Reviewerly/exHarmony huggingface dataset:
HyperRTV: Neural-Enhanced Adaptive Real-Time Video Streaming Based on Terminal-Edge Collaboration

Video has become the primary source of Internet traffic due to the advance of streaming media technology and the surge in user demand for real-time video streaming applications. In this case, neural-enhanced video streaming and mobile edge computing are proposed to improve video content quality on nearby MEC (Mobile Edge Computing) devices and to maintain low interaction delay under limited bandwidth. In order to maximize the architectural advantages brought by MEC, we designed HyperRTV, a terminal-edge collaborative real-time video transmission system with neural-enhanced streaming. HyperRTV consists of three key components: 1) a video super-resolution structure that uses hardware-accelerated DNNs to satisfy latency limits; 2) an adaptive bitrate controller that can dynamically adjust the bitrate under various network conditions; 3) a heuristic task offloading strategy based on the ant colony algorithm to further meet the heterogeneity and dynamics of the MEC environment. Experiments show that HyperRTV decreases bandwidth consumption by 46% on average and achieves a 48%-65% reduction in latency, consistently maintaining better visual quality and higher stability in unpredictable networks. Our task offloading strategy can reduce the execution and transfer time by 16.1%-19.5% in a MEC environment with multi-task requests.
A Blockchain-Based Privacy-Preserving Data Sharing Scheme with Security-Enhanced Access Control

In the data-driven economy, data sharing is a key approach to unleashing the true value of data. Blockchain, as a decentralized ledger, can provide a trusted data sharing platform in an untrusted environment. However, existing blockchain-based data sharing schemes suffer from inefficiency and inadequate protection of security and privacy. To address the above issues, we propose a blockchain-based privacy-preserving data sharing scheme with security-enhanced access control. In the scheme, a secure data sharing architecture using dual-blockchain and the interplanetary file system (IPFS) is presented to provide decentralized and scalable storage. Based on the architecture, a blockchain-assisted multi-authority attribute-based encryption (BA-MA-ABE) algorithm with efficient attribute revocation and computation is designed in our work. Our BA-MA-ABE lever-ages blockchain to securely manage partial decryption keys and provides fine-grained access control over encrypted data. We also devise smart contracts that can support traceable access control over the flow of data while protecting user identity privacy with verifiable attribute credentials. In comparison with some existing work, our scheme shows more comprehensive security features with lower user computation overhead.
The Analysis of Provisions of the Theory of Graves’s Emergent Levels of Biopsychosocial Systems

Since early 21 century, there have been numerous works in scientific and popular literature devoted to the theory of emergent levels biopsychosocial systems by Clare Graves and theories derived from it, such as Spiral Dynamic. The theory was developed on the basis of the analysis of significant amount of data gathered to reveal patterns of «normal» behavior of an individual. The theory is widely used in business consulting and other spheres. As popularization of any theory can lead to its mythologization, it is necessary to analyze basic provisions of Graves’s theory and its derivatives to elicit the provisions that are scientifically grounded, have a hypothetical status, and require experimental verification, as well as pseudoscientific popular views. The present review is an attempt of such analysis.
EDUCATION IN THE FIELD OF CYBERSECURITY AT UNIVERSITIES IN POLAND

The article characterizes the current state of development of cybersecurity education in highereducation institutions in Poland. The purpose of the study is to characterize the models of cybersecurity education, to verify the development trends and to attempt to estimate further directionsin higher education for cyber security professionals. The compiled material, obtained frompublicly available databases and curricula, allowed us to formulate conclusions related to theeducation process itself, as well as to the confirmation of skills. A quantitative as well as qualitativeanalysis of the data was carried out, focused on the content of study programs. The profile ofa cybersecurity graduate varies by the leading institution, as well as by the discipline under whichthe education is provided. Recent years show a significant increase in new majors with a socialhumanitiesprofile, which, in addition to technological issues, pay attention to the broad contextof human functioning in cyberspace. An important addition to cybersecurity education, apartfrom formal confirmation of skills during higher education, is the certification process. This is anexample of informal education, but as global experience shows, it is equally important, both fromthe perspective of improving specialized skills and as a formal requirement in the recruitment andemployment search process.
Engineering Education Practice Using Project Management Methods

