Reviewerly/exHarmony huggingface dataset:
Data Augmentation for Deep Receivers

Deep neural networks (DNNs) allow digital receivers to learn to operate in complex environments. To do so, DNNs should preferably be trained using large labeled data sets with a similar statistical relationship as the one under which they are to infer. For DNN-aided receivers, obtaining labeled data conventionally involves pilot signalling at the cost of reduced spectral efficiency, typically resulting in access to limited data sets. In this paper, we study how one can enrich a small set of labeled pilots data into a larger data set for training deep receivers. Motivated by the widespread use of data augmentation techniques for enriching visual and text data, we propose dedicated augmentation schemes that exploits the characteristics of digital communication data. We identify the key considerations in data augmentations for deep receivers as the need for domain orientation, class (constellation) diversity, and low complexity. Following these guidelines, we devise three complementing augmentations that exploit the geometric properties of digital constellations. Our combined augmentation approach builds on the merits of these different augmentations to synthesize reliable data from a momentary channel distribution, to be used for training deep receivers. Furthermore, we exploit previous channel realizations to increase the reliability of the augmented samples. The superiority of our approach is numerically evaluated for training several deep receiver architectures in different channel conditions. We consider both linear and non-linear synthetic channels, as well as the COST 2100 channel generator, for both single-input single-output and multiple-input multiple-output scenarios. We show that our combined augmentations approach allows DNN-aided receivers to achieve gains of up to 1 dB in bit error rate and of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 3$ </tex-math></inline-formula> in spectral efficiency, compared to regular non-augmented training. Moreover, we demonstrate that our augmentations benefit training even as the number of pilots increases, and perform an ablation study on the different augmentations, which shows that the combined approach surpasses each individual augmentation technique.
Political Science Education in the Educational Environment of University: Valuable Semantic Foundations

Political science education over the past thirty years has become the most important element of the humanitarian sphere of the Russian university. At the same time, a discussion continues in the scientific and pedagogical community about the status of political science education in the professional training of specialists in demand for the country, masters and research teachers. One of the main questions postulated in the expert community is to determine the role of the science of politics in preventing the emergence of conflict situations in the educational space of the university on the basis of the "politicization" of the worldview positions of participants in educational relations. The transition in 2010 - 2020 to a competency-based model for the implementation of basic professional educational programs and the introduction of project technologies in the educational process led to a serious transformation of the architecture of education and upbringing at the university. The article presents arguments in favor of the author's hypothesis about the need to introduce programs of political and legal education at leading universities for all participants in the educational process. The author comes to the conclusion that the approbation of such programs will qualitatively update the content of the educational work of innovative teachers and methodologically strengthen the cultural component in the professional outlook of students. The educational component is also emphasized. The content of the programs should include didactic materials containing recommendations on how to quickly respond to typical conflict situations on the grounds of a mismatch of political orientations among the youth. The materials contained in the article are addressed to graduate students, young professionals and researchers of higher education.
Versatile Video Coding-Based Coding Tree Unit Level Image Compression With Dual Quantization Parameters for Hybrid Vision

Image analysis based on machine vision is hugely manipulated in the smart industry. Good-quality images are required for outstanding machine analysis results, but handling high-definition images could be problematic in a constrained environment such as a low-bandwidth network or low-capacity storage. Lowering the image resolution might be a straightforward solution for reducing image data, but it would occasion much information loss, leading to the deterioration of machine vision. Moreover, human supervision could be necessary for a contingency that machine vision cannot control.Therefore, an innovative image compression method considering machine and human vision is required; more compression efficiency than the state-of-the-art codec, praiseworthy machine vision performance, and human-recognizable quality. In this paper, we propose Versatile video coding(VVC) based image compression for hybrid vision, i.e., machine vision and human vision. Our work provides a coding tree unit(CTU) level image compression with dual quantization parameters (QPs) according to the quantization parameter map and the saliency extracted by the object detection network; in the salient region, the proposed method maintains high quality with low QP but degrades the quality with high QP in the non-salient region.Compared with VVC, the proposed compression method achieves a bitrate reduction of up to 25.5% in machine vision tasks, proving more compression efficiency and still admirable machine vision performance. From the perspective of human vision, the proposed method provides human-perceptible image quality, preserving acceptable objective quality values.
Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

