Reviewerly/exHarmony huggingface dataset:
Structuring the Post-COVID-19 Process of Digital Transformation of Engineering Education in the Russian Federation

The temporality of the socio-technological transformations of industry 4.0 has exacerbated the problem of introducing and using digital technologies in the educational process. The development in this area is due to the presence of unique global challenges, the availability of digital devices, and the experience of distance learning due to the COVID-19 pandemic. As part of the work, a statistical study was conducted aimed at establishing the average level of proficiency in the basic skills of independent search for information on the Internet. A classification of digital solutions is formulated, and an attempt is made to synthesize criteria for the successful integration of digital solutions into the educational process, the following groups are distinguished: organizational, auxiliary, structural, and specialized. Independent searching for information is considered an element of the educational process, which changes almost uncontrollably, unlike other solutions in the field of digitalization of education. The process of digitalization is presented as a set of four elements, the successful implementation of which will allow the effective implementation of digital technologies in the educational process. An element of digital infrastructure is highlighted; an element of organization, an element of integrating teachers into a new digital educational environment, and a student element. It is concluded that it is necessary to introduce courses of additional competencies dedicated to working with information; the idea of integrating master’s students into research aimed at solving modern global challenges with the help of digital technologies is proposed.
Retinex-Based Relighting for Night Photography

The lighting up of buildings is one form of entertainment that makes a city more colorful, and photographers sometimes change this lighting using photo-editing applications. This paper proposes a method for automatically performing such changes that follows the Retinex theory. Retinex theory indicates that the complex scenes caught by the human visual system are affected by surrounding colors, and Retinex-based image processing uses these characteristics to generate images. Our proposed method follows this approach. First, we propose a method for extracting a relighting saliency map using Retinex with edge-preserving filtering. Second, we propose a sampling method to specify the lighting area. Finally, we composite the additional light to match the human visual perception. Experimental results show that the proposed sampling method is successful in keeping the illuminated points in bright locations and equally spaced apart. In addition, the proposed various diffusion methods can enhance nighttime skyline photographs with various expressions. Finally, we can add in a new light by considering Retinex theory to represent the perceptual color.
Generic Patient-Centered Blockchain-Based EHR Management System

Accessing healthcare services by several stakeholders for diagnosis and treatment has become quite prevalent owing to the improvement in the industry and high levels of patient mobility. Due to the confidentiality and high sensitivity of electronic healthcare records (EHR), the majority of EHR data sharing is still conducted via fax or mail because of the lack of systematic infrastructure support for secure and reliable health data transfer, delaying the process of patient care. As a result, it is critically essential to provide a framework that allows for the efficient exchange and storage of large amounts of medical data in a secure setting. The objective of this research is to develop a Patient-Centered Blockchain-Based EHR Management (PCEHRM) system that allows patients to manage their healthcare records across multiple stakeholders and to facilitate patient privacy and control without the need for a centralized infrastructure by means of granting or revoking access or viewing one’s records. We used an Ethereum blockchain and IPFS (inter-planetary file system) to store records because of its advantage of being distributed and ensuring the immutability of records and allowing for the decentralized storage of medical metadata, such as medical reports. To achieve secure a distributed, and trustworthy access control policy, we proposed an Ethereum smart contract termed the patient-centric access control protocol. We demonstrate how the PCEHRM system design enables stakeholders such as patients, labs, researchers, etc., to obtain patient-centric data in a distributed and secure manner and integrate utilizing a web-based interface for the patient and all users to initiate the EHR sharing transactions. Finally, we tested the proposed framework in the Windows environment by compiling a smart contract prototype using Truffle and deploy on Ethereum using Web3. The proposed system was evaluated in terms of the projected medical data storage costs for the IPFS on blockchain, and the execution time for a different number of peers and document sizes. The findings of the study indicate that the proposed strategy is both efficient and practicable.
Constructing Spatio-Temporal Graphs for Face Forgery Detection

