Reviewerly/exHarmony huggingface dataset:
Training CNN Classifiers Solely on Webly Data

Abstract Real life applications of deep learning (DL) are often limited by the lack of expert labeled data required to effectively train DL models. Creation of such data usually requires substantial amount of time for manual categorization, which is costly and is considered to be one of the major impediments in development of DL methods in many areas. This work proposes a classification approach which completely removes the need for costly expert labeled data and utilizes noisy web data created by the users who are not subject matter experts. The experiments are performed with two well-known Convolutional Neural Network (CNN) architectures: VGG16 and ResNet50 trained on three randomly collected Instagram-based sets of images from three distinct domains: metropolitan cities, popular food and common objects - the last two sets were compiled by the authors and made freely available to the research community. The dataset containing common objects is a webly counterpart of PascalVOC2007 set. It is demonstrated that despite significant amount of label noise in the training data, application of proposed approach paired with standard training CNN protocol leads to high classification accuracy on representative data in all three above-mentioned domains. Additionally, two straightforward procedures of automatic cleaning of the data, before its use in the training process, are proposed. Apparently, data cleaning does not lead to improvement of results which suggests that the presence of noise in webly data is actually helpful in learning meaningful and robust class representations. Manual inspection of a subset of web-based test data shows that labels assigned to many images are ambiguous even for humans. It is our conclusion that for the datasets and CNN architectures used in this paper, in case of training with webly data, a major factor contributing to the final classification accuracy is representativeness of test data rather than application of data cleaning procedures.
Enterprise Modelling Assistance: Edge Prediction Improvement Using Textual Information

Today, enterprise modelling is still a highly manual task. There are exist some assistance techniques but they are mostly limited to pattern libraries and pre-defined rules, which limits their functionality and makes them non-flexible. Application of machine learning techniques to support enterprise modelers is a promising approach. However, one of the main problems in this area today is the absence of model repositories that could be used for training what causes the necessity to train machine learning models on small data. In this paper we study which textual information from the model and how can be used to increase the efficiency of the edge prediction task, which is one of the key tasks in graph-structured problems like enterprise modelling. The comparative analysis shows that application of FastText method provides a better result for node names embedding, and consideration of node names and descriptions significantly increases the edge prediction quality. The built model has been successfully validated on a test case scenario simulating the enterprise model building process.
Using the innovative I-learning platform in the education system

Today, due to the rapid development of digital technology, every teacher has the opportunity to competently form their own educational resources. The number of platforms, services, sites, special courses, allowing to offer educational resources is growing in the market. Creation of competitive educational resources and their wide distribution is one of the urgent problems of today. In this article we will analyze open educational resources offered for mass use, identify the most widely used topics and demand for them, and explore directions for development. To this end, search services and open educational resources of the Internet will be available. The results of the study identified two main areas: first, the fact that teachers need proper training and professional development in relation to online learning to successfully meet the requirements for learning in an online environment. The results of this study showed that the main goal of the innovation process is the renewal of education, the optimal use of new technologies, the study of Innovative Learning gave relatively new results. Secondly, to determine the impact of educational resources to improve the quality of learning, it is necessary to introduce a form of assessment of student achievement according to their cognitive level in order to measure learning outcomes. It is also very important for the teacher to use a common assessment form to track progress in the virtual environment. The results confirm the idea of the theory of conceptual self-improvement, for effective organization of innovative learning teachers need appropriate professional development. The results of this study can improve the experience of using the educational environment on the Internet with the help of an innovative platform I-learning.
Mutational Chemotaxis Motion Driven Moth-Flame Optimizer for Engineering Applications

Moth-flame optimization is a typical meta-heuristic algorithm, but it has the shortcomings of low-optimization accuracy and a high risk of falling into local optima. Therefore, this paper proposes an enhanced moth-flame optimization algorithm named HMCMMFO, which combines the mechanisms of hybrid mutation and chemotaxis motion, where the hybrid-mutation mechanism can enhance population diversity and reduce the risk of stagnation. In contrast, chemotaxis-motion strategy can better utilize the local-search space to explore more potential solutions further; thus, it improves the optimization accuracy of the algorithm. In this paper, the effectiveness of the above strategies is verified from various perspectives based on IEEE CEC2017 functions, such as analyzing the balance and diversity of the improved algorithm, and testing the optimization differences between advanced algorithms. The experimental results show that the improved moth-flame optimization algorithm can jump out of the local-optimal space and improve optimization accuracy. Moreover, the algorithm achieves good results in solving five engineering-design problems and proves its ability to deal with constrained problems effectively.
Application of FCM Clustering Algorithm in Digital Library Management System

