Reviewerly/exHarmony huggingface dataset:
Comparison of Performance and Costs of CaaS and RDBaaS Services

Public clouds like AWS, Azure, GCP, and OCI offer a range of services including Container as a Service (CaaS) and Relational Database as a Service (RDBaaS). From the perspective of an IT system provider there is a notable lack of information on the overall performance that can be achieved when using specific configurations of these interdependent CaaS and RDBaaS services. To address this issue and avoid incorrect architectural assumptions, it was decided to empirically evaluate the combined performance offered by CaaS and RDBaaS services, considering their hardware configurations selected based on a predetermined cost constraint. The experiments were conducted in two stages. The first stage was aimed at narrowing down the set of investigated CaaS and RDBaaS services. Using the measurements collected in the second stage, a statistical analysis was performed, comparing the performance achieved in the cloud environments with the performance obtained in the on-premise environment, taking cost constraints into account. The experiments conducted for the cloud services provided insights into their limitations and performance, enabling informed architectural decisions during the design of complex IT systems. The results of the analysis confirmed that the performance obtained for the studied combinations of services and their associated costs significantly varied among the different cloud providers.
WFF-EGNN: Encrypted Traffic Classification Based on Weaved Flow Fragment via Ensemble Graph Neural Networks

Traffic analysis plays an essential role in network management and security protection under the premise of fully protecting user privacy. Unfortunately, encryption dramatically reduces the disclosure of traffic information, making encrypted traffic analysis more challenging than plaintext traffic analysis, especially in the environment of new encryption protocols (e.g., TLS-1.3, QUIC). The existing tensor-based methods mainly focus on optimizing packet length sequence features and introducing the latest deep learning model. However, the tensor-based features cannot sufficiently express the structured non-Euclidean Markov properties inside the encrypted traffic. This paper proposes a novel traffic graphical expression model named Weaved Flow Fragment (WFF) to transform a packet sequence into a graph, which better represents the packet sequence’s inner relationship than the tensor. WFF also considers the co-evolution relationship and the cross-direction change relationship in the bidirectional flow, breaking through the limitation that the tensor-like length sequence only considers the adjacent Markov properties. Then, we use the latest graph convolutional networks, gated graph neuron networks, and capsule graph neural networks to implement classification based on WFF, respectively. Further, to give full play to the advantages of different graph neural network classifiers to improve classification effect in large-scale data scenarios, we proposed the ensemble graph neural network architecture with several ensemble mechanisms to reduce the possibility of classification error caused by overfitting and model concerns. Experiments show that our classification effect is much better than the state-of-the-art methods (achieved 99.25% F1-score) in an open-world environment, and the model size is reduced by 99.1%.
Image Processing with Reservoir Neural Network

Reservoir neural network (RNN) is a powerful tool for solving complex machine learning problems. The reservoir is a recurrent part of the network having a large size and rare internal connections which are most often set randomly and remain fixed. The idea of the RNN is to train only part of the network using a simple classification/regression technique and leave most of the network (reservoir) fixed. At the same time, all RNN advantages are preserved, and the training time is significantly reduced. The work performed optimization and research of methods that improve the reservoir ability to solve problems of image classification. These methods are based on the reservoir output data transformation before they are fed to the RNN output layer. In the work, the optimal parameters values for the methods Infomax and SpaRCe were obtained, which provide a minimum error in image classification. Using the example of image classification from the MNIST handwritten digit database, it is shown that: 1. Reservoir networks are trained much faster than convolutional networks, although they are inferior to the latter in terms of image classification accuracy. 2. ESN (echo-state network) with principal component projector (PCA) gives more accurate results than ESN, Infomax and SpaRCe networks, but is slower.
Blockchain Design and Modelling

Ontology engineering, along with semantic Web technologies, allow the semantic development and modeling of the operational flow required for blockchain design. The semantic Web, in accordance with W3C, provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries and can be seen as an integrator for various content, applications and information systems. The most widely used blockchain modelling system, by abstract representation, description and definition of structure, processes, information and resources, is the enterprises modelling. Enterprise modelling uses domain ontologies by model representation languages.
Zero-Shot NER via Extractive Question Answering

