Reviewerly/exHarmony huggingface dataset:
Advanced Forecasting Methods of 5-Minute Power Generation in a PV System for Microgrid Operation Control

This paper concerns very-short-term (5-Minute) forecasting of photovoltaic power generation. Developing the methods useful for this type of forecast is the main aim of this study. We prepared a comprehensive study based on fragmentary time series, including 4 full days, of 5 min power generation. This problem is particularly important to microgrids’ operation control, i.e., for the proper operation of small energy micro-systems. The forecasting of power generation by renewable energy sources on a very-short-term horizon, including PV systems, is very important, especially in the island mode of microgrids’ operation. Inaccurate forecasts can lead to the improper operation of microgrids or increasing costs/decreasing profits for microgrid operators. This paper presents a short description of the performance of photovoltaic systems, particularly the main environmental parameters, and a very detailed statistical analysis of data collected from four sample time series of power generation in an existing PV system, which was located on the roof of a building. Different forecasting methods, which can be employed for this type of forecast, and the choice of proper input data in these methods were the subject of special attention in this paper. Ten various prognostic methods (including hybrid and team methods) were tested. A new, proprietary forecasting method—a hybrid method using three independent MLP-type neural networks—was a unique technique devised by the authors of this paper. The forecasts achieved with the use of various methods are presented and discussed in detail. Additionally, a qualitative analysis of the forecasts, achieved using different measures of quality, was performed. Some of the presented prognostic models are, in our opinion, promising tools for practical use, e.g., for operation control in low-voltage microgrids. The most favorable forecasting methods for various sets of input variables were indicated, and practical conclusions regarding the problem under study were formulated. Thanks to the analysis of the utility of different forecasting methods for four analyzed, separate time series, the reliability of conclusions related to the recommended methods was significantly increased.
VCNet: A generative model for volume completion

We present VCNet, a new deep learning approach for volume completion by synthesizing missing subvolumes. Our solution leverages a generative adversarial network (GAN) that learns to complete volumes using the adversarial and volumetric losses. The core design of VCNet features a dilated residual block and long-term connection. During training, VCNet first randomly masks basic subvolumes (e.g., cuboids, slices) from complete volumes and learns to recover them. Moreover, we design a two-stage algorithm for stabilizing and accelerating network optimization. Once trained, VCNet takes an incomplete volume as input and automatically identifies and fills in the missing subvolumes with high quality. We quantitatively and qualitatively test VCNet with volumetric data sets of various characteristics to demonstrate its effectiveness. We also compare VCNet against a diffusion-based solution and two GAN-based solutions.
Leveraging Synthetic Data and PU Learning For Phishing Email Detection

Imbalanced data classification has always been one of the most challenging problems in data science especially in the cybersecurity field, where we observe an out-of-balance proportion between benign and phishing examples in security datasets. Even though there are many phishing detection methods in literature, most of them neglect the imbalanced nature of phishing email datasets. In this paper, we examine the imbalanced property by varying legitimate to phishing class ratios. We generate new synthetic instances using a generative adversarial network model for long sentences (LeakGAN) to balance out the training process and ameliorate its impact on classification. These synthetic instances are labeled by positive-unlabeled learning and added to the initial imbalanced training set. The resulting dataset is given to the Bidirectional Encoder Representations from Transformers (BERT) model for sequence classification. We compare several state-of-the-art methods from the literature against our approach, which achieves a high performance throughout all the imbalanced ratios reaching an F1-score of 99.6% for the most extreme imbalanced ratio and an F1-score of 99.8% for balanced cases.
Analysis of Performance of University Groups Belonging to “Leadership in the Region and/or Industry” Track of “Priority 2030” Program

This article presents the comprehensive study results of the performance of universities which are the winners of the “Leadership in the region and/or industry” track of the “Priority 2030” program. The research included a comparison of the results of 3 groups of universities of this track, determined by the results of the competition. The participation of these groups of universities in the main strategic initiatives in the field of the higher education system development in 2006–2020 is considered; indicators for the main areas of university activities were selected and grouped, namely, educational, scientific and innovation, international and financial. The comparative analysis of aggregate indicators of recent years is based on the Displaced Ideal Method. This made it possible not only to determine the current positions of universities belonging to the examined track, but also to identify a number of problems in their activities that need to be solved in the coming years to strengthen their contribution to the socio-economic development of the regions. The results of the comprehensive analysis can be used not only by the current participants of the “Leadership in the region and/or industry” track of the “Priority 2030” program, but also by universities that only plan to take part in this program in the near future and the expert community engaged in research in the field of higher education development in Russia.
HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks

Federated learning (FL) is a new artificial intelligence concept that enables Internet-of-Things (IoT) devices to learn a collaborative model without sending the raw data to centralized nodes for processing. Despite numerous advantages, low computing resources at IoT devices and high communication costs for exchanging model parameters make applications of FL in massive IoT networks very limited. In this work, we develop a novel compression scheme for FL, called <i>high-compression federated learning (HCFL)</i>, for very large scale IoT networks. HCFL can reduce the data load for FL processes without changing their structure and hyperparameters. In this way, we not only can significantly reduce communication costs, but also make intensive learning processes more adaptable on low-computing resource IoT devices. Furthermore, we investigate a relationship between the number of IoT devices and the convergence level of the FL model and thereby better assess the quality of the FL process. We demonstrate our HCFL scheme in both simulations and mathematical analyses. Our proposed theoretical research can be used as a minimum level of satisfaction, proving that the FL process can achieve good performance when a determined configuration is met. Therefore, we show that HCFL is applicable in any FL-integrated networks with numerous IoT devices.
Design and Implementation of a Steganography-based System that Provides Protection for Breast Cancer Patient's Data

In recent years, several data breach attacks have occurred in the healthcare systems. These attacks violated the confidentiality of the Health Information System (HIS) and resulted in the exposure of a massive number of medical records. This paper aims to provide protection and privacy to the breast cancer patients' data, by using digital mammograms as the cover image in the steganography process. The steganography method used is the Least Significant Bit (LSB) was enhanced by integrating cryptography of the medical data using Advanced Encryption Standard (AES) and RSA encryption for the secret key. Also, the K-Means algorithm is used in the segmentation process of the mammogram to choose the image segment that is used in the hiding process. This paper resulted in enhancing the Mean Squared Error (MSE) values in the Stego-Images to reach 0.00257 and the Peak Signal to Noise Ratio (PSNR) to reach 78.79652 with 0% loss or manipulation in the medical data.
Development of Tomato Harvest Support System Using Mixed Reality Head Mounted Display

At present, many Japanese tomato farmers harvest manually to select harvest candidates considering ripeness and blemishes of each fruit. Therefore, part-time workers and new farmers have problems such as stability of harvest quality and work efficiency because they do not have a good eye for harvest candidates. Thus, we propose a system using Head Mounted Display (HMD) and User Datagram Protocol (UDP) that a computer gets camera images of a subjective perspective in real-time and finally, HMD presents the information which is obtained by image processing in Mixed Reality (MR) space. This system enables to tell the information such as harvest candidates, sugar content, and acidity of tomatoes to workers without disturbing the harvesting operation. In this paper, we describe the adopted HMD and the original architecture that we devised to achieve harvesting support in mixed reality except for image processing part, and moreover, we validate the proposed system.
Recovery Time Evaluation of Ad-hoc Routing Protocols in IoT-Blockchain

When deploying the IoT-Blockchain system on an infrastructure-based network (e.g., Wi-Fi), we will face the single point of failure problem (e.g., failure of an access point harm the whole system). Using ad-hoc routing protocols can avoid such a problem, and the system can recover the communication automatically if an IoT device fails. This paper investigates the recovery time of two routing protocols (i.e., Optimized Link State Routing (OLSR) and a Better Approach To Mobile Ad-hoc Network (BATMAN)) in an IoT-Blockchain system after a network failure happened without and with a blockchain connection. We have used mininet-wifi and private Ethereum blockchain to build the IoT-Blockchain, where we deployed the two routing protocols. The evaluation results show that the performance of BATMAN is better than OLSR&#x2019;s. Specifically, the recovery time of BATMAN is 8.485, 7.355 seconds faster than OLSR when not having and having a blockchain connection, respectively.
A Survey of Modern Crawler Methods

Web crawler is a kind of computer program to browse the World Wide Web (WWW) automatically and efficiently. In the information age, due to the explosive growth of Internet pages, it has become exceedingly difficult and time-consuming for people to search information, therefore people need the help of crawler to get the information they need. In this paper, the existing crawler technology is briefly summarized, the purpose is for beginners or people interested in the field of crawler can have a preliminary understanding and cognition of crawler. This paper first introduces the background of crawler technology and its classification and use. Then it summarizes some common libraries and frameworks of modern crawlers through tables, which is convenient for readers to compare and understand. Secondly, this paper reviews the literature of modern crawler technology, and describes how people realize crawler application through library or framework. The practical application of crawler technology is also briefly explained.
Discovering diverse solutions in deep reinforcement learning by maximizing state–action-based mutual information