Traditional library systems are gradually being replaced by digital libraries. Digital libraries are developing from simple database-based storage and retrieval to knowledge-based implementation. The fuzzy C-means (FCM) clustering algorithm is an example of data collection and data processing technology. It evaluates and draws conclusions based on mathematics, large data, and other technologies. In order to better improve the digital library management system, this paper applied FCM clustering algorithm to the digital library management system. Based on the in-depth study of the FCM clustering algorithm, this paper built a digital library management system. The clustering algorithm was used to cluster library borrowing records and reader information. It provided technical support and suggestions on library collection construction and book purchase and promoted book management to form a good spitting cycle. The experimental results extracted during the evaluation phase demonstrated that the overall error rate of the suggested FCM clustering algorithm for information clustering is 3.66%, which is better than the existing comparative models. This shows that applying the FCM clustering algorithm to a digital library management system has some practical significance.
Satellite-based continuous-variable quantum key distribution under the Earth’s gravitational field

Long-distance communication protocols cannot ignore the existence of the Earth's gravitational field and its effects on quantum states. In this work, we show a very general method to consider the effects of the Earth's gravitational field on continuous-variable quantum key distribution protocols. Our results show that the Earth's gravitational field erodes the ability of the two parties to perform QKD in all the protocols. However, our findings also exhibit some interesting features, i.e., the key rates initially increase for a specific range of height parameter $$h\simeq r_A/2$$ and then gradually decrease with the increasing of the orbits of satellite h. A possible explanation is also provided in our analysis, considering the fact that gravitational frequency shift and special relativistic effects play different roles in the key rates. In addition, our findings show that the change in key rate effected by gravitational frequency shift can be determined at a level of $$<1.0\%$$ within the satellite height at geostationary Earth orbits. Our work could provide some interesting possibilities to reduce the loss key rate through the control of the orbital height of satellites.
Information scrambling dynamics in a fully controllable quantum simulator

Quantum information scrambling has received tremendous attention owing to its key concept in the fields of quantum chaos and quantum gravity. Here, with exploiting a fully controllable superconducting quantum processor, we study the verified scrambling in a 1D spin chain by an analog superconducting quantum simulator with the signs and values of individual driving and coupling terms fully controllable. By engineering opposite Hamiltonians on two subsystems, we measure the temporal and spatial patterns of out-of-time ordered correlators (OTOC), with the Hamiltonian mismatch and the decoherence extracted quantitatively from the scrambling dynamics, providing a benchmarking tool for probing not only the spatial but the temporal patterns of scrambling dynamics in many-body systems. Our work demonstrates the superconducting system with strong controllability as a powerful quantum simulator.
Bounded pool mining and the bounded Bitcoin price

We present a simple model featuring the supply side of the Bitcoin ecosystem, i.e. the market structure of “mining”, to rationalize the relationship between the Bitcoin price volatility and the market concentration in pool mining. An individual miner optimally chooses to operate individually or to delegate the mining capacity in hashrates to a mining pool. The mining pool entertains the trade-off between compromising the network derived from its market power and maintaining sufficient hashrate delegations from dispersed miners. We show that a mining pool finds it optimal to be self-constrained in size while maintaining a positive probability of compromising the network in equilibrium. As a result, the bounded market concentration in pooled mining caps the Bitcoin price fluctuations. We also document important empirical evidence which is consistent with our model predictions.
A Virtual Machine Consolidation Algorithm Based on Dynamic Load Mean and Multi-Objective Optimization in Cloud Computing

High energy consumption and low resource utilization have become increasingly prominent problems in cloud data centers. Virtual machine (VM) consolidation is the key technology to solve the problems. However, excessive VM consolidation may lead to service level agreement violations (SLAv). Most studies have focused on optimizing energy consumption and ignored other factors. An effective VM consolidation should comprehensively consider multiple factors, including the quality of service (QoS), energy consumption, resource utilization, migration overhead and network communication overhead, which is a multi-objective optimization problem. To solve the problems above, we propose a VM consolidation approach based on dynamic load mean and multi-objective optimization (DLMM-VMC), which aims to minimize power consumption, resources waste, migration overhead and network communication overhead while ensuring QoS. Fist, based on multi-dimensional resources consideration, the host load status is objectively evaluated by using the proposed host load detection algorithm based on the dynamic load mean to avoid an excessive VM consolidation. Then, the best solution is obtained based on the proposed multi-objective optimization model and optimized ant colony algorithm, so as to ensure the common interests of cloud service providers and users. Finally, the experimental results show that compared with the existing VM consolidation methods, our proposed algorithm has a significant improvement in the energy consumption, QoS, resources waste, SLAv, migration and network overhead.
Autonomous Driving Robot Wheelchairs for Smart Hospitals