Although the task of named entity recognition (NER) is usually solved as a sequence tagging problem via traditional supervised learning approaches which require the presence of a substantially sized annotated dataset, recent works aiming to utilize pretrained extractive question-answering (QA) models have shown significant few and zero-shot capabilities. This work aims to further investigate their applicability in zero-shot setting i.e. without explicit fine-tuning. We construct a test dataset and conduct a series of experiments to determine the shortcomings inherent in using QA models for the NER task. Our findings demonstrate such problems as weak prompt robustness, high false positive rate, trouble distinguishing between semantically close entity types and more. We suggest that addressing these problems is a crucial first step in improving the extractive QA NER approach. We conduct most of our experiments using DeepPavlov’s models within their framework.
An Intelligent Day Ahead Solar Plant’s Power Forecasting System

The power production of a photovoltaic system has complex nonlinear dynamic with uncertainties since solar radiation and temperature fluctuate. Thereby, it is complicated to approximate this complex dynamic by conventional algorithms while machine learning algorithms provide the required forecast’s performance. We solved the intelligent day ahead solar plant’s power forecasting task based on the modified neural net with a developed fuzzy attention mechanism. In contrast with existed fuzzy attention mechanisms which use classical membership function we consider an attention mechanism’s context vector as fuzzy measures. The developed fuzzy attention mechanism selects from all signals which provided by classical attention mechanism only important signal based on fuzzy measures and the fuzzy integral. The comparison simulations study results of the intelligent day ahead solar plant’s power forecasting system based on the created modified neural net with a fuzzy attention mechanism reveal its advantages and competitive performance, as compared to a classical RNN. The modified neural net with a fuzzy attention mechanism has competitive performance as compared to a modified fuzzy neuronet and RNN for day ahead forecasting of the hourly solar plants’ power.
SAMDIT: Systematic Study of Adding Memory to Divided Input in the Transformer to Process Long Documents

Recently, processing long documents using a transformer has attracted the attention of the research society. This paper suggests a method to process long documents, which combines segmentation, unified relative positional encoding for slot tokens, masking technique, and additional memory slots related to segments. The main results show on-par performance for using our method to process long documents with the baseline and superior performance compared to SLED. Memory content was studied and analyzed and revealed extensive interaction between memory slots and related chunk.
Dog Face Recognition Using Vision Transformer

The demand for effective, efficient and safe methods for animal identification has been increasing significantly, due to the need for traceability, management, and control of this population, which grows at higher rates than the human population, particularly pets. Motivated by the efficacy of modern human identification methods based on face biometrics features, in this paper, we propose a dog face recognition method based on vision transformers, a deep learning approach that decomposes the input image into a sequence of patches and applies self-attention to these patches to capture spatial relationships between them. Results obtained on DogFaceNet, a public database of dog face images, show that the proposed method, which uses the EfficientFormer-L1 architecture, outperforms the state-of-the-art method proposed previously in literature based on ResNet, a deep convolutional neural network.
Communicative strategies of Mark Hamill’s personal branding

The article deals with linguistic aspects of personal branding, exploring communicative strategies and stylistic devices used for the purposes creating, maintaining and promoting celebrity’s personal brand. Branding is an indispensable instrument of modern-day marketing and its focus has long since shifted from companies to human brands. Self-branding has become an important component of a successful public career and its linguistic realization gains increasingly more weight. Multiple online platforms and social media grew beyond their initial purposes and became primary mediators of personal branding initiatives. The present research analyses Mark Hamill’s Twitter activity in 2021 and identifies main communicative strategies that facilitate self-promotion. The results suggest that the strategy of establishing the rapport with the target audience, the strategy of the display of the social awareness, career promotion strategy and the strategy of creating the sense of exclusivity are the main communicative strategies of Mark Hamill’s personal branding. Each one of them establishes or perpetuates one or multiple traits associated with the actor, thus building and strengthening his personal brand. On the other hand, all communicative strategies are realized through certain linguistic and rhetorical means, which facilitate their implementation and help produce the desired effect on the target audience. The present research identified various linguistic and stylistic devices used in Mark Hamill’s personal branding, namely quotation, parallelism, irony, parody, creolized text (hashtags, emoji, uppercase letters, expressive punctuation, pictures, gifs, cartoons, videos), hypertext, slogans, rhymes, rhetorical questions and clichés. The research showed that Twitter activity is extremely instrumental in attracting attention, projecting the desired image of oneself and connecting to the target audience, and, thus, effective from the personal branding perspective.
Dentistry and metaverse: A deep dive into potential of blockchain, NFTs, and crypto in healthcare