Reinforcement learning algorithms are typically limited to learning a single solution for a specified task, even though diverse solutions often exist. Recent studies showed that learning a set of diverse solutions is beneficial because diversity enables robust few-shot adaptation. Although existing methods learn diverse solutions by using the mutual information as unsupervised rewards, such an approach often suffers from the bias of the gradient estimator induced by value function approximation. In this study, we propose a novel method that can learn diverse solutions without suffering the bias problem. In our method, a policy conditioned on a continuous or discrete latent variable is trained by directly maximizing the variational lower bound of the mutual information, instead of using the mutual information as unsupervised rewards as in previous studies. Through extensive experiments on robot locomotion tasks, we demonstrate that the proposed method successfully learns an infinite set of diverse solutions by learning continuous latent variables, which is more challenging than learning a finite number of solutions. Subsequently, we show that our method enables more effective few-shot adaptation compared with existing methods.
Reduced‐order multisensory fusion estimation with application to object tracking

This paper investigates the track-to-track state estimation for a class of linear time-varying multisensory systems. We propose a novel low-complexity reduced-order filter (ROF) under the Kalman filtering framework. Unlike the majority of previous track-to-track strategies, the proposed fusion strategy applies only to special variables or required components that contain critical information about a target system of interest. Also, unlike existing suboptimal fusion filters such as the covariance intersection, the proposed ROF algorithm makes use of nonzero cross-covariances between local filters that greatly improve its estimation accuracy. The theoretical aspect of ROF application to multisensory systems with identical sensors is also thoroughly investigated. Finally, we show the effectiveness and accuracy of the ROF when applied to objects (including a drone) performing a two-dimensional maneuver using numerical simulations.
An Adaptive Embedding Network with Spatial Constraints for the Use of Few-Shot Learning in Endangered-Animal Detection

Image recording is now ubiquitous in the fields of endangered-animal conservation and GIS. However, endangered animals are rarely seen, and, thus, only a few samples of images of them are available. In particular, the study of endangered-animal detection has a vital spatial component. We propose an adaptive, few-shot learning approach to endangered-animal detection through data augmentation by applying constraints on the mixture of foreground and background images based on species distributions. First, the pre-trained, salient network U2-Net segments the foregrounds and backgrounds of images of endangered animals. Then, the pre-trained image completion network CR-Fill is used to repair the incomplete environment. Furthermore, our approach identifies a foreground–background mixture of different images to produce multiple new image examples, using the relation network to permit a more realistic mixture of foreground and background images. It does not require further supervision, and it is easy to embed into existing networks, which learn to compensate for the uncertainties and nonstationarities of few-shot learning. Our experimental results are in excellent agreement with theoretical predictions by different evaluation metrics, and they unveil the future potential of video surveillance to address endangered-animal detection in studies of their behavior and conservation.
Assessment on potential security threats and introducing novel data security model in cloud environment

Cloud computing technology is the most important development in engineering. The technology is service-oriented, prioritizing cost reduction, equipment reduction, and payment only for used services. This article reviews current research on security risks and possible solutions for cloud computing. Cloud computing refers to Information systems, network, and device storage are examples of customer-provided services. Because the cloud is operated remotely, it can be accessed from anywhere. Computer service is used by the cloud provider. Unauthorized individuals may access critical information on the cloud, making it insecure. As a consequence, to ensure comprehensive data security, specialist data protection encryption technology is deployed. The topic of data security in cloud computing is discussed in this article. This is a study of cloud data and the security challenges that come with it. To achieve a better result than previous research endeavors with unique constraints, this study used two data encryption and decryption algorithms. The suggested approach is both quicker and easier to use than the RSA-Blowfish method.
DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning

Differential Privacy (DP) has emerged as a rigorous formalism to quantify privacy protection provided by an algorithm that operates on privacy sensitive data. In machine learning (ML), DP has been employed to limit inference/disclosure of training examples. Prior work leveraged DP across the ML pipeline, albeit in isolation, often focusing on mechanisms such as gradient perturbation.
Social Networking Security during COVID-19: A Systematic Literature Review

