Reviewerly/exHarmony huggingface dataset:
Complex-Valued Densely Connected Convolutional Networks

In recent years, deep learning has made significant progress in computer vision. However, most studies focus on the algorithms in real field. Complex number can have richer characterization capabilities and use fewer training parameters. This paper proposes a complex-valued densely connected convolutional network, which is complex-valued DenseNets. It generalizes the network structure of real-valued DenseNets to complex field and constructs the basic architectures including complex dense block and complex transition layers. Experiments were performed on the CIFAR-10 database and CIFAR-100 datasets. Experimental results show the proposed algorithm has a lower error rate and fewer parameters than real-valued DenseNets and complex-valued ResNets.
A Study on Natural Language Processing Classified News

In recent years, since the Artificial Intelligence (AI) grows up, the machine has better judgment then the human. In this paper, we used Artificial Intelligence to train computer such that it can classify news according to the content of the news. When the category of news did not mark or flag error, the computer can quickly mark the correct news category to reduce the cost and time of human resource. Furthermore, we can build a news classification system for social networks. The system can classify the news from different news media. We used web crawler, data preprocessing, Jieba and NLP to train the computer. After many times to trainings, a large amount of training data, the experimental results show that the accuracy rate of news classification is 97.43%.
MRI Super-Resolution using Laplacian Pyramid Convolutional Neural Networks with Isotropic Undecimated Wavelet Loss

High spatial resolution of Magnetic Resonance images (MRI) provide rich structural details to facilitate accurate diagnosis and quantitative image analysis. However the long acquisition time of MRI leads to patient discomfort and possible motion artifacts in the reconstructed image. Single Image Super-Resolution (SISR) using Convolutional Neural networks (CNN) is an emerging trend in biomedical imaging especially Magnetic Resonance (MR) image analysis for image post processing. An efficient choice of SISR architecture is required to achieve better quality reconstruction. In addition, a robust choice of loss function together with the domain in which these loss functions operate play an important role in enhancing the fine structural details as well as removing the blurring effects to form a high resolution image. In this work, we propose a novel combined loss function consisting of an L1 Charbonnier loss function in the image domain and a wavelet domain loss function called the Isotropic Undecimated Wavelet loss (IUW loss) to train the existing Laplacian Pyramid Super-Resolution CNN. The proposed loss function was evaluated on three MRI datasets - privately collected Knee MRI dataset and the publicly available Kirby21 brain and iSeg infant brain datasets and on benchmark SISR datasets for natural images. Experimental analysis shows promising results with better recovery of structure and improvements in qualitative metrics.
Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data

This paper proposes a novel undersampling method, for dealing with imbalanced datasets. The proposal is based on a novel instance importance measure (also introduced in this paper), and is able to balance hybrid and incomplete data. The numerical experiments carried out show the proposed undersampling algorithm outperforms others algorithms of the state of art, in well-known imbalanced datasets.
Forecasting Using Information and Entropy Based on Belief Functions

This paper introduces an entropy-based belief function to the forecasting problem. While the likelihood-based belief function needs to know the distribution of the objective function for the prediction, the entropy-based belief function does not. This is because the observed data likelihood is somewhat complex in practice. We, thus, replace the likelihood function with the entropy. That is, we propose an approach in which a belief function is built from the entropy function. As an illustration, the proposed method is compared to the likelihood-based belief function in the simulation and empirical studies. According to the results, our approach performs well under a wide array of simulated data models and distributions. There are pieces of evidence that the prediction interval obtained from the frequentist method has a much narrower prediction interval, while our entropy-based method performs the widest. However, our entropy-based belief function still produces an acceptable range for prediction intervals as the true prediction value always lay in the prediction intervals.
Spin-state estimation using the Stern-Gerlach experiment

We propose a state estimation scheme for spins, using a modified setup of the Stern-Gerlach experiment, in which a beam of neutral spin-1/2 point particles interacts with a quadrupolar magnetic field. The proposed linear inversion estimation procedure, based on a quadrant intensity detector, requires a suitable initial spatial state of the beam. The statistical characterization of the estimator of the initial spin state allows us not only to associate an error to the estimated parameters, but also to define a measure for comparing estimation procedures corresponding to different Stern-Gerlach setups.
Object Tracking in Satellite Videos Based on a Lightweight Network