Blockchain technology and the metaverse have the potential to revolutionize dentistry and healthcare by enhancing data security, patient empowerment, disaster victim identification, and the delivery of dental services. In this review, we discuss the current state of the art in the field of dentistry and the future of dentistry, highlighting the advantages and challenges of utilizing blockchain technology for disaster victims’ identification. Blockchain's applications in Disaster Victim Identification (DVI) offer a humanitarian dimension, helping bring solace to families in times of tragedy. Moreover, blockchain's potential to establish virtual health clinics and telemedicine platforms could bridge healthcare gaps in underserved regions.
Influence of environmental factors on the power produced by photovoltaic panels artificially weathered

Solar energy is an important renewable energy source and a great option to mitigate the greenhouse gases produced by fossil fuels in electricity production. The common way to use solar energy is through photovoltaic technology. This technology converts solar photons into electricity. However, power production by photovoltaic panels (PV) is strongly related to environmental, manufactured, and maintenance factors. The present work analyzed the exposure of PV panels to environmental conditions at different stages in their lifetime (new, five, ten, 15 years, and 20 years). For this purpose, the PV panel was subjected to a simulated weathering process. A Generalized Linear Model (GLM) approach was used to evaluate the influence of environmental conditions (meteorological and air pollution) on power production, which follows a Binomial Negative distribution due to the overdispersion. The proposed method was performed in the tropical Andean during the winter season and in the presence of the El Niño Southern Oscillation (ENSO). Generally, the specific humidity and temperature were the meteorological significant covariates, while the PM2.5 was the air pollution significant covariate. Higher power production efficiencies were obtained in the presence of precipitation and wind velocity as significant covariates. The model evaluation had adequate criteria values, NSE between 0.76 and 0.96, and correlation coefficient between 0.87 and 0.98 using a new and weathered 15-year PV panel, respectively.
GAIN: Decentralized Privacy-Preserving Federated Learning

Federated learning enables multiple participants to cooperatively train a model, where each participant computes gradients on its data and a coordinator aggregates gradients from participants to orchestrate training. To preserve data privacy, gradients need to be protected during training. Pairwise masking satisfies the requirement, which allows participants to blind gradients with masks and the coordinator to perform aggregation in the blinded field. However, the solution would leak aggregated results to external adversaries (e.g., an adversarial coordinator), which suffers from quantity inference attacks. Additionally, existing pairwise masking-based schemes rely on a central coordinator and are vulnerable to the single-point-of-failure problem. To address these issues, we propose a decentralized privacy-preserving federated learning scheme called GAIN. GAIN blinds gradients with masks and encrypts blinded gradients using additively homomorphic encryption, which ensures the confidentiality of gradients, and discloses nothing about aggregated results to external adversaries to resist quantity inference attacks. In GAIN, we design a derivation mechanism for generation of masks, where masks are derived from shared keys established by a single key agreement. The mechanism reduces the computation and communication costs of existing schemes. Furthermore, GAIN introduces smart contracts over blockchains to aggregate gradients in a decentralized manner, which addresses the single-point-of-failure problem. Smart contracts also provide verifiability for model training. We present security analysis to demonstrate the security of GAIN, and conduct comprehensive experiments to evaluate its performance.
A novel color image tampering detection and self-recovery based on fragile watermarking

In industrial applications, sensing devices' image data must be accurately identified and recovered to prevent economic losses. Nevertheless, most industrial image watermarking methods focus on grayscale images with difficulty in achieving superior performance in three parameters: watermarked image quality, tamper detection and content recovery quality. To resolve this issue, a color image tampering detection and self-recovery method based fragile watermarking scheme is proposed in this paper. In the proposed scheme, block-based regular marker with pixel-based continuous marker is used for watermark embedding to enhance the quality of watermarked images. Then, a feature extraction-based tampering detection scheme for diagonal blocks combined with three-layer authentication is developed to resist tampering attacks of diverse shapes and sizes. Additionally, the block pixel-level recovery mechanism along with the Smoothing Inpainting algorithm is designed to implement the high-quality recovery of tampered images. Compared with recent methods, experimental results demonstrate the superiority of the scheme on tamper detection and image self-recovery, in terms of the image recovery robustness, tamper localization and attack resistance. Our scheme has a PSNR value of 25.4921 dB and an SSIM value of 0.7728 for the recovered image at 90% tampering rate.
Novelty detection technology for Alzheimer's disease based on autoencoder feature extraction and MCD classification