During the Covid-19 Pandemic, the usage of social media networks increased exponentially. People engage in education, business, shopping, and other social activities (i.e., Twitter, Facebook, WhatsApp, Instagram, YouTube). As social networking expands rapidly, its positive and negative impacts affect human health. All this leads to social crimes and illegal activities like phishing, hacking, ransomware, password attacks, spyware, blackmailing, Middle-man-attack. This research extensively discusses the social networking threats, challenges, online surveys, and future effects. We conduct an online survey using the google forms platform to collect the responses of social networking sites (SNS) users within Pakistan to show how SNS affects health positively and negatively. According to the collected response, we analyzed that 50% of the users use SNS for education purposes, 17.5% use it for shopping purposes, 58.2% use it for entertainment, 37.1% use it for communication, and 9.8% use it for other purposes. According to the response, the excessive use of SNS affects the health that 9.8% users face the physical threat, 42.8% user faces mental health issues due to excessive or inappropriate use of SN, and 50.5% users feel moral threat using Social sites. Finally, we conclude our paper by discussing the open challenges, conclusions, and future directions.
Strategy for the Development of Librarianship in Russia: Variety of Experiences and Ideas

The article presents the results of the Annual Meeting of the Heads of Federal and Central Regional Libraries of Russia, held on November 18—19, 2021. The main topic of the Meeting is “Strategy for the Development of Librarianship in the Russian Federation up to 2030. Implementation Mechanisms”. Organizers are the Ministry of Culture of the Russian Federation, the Russian State Library (RSL) and the National Library of Russia (NLR). 169 people from 34 regions of the Russian Federation took part in the meeting, held at the RSL; 5320 connections were recorded during the broadcasts. Special attention was focused on the following areas: launch of the federal departmental and regional projects to solve the key problems of libraries’ activities; development of the library holdings of Russia; digitalization of libraries. Within the framework of the meeting, the plenary session took place; the winners of three All-Russian competitions were awarded (“Libraries. PROmotion”, “Library Analytics”, as well as scientific works on library science, bibliography and book studies). Two sections worked (“Federal, departmental and regional projects, modernization of the countryʼs library system” and “Development of the library holdings”), and the conference “Russian Electronic Knowledge Space” was conducted. Following the results of the Meeting, proposals were formulated to help solve a number of problems facing modern libraries. The author notes the desire of the participants to cooperate in line with the unified library policy. The successful implementation of the “Strategy for the Development of Librarianship in the Russian Federation up to 2030” requires coordinated efforts of the governing bodies in the field of culture and libraries at all levels, primarily federal and central regional libraries. Serious tasks are set for the Russian State Library and the National Library of Russia.
Path Planning under The Hull bottom of Painting Robot Based on Heuristic Multi-robot Cooperation in Ship Manufacturing

In the whole world, the economic loss caused by hull corrosion is enormous. Ship painting has become an important part of ship manufacturing process because it can effectively alleviate the corrosion of ship. The manual painting has disadvantages both in the quality and the efficiency. However, the research of automatic sprayers for a ship hull is not widely used because of the complex environment in the shipyard dock and the huge differences in both size and shape of ships to be repaired. Therefore, this paper pro-posed a new method: according to the ship size and blocks distribution in the blocks&#8217; layout of ship yards, the grid method was used to generate the map model; to solve the problems of high rerouting rate, low coverage and large consumption of calculation in the global path planning, a regional division method was proposed to divide the whole area; to shorten the dock occupancy time, a path planning algorithm based on multi robots heuristic cooperation was proposed. Simulation results and experi-mental data show that the full coverage path planning algorithm proposed in this paper has satisfactory adaptability.
HoloLogger: Keystroke Inference on Mixed Reality Head Mounted Displays

When using personal computing services in mixed reality (MR) such as online payment and social media, sensitive information and account passwords must be typed in MR. To design secure MR systems and build up user trust, it is imperative to first understand the security threat to the sensitive MR input. Although keystroke inference attacks by analyzing human-computer interaction in videos or via wireless signals have been successful, they require placing extra hardware near the user which is easily noticeable in practice. In this paper, we expose a more dangerous malware-based attack through the vulnerability that no permission is required for accessing MR motion data. We aim to monitor MR headset motion and infer the user input through a benign App. Realizing the attack system requires addressing unique challenges in MR such as six-degree-of-freedom (6DoF) device motion and no explicit motion signal for keystroke identification. To this end, we present HoloLogger, the first malware-based keystroke inference attack system on HoloLens. HoloLogger is empowered by a 6DoF-head-motion-driven key tracking scheme and an air-tap-pattern-based keystroke inference framework. Extensive evaluations with 25 users and 750 inference trials of passwords consisting of 4&#x2013;8 lowercase English letters demonstrate that HoloLogger successfully achieves a top-5 accuracy of 93%. HoloLogger is also robust in various environments such as different user positions and input categories.