Recently, advanced development of facial manipulation techniques threatens web information security, thus, face forgery detection attracts a lot of attention. It is clear that both spatial and temporal information of facial videos contains the crucial manipulation traces, which are inevitably created during the generation process. However, most existing face forgery detectors only focus on the spatial artifacts or the temporal incoherence, and they are struggling to learn a significant and general kind of representations for manipulated facial videos. In this work, we propose to construct spatial-temporal graphs for fake videos to capture the spatial inconsistency and the temporal incoherence at the same time. To model the spatial-temporal relationship among the graph nodes, a novel forgery detector named Spatio-Temporal Graph Network (STGN) is proposed, which contains two kinds of graph-convolution-based units, the Spatial Relation Graph Unit (SRGU) and the Temporal Attention Graph Unit (TAGU). To exploit spatial information, the SRGU models the inconsistency between each pair of patches in the same frame, instead of focusing on the low-level local spatial artifacts which are vulnerable to samples created by unseen manipulation methods. And, the TAGU is proposed to model the long-distance temporal relation among the patches at the same spatial position in different frames with a graph attention mechanism based on the inter-node similarity. With the SRGU and the TAGU, our STGN can combine the discriminative power of spatial inconsistency and the generalization capacity of temporal incoherence for face forgery detection. Our STGN achieves state-of-the-art performances on several popular forgery detection datasets. Extensive experiments demonstrate both the superiority of our STGN on intra manipulation evaluation and the effectiveness for new sorts of face forgery videos on cross manipulation evaluation.
A Survey on Soft Computing Techniques for Federated Learning- Applications, Challenges and Future Directions

Federated Learning is a distributed, privacy-preserving machine learning model that is gaining more attention these days. Federated Learning has a vast number of applications in different fields. While being more popular, it also suffers some drawbacks like high communication costs, privacy concerns, and data management issues. In this survey, we define federated learning systems and analyse the system to ensure a smooth flow and to guide future research with the help of soft computing techniques. We undertake a complete review of aggregating federated learning systems with soft computing techniques. We also investigate the impacts of collaborating various nature-inspired techniques with federated learning to alleviate its flaws. Finally, this paper discusses the possible future developments of integrating federated learning and soft computing techniques.
An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation

With the support of cloud computing technology, it is easier for financial institutions to obtain more key information about the whole industry chain. However, the massive use of financial data has many potential risks. In order to better cope with this dilemma and better protect the financial privacy of users, we propose a privacy protection model based on cloud computing. The model provides four levels of privacy protection according to the actual needs of users. At the highest level of protection, the server could not access any information about the user and the raw data, nor could it recover the computational characteristics of the data. In addition, due to the universality of the mathematical principle of linear operators, the model could effectively protect and accelerate all models based on linear operations. The final results showed that the method can increase the speed by 10 times, compared with the privacy protection method that only uses local computing power instead of the cloud server. It can also effectively prevent the user’s privacy from being leaked with relatively minimal delay cost, compared with no privacy protection method. Finally, we design a multi-user scheduling model to deploy the model in a real scenario, which could maximise server power and protect user privacy as well.
Real-Time Finger-Writing Character Recognition via ToF Sensors on Edge Deep Learning

Human–computer interaction is demanded for natural and convenient approaches, in which finger-writing recognition has aroused more and more attention. In this paper, a device-free finger-writing character recognition system based on an array of time-of-flight (ToF) distance sensors is presented. The ToF sensors acquire distance values between sensors to a writing finger within a 9.5 × 15 cm square on a surface at specific time intervals and send distance data to a low-power microcontroller STM32F401, equipped with deep learning algorithms for real-time inference and recognition tasks. The proposed method enables one to distinguish 26 English lower-case letters by users writing with their fingers and does not require one to wear additional devices. All data used in this work were collected from 21 subjects (12 males and 9 females) to evaluate the proposed system in a real scenario. In this work, the performance of different deep learning algorithms, such as long short-term memory (LSTM), convolutional neural networks (CNNs) and bidirectional LSTM (BiLSTM), was evaluated. Thus, these algorithms provide high accuracy, where the best result is extracted from the LSTM, with 98.31% accuracy and 50 ms of maximum latency.
Multilingual Speech Recognition for Turkic Languages