Object tracking is a hot topic in computer vision. The significantly developed satellite video technology makes object tracking in satellite videos possible. In recent years, Convolutional Neural Network (CNN)-based trackers have achieved satisfying performance in the visual object tracking field. However, CNN cannot be directly applied to object tracking in satellite videos due to the following two reasons. First, the feature map size generally decreases as the network layer deepens, which is unsuitable for the small targets in satellite videos. Second, CNN-based trackers commonly need extensive data to train the network parameters, while few labeled satellite videos are available. Therefore, in this paper, we design a lightweight network for the satellite video tracking task. On one hand, the network generates a response map with the same size as the input image and reserves the spatial resolution of targets. On the other hand, the network parameters are transferred from an existing network and trained with the initial annotated frame, thus no extra data are needed. To make a fair comparison between existing trackers, we further propose a simulated benchmark based on the UAV123 dataset according to the imaging characteristics of satellite videos. Experiments are conducted to compare our method with other state-of-the-art trackers on both the simulated benchmark and real satellite videos and experimental results demonstrate the superiority of our proposed algorithm.
Risks of Privacy-Enhancing Technologies: Complexity and Implications of Differential Privacy in the Context of Cybercrime

In recent years, the swift expansion of technology-enabled data harvesting has infiltrated modern life and led to the collection of massive amounts of private data. As a result, the preservation of individual privacy has become a salient concern for the general public. Combined with an increase in the frequency and prevalence of cybercrime, more of the public now face the very real risk of privacy loss associated with illegitimate use of private data. Differential Privacy has emerged as a relatively new privacy-preserving method with the potential to significantly reduce the likelihood of harmful data disclosures stemming from malicious use. However, research has not explicitly investigated Differential Privacy from the perspective of criminal justice or examined the utility of Differential Privacy as a possible situational crime prevention measure to cybercrime. Therefore, this chapter explores the proliferation of cybercrime through advances in technology and briefly examines other privacy-preserving methods before discussing the possible use of Differential Privacy as a viable countermeasure to cybercrime. The chapter concludes with a discussion of several practical considerations related to the use of Differential Privacy as a tool in the fight against cybercrime and offers recommendations for future research.
The Effect of Digital Economy and Artificial Intelligence on The Participants of The School Educational Process

The article examines the nature of the effect of the digital economy and artificial intelligence on the participants of the school educational process. The study deploys a Likert scale questionnaire, focused interview, and IBM SPSS Statistics 22 as the main instruments. The authors determine the status of a teacher in the AI-teacher system and identify the nature of AI’s influence on the students and communication within the teacher-student-teacher system. Support is provided for the hypothesis suggesting that the effect of the digital economy and AI on the participants of the school educational process (teachers and students) is complex and controversial and causes the differences in the teachers’ positions depending on their age, working experience, and the subject of specialization. The primary advantage of the article is the identification of stable relationships in the teacher-student-teacher system developing with the help of AI. The study indicates the teacher’s advantage over AI in teaching technique and social influence, as well as the negative effect of AI on students’ health. For the first time, the authors examine the nature of role changes in the AI-teacher system in the context of a global pandemic. The danger of AI replacing teachers with the help of teachers themselves is identified. The acquired results may be used by theorists and practitioners for further development of the foundations of the system of education and human interaction with AI in the digital economy.
Trust based authentication scheme (tbas) for cloud computing environment with Kerberos protocol using distributed controller and prevention attack

Purpose This paper aims to discuss the Silver and Golden ticket exploits that usually exists in the existing systems. To overcome these challenges, the data is first encrypted and then the ticket is granted to the validated user. The users are validated using the user privileges. The security levels of the proposed model are compared with the existing models and provide a better performance using the Key Distribution Centre (KDC). The number of authentication and authorization levels present in the existing and proposed model is also evaluated. Design/methodology/approach The methodology designed in this paper is discussed in this section. The existing models are designed in such a way that the client ID first asked to send an authorization request to the Authentication Server. The server looks up the user in its database and then sends back a ticket generated by it to the client to obtain services for the Service center. Numerous models have some additional features to these systems where the theme of KDC was introduced. The Key Distribution Centre (KDC), which is a set of nodes in a network where the data could be distributed and stored, such that any kind of attack on a single KDC will not impact other KDC and the data stored in it. The nodes other than the KDC in the network are termed as the slave nodes. The slave nodes communicate with each other within the network depending on the topology of the entire network. In this paper, the authors have used the Kerberos protocol for adding more security functions in the entire network. The system developed consists of a client, server and a set of nodes connected to each other in a ring fashion. Findings The proposed model provides security to the information being used by making use of the Kerberos protocol. Additional features and algorithms such as the use of the ticket-granting approach have been added at the protocol to make it more secure than the existing models. The ticket generation is done at the server-side that makes the user have proper authentication to make use of the services available from the server-side. The model is designed in such a way that it could remain operational even during the time of denial of service. As future work, use of machine learning and deep learning could be used to predict the attack on the network well before it is being misused. Originality/value The paper discusses the Silver and Golden ticket exploits that usually exists in the existing systems. To overcome these challenges, the data is first encrypted and then the ticket is granted to the validated user. The users are validated using the user privileges. The security levels of the proposed model are compared with the existing models and provide a better performance using the Key Distribution Centre (KDC). The number of authentication and authorization levels present in the existing and proposed model is also evaluated.
Diagnose Expert System Computer Malfunction Certainty Factor Method

