Reviewerly/exHarmony huggingface dataset:
A Cloud-Based Approach to Big Graphs

Analyzing graphs, networks of nodes and links, becomes more difficult with increasing scale, especially when modeling real-world problems where skew distribution in the data exacerbates resource contention. The greater storage capacity of disks combined with support for many more IO ports than DIMM slots, can bend the growth curve of system size as graph scales continue to accelerate. Modern computer architecture is optimized for consistent and predictable access to data, instituting a hierarchy of memory subsystems to keep the most relevant data proximal to the processing unit. As large computing systems are increasingly assembled from lower cost, commodity computer components which are less resilient, the task of fault-tolerance is further levied onto the software stack. The performance gap between memory and disk will narrow with solid-state drives and new advances in disk subsystems that resemble memory architectures.
Real Time Multiple-Object Detection Based On Enhanced SSD

Object detection and recognition are widely employed in a variety of computer vision applications, and numerous techniques have been suggested in the literature. Object detection is one of the areas of computer vision that has seen a lot of achievement. In the last decade, various applications of computer vision have been presented. Some of these include autonomous cars, video surveillance, and scene interpretation. Visual recognition systems, including image categorization, localization, and detection, are at the heart of all of these applications and have gathered a lot of research attention. These visual identification algorithms have achieved extraordinary performance because of considerable advancements in neural networks, particularly deep learning (DL). Object detection techniques in real-time systems, on the other hand, necessitate minimal computing time and a thorough performance assessment prior to deployment. In this paper multiple image detection approach is used to identify and locate the object concerning their class label, and an enhanced feature map is used for object detection. All this is possible due to the recent advancement in DL along with image processing. These images, fed to the model, come from the video source. To tackle the problem related to the aspect ratio, the model uses the multi-scale feature map together with a distinct filter for diverse default boxes.
Towards a scalable mixed-species ion trap quantum computer with one laser-free species

The 53rd Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics will take place from May 30 – June 3, 2022 in Orlando, Fl, USA. <a href="https://morressier.zoom.us/j/89880865272?pwd=SWxTRGhra1ZOSDBrSkhVZDBzVDR6UT09/">Virtual Help Desk</a>
Group recommendation in Telegram by membership graph analyzing

Telegram currently has nearly 500 million monthly active users. It is known as a social network in many countries, such as Iran. Due to the diversity and increase of groups in this messenger, it has become difficult to find groups that are related to the users’ interests; so it is better for users to find suitable groups with the help of recommender systems. In this paper, we present a new collaborative filtering method for a group recommender system. It is based on the users’ interests, using and analyzing the graph of users’ membership. The proposed method mainly consists of two parts. In the first step, the main user and top similar users are extracted, then we list the top similar users’ groups. For each similar user, we calculate a score based on the public and the number of user’s groups for any group in the list. We consider the total score of all the top similar users for each group in the list as the group rank. In the second step, due to the existence of groups with only one similar user, the score of popular groups changes according to the number of dissimilar members in each group. Experiments have been performed to determine the effectiveness of these methods on Telegram data. The actual data of this research includes more than 700,000 supergroups and 70 million Telegram users. To evaluate the accuracy and performance of this research precision, recall and f-measure are used and the results confirm the usefulness and effectiveness of the proposed method.
Intake

OpflowVolume 48, Issue 5 p. 2-2 Departments Intake First published: 01 June 2022 https://doi.org/10.1002/opfl.1682Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume48, Issue5June 2022Pages 2-2 RelatedInformation
Teamplay

This chapter highlights how AI and physics mutually affect each other in both directions. On the one hand, AI in the form of machine learning (ML) is an increasingly essential tool in physical research. Examples are presented where ML enabled the classification of 111,000,000 astronomic light sources, the study of physical conditions short after the Big Bang, and the discovery of the “God particle”. It is demonstrated how ML enables us to handle Big Data, dissect signal from noise, and relate computer simulations to experiment. Furthermore, ML is shown to (re)discover fundamental laws by Newton, Maxwell, and Einstein – as well as from thermodynamics and statistical mechanics – within seconds! On the other hand, physics is inversely presented as having a strong impact on machine learning. First, it provides a theoretical framework to understand AI through the connection between statistical mechanics and learning theory and yields a fundamental understanding of why deep learning works so well. Second, it drives the development of new hardware platforms providing help with expensive information processing pipelines by laying the foundations of advanced analog computing and quantum computing. In 2020, a prototype quantum computer was released which was claimed to be able to calculate 100,000 billion times faster than today’s best supercomputers!
Quality assessment with deep learning for imaging applications

