Reviewerly/exHarmony huggingface dataset:
Managing the Scientific Activity of Creative Employees in Higher Education Using Scientometric Indicators

The phenomenon of creativity is considered from the standpoint of an indicator that determines a person’s ability to scientific creativity. The genesis of the creativity theory is presented and issues requiring additional research are highlighted. The tests and methods aimed at identifying a person’s creative abilities are analysed; their generality is shown and both positive points and the restrictions related to their application difficulties to large creative groups are separately identified. The indicators are justified that are advisable to be used when testing universities’ and scientific institutions’ employees. The feasibility of applying open sources based on the scientific electronic library “E-library” in assessing the creative abilities of personalities involved in research and pedagogical activities is shown. The authors propose to use histograms that determine each of the employees’ contribution to the university’s overall scientific potential as the initial data for the subsequent research. The law of distributing these histograms is established and the curves approximating them are identified. The choice of the threshold determining the team’s creative part is justified based on the Pareto principle. The commonality of the distribution law is proven and numerical indicators are obtained allowing one to quickly conduct calculations on choosing the threshold of making a decision. Examples of four universities demonstrate the procedure for applying the developed approach. The article demonstrates the processing data of the results characterising the contribution of the creative part of scientific and pedagogical groups to forming the intellectual potential of universities issued in open publications. The conclusions are formulated and the directions of further research are determined.
WGM-dSAGA: Federated Learning Strategies with Byzantine Robustness Based on Weighted Geometric Median

Federated learning techniques accomplish federated modeling and share global models without sharing data. Federated learning offers a good answer to complex data and privacy security issues. Although there are many ways to target federated learning, Byzantine attacks are the ones we concentrate on. Byzantine attacks primarily impede learning by tampering with the local model parameters provided by a client to the master node throughout the federation learning process, leading to a final global model that diverges from the optimal solution. To address this problem, we combine aggregation rules with Byzantine robustness using a gradient descent optimization algorithm based on variance reduction. We propose a WGM-dSAGA method with Byzantine robustness, called weighted geometric median-based distributed SAGA. We replace the original mean aggregation strategy in the distributed SAGA with a robust aggregation rule based on weighted geometric median. When less than half of the clients experience Byzantine attacks, the experimental results demonstrate that our proposed WGM-dSAGA approach is highly robust to different Byzantine attacks. Our proposed WGM-dSAGA algorithm provides the optimal gap and variance under a Byzantine attack scenario.
LPG-Based Knowledge Graphs: A Survey, a Proposal and Current Trends

A significant part of the current research in the field of Artificial Intelligence is devoted to knowledge bases. New techniques and methodologies are emerging every day for the storage, maintenance and reasoning over knowledge bases. Recently, the most common way of representing knowledge bases is by means of graph structures. More specifically, according to the Semantic Web perspective, many knowledge sources are in the form of a graph adopting the Resource Description Framework model. At the same time, graphs have also started to gain momentum as a model for databases. Graph DBMSs, such as Neo4j, adopt the Labeled Property Graph model. Many works tried to merge these two perspectives. In this paper, we will overview different proposals aimed at combining these two aspects, especially focusing on possibility for them to add reasoning capabilities. In doing this, we will show current trends, issues and possible solutions. In this context, we will describe our proposal and its novelties with respect to the current state of the art, highlighting its current status, potential, the methodology, and our prospect.
Minimum complexity integrated photonic architecture for delay-based reservoir computing

Reservoir computing is an analog bio-inspired computation scheme for efficiently processing time-dependent signals, the photonic implementations of which promise a combination of massive parallel information processing, low power consumption, and high-speed operation. However, most of these implementations, especially for the case of time-delay reservoir computing, require extensive multi-dimensional parameter optimization to find the optimal combination of parameters for a given task. We propose a novel, largely passive integrated photonic TDRC scheme based on an asymmetric Mach-Zehnder interferometer in a self-feedback configuration, where the nonlinearity is provided by the photodetector, and with only one tunable parameter in the form of a phase shifting element that, as a result of our configuration, allows also to tune the feedback strength, consequently tuning the memory capacity in a lossless manner. Through numerical simulations, we show that the proposed scheme achieves good performance -when compared to other integrated photonic architectures- on the temporal bitwise XOR task and various time series prediction tasks, while greatly reducing hardware and operational complexity.
Blockchain-based cryptographic approach for privacy enabled data integrity model for IoT healthcare