Pedestrian detection and tracking are vital elements of today's surveillance systems, which make daily life safe for humans. Thus, human detection and visualization have become essential inventions in the field of computer vision. Hence, developing a surveillance system with multiple object recognition and tracking, especially in low light and night-time, is still challenging. Therefore, we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night. In particular, we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared (IR) images using machine learning and tracking them using particle filters. Moreover, a random forest classifier is adopted for image segmentation to identify pedestrians in an image. The result of detection is investigated by particle filter to solve pedestrian tracking. Through the extensive experiment, our system shows 93% segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof classes. Moreover, the system achieved a detection accuracy of 90% using multiple template matching techniques and 81% accuracy for pedestrian tracking. Furthermore, our system can identify that the detected object is a human. Hence, our system provided the best results compared to the state-of-art systems, which proves the effectiveness of the techniques used for image segmentation, classification, and tracking. The presented method is applicable for human detection/tracking, crowd analysis, and monitoring pedestrians in IR video surveillance.
Optimizing Resource Allocation Framework for Multi-Cloud Environment

Cloud computing makes dynamic resource provisioning more accessible. Monitoring a functioning service is crucial, and changes are made when particular criteria are surpassed. This research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service (QoS), estimating the required resources, and modifying allotted resources depending on workload and parallelism due to resources. Resource allocation is a complex challenge due to the versatile service providers and resource providers. The engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of service. The objective of a coherent and rational resource allocation is to attain the quality of service. It also includes identifying critical parameters to develop a resource allocation mechanism. A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud environment. The three main parameters of the proposed framework are data accessibility, optimization, and collaboration. Using an optimization technique, these three segments are further divided into subsets for resource allocation and long-term service quality. The CloudSim simulator has been used to validate the suggested framework. Several experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained QoS. The results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.
Text Simplification Using Transformer and BERT

Reading and writing are the main interaction methods with web content. Text simplification tools are helpful for people with cognitive impairments, new language learners, and children as they might find difficulties in understanding the complex web content. Text simplification is the process of changing complex text into more readable and understandable text. The recent approaches to text simplification adopted the machine translation concept to learn simplification rules from a parallel corpus of complex and simple sentences. In this paper, we propose two models based on the transformer which is an encoder-decoder structure that achieves state-of-the-art (SOTA) results in machine translation. The training process for our model includes three steps: preprocessing the data using a subword tokenizer, training the model and optimizing it using the Adam optimizer, then using the model to decode the output. The first model uses the transformer only and the second model uses and integrates the Bidirectional Encoder Representations from Transformer (BERT) as encoder to enhance the training time and results. The performance of the proposed model using the transformer was evaluated using the Bilingual Evaluation Understudy score (BLEU) and recorded (53.78) on the WikiSmall dataset. On the other hand, the experiment on the second model which is integrated with BERT shows that the validation loss decreased very fast compared with the model without the BERT. However, the BLEU score was small (44.54), which could be due to the size of the dataset so the model was overfitting and unable to generalize well. Therefore, in the future, the second model could involve experimenting with a larger dataset such as the WikiLarge. In addition, more analysis has been done on the model’s results and the used dataset using different evaluation metrics to understand their performance.
Scalable Blockchain Technology for Tracking the Provenance of the Agri-Food