To establish stable video operations and services while maintaining high quality of experience, perceptual video quality assessment becomes an essential research topic in video technology. The goal of image quality assessment is to predict the perceptual quality for improving imaging systems' performance. The paper presents a novel visual quality metric for video quality assessment. To address this problem, we study the of neural networks through the robust optimization. High degree of correlation with subjective estimations of quality is due to using of a convolutional neural network trained on a large amount of pairs video sequence-subjective quality score. We demonstrate how our predicted no-reference quality metric correlates with qualitative opinion in a human observer study. Results are shown on the MCL-V dataset with comparison existing approaches.
Protecting Deep Neural Network Intellectual Property with Architecture-Agnostic Input Obfuscation

Deep Convolutional Neural Networks (DCNNs) have revolutionized and improved many aspects of modern life. However, these models are increasingly more complex, and training them to perform at desirable levels is difficult undertaking; hence, the trained parameters represent a valuable intellectual property (IP) asset which a motivated attacker may wish to steal. To better protect the IP, we propose a method of lightweight input obfuscation that is undone prior to inference, where input data is obfuscated in order to use the model to specification. Without using the correct key and unlocking sequence, the accuracy of the classifier is reduced to a random guess, thus protecting the input/output interface and mitigating model extraction attacks which rely on such access. We evaluate the system using a VGG-16 network trained on CIFAR-10, and demonstrate that with an incorrect deobfuscation key or sequence, the classification accuracy drops to a random guess, with an inference timing overhead of 4.4% on an Nvidia-based evaluation platform. The system avoids the costs associated with retraining and has no impact on model accuracy for authorized users.
Detecting trojans in satellite imagery AI applications

The use of deep learning in multi-domain operations to analyze satellite imagery is becoming particularly important. As deep learning models are computationally expensive to train and require vast amounts of data, there is an increasing trend towards the outsourcing of model training to the cloud, relying on pre-trained models and use of third party datasets. This poses serious security challenges and exposes users to adversarial attacks that aim to disrupt the training pipeline and insert Trojan behavior (backdoors) into the AI system. In this work, we demonstrate a method based on Generative Adversarial Networks (GANs) to automatically detect Trojans in deep learning computer vision models with a high detection accuracy (89%). We pick a land usage classification problem on satellite imagery for this demonstration. These results can easily be extended to other computer visons problems such as object detection. This technique is agnostic to the internal architecture of the deep learning network in question. We make no hard assumptions about the nature of the Trojan - size or pattern of the trigger, the targeted classes and the method of trigger injection.
Identifying Scientific Trends Through Recombinant Memetics

The first two decades of the twenty-first century can be perceived as the period of information generation, verbal and audiovisual formulation and spread. The field of social psychology usually looks into the mechanisms of social behaviour change under certain conditions or as a result of certain events. Corpora have been used to identify the frequency of the words in both spoken and written language. New technologies have recently necessitated great changes within the world of the education system, teaching and learning, information exchange; science and scholarship; daily social interactions, and mass media coverage of burning events. During the lockdown period, modern language teachers have been using the newest available technological devices such as computers, laptops, mobile phones, smartphones, tablets, OHPs, e-books, blackboard software, digital flashcards, particularly frequently.
The Effect of Small Group Collaborative Learning Activities on the Improvement of Linguistic Competence in Children with Moderate Intellectual Disabilities

Purpose: This study sought to investigate the enhancement of linguistic competence through small group collaborativelearning in children with moderate intellectual disabilities. Methods: The subjects included three children selected from aclass of six-year-olds at a kindergarten in Mokpo, Jeonnam. The selected children had moderate intellectual disabilitiesand no paresthesia or physical disabilities. The control group consisted of children who were in a special class for thehandicapped at a kindergarten attached to an elementary school in Mokpo and receiving home education. Results: Thefindings were as follows: first, the small group collaborative learning increased the communication skills of the childrenwith moderate intellectual disabilities; second, it improved their comprehension capabilities; third, it improved their ex pressive abilities; finally, it did not enhance their reading and writing skills. Conclusion: Small group collaborative learningincreased the communication skills, comprehension, and expressive abilities of children with moderate intellectual dis abilities, but did not increase the reading and writing skills of children with moderate intellectual disabilities.
Learning to guide multiple heterogeneous actors from a single human demonstration via automatic curriculum learning in StarCraft II