The development of Internet of Things (IoT) technologies allowed the rapid generation of massive amounts of data by people. Although moving data to a server is a useful solution for storage, the owner of the data loses control, which results in security lapses. An efficient method for cloud-based data models is data integrity. In order to protect data privacy in IoT healthcare, this paper develops a data integrity technique. The Data Owner, IoT server, Key Generator Center, and Auditor are the four entities that make up this model. Here, the ability to verify the accuracy of the outsourced data is present in the auditor, data owner, and IoT server. The setup, storage, and verification phase are the three stages that make up the data integrity model. Here, the proposed Taylor-based Border Collie Cat optimisation is used to generate integrity keys in the best possible way. Here, the Taylor series and Border Collie Cat optimisation (BCCO) are combined to derive the proposed Taylor-based BCCO. Python is used to carry out the proposed strategy’s implementation. The proposed method offered enhanced performance with highest normalised variance of 0.710, highest conditional privacy of 2.880, and smallest computation time of 0.179 sec using heart disease dataset.
A Semantic Segmentation Framework for Hyperspectral Imagery Based on Tucker Decomposition and 3DCNN Tested with Simulated Noisy Scenarios

The present work, unlike others, does not try to reduce the noise in hyperspectral images to increase the semantic segmentation performance metrics; rather, we present a classification framework for noisy Hyperspectral Images (HSI), studying the classification performance metrics for different SNR levels and where the inputs are compressed. This framework consists of a 3D Convolutional Neural Network (3DCNN) that uses as input data a spectrally compressed version of the HSI, obtained from the Tucker Decomposition (TKD). The advantage of this classifier is the ability to handle spatial and spectral features from the core tensor, exploiting the spatial correlation of remotely sensed images of the earth surface. To test the performance of this framework, signal-independent thermal noise and signal-dependent photonic noise generators are implemented to simulate an extensive collection of tests, from 60 dB to −20 dB of Signal-to-Noise Ratio (SNR) over three datasets: Indian Pines (IP), University of Pavia (UP), and Salinas (SAL). For comparison purposes, we have included tests with Support Vector Machine (SVM), Random Forest (RF), 1DCNN, and 2DCNN. For the test cases, the datasets were compressed to only 40 tensor bands for a relative reconstruction error less than 1%. This framework allows us to classify the noisy data with better accuracy and significantly reduces the computational complexity of the Deep Learning (DL) model. The framework exhibits an excellent performance from 60 dB to 0 dB of SNR for 2DCNN and 3DCNN, achieving a Kappa coefficient from 0.90 to 1.0 in all the noisy data scenarios for a representative set of labeled samples of each class for training, from 5% to 10% for the datasets used in this work. The source code and log files of the experiments used for this paper are publicly available for research purposes.
Hazard-informed optimization of seismic networks for earthquake early warning—the case of the Lower Rhine Embayment (western Germany)

Earthquake early warning (EEW) systems can serve as a viable solution to protect specific hazard‐prone targets (major cities or critical infrastructure) against harmful seismic events. Using the example of the Lower Rhine Embayment (western Germany), we present a novel approach for evaluating and optimizing seismic networks for EEW purposes. The network optimization is applied to simulated earthquake scenarios from a hazard-compatible stochastic catalog, which represents a realization of the seismicity in the target area over a given period of time. We propose a densification of the existing network in the area by pre-selecting a number of potential sites with an optimal station configuration using a microgenetic algorithm, minimizing an appropriate cost function associated with the network layout. We show that the new decentralized network significantly improves the warning time and the accuracy of the warnings for levels of shaking for threshold levels of at least 0.02 g. Although the accuracy of the alerts for other cities outside the target area varies depending on their location, we demonstrate that the updated network layout will also improve the warning times for neighboring cities.
Image Classification Based on Layered Gradient Clipping Under Differential Privacy