This paper introduces a robot wheelchair with autonomous driving for smart hospitals. The robot is with multiple ultrasonic sensors, Wi-Fi/Bluetooth modules and etc. Thus, the robot wheelchair safely moves patients with mobility difficulties to their destinations in a large and complex structure of hospitals. In addition, the robot wheelchair collects obstacle information and send it to the control server in smart hospitals. Experiment results show that the robot wheelchair moves without collision and collects obstacle information.
An Improved Approach for Generative Model-Based Product Image Anomaly Detection

Product anomaly detection is an essential problem in the industry. Researchers have tried to solve anomaly detection using machine learning to reduce the burden on human inspectors. In recent years, anomaly detection in product images using deep neural networks has been actively studied. In this paper, we propose an improved approach for anomaly detection using generative models. We experiment on the MVTec AD dataset, a real-world public dataset, and show that our method is applicable to real industrial products. We also confirm that performance improvements are achieved on several generative models.
ZT&amp;T: Secure blockchain-based tokens for service session management in Zero Trust Networks

In the current digitalized world, the number of interconnected entities is constantly growing. Users and devices are no longer confined to the same physical or geographical region when participating in network connections. Due to this, the line that delimits the network perimeter can no longer be identified. Traditional castle-and-moat security approaches cannot accommodate the latent threats that may occur in these borderless network deployments. Zero Trust Networking, alongside Software Defined Perimeter (SDP) concepts, aims to extend the perimeter of trust to every entity connected to the network regardless of their physical location. Authentication, access control, and verification methods must constantly be applied for all the participants in the network, as everything is considered untrustworthy. The requirement of complex security mechanisms alongside constant trust assurance for every interaction may challenge the realization of the Zero Trust vision. Blockchain is an emergent technology that can be integrated into Zero Trust to leverage these requirements. The data decentralization, anonymity, cryptography, and immutable record of transactions can provide the required security for Zero Trust. In this work, we propose a mechanism for assuring secure service session management with the use of blockchain capabilities. Non-Fungible-Tokens (NFT) are applied to access and provider tokens representing a policy agreement for service consumption. The tokens are mapped to the public addresses of entities registered in the blockchain. The access and provider tokens are encoded with metadata defining the service sessions' expiration. The proposal is realized through the use of an emerging open source Zero Trust platform (OpenZiti), a private Ethereum blockchain (Hyperledger Besu), and a session manager decentralized application (ZT&T) for handling the creation of policies and blockchain interaction.
Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures

Handling missing values (MVs) and feature selection (FS) are vital preprocessing tasks for many pattern recognition, data mining, and machine learning (ML) applications, involving classification and regression problems. The existence of MVs in data badly affects making decisions. Hence, MVs have to be taken into consideration during preprocessing tasks as a critical problem. To this end, the authors proposed a new algorithm for manipulating MVs using FS. Bayesian ridge regression (BRR) is the most beneficial type of Bayesian regression. BRR estimates a probabilistic model of the regression problem. The proposed algorithm is dubbed as cumulative Bayesian ridge with similarity and Luca’s fuzzy entropy measure (CBRSL). CBRSL reveals how the fuzzy entropy FS used for selecting the candidate feature holding MVs aids in the prediction of the MVs within the selected feature using the Bayesian Ridge technique. CBRSL can be utilized to manipulate MVs within other features in a cumulative order; the filled features are incorporated within the BRR equation in order to predict the MVs for the next selected incomplete feature. An experimental analysis was conducted on four datasets holding MVs generated from three missingness mechanisms to compare CBRSL with state-of-the-art practical imputation methods. The performance was measured in terms of R2 score (determination coefficient), RMSE (root mean square error), and MAE (mean absolute error). Experimental results indicate that the accuracy and execution times differ depending on the amount of MVs, the dataset’s size, and the mechanism type of missingness. In addition, the results show that CBRSL can manipulate MVs generated from any missingness mechanism with a competitive accuracy against the compared methods.
Hidden Monitoring Based on Keystroke Dynamics in Online Examination System

