mechanism behind this dissociation is an inability to integrate across multiple slices to form a 3D localization response. The central slices of 3D classification images are remarkably similar to the corresponding 2D classification images. Conclusions: 2D and 3D tasks show similar weighting patterns between 2D images and the central slice of 3D images. There is relatively little weighting across slices in the 3D tasks, leading to lower task efficiency with respect to the ideal observer.
Augmented Reality with Maps for Off-Screen POI Awareness

Augmented Reality (AR) applications for mobile devices are common nowadays, however the information superimposed over the image captured by the device's camera is limited to its field of view. To address this problem in the visualization of points of interest (POI), we have combined the AR view with the display of the POI's location in a map to increase user awareness about nearby POI. It gives insight about the orientation and distance between the POI and the user. We developed 3 solutions: (1) including cues to off-screen POI in a frame at the border of the AR view and providing an alternative map view; (2) combining the AR view with a mini-map superimposed on its top left corner; and (3) combining the AR view with a circular radar view on its top left corner. A usability study performed to compare these approaches revealed that, although all the approaches were positively appreciated, the solution with a mini-map was the preferred one.
Understanding and Detecting International Revenue Share Fraud

Premium rate phone numbers are often abused by malicious parties (e.g., via various phone scams, mobile malware) as a way to obtain monetary benefit.This benefit comes from the 'revenue share' mechanism that enables the owner of the premium rate number to receive some part of the call revenue for each minute of the call traffic generated towards this number.This work focuses on International Revenue Share Fraud (IRSF), which abuses regular international phone numbers as the so-called International Premium Rate Numbers (IPRN).IRSF often involves multiple parties (e.g., a fraudulent telecom operator in collaboration with a premium rate service provider) who collect and share the call revenue, and is usually combined with other fraud schemes to generate call traffic without payment.Although this fraud scheme has been around for several years, it remains to be one of the most common phone fraud schemes, reportedly leading to billions of dollars of losses every year.In this paper we explore the IRSF ecosystem from multiple angles, via: (i) A telephony honeypot that observes IRSF attempts towards an unused phone number range (i.e., a phone number gray space), (ii) A dataset of more than 3 Million test IPRNs and more than 206K test call logs we collected from several online IPRN service providers during 4 years, and finally, (iii) A realworld call data set from a small European operator, involving 689K call records, that we analyze to find IRSF cases.By leveraging our observations from (ii), we propose several machine learning features that can be used in IRSF detection.We validate our approach on the dataset in (iii), achieving 98% accuracy with a 0.28% false positive rate in detecting the fraudulent calls.
Shot detection using skeleton position in badminton videos

This paper proposes a shot detection method using the poses of a player in a badminton video sequence. In the proposed method, the hit timing is detected by focusing on the arm movements of the player and analysing the swing movement using skeletal information. The simple shot information is estimated by connecting player positions in the hit timing frame. By performing an experiment to verify hit timing detection, we confirmed that the detection is highly accurate and shot information detection is achieved.
Multilayer Network Representation Learning Method Based on Random Walk of Multiple Information

Network representation learning aims to map nodes in the network into low-dimensional dense vectors, which can be widely used to solve the network analysis tasks. Existing methods mainly focus on single-layer homogeneous networks. However, many real-world networks consist of multiple types of nodes and edges, which are called multilayer networks. The problem of how to capture node information and use multi-type relational information is a major challenge of multilayer network representation learning. To address this problem, we propose a method of random walk of multiple information, called IFMNE, to efficiently preserve and learn node information and multi-type relational information into a unified space. This method combines node structure information with network topology information to obtain the node random walk sequence, and trains the node walk sequence on the neural network model. Experimental results are performed on five real multilayer networks, and the embedding vectors were evaluated by link prediction task. The accuracy was significantly improved on the basis of low time complexity compared with the baseline methods.
Optimal Sampling Gaps for Adaptive Submodular Maximization

Running machine learning algorithms on large and rapidly growing volumes of data is often computationally expensive, one common trick to reduce the size of a data set, and thus reduce the computational cost of machine learning algorithms, is probability sampling. It creates a sampled data set by including each data point from the original data set with a known probability. Although the benefit of running machine learning algorithms on the reduced data set is obvious, one major concern is that the performance of the solution obtained from samples might be much worse than that of the optimal solution when using the full data set. In this paper, we examine the performance loss caused by probability sampling in the context of adaptive submodular maximization. We consider a simple probability sampling method which selects each data point with probability at least r. If we set r=1, our problem reduces to finding a solution based on the original full data set. We define sampling gap as the largest ratio between the optimal solution obtained from the full data set and the optimal solution obtained from the samples, over independence systems. Our main contribution is to show that if the sampling probability of each data point is at least r and the utility function is policywise submodular, then the sampling gap is both upper bounded and lower bounded by 1/r. We show that the property of policywise submodular can be found in a wide range of real-world applications, including pool-based active learning and adaptive viral marketing.
Training STEAM Educators in the COVID-19 emergency situation: Redesigning teaching

With the irruption of the COVID-19, the Institute for Educational Sciences (ICE) of the Universidad Politécnica de Madrid (UPM), specialized in Science, Technology, Engineering, Arts and Mathematics (STEAM) educators' training, had to redesign its programs. This abrupt adaptation was conducted relying on Information and Communication Technologies (ICT) that faculty and students had not all previously handled. A comparison of students' opinions comparing pre-COVID face-to-face teaching and COVID distance teaching was carried out. Analyses show that eLearning during COVID has, in general, affected objectives, contents and pedagogical performance. Although participants value the efforts of the professors to adapt their teaching to a new situation, all indicators decreased, except in one program. Consequently, the ICE's programs need to be re-designed to meet their students' demands, as it is not enough to literally apply face-to-face teaching to a virtual distance environment. It is necessary to transform the educational practice, including diverse learning activities for competences achievement. Furthermore, the ICE, as a center specialized in teacher training, has a huge responsibility in helping UPM faculty to move forward, providing training in all the areas needed for a transition from face-to-face and blended Learning (b-Learning) teaching to its combination with bimodal synchronous teaching (d- Learning), as future generations will demand these multiple modalities.
Accelerating Distributed SGD With Group Hybrid Parallelism