The "Project Management Exercise" lecture is offered as an elective course to allow students to experience problem-solving methods through practical project management. In FY2021, we designed and manufactured a device (stirring mechanism) to improve the efficiency of hydrate production experiments under the theme of "Provision of a stirring mechanism inside a pressure vessel. In order to complete the deliverables (stirring mechanism) to the level required by the client in a relatively short period of time, advance planning is extremely important. In this project, because we were able to create a solid project plan, we were able to proceed with the project with almost no problems, and we were able to submit deliverables that met the client's requirements by the scheduled date. As described above, it was confirmed that even in a project with relatively high expertise and a high level of requirements, the use of project management methods can increase the students' achievement in solving problems.
Powerful SEM: An Easy and Powerful Auxiliary for Structural Equation Modelling Analysis

Powerful SEM is a free R package that addresses some important issues in structural equation modelling. It can calculate sample size and statistical power of structural equation modeling (SEM) more easily, and provide adjusted model fit indices (Bollen Stine P correction), to make it more convenient to conduct SEM analysis
AR Assistant for Indoor Navigation

In this study, we discuss the problem of indoor autonomous robotic navigation. By using an Android smartphone and the JAQL robot, a system is created dedicated for indoor navigation based on AR is developed. We want to learn more about how augmented reality can now be implemented to solve the problems of navigating without GPS. We created a smartphone application that serves as the system's eyes and brain at the same time using Google's AR core. We emphasize AR core's features and describe how they were used in all of our tasks. Assessment of the performance of the whole architecture is done and a report of the findings from a series of tests conducted under various conditions in a real-world setting is made. We finish by examining the system's advantages and disadvantages as well as potential future developments.
A New Extrapolation Economy Cascadic Multigrid Method for Image Restoration Problems

In this paper, a new extrapolation economy cascadic multigrid method is proposed to solve the image restoration model.The new method combines the new extrapolation formula and quadratic interpolation to design a nonlinear prolongation operator, which provides more accurate initial values for the fine grid level.An edge preserving denoising operator is constructed to remove noise and preserve image edges.The local smoothing operator reduces the influence of staircase effect.The experiment results show that the new method not only improves the computational efficiency but also ensures good recovery quality.
Identifikasi Kesalahan Pengerjaan Materi Dilatasi Waktu Berdasarkan Newman Error Analysis (NEA)

The purpose of this paper is to identify student errors in working on time dilation questions based on Newman Error Analysis (NEA). This research is a qualitative descriptive study. Data collection was carried out through a written test which contained 5 time dilation questions and a non-written test in the form of an interview. The results of this study indicate that the types of errors made by students are 20.0% understanding errors, 5.19% transformation errors, 26.67% skills process errors and 6.67% final answer writing errors. Meanwhile, based on the results of interviews with students, it shows that students' mistakes are caused by students' lack of understanding in interpreting time dilation questions, students are less thorough and in a hurry in working on questions and have not been able to use formulas correctly.
Research on Digital Campus Construction Based on SOA Service Architecture

The construction of educational informatization improves the comprehensive competitiveness, academic status and school-running level of colleges and universities. Information technology has also become the main driving force for colleges and universities to develop and change their school-running models, improve management efficiency and school-running levels, and create brands. At present, the software, hardware and implementation technologies used in information systems are not the same, and each application system is also maintained separately. These isolated application systems operate independently, cannot be interconnected, and have different data. There is no way to achieve data association, exchange and sharing. "Information The problem of isolated islands has become increasingly prominent. This paper is based on Service-Oriented Architecture (Service-Oriented Architecture, SOA) from the perspective of business operations and processes, through the IT standard architecture and service modeling methods, integrating internal resources of universities, sharing existing resources between systems, Realize the construction goal of smart campus.
Summarization rewriting based on SOP and scoped-context

Automatic summarization technology uses some concise sentences to summarize the general idea of the article so that readers can understand the article's main content only by reading the abstract. Instead of simply selecting and rearranging sentences from the original text — just as the general extractive summarization does, the rewriting system model based on seq2seq applies extracted sentences as input to generate more consistent summaries with human language conventions. The BERT pre-trained model has also been applied which achieved good results. To increase coherence, we propose the Rewriting Summarization Based on Sentence Order Prediction (SOP) and Scoped-Context: after the extractive summarization, we use the SOP tasks of the ALBERT model to reorder the sentence sequence; In the abstractive summarization, we apply the group sub-tag-based attention mechanism problem to the seq2seq situation. To further reduce redundancy and irrelevance, each extracted sentence is taken as the input of the rewriter altogether, with its context within a specific scope. Our method can enhance model performance, achieve higher ROUGE scores, and maintain lower computational complexity.
Total Probability Decomposition Algorithm for Computer Network Reliability Analysis Based on Cloud Computing