Traditionally, learning from human demonstrations via direct behavior cloning can lead to high-performance policies given that the algorithm has access to large amounts of high-quality data covering the most likely scenarios to be encountered when the agent is operating. However, in real-world scenarios, expert data is limited and it is desired to train an agent that learns a behavior policy general enough to handle situations that were not demonstrated by the human expert. Another alternative is to learn these policies with no supervision via deep reinforcement learning, however, these algorithms require a large amount of computing time to perform well on complex tasks with high-dimensional state and action spaces, such as those found in <i>StarCraft II</i>. Automatic curriculum learning is a recent mechanism comprised of techniques designed to speed up deep reinforcement learning by adjusting the difficulty of the current task to be solved according to the agent's current capabilities. Designing a proper curriculum, however, can be challenging for sufficiently complex tasks, and thus we leverage human demonstrations as a way to guide agent exploration during training. In this work, we aim to train deep reinforcement learning agents that can command multiple heterogeneous actors where starting positions and overall difficulty of the task are controlled by an automatically-generated curriculum from a single human demonstration. Our results show that an agent trained via automated curriculum learning can outperform state-of- the-art deep reinforcement learning baselines and match the performance of the human expert in a simulated command and control task in <i>StarCraft II</i> modeled over a real military scenario.
Heterogeneous Collaborative Refining for Real-Time End-to-End Image-Text Retrieval System

The image-text retrieval task currently suffers from high search latency due to the cost of image feature extraction and semantic alignment calculation. We propose a real-time image-text retrieval system for edge-end servers with low-power AI accelerator cards. The procedure is conspicuously sped up by selectively placing part of the deep learning calculation on accelerator devices with a heterogeneous collaborative computation scheme. We also design a lightweight GCN optimization method, which directly transfers the correlation between the image detection areas in projection to reduce computational redundancy. Our other contributions include performance analyses of models with different weights for industrial reference in practical applications. It is the first GCN-based image-text retrieval system to perform a real-time end-to-end search to the best of our knowledge. Experiments show that the system can process 20 image-to-text retrievals per second with high accuracy.
Effects of spatial augmented reality assistance on the efficiency of prefabricating timber frame walls

Assistance systems have the potential to support workers in various assembly tasks. Especially in mass customized prefabrication, the correct objects need to be selected and positioned accurately. Using conventional paper plans as assembly guidance, skilled workers need to spend precious time carefully reading plans for the individually designed elements. Augmented reality (AR) systems can help to integrate information for assembly tasks directly into the work environment, which should improve time efficiency and error rates. However, AR systems are often not ergonomic or accurate enough (e.g. smartglasses) or expensive (e.g. laser projectors). In the present study, the effect of a simple projection based spatial augmented reality (SAR) system was studied for building prefabricated wall elements under laboratory conditions, using printed CAD drawings as reference. Each subject built a timber frame wall element with dimensions close to reality. Using the SAR system, the completion times were reduced by nearly 50%, the error rate of subjects with a low experience level decreased and the reported task load measured according to the NASA raw task load index using the SAR system was significantly lower.
Retracted: Intelligentization of Tourism Japanese Environmental Scenic Spots Based on Intelligent Translation Hardware System

Based on the intelligent translation hardware system, this article discusses the existing situation of the Japanese explanation of the 5A-level scenic spots in Jiangxi Province from the perspective of the Japanese explanation of the scenic spot, combined with the relevant information of the Lushan Scenic Area in Jiangxi Province, and raises and analyzes the problems to achieve the purpose of improving the problem., I hope to provide reference materials for the actual Japanese explanation work of the scenic spot. Establish a variety of translation projects that suit students' academic abilities and interests, and obtain better teaching results. Practice has shown that the practical, collaborative, and interactive features of the translation workshop teaching model have significant meanings for cultivating students' translation ability and comprehensive literacy, and are worthy of being tried and promoted in teaching activities.
A Shuttle-Efficient Qubit Mapper for Trapped-Ion Quantum Computers