An expert system is a knowledge base system that solves problems using an expert's knowledge that is entered into a computer, thereby increasing productivity, Because an expert can work faster than a human lay works like an expert. Expert systems Also solve problems by imitating the ways in the which an expert expert offer section with problems in his field, one of the which is in the field of computer repair, the problem of computer damage Becomes a fairly complicated problem, this problem is Generally experienced by individuals and institutions. One of them is in school institutions that have computer laboratories. to diagnose computer use can damage the certainty factor method that helps identify damage to the computer and find the cause of damage to the computer based on the symptoms that occur and the solution to repair it. Certainty Factor is one of the techniques used to deal with uncertainty in decision making. In dealing with a problem, answers are Often found that do not have full certainty. This uncertainty is influenced by two factors items, namely the uncertain rules and user uncertain answers. Uncertain rules are rules of symptoms that are determined for a damage.
Player Style Clustering without Game Variables

Player clustering when applied to the field of video games has several potential applications. For example, the evaluation of the composition of a player base or the generation of AI agents with identified playing styles. These agents can then be used for either the testing of new game content or used directly to enhance a player’s gaming experience. Most current player clustering techniques focus on the use of internal game variables. This raises two main issues: (1) the availability of game variables, as source code access is required to log them and hence limits the data sources that can be used, and (2) the choice of game variables can introduce unintended bias in the types of play style extracted. In this work, a hybrid unsupervised frame encoder and a ‘reference-based’ clustering algorithm are both proposed and combined to allow clustering from raw game play videos. It is shown that the proposed methods are most beneficial when the types of play styles are unknown.
Digital Transformation of Additional Professional Education: Features of the LK-14 Educational Platform

The research results include the specifics of APE modernization in the region and the model of targeted personalized training of teachers of the republic. The qualitative novelty of this model stems from the fact that developing teachers’ competencies involves identifying gaps in their professional knowledge and building individual learning paths.
Assessing the changeability of component-based system design: a controlled experiment

Component-based software system (CBSS) must be designed and implemented not only to meet the current customers’ requirements, but also to be receptive to future changes. Therefore, most often, one or more components of the system may need to be changed. Usually, designers do not exactly know what the future state looks like. The need for change continues to increase as technology evolves. For the reusable software components, this modification may be compromised, perhaps for the system architecture that comprises components or interfaces that are difficult to change. An essential way for controlling and managing such changes is to develop metrics as an indicator of changeability. The aim of this paper is to present the relationship between the proposed component information flow-based measures and changeability of software components. Although the component information flow complexity (CIFC) and component coupling (CC) could make a significant contribution to the understanding of software design processes, they need to be adjusted to an external quality attributed to determine the usefulness of the metrics. A controlled change experiment was conducted involving eighteen software components and twelve changes for verifying certain hypotheses to justify the validity of the measured external attribute. The empirical findings showed that there was a significant statistical correlation between the proposed information-flow-based metrics and changeability of software components. CBSS designers can achieve some quality insight in terms of changeability if the metrics are applied in the right context.
Global Biased Pruning Considering Layer Contribution

Convolutional neural networks (CNNs) have made impressive achievements in many areas, but these successes are limited by storage and computing costs. Filter pruning is a promising solution to accelerate and compress CNNs. Most existing methods for filter pruning only consider the role of the filter itself, ignoring the characteristics of the layer. In this paper, we propose a global biased filter pruning method considering layer contribution, which tends to preferentially remove weak filters in weak layers. The impact of each layer on final performance is quantitatively analyzed, and such the improvement between adjacent layers is exploited to represent the layer contribution and determine the weak layers. We introduce layer weight and Taylor expansion to jointly evaluate the filters in different layer, and remove the least important filters to compress CNNs. And then, fine-tune the CNNs to restore their predictive power. The experiment results show that the proposed approach could crop 92.63%, 99.06%, 57.60% and 58.97% parameters of VGG16, MobileNetV1, ResNet32, and ResNet56 respectively on CIFAR10, 78.29% and 62.28% parameters of VGG16 and ResNet56 respectively on CIFAR100, which outperforms other methods, and removes 92.30% parameters on Tiny-Yolov2 with a negligible mAP loss.
Survey on the State-Of-The-Art Methods for Objective Video Quality Assessment in Recognition Tasks