Due to an increase in agricultural mislabeling and carelesshandling of non-perishable foods in recent years, consumers have been calling for the food sector to be more transparent. Due to information dispersion between divisions and the propensity to record inaccurate data, current traceability solutions typically fail to provide reliable farm-to-fork histories ofproducts. The three most enticing characteristics of blockchain technology areopenness, integrity, and traceability, which make it a potentially crucial tool for guaranteeing the integrity and correctness of data. In this paper, we suggest a permissioned blockchain system run by organizations, such as regulatory bodies, to promote the origin-tracking of shelf-stable agricultural products. We propose a four-tiered architecture, parallel side chains, Zero-Knowledge Proofs (ZKPs), and Interplanetary File Systems (IPFS). These ensure that information about where an item came from is shared, those commercial competitors cannot get to it, those big storage problems are handled, and the system can be scaled to handle many transactions at once. Thesolution maintains the confidentiality of all transaction flows whenprovenance data is queried utilizing smart contracts and a consumer-grade reliance rate. Extensive simulation testing using Ethereum Rinkeby and Polygon demonstrates reduced execution time, latency, and throughput overheads.
A Federated Named Entity Recognition Model with Explicit Relation for Power Grid

The power grid operation process is complex, and many operation process data involve national security, business secrets, and user privacy. Meanwhile, labeled datasets may exist in many different operation platforms, but they cannot be directly shared since power grid data is highly privacy-sensitive. How to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart grid. Therefore, this paper proposes federated learning named entity recognition method for the power grid field, aiming to solve the problem of building a named entity recognition model covering the entire power grid process training by data with different security requirements. We decompose the named entity recognition (NER) model FLAT (Chinese NER Using Flat-Lattice Transformer) in each platform into a global part and a local part. The local part is used to capture the characteristics of the local data in each platform and is updated using locally labeled data. The global part is learned across different operation platforms to capture the shared NER knowledge. Its local gradients from different platforms are aggregated to update the global model, which is further delivered to each platform to update their global part. Experiments on two publicly available Chinese datasets and one power grid dataset validate the effectiveness of our method.
A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

Artificial Intelligence (AI) is finding increasing application in healthcare monitoring. Machine learning systems are utilized for monitoring patient health through the use of IoT sensor, which keep track of the physiological state by way of various health data. Thus, early detection of any disease or derangement can aid doctors in saving patients’ lives. However, there are some challenges associated with predicting health status using the common algorithms, such as time requirements, chances of errors, and improper classification. We propose an Artificial Krill Herd based on the Random Forest (AKHRF) technique for monitoring patients’ health and eliciting an optimal prescription based on their health status. To begin with, various patient datasets were collected and trained into the system using IoT sensors. As a result, the framework developed includes four processes: preprocessing, feature extraction, classification, and result visibility. Additionally, preprocessing removes errors, noise, and missing values from the dataset, whereas feature extraction extracts the relevant information. Then, in the classification layer, we updated the fitness function of the krill herd to classify the patient’s health status and also generate a prescription. We found that the results from the proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity, specificity, Area under the Curve (AUC), accuracy, precision, recall, and F-measure.
Blockchain Mobile Wallet with Secure Offline Transactions

There has been an increase in the adoption of mobile payment systems worldwide in the past few years. However, poor Internet connection in rural regions continues to be an obstacle to the widespread use of such technologies. On top of that, there are significant problems with the currently available offline wallets; for instance, the payee cannot verify the number of coins received without access to the Internet. Additionally, it has been demonstrated that some existing systems are susceptible to false token generation, and some do not even permit the user to divide the offline token into smaller portions to be used as change. This paper proposes a blockchain-based wallet system that provides a secure mobile payment service even if a user cannot access a reliable Internet connection. Our approach relies on Bluetooth and digital signatures to establish and build a trust connection between the parties. The proposed solution overcomes the main limitations of existing systems that use offline transactions, such as the generation of fake offline tokens and the indivisibility of offline tokens. The user buys Offline Tokens (OTs) from a server called an Offline Token Manager (OTM) to use them later to perform offline transactions. Each mobile device must store a single, signed offline token transaction to prevent fake tokens. On the other hand, all offline transactions will be kept as a history in a particular local database. Finally, when the receiver becomes online, it will send a convert request to the OTM to change the value of the OTs to the appropriate amount in real coins. This step requires a connection to the Internet. To evaluate the effectiveness of the system, the Solidity programming language was used to develop a smart contract on the Ethereum blockchain with a backend application programming interface (API) and an android mobile application. The proposed method has an advantage over other prominent wallets.
Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