The emergence of cloud computing has profoundly changed the business computing model. People can obtain on-demand computing power without purchasing a large number of expensive specialized equipment. Currently, it has been widely used in the fields of defense and military, transportation, industrial manufacturing and social computing. How to use the existing cloud computing to build highly reliable and highly available application computing, and ensure that the computing runs stably along the expected track and towards the expected goal; How to objectively and scientifically design reliable cloud computing and dynamically and efficiently evaluate the reliability of cloud computing is an important problem in reliability analysis. Reliability plays a crucial role in the non-functional attributes of a service. The reliability of a service directly determines whether it can fulfill the business needs of the service requester according to certain requirements. Increasing the reliability of cloud services can reduce the service failure rate and reduce the service cost of cloud service providers to some extent. Therefore, the probability decomposition method adopted in this paper is an important method to calculate the network reliability. This method is applicable to both undirected and directed networks. It is easy to realize. The selection of decomposition elements does not depend on manpower. The experimental results show that the efficiency is as high as 61.5%.
Performance Analysis of Different Optimization Algorithms for Multi-Class Object Detection

Object recognition is a significant approach employed for recognizing suitable objects from the image. Various improvements, particularly in computer vision, are probable to diagnose highly difficult tasks with the assistance of local feature detection methodologies. Detecting multi-class objects is quite challenging, and many existing researches have worked to enhance the overall accuracy. But because of certain limitations like higher network loss, degraded training ability, improper consideration of features, less convergent and so on. The proposed research introduced a hybrid convolutional neural network (H-CNN) approach to overcome these drawbacks. The collected input images are pre-processed initially through Gaussian filtering to eradicate the noise and enhance the image quality. Followed by image pre-processing, the objects present in the images are localized using Grid Guided Localization (GGL). The effective features are extracted from the localized objects using the AlexNet model. Different objects are classified by replacing the concluding softmax layer of AlexNet with Support Vector Regression (SVR) model. The losses present in the network model are optimized using the Improved Grey Wolf (IGW) optimization procedure. The performances of the proposed model are analyzed using PYTHON. Various datasets are employed, including MIT-67, PASCAL VOC2010, Microsoft (MS)-COCO and MSRC. The performances are analyzed by varying the loss optimization algorithms like improved Particle Swarm Optimization (IPSO), improved Genetic Algorithm (IGA), and improved dragon fly algorithm (IDFA), improved simulated annealing algorithm (ISAA) and improved bacterial foraging algorithm (IBFA), to choose the best algorithm. The proposed accuracy outcomes are attained as PASCAL VOC2010 (95.04%), MIT-67 dataset (96.02%), MSRC (97.37%), and MS COCO (94.53%), respectively.
Review and Prospects of Proactive Administration in Republic of Korea’s Public Service Sector

Purpose: This article is to review the current state of proactive administration in the public service sector of Republic of Korea (ROK) and to present prospects for the people’s better life.&#x0D; Method: This study reviews previous research to identify obstacles of proactive administration and approaches an empirical analysis of progress in proactive administration to address mitigation options.&#x0D; Results: Public servants hesitated to perform their duties creatively and proactively, fearing that they would be punished if the results were not good. On the other hand, some public servants are abusing the purpose and incentives of proactive administration. Therefore, it is necessary to identify, predict, and mitigate issues that may or may occur in the process of promoting proactive administration.&#x0D; Conclusion: ROK government-wide efforts for proactive administration are necessary for the public service society to move in the right direction. The most important thing is the proper perception of public servants' proactive administration. To this end, it is essential to strengthen and actively utilize the teaching capabilities and skills of dedicated lecturers in connection with compulsory education. In particular, fair and strict punishment should be imposed on public servants who abuse proactive administrative incentives.
Explaining Model Confidence Using Counterfactuals

Displaying confidence scores in human-AI interaction has been shown to help build trust between humans and AI systems. However, most existing research uses only the confidence score as a form of communication. As confidence scores are just another model output, users may want to understand why the algorithm is confident to determine whether to accept the confidence score. In this paper, we show that counterfactual explanations of confidence scores help study participants to better understand and better trust a machine learning model's prediction. We present two methods for understanding model confidence using counterfactual explanation: (1) based on counterfactual examples; and (2) based on visualisation of the counterfactual space. Both increase understanding and trust for study participants over a baseline of no explanation, but qualitative results show that they are used quite differently, leading to recommendations of when to use each one and directions of designing better explanations.
Management of pulmonary thromboembolism