Alzheimer's disease (AD) is a neurodegenerative disorder that affects the life quality of millions of people worldwide. To diagnose new cases in a timely manner, we propose a new novelty detection technique that combines Autoencoder and Minimum Covariance Determinant (MCD). The technique consists of two steps: first, we use an Autoencoder to extract low-dimensional and discriminative features from the publicly available ADNI dataset, where we only train the Autoencoder with normal data, making the abnormal data more distinguishable in the feature space; second, based on the features of normal data, we use MCD to construct a decision boundary, and judge the degree of abnormality by the distance of the test point to the boundary. Compared with traditional methods without using Autoencoder, our technique has significant advantages in terms of accuracy and sensitivity, and can effectively deal with data imbalance problem. Experimental results show that our method can efficiently detect novel AD cases, and has a wide range of application prospects.
Incoherent optical cryptosystem based on hybrid wave aberration

An incoherent optical cryptosystem based on hybrid wave aberration is proposed in this study. The proposed cryptosystem is designed with different channels to divide the object. In each channel, different wave aberrations are allocated to form the hybrid wave aberration. When the object is imaged by the channel with huge wave aberrations, its corresponding image will be blurry enough to form a ciphertext. Because the different wave aberrations in different channels can be regarded as the key, the proposed optical cryptosystem can achieve multi-key encryption as well as local encryption. The proposed optical cryptosystem enhances the security of incoherent optical cryptosystem through the quantity of keys and the special construction method of ciphertext.
Insights into security and privacy issues in smart healthcare systems based on medical images

The advent of the fourth industrial revolution along with developments in other emerging technologies, such as Internet of Things, big data, artificial intelligence as well as cloud and quantum computing, smart healthcare systems (SHS) are becoming ubiquitous in our daily lives. Meanwhile, patients, doctors, and other medical personnel rely on the safe and efficient storage, transmission, and analysis of medical images and electronic health records for successful diagnosis, treatment, and management of different ailments. Moreover since, for various reasons, medical images are always the target of different illicit criminal activities, studies to utilise advanced information technologies to safeguard the confidentiality, integrity, and availability of such data have become a major priority in all SHS platforms. Our study evaluates recent efforts to deploy emerging technologies to design, secure, and enhance the efficiency of SHS that are based on medical images. It is hoped that this work will stimulate further interest aimed at the pursuit of more advanced algorithms and frameworks covering all aspects of security and privacy in emerging and future smart healthcare applications.
PolyFLAG_SVM: a Polymorphic Federated Learning Aggregation of Gradients Support Vector Machines Framework

The critical importance of user privacy in the context of Machine Learning is a hot research topic because it hinders data collection. Consequently, Federated Learning (FL) has emerged as a solution to the privacy problem. Instead of collecting users’ data to train smart models, FL exchanges models with clients, which are trained and sent back to the server for aggregation and global model updating. However, FL still faces some hurdles, such as vulnerability to inference and poisoning attacks. For this reason, this paper proposes PolyFLAG_SVM: Polymorphic Federated Learning Aggregation of Gradients Support Vector Machines Framework. PolyFLAG_SVM is a novel, secure, communication-efficient framework that provides several variants of Support Vector Machines models that follow the Gradient Descent Update technique. The confidence in the security of the proposed model is the result of the polymorphism of the encryption keys used, which guarantees that a cracked or leaked key is useless, since it is not used twice within the FL cycle. Moreover, the proposed framework is communication efficient due to the small size of messages exchanged between servers and clients. The proposed model is explained in detail and evaluated appropriately in this paper.
Accurate generation of stochastic dynamics based on multi-model generative adversarial networks

Generative Adversarial Networks (GANs) have shown immense potential in fields such as text and image generation. Only very recently attempts to exploit GANs to statistical-mechanics models have been reported. Here we quantitatively test this approach by applying it to a prototypical stochastic process on a lattice. By suitably adding noise to the original data we succeed in bringing both the Generator and the Discriminator loss functions close to their ideal value. Importantly, the discreteness of the model is retained despite the noise. As typical for adversarial approaches, oscillations around the convergence limit persist also at large epochs. This undermines model selection and the quality of the generated trajectories. We demonstrate that a simple multi-model procedure where stochastic trajectories are advanced at each step upon randomly selecting a Generator leads to a remarkable increase in accuracy. This is illustrated by quantitative analysis of both the predicted equilibrium probability distribution and of the escape-time distribution. Based on the reported findings, we believe that GANs are a promising tool to tackle complex statistical dynamics by machine learning techniques.