For fuzzy systems to be implemented effectively, the fuzzy membership function (MF) is essential. A fuzzy system (FS) that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output (SISO) FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output. Utilizing a variety of non-linear techniques, a SISO FS is simulated. The results of FS experiments conducted in comparable conditions are then compared. The simulated results and the results of the experimental setup agree fairly well. The findings of the suggested model demonstrate that the relative error is abated to a sufficient range (≤ ± 10%) and that the mean absolute percentage error (MPAE) is reduced by around 66.2%. The proposed strategy to reduce MAPE using an FS improves the system’s performance and control accuracy. By using the best input and output MFs protocol, the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs. The proposed fuzzy system performed far better than other modern days approaches available in the literature.
The Globalization of Cashless Transactions Using Blockchain Technology to Preventing Money Laundering and The Changing Trends in The Cryptocurrency Market: A Learning Experience of Polish and EU Laws

Abstract Summary The COVID-19 pandemic experience has driven us to rely on technology so much on the development and expansion of technology, including cashless transactions. Criminal groups may become more interested in electronic payments and virtual currencies because of increased traffic in these areas. When comparing the second half of the 2019 to the first half of the 2020, the number of fraudulent card transactions increased by 11.4%. New developments in virtual currency trading regulations, including the digital finance package, which includes, among other things, a draught regulation from the European Parliament and an amended act to combat money laundering and terrorist financing, among others. For the reasons stated above, these regulations may lead to a virtual currency market collapse and the withdrawal of investors and the siphoning of money into Asian markets as a result. The current regulations are a manifestation of total regulation and do not encourage technological advancement.
Slightly off-axis interferometry based on spatial-carrier phase-shifting differential method

In this paper, a spatial-carrier phase-shifting differential-based slightly off-axis interferometry (SCPSD) is proposed. First, two interferograms are obtained by single exposure method combined with spatial-carrier phase-shifting (SCPS) method. On this basis, the two interferograms are removed background item via filtering and differential operation, and are further normalized. Finally, the phase related parameters are solved using the Lissajou ellipse fitting method to obtain the measured phase. Simulation and experimental results show that this method can achieve fast and high-accuracy phase extraction with slightly off-axis. The performance analysis indicates that this method has better performance than the common SCPS methods in resisting noise and different phase heights under the condition of slightly off-axis. Moreover, the proposed method sacrifices less space bandwidth product than traditional off-axis interferometry, and requires only one exposure compared with time-domain phase-shifting interferometry, which can be used to achieve high-accuracy dynamic phase measurement.
Triple Robustness Augmentation Local Features for multi-source image registration

With the proliferation of a wide variety of sensors, accurate multi-source image registration is crucial for many remote sensing image processing tasks. However, the registration of multi-source images faces the challenges of rotations, scales, and domain transformations caused by significant differences in shooting time, viewing angle, and sensor imaging modes. To cope with this problem, we propose a deep learning-based registration method named TRFeat, which aims to comprehensively improve the rotation, scale, and cross-domain robustness of local features. First, we introduce a special circular sampling convolutional layer to replace the standard square convolutional layer, in order to enhance the rotational robustness of local features. Second, we design a scale pyramid backbone network architecture to improve the robustness of the network to scale transformations. Third, we promote the use of hypercolumn domain alignment loss to extract cross-domain robust local descriptors for images from different sources. In addition, we develop a novel keypoint detection training framework based on iterative refinement supervision to obtain repeatable and reliable keypoints localization in multi-source images. Finally, we conduct thorough experiments on five multi-source datasets. Extensive experimental results validate that our TRFeat outperforms other state-of-the-art hand-crafted (e.g. RIFT) and deep learning-based methods (e.g. ASLFeat). Specifically, our TRFeat achieves an MMA@3 of 76.08% on the HPatches dataset and an RMSE of 3.38 on the Xiang dataset. The code is available at https://github.com/vignywang/TRFeat.