The COVID-19 pandemic has accelerated the development of distance learning technologies, where online tests and exams play an important role. In online testing, the detection of various types of academic fraud, including cases where the examinee is falsely represented by another person, is of particular importance. Continuous biometric (behavioral) authentication can be a solution to counter unauthorized access. This paper proposes an authentication technology based on keystroke dynamics and hidden monitoring. An application for collecting and updating keystroke dynamics profiles of domain users and their continuous authentication is developed. The efficiency of reducing the dimension of the keystroke feature space based on the frequency of alphabetic letters is demonstrated. Results of popular performance metrics (FAR, FRR, ERR, ROC, and DET) are significantly improved already when evaluating only metric distances. For instance, ERR is reduced from 10.1% to 0.79%, which is comparable to the estimates provided by the kNN method for its optimal parameters.
Security and Privacy Threats and Requirements for the Centralized Contact Tracing System in Korea

As COVID-19 became a pandemic worldwide, contact tracing technologies and information systems were developed for quick control of infectious diseases in both the private and public sectors. This study aims to strengthen the data subject’s security, privacy, and rights in a centralized contact tracing system adopted for a quick response to the spread of infectious diseases due to climate change, increasing cross-border movement, etc. There are several types of contact tracing systems: centralized, decentralized, and hybrid models. This study demonstrates the privacy model for a centralized contact tracing system, focusing on the case in Korea. Hence, we define security and privacy threats to the centralized contact tracing system. The threat analysis involved mapping the threats in ITU-T X.1121; in order to validate the defined threats, we used LIDDUN and STRIDE to map the threats. In addition, this study provides security requirements for each threat defined for more secure utilization of the centralized contact tracing system.
Making attention mechanisms more robust and interpretable with virtual adversarial training

Although attention mechanisms have become fundamental components of deep learning models, they are vulnerable to perturbations, which may degrade the prediction performance and model interpretability. Adversarial training (AT) for attention mechanisms has successfully reduced such drawbacks by considering adversarial perturbations. However, this technique requires label information, and thus, its use is limited to supervised settings. In this study, we explore the concept of incorporating virtual AT (VAT) into the attention mechanisms, by which adversarial perturbations can be computed even from unlabeled data. To realize this approach, we propose two general training techniques, namely VAT for attention mechanisms (Attention VAT) and "interpretable" VAT for attention mechanisms (Attention iVAT), which extend AT for attention mechanisms to a semi-supervised setting. In particular, Attention iVAT focuses on the differences in attention; thus, it can efficiently learn clearer attention and improve model interpretability, even with unlabeled data. Empirical experiments based on six public datasets revealed that our techniques provide better prediction performance than conventional AT-based as well as VAT-based techniques, and stronger agreement with evidence that is provided by humans in detecting important words in sentences. Moreover, our proposal offers these advantages without needing to add the careful selection of unlabeled data. That is, even if the model using our VAT-based technique is trained on unlabeled data from a source other than the target task, both the prediction performance and model interpretability can be improved.
Modeling Health Data Using Machine Learning Techniques Applied to Financial Management Predictions

Health management has steadily improved in performance and accuracy using IT technology. Hospitals and health institutions hold an enormous number of data in their software applications, which can be used with Big Data methodologies to extract useful information. One of the most challenging aspects of health institutional management is financial management; billing prediction is a key aspect to maintain a predictable service level for patients, avoiding unpleasant surprises and anticipating treasury management. Using patient data from public patient databases and applying a machine learning approach, this article offers a model that helps to make more precise and detailed financial plans.
Remote Training for Medical Staff in Low-Resource Environments Using Augmented Reality

This work aims to leverage medical augmented reality (AR) technology to counter the shortage of medical experts in low-resource environments. We present a complete and cross-platform proof-of-concept AR system that enables remote users to teach and train medical procedures without expensive medical equipment or external sensors. By seeing the 3D viewpoint and head movements of the teacher, the student can follow the teacher's actions on the real patient. Alternatively, it is possible to stream the 3D view of the patient from the student to the teacher, allowing the teacher to guide the student during the remote session. A pilot study of our system shows that it is easy to transfer detailed instructions through this remote teaching system and that the interface is easily accessible and intuitive for users. We provide a performant pipeline that synchronizes, compresses, and streams sensor data through parallel efficiency.