Convolutional neural networks (CNNs) are widely used in the field of image classification. At the same time, users face the risk of privacy leakage because adversaries can reverse private information from the training parameters of CNNs. Adding Gaussian noise to the training parameters is an effective means to prevent adversaries from stealing private, but this tends to reduce the utility of the models. Therefore, how to find a balance between privacy and utility has become a hot research topic. In this paper, to improve the image classification ability of CNN models under differential privacy protection, we propose an image classification algorithm based on layered gradient clipping under differential privacy, ICGC-DP for short. Firstly, the gradient tensor is layered according to the neural network model. Secondly, for each layered gradient tensor, the median of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${L_{2}}$ </tex-math></inline-formula> norms is used as the clipping threshold. Moreover, to prevent the sensitivity from converging to zero, we add a bound on the sensitivity to ensure that all gradients can be protected by differential privacy. To further improve the classification utility of ICGC-DP, we design an adaptive weighted fusion module for it. The module assigns weights to prediction tensors according to the variance between them. We conduct comprehensive experiments on the Mnist, FashionMnist and CIFAR10 datasets, respectively. The experimental results show that, when the privacy budget <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon = 2.0$ </tex-math></inline-formula> , which indicates that the algorithm adds a large noise, ICGC-DP achieves 97.36%, 88.72% and 72.63% classification accuracy for the Minist, FasionMnist and CIFAR10 datasets, respectively; when the privacy budget <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon = 8.0$ </tex-math></inline-formula> , which means the algorithm adds less noise, the classification accuracy of ICGC-DP for Minist, FasionMnist and CIFAR10 datasets reaches 97.81%, 89.49% and 74.41%, respectively.
Bridging the Reality Gap Between Virtual and Physical Environments Through Reinforcement Learning

Creating Reinforcement learning(RL) agents that can perform tasks in the real-world robotic systems remains a challenging task due to inconsistencies between the virtual-and the real-world. This is known as the “reality-gap” which hinders the performance of a RL agent trained in a virtual environment. The research describes the techniques used to train the models, generate randomized environments, reward function, and techniques utilized to transfer the model to the physical environment for evaluation. For this investigation, a low-cost 3-degrees-of-freedom (DOF) Steward platform was 3D modeled and created virtually and physically. The goal of the 3D-Stewart platform was to guide and balance the marble towards the center. Custom end-to-end APIs were developed to interact with the Godot game engine, manipulate physics and dynamics, interact with the in-game lighting and perform environment randomizations. Two RL algorithms: Q-learning and Actor-Critic, were implemented to evaluate the performance by using domain randomization and induced noise to bridge the reality gap. For Q-learning, raw frames were used to make predictions while Actor-Critic utilized marble position, velocity vector and relative position by pre-processing captured frames. The experimental results show the effectiveness of domain randomization and introduction of noise during the training.
Machine Learning Based Approach for Traffic Rule Violation Detection

The goal of this paper is to design an automated system model to monitor the violation of traffic rules, specifically the number of people sitting on a two-wheeler. Typically, in areas near the security offices, people tend to follow the rules, but in areas where no one is watching, people violate the rules. In our case of an organizational campus, if there are three people traveling on a two-wheeler but when they encounter a security guard, one of the persons gets down and walks ahead of the guarded area and then again sits back on the vehicle. In such cases, efficient methods are required to monitor the violation of specified traffic rules without human intervention. For the above-mentioned challenge, a deep learning-based solution is provided where the process starts with object recognition using YOLOv3 (You Only Look Once) model, using which a person sitting on any particular vehicle is identified based on a minimum threshold distance. Also, for the distance calculation, a depth estimation algorithm which helps us in finding the 3-D distance between objects from a 2-D image is implemented. Moreover, the number plate of the vehicle violating the above-mentioned rule is identified for easy identification of the person violating the rule. The proposed approach is implemented on a real time video streaming dataset. The simulation results show the efficiency of the proposed approach in terms of accuracy, precision and recall as 91%, 86% and 94% respectively.
Identifying Image of the Correct Use of Face Mask Using Semantic Segmentation Technique

In recent years, the world is facing Covid-19 pandemic which has spread to more than 200 countries. WHO recommend everyone to always wearing a mask and keeping a distance to reduce transmission since Covid-19 is very susceptible to infection in a crowded area. In fact, many people misuse masks, such as wearing a mask but not covering the nose, and thus, monitoring the correct use of masks on a large-scale area is not easy. A technology implementing a high precision computer vision is needed to help monitoring the correct use of human mask automatically. This paper proposes a deep learning method that performs semantic segmentation and classification tasks to precisely identify the use of human face mask. Since it is rarely done so far, a sufficient dataset for this task is still lacking. Therefore, we also construct a dataset for face mask semantic segmentation task, including the fine-grained annotated ground truth. Based on our experiments, the proposed method that uses U-Net base model provides the best Mean IoU performance, which is 95%, compared to several comparative models. The segmentation output is then forwarded to a classification process, to decide whether it is a correct or an incorrect use of mask, and provides an accuracy rate that reaches 100%. Details of the experimental results are shown both quantitatively and qualitatively in this paper. The current results of this study may inspire the development of a better system in the future.
Book Recommender System using Convolutional Neural Network