This paper is a technical report, presenting a survey on the state-of-the-art methods for objective video quality assessment in recognition tasks. It bases on the most up-to-date solutions, developed by various research teams. The study considers, among others, solutions developed by the AGH University research team, including the contributions to ITU-T Recommendation P.912 (dealing with video quality assessment methods for recognition tasks) as well as the video quality indicators (available at http://vq.kt.agh.edu.pl/). In particular, we consider evaluation metrics based on a trade-off between computer vision performance and compression efficiency.
A Trade-Off Negotiation Strategy for Pareto-Optimal Service Composition with Additive QoS-constraints

Abstract QoS-based service composition enables the development of complex business applications. They are composed of distributed services characterized by QoS attributes representing non-functional characteristics, such as cost, execution time, and reliability. With the proliferation of services on the Internet, more candidates for each component service may be available with different QoS values. Hence, the ones satisfying QoS global constraints required for the application have to be selected. Automated negotiation is adopted to select component services in a dynamic market of services where provided QoS values may vary according to different market strategies. Nevertheless, when dealing with multiple QoS attributes, multiple service providers, and not shared information, it is difficult to guarantee the formal properties of the negotiation outcomes. In the present work, we propose a trade-off negotiation strategy that allows reaching a Pareto-optimal agreement, if it exists. The agreement consists of the QoS values of component services that are the ones selected to provide the complete application. The strategy exploits both the competition, that is due to multiple services providing the same functionality with different QoS values, and the cooperation among the providers of the different component services, that are necessary to meet the required end-to-end QoS constraints.
Active matrix factorization for surveys

Amid historically low response rates, survey researchers seek ways to reduce respondent burden while measuring desired concepts with precision.We propose to ask fewer questions of respondents and impute missing responses via probabilistic matrix factorization.A variance-minimizing active learning criterion chooses the most informative questions per respondent.In simulations of our matrix sampling procedure on real-world surveys as well as a Facebook survey experiment, we find active question selection achieves efficiency gains over baselines.The reduction in imputation error is heterogeneous across questions and depends on the latent concepts they capture.Modeling responses with the ordered logit likelihood improves imputations and yields an adaptive question order.We find for the Facebook survey that potential biases from order effects are likely to be small.With our method, survey researchers obtain principled suggestions of questions to retain and, if desired, can automate the design of shorter instruments.
Preparing Students of the Institute of Physical Education and Sport to Use Distant Learning Technologies in the Course of Pandemic

The originality of this study is in providing the facts that the high proficiency and professional capacity of graduates of physical culture institutes, their ability and competence to adjust the accumulated knowledge and skills to new goals and objectives of education, regularly changing conditions of professional activity can be formed and developed through the effective use of DL technologies. adapted to the solution of occupational tasks.
Designing different features of an interactive tabletop application to support collaborative problem-solving

The design space of tangible and multi-touch tabletop interfaces is complex, and little is known about how the different characteristics of tangible and multi-touch interactive features affect collaboration strategies. With this work, we report on five different features designed for an interactive tabletop application to support collaborative problem-solving. We present the design details and describe preliminary results obtained from a user study with 15 participants.
Introducing data science with data moves and <scp>CODAP</scp>

Abstract This paper describes a short module [ 3 ] for introducing data science to senior school students or other data‐science beginners. The design focuses on “data moves.” Students use CODAP [ 5 ] to do their work.
Mathematical Modelling and Identification of a Quadrotor

Motivated by the important growth of VTOL vehicles research such as quadrotors and to a small extent autonomous flight, a quadrotor dynamical model is presented in this work. The purpose of this study is to get a better understanding of its flight dynamics. It is an underactuated system. So, a simplified and clear model is needed to implement controllers on these kind of unmanned aerial systems. In addition, a computational tool is used for validation purposes. For future works embedded or intelligent control systems can be developed to control them. Gyroscopic and some aerodynamics effects are neglected.