Trapped-ion (TI) quantum computer is one of the forerunner quantum technologies. Execution of a quantum gate in multiple trap TI system may frequently involve ions from two different traps, hence one of the ions needs to be shuttled (moved) between traps to be co-located, degrading fidelity, and increasing the program execution time. The choice of initial mapping influences the number of shuttles. The existing Greedy policy neglects the depth of the program at which a gate is present. Intuitively, the contribution of the late-stage gates to the initial mapping is less since the ions might have already shuttled to a different trap to satisfy other gate operations. In this paper, we target this gap and propose a new program adaptive policy especially for programs with considerable depth and high number of qubits (valid for practical-scale quantum programs). Our technique achieves an average reduction of 9% shuttles/program (with 21.3% at best) for 120 random circuits and enhances the program fidelity up to 3.3X (1.41X on average).
Methodology of social sciences and humanities: Ideas and technologies of research skill and art

Review of the textbook of I.V. Ponkin and, A.I. Lapteva “Methodology of scientific research and practical analytics” (third edition, Moscow, 2022).
THE ADVANTAGES OF DEVELOPING WEB APPLICATIONS USING THE SPRING MVC FRAMEWORK

This article discusses the advantages of creating a web application in the mvc model using the spring boot framework and its advantages over other technologies. Using Spring Boot technology when creating web applications gives the programmer a lot of advantages in terms of customizing and writing program code. You will need to repeat the same settings every time you develop an enterprise Java application.
Viewing Browser Content from Extreme Angles with Partial Perspective Corrections

Displays viewed from extreme angles become perceptually distorted due to perspective projections. Perspective correction has been discussed as a remedy, yet there are few general and practical implementations available to users. This work explores modern browser technology for realizing perspective correction in practice, and whether partial perspective corrections is feasible. Three prototypes are explored, two where the corrections are configured manually and one where the corrections are determined automatically through camera-based vantage point measurements. Browser transformations are used to perform perspective corrections to content in real-time.
About Chinese Class Design in connection with service learning

In this study, we designed and operated a Chinese class model linked to the service - learning of the Chinese language international education major, and discussed additional considerations and improvements related to the learning performance. This class model can be designed through the process of selecting and consulting target schools, determining the time and turn of activities, coordinating class hours, determining and recruiting the number of required personnel, selecting participants, providing conference room information, and related workshops. Through the operation of this class model, the learner’s Chinese skills have been remarkably improved, which is a level that teachers and students can feel. Many universities in China have majors in Chinese language international education, and it is not easy for undergraduate majors to have practical opportunities. Therefore, we expect that the Chinese teaching model, which is linked to the service learning of the Chinese language international education major of the Chinese university, will be a teaching method that will provide practical opportunities for Chinese students and guarantee better learning effects for Korean students.
Improving Findability of Digital Assets in Research Data Repositories Using the W3C DCAT Vocabulary

Research data management requires stable, trustworthy repositories to safeguard scientific research results. In this context, rich markup with metadata is crucial for the discoverability and interpretability of the relevant resources. SEEK is a web-based software to manage all important artifacts of a research project, including project structures, involved actors, documents and datasets. SEEK is organized along the ISA model (Investigation - Study - Assay). It offers several machine-readable serializations, including JSON and RDF. In this paper, we extend the power of RDF serialization by leveraging the W3C Data Catalog Vocabulary (DCAT). DCAT was specifically designed to improve interoperability between digital assets on the Web and enables cross-domain markup. By using community-consented gold standard vocabularies and a formal knowledge description language, findability and interoperability according to the FAIR principles are significantly improved.
A successive approach to enhancement of infrared facial images

As it becoming more and more reliable and mature, the technology of face recognition has been widely applied to nowadays life. However, conventionally, face recognition uses the visible light as the imaging spectrum and is thus limited under lighting conditions (such as nighttime) and bad atmospheric conditions (such as rain, fog). Infrared face imaging provides an alternative solution to above problem. Nonetheless, infrared (IR) facial images are usually in low quality due to limitation of current imaging devices as well as atmospheric noises and disturbance. This situation restrains the face recognition system from performing well. Therefore, enhancing of low-quality IR facial images is crucial to a practical IR face recognition system. We in this research work propose to address the problem of IR facial image enhancement by a succession of IR facial denoising and IR facial deblurring. The former is realized via a deep neural network of denoising while the latter is achieved by a blind deconvolution algorithm. The denoising DNN is trained on the Waterloo Exploration database and tested on our multispectral face dataset. The metrics of PSNR and running time are used to compare between different denoising methods including both traditional ones and deep learning-based ones. The metric of Tenegrad is used to evaluate the deblurring method involved. Overall, image quality is improved significantly, which in turn proves our proposed framework of successive IR face enhancement to be beneficial.