The use of technology cannot be avoided along with the times. Searching, reading, and buying books online is one of the uses of technology. Several online book websites have provided free and paid online book services to users, such as gramedia.com, openlibrary.org, etc. Books available on online services have various types and ratings, depending on user interests. However, some book enthusiasts sometimes find it difficult to find other books that match their desires. Book recommender systems have been widely developed by utilizing collaborative filtering. However, collaborative filtering still leaves problems in terms of scalability and sparsity. To overcome this problem, we propose an approach for utilizing the Convolutional Neural Network (CNN) algorithm in a book recommender system. CNN has advantages in solving large data problems (scalability) and sparsity problems. We use book recommendation dataset from GitHub. To evaluate system performance, we combine several parameters, such as filter size, kernel size, and epoch. The best model is obtained by a combination of parameters with filter size of 8, kernel of 2, and epoch of 50 with MAE, MAPE, and MSE values are 1.361, 23,766, and 3,034, respectively.
A Layer Selection Optimizer for Communication-Efficient Decentralized Federated Deep Learning

Federated Learning (FL) systems orchestrate the cooperative training of a shared machine learning (ML) model across connected devices.Recently, decentralized FL architectures driven by consensus have been proposed to enable the devices to share and aggregate the ML parameters via direct sidelink communications.The approach has the advantage of promoting the federation among the agents even in the absence of a server, but may require an intensive use of communication resources compared to vanilla FL methods.This paper proposes a communication-efficient design of consensus-driven FL optimized for training of deep neural networks (DNNs).Devices independently select fragments of the DNN to be shared with neighbors on each training round.Selection is based on a local optimizer that trades model quality improvement with sidelink communication resource savings.The proposed technique is validated on a vehicular cooperative sensing use case characterized by challenging real-world datasets and complex DNNs typically employed in autonomous driving with up to 40 trainable layers.The impact of layer selection is analyzed under different distributed coordination configurations.The results show that it is better to prioritize the DNN layers possessing few parameters, while the selection policy should optimally balance gradient sorting and randomization.Latency, accuracy and communication tradeoffs are analyzed in detail targeting sustainable federation policies.
Disjoint Paths Construction and Fault-Tolerant Routing in BCube of Data Center Networks

BCube is a promising structure of data center network, as it can significantly improve the performance of typical applications. With the expansion of network scale and increasement of complexity, reliability and stability of networks have become more essential. In this paper, we study the fault-tolerant routings in BCube. First, we design a fault-tolerant routing algorithm based on node disjoint multi-paths. The proposed multi-path routing has stronger fault tolerance, since each path has no other common nodes except the source node and the destination node. Second, we investigate an effective fault-tolerant routing based on routing capabilities algorithm for BCube. The proposed algorithm has higher fault tolerance and success rate of finding feasible routes, since it does not limit the faults number. Third, we present an adaptive path finding algorithm for establishing virtual links between any two nodes in BCube, which can shorten the diameter of BCube. Extensive simulation results show that the proposed routing scheme outperforms the existing popular algorithms. Compared with the state-of-the-art fault-tolerant routing algorithms, the proposed algorithm has a 21.5% to 25.3% improvement on both throughput and packet arrival rate. Meanwhile, it reduces the average latency of 18.6% and the maximum latency of 23.7% in networks.
Color Image Cryptography Using Block and Pixel-Wise Permutations with 3D Chaotic Diffusion in Metaverse

All multimedia data types, including digital images, audio, text, and video, are now freely accessible online due to the phenomenal growth of internet technology solutions and wireless telecommunication systems, including internet and cellular networks. Cryptographic algorithms are needed to provide a certain level of security, dependability, and privacy and to keep sensitive information from being accessed. At the same time, data is being collected and sent. This paper presents a novel and effective system of color image cryptographic algorithms based on image pixel shuffling, which involves repeatedly splitting the image into a predetermined number of blocks and sub-blocks and spinning each clockwise by 90°. By obscuring the statistical makeup of the pixels, two dimensions of Arnold cat mapping were then used to distort further the connection between neighboring pixels in the plaintext image. The image pixels are returned to their original places during the decryption process. The Henon mapping algorithm is employed for image encryption to decrease the correlation between the ciphertext and the plaintext image. The encryption above algorithm was tried and examined on a few color images, and the results demonstrated a sufficient level of security to withstand differential and statistical attacks.