The scale of model parameters and datasets is rapidly growing for high accuracy in various areas. To train a large-scale deep neural network (DNN) model, a huge amount of computation and memory is required; therefore, a parallelization technique for training large-scale DNN models has attracted attention. A number of approaches have been proposed to parallelize large-scale DNN models, but these schemes lack scalability because of their long communication time and limited worker memory. They often sacrifice accuracy to reduce communication time. In this work, we proposed an efficient parallelism strategy named group hybrid parallelism (GHP) to minimize the training time without any accuracy loss. Two key ideas inspired our approach. First, grouping workers and training them by groups reduces unnecessary communication overhead among workers. It saves a huge amount of network resources in the course of training large-scale networks. Second, mixing data and model parallelism can reduce communication time and mitigate the worker memory issue. Data and model paralleism are complementary to each other so the training time can be enhanced when they are combined. We analyzed the training time model of the data and model parallelism, and based on the training time model, we demonstrated the heuristics that determine the parallelization strategy for minimizing training time. We evaluated group hybrid parallelism in comparison with existing parallelism schemes, and our experimental results show that group hybrid parallelism outperforms them.
Acoustic Modeling for Multi-Array Conversational Speech Recognition in the Chime-6 Challenge

This paper presents our main contributions of acoustic modeling for multi-array multi-talker speech recognition in the CHiME-6 Challenge, exploring different strategies for acoustic data augmentation and neural network architectures. First, enhanced data from our front-end network preprocessing and spectral augmentation are investigated to be effective for improving speech recognition performance. Second, several neural network architectures are explored by different combinations of deep residual network (ResNet), factorized time delay neural network (TDNNF) and residual bidirectional long short-term memory (RBiLSTM). Finally, multiple acoustic models can be combined via minimum Bayes risk fusion. Compared with the official baseline acoustic model, the proposed solution can achieve a relatively word error rate reduction of 19% for the best single ASR system on the evaluation data, which is also one of main contributions to our top system for the Track 1 tasks of the CHiME-6 Challenge.
Accessibility of Microsoft Teams and Moodle Services for the Implementation of E-Learning for Students with Disabilities in Institutions of Higher Education in Ukraine

The current study investigates the ways to increase accessibility to higher education for students with disabilities. The importance of implementation e-learning for such students as one of the preconditions for effective social and psychological adaptation and further successful integration of persons with disabilities into society is emphasized. A survey of students with disabilities was conducted to identify their needs in educational process. The main difficulties that they overcome in the process of education in institutions of higher education are identified. The main peculiarities and advantages of the distance learning course «Adaptive Physical Education» are described created in the Moodle system. The differences and benefits of Microsoft Teams over the Moodle platform in the process of e-learning for students with disabilities are described. The process of organizing of «Adaptive Physical Education» distance learning course for students with disabilities on the Microsoft Teams platform and the purpose of implementation it in the institution of higher education are emphasized.
Decision Support System for Selection of Thesis Advisors Supervisors Thesis Advisors according to the Field of Science Using the AHP Method

Thesis is a scientific work and a condition that must be taken in order to achieve a bachelor's degree. Thesis supervisors play an important role because they have the responsibility and ensure that students are able to compile the thesis properly so that it is ready for testing and quality. In higher education, a system is needed that can help and ease a job, especially in the selection of thesis supervisors. In selecting the thesis supervisor, it is still based on subjectivity, which causes the title submitted by students to not match the field of science of the thesis supervisor so that the lecturer appointed to guide the topic of the title submitted by students has a different field of expertise from the student's title. In this determination, there are sometimes less than optimal decisions where the appointed lecturer is not in accordance with the thesis topic proposed by the student. This system is made using the AHP method so that the programmed system can be carried out more effectively. AHP method is one of the methods used in decision support systems with the calculation process by comparing each criterion.
Cross-Domain and Disentangled Face Manipulation With 3D Guidance

Face image manipulation via three-dimensional guidance has been widely applied in various interactive scenarios due to its semantically-meaningful understanding and user-friendly controllability. However, existing 3D-morphable-model-based manipulation methods are not directly applicable to out-of-domain faces, such as non-photorealistic paintings, cartoon portraits, or even animals, mainly due to the formidable difficulties in building the model for each specific face domain. To overcome this challenge, we propose, as far as we know, the first method to manipulate faces in arbitrary domains using human 3DMM. This is achieved through two major steps: 1) disentangled mapping from 3DMM parameters to the latent space embedding of a pre-trained StyleGAN2 [1] that guarantees disentangled and precise controls for each semantic attribute; and 2) cross-domain adaptation that bridges domain discrepancies and makes human 3DMM applicable to out-of-domain faces by enforcing a consistent latent space embedding. Experiments and comparisons demonstrate the superiority of our high-quality semantic manipulation method on a variety of face domains with all major 3D facial attributes controllable – pose, expression, shape, albedo, and illumination. Moreover, we develop an intuitive editing interface to support user-friendly control and instant feedback. Our project page is <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://cassiepython.github.io/cddfm3d/index.html</uri> .