This study aimed to identify the clinical management of pulmonary thromboembolism in the literature. The methodology selected for carrying out this research is an integrative literature review study, where a survey of data was carried out in the scientific bases: LILACS and SCIELO. The results showed that the clinical management starts after the diagnosis, initially, the diagnosis and confirmation of this condition for treatment represent a challenge for physicians who need to monitor this suspicion and perform tests in a timely manner. The importance of investigating the patient's clinical history and considering the risk factors for embolism is emphasized. Clinical management of pulmonary embolism must be performed in the hospital, and clinical response rates vary according to the size of the embolism, the affected area, and the delay in diagnosis. In the hospital, the main measures are to provide oxygen, analgesics to control pain, and anticoagulants to prevent existing clots from progressing and to prevent new clots from forming.
Adaptive Training and Aggregation for Federated Learning in Multi-tier Computing Networks

Multi-tier computing (MC) utilizes computing resources from the cloud, fog, edge, and end layers to promote intelligent Internet of Things (IoT) applications. Federated learning (FL) in MC offers a prospective distributed and privacy-preserving framework to deploy deep learning applications in different layers. Due to time-varying network topologies, wireless channel states, and computational workloads, MC faces a dynamic and uncertain environment, which poses additional challenges to FL task processing. In this paper, we propose a novel adaptive training and aggregation federated learning (ATAFL) framework. Specifically, local model training can be performed at end devices, edge nodes, and fog nodes. The global aggregator can choose from the edge, fog, and cloud layers. A joint optimization problem of training, aggregation node selection, and resource allocation is further formulated to minimize system latency and energy consumption. Moreover, digital twin and deep reinforcement learning (DRL) techniques are integrated into the MC network to design optimal node selection and resource allocation strategies based on captured state information of the MC system. We implement a prototype, and experimental results show that our proposed DRL-based algorithm reduces system latency and energy consumption compared with the benchmark algorithms.
Frame-Level Label Refinement for Skeleton-Based Weakly-Supervised Action Recognition

In recent years, skeleton-based action recognition has achieved remarkable performance in understanding human motion from sequences of skeleton data, which is an important medium for synthesizing realistic human movement in various applications. However, existing methods assume that each action clip is manually trimmed to contain one specific action, which requires a significant amount of effort for annotation. To solve this problem, we consider a novel problem of skeleton-based weakly-supervised temporal action localization (S-WTAL), where we need to recognize and localize human action segments in untrimmed skeleton videos given only the video-level labels. Although this task is challenging due to the sparsity of skeleton data and the lack of contextual clues from interaction with other objects and the environment, we present a frame-level label refinement framework based on a spatio-temporal graph convolutional network (ST-GCN) to overcome these difficulties. We use multiple instance learning (MIL) with video-level labels to generate the frame-level predictions. Inspired by advances in handling the noisy label problem, we introduce a label cleaning strategy of the frame-level pseudo labels to guide the learning process. The network parameters and the frame-level predictions are alternately updated to obtain the final results. We extensively evaluate the effectiveness of our learning approach on skeleton-based action recognition benchmarks. The state-of-the-art experimental results demonstrate that the proposed method can recognize and localize action segments of the skeleton data.
Model Protection Scheme Against Distillation Attack in Internet of Vehicles

Aiming at the problems of model security and user data disclosure caused by the deep learning model in the Internet of Vehicles scenario, which can be stolen by malicious roadside units or base stations and other attackers through knowledge distillation and other techniques, this paper proposes a scheme to strengthen prevent against distillation. The scheme exploits the idea of model reinforcement such as model self-learning and attention mechanism to maximize the difference between the pre-trained model and the normal model without sacrificing performance. It also combines local differential privacy technology to reduce the effectiveness of model inversion attacks. Our experimental results on several datasets show that this method is effective for both standard and data-free knowledge distillation, and provides better model protection than passive defense.
Announcing new resources from the FORCE11-COPE Research Data Publishing Ethics Working Group: flowcharts!

Recognizing the need for dialogue on best practices around ethical challenges in data publication, the FORCE11 Research Data Publication Ethics Working Group started in early 2021 as a multi-stakeholder community effort involving representatives of data repositories, journals, researchers, institutional research integrity officers and libraries.