The primary aim of this study was to contribute to the development of multilingual automatic speech recognition for lower-resourced Turkic languages. Ten languages—Azerbaijani, Bashkir, Chuvash, Kazakh, Kyrgyz, Sakha, Tatar, Turkish, Uyghur, and Uzbek—were considered. A total of 22 models were developed (13 monolingual and 9 multilingual). The multilingual models that were trained using joint speech data performed more robustly than the baseline monolingual models, with the best model achieving an average character and word error rate reduction of 56.7%/54.3%, respectively. The results of the experiment showed that character and word error rate reduction was more likely when multilingual models were trained with data from related Turkic languages than when they were developed using data from unrelated, non-Turkic languages, such as English and Russian. The study also presented an open-source Turkish speech corpus. The corpus contains 218.2 h of transcribed speech with 186,171 utterances and is the largest publicly available Turkish dataset of its kind. The datasets and codes used to train the models are available for download from our GitHub page.
Real-time computer-generated frequency-carrier Moiré profilometry with three-frequency heterodyne temporal phase unwrapping

A real-time computer-generated frequency-carrier Moiré profilometry with three-frequency heterodyne temporal phase unwrapping is proposed. While three sinusoidal gratings shared the same direct current(DC) component and the same amplitude but with designed different frequencies are projected onto the measured object, three corresponding deformed patterns can be captured by camera. Then, the shared DC component is extracted from the highest-frequency pattern. And based on it, three AC components of the captured deformed patterns can also be effectively extracted by normalization. From this way, the number of the needed gratings can be reduced from six to three, and the disturbance of the object’s nonuniform reflectivity can be avoided efficiently. By using computer-generated frequency-carrier Moiré profilometry(CGFCMP), the carrier frequency designed as pure sinusoidal and cosinusoidal gratings is optimized in order to effectively extract the computer-generated frequency-carrier Moiré fringes, and finally-three wrapped phases at different frequencies can be obtained. Then the wrapped phase with the highest-frequency grating can be unwrapped by three-frequency heterodyne temporal phase unwrapping algorithm. The simulation and experimental results verify the feasibility and flexibility of the proposed method. It is suitable to measure the complex surface with abrupt slope and multiple isolated objects, and has prospective application in real-time three-dimensional measurement.
Incentive-Driven Information Sharing in Leasing Based on a Consortium Blockchain and Evolutionary Game

Blockchain technology (BCT) provides a new way to mitigate the default risks of lease contracts resulting from the information asymmetry in leasing. The conceptual architecture of a consortium blockchain-based leasing platform (CBLP) is first proposed to facilitate information sharing between small and medium-sized enterprises (SMEs, the “lessees”) and leasing firms (LFs, the “lessors”). Then, based on evolutionary game theory (EGT), this study builds a two-party game model and analyzes the influences of four types of factors (i.e., information sharing, credit, incentive–penalty, and risk) on SMEs’ contract compliance or default behaviors with/without blockchain empowerment. The primary findings of this study are as follows: (1) SMEs and LFs eventually evolve to implement the ideal “win–win” strategies of complying with the contract and adopting BCT. (2) The large residual value of the leased asset can tempt SMEs to conduct a default action of unauthorized asset disposal, while leading LFs to access the CBLP to utilize information shared on-chain. (3) When the maintenance service is outsourced instead of being provided by lessors, the maintenance fee is not a core determinant affecting the equilibrium state. (4) There is a critical value concerning the default penalty on-chain to incentivize the involved parties to keep their commitments. (5) The capability of utilizing information, storage overhead, and security risk should all be taken into consideration when deciding on the optimal strategies for SMEs and LFs. This study provides comprehensive insights for designing an incentive mechanism to encourage lessees and lessors to cooperatively construct a sustainable and trustworthy leasing environment.
Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review

Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN-long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images.
A Generic Service to Provide In-Network Aggregation for Key-Value Streams

Key-value stream aggregation is a common operation in distributed systems, which requires intensive computation and network resources. We propose a generic in-network aggregation service for key-value streams, ASK, to accelerate the aggregation operations in diverse distributed applications. ASK is a switch-host co-designed system, where the programmable switch provides a best-effort aggregation service, and the host runs a daemon to interact with applications. ASK makes in-depth optimization tailored to traffic characteristics, hardware restrictions, and network unreliable natures: it vectorizes multiple key-value tuples’ aggregation of one packet in one switch pipeline pass, which improves the per-host’s goodput; it develops a lightweight reliability mechanism for key-value stream’s asynchronous aggregation, which guarantees computation correctness; it designs a hot-key agnostic prioritization for key-skewed workloads, which improves the switch memory utilization. We prototype ASK and use it to support Spark and BytePS. The evaluation shows that ASK could accelerate pure key-value aggregation tasks by up to 155 times and big data jobs by 3-5 times, and be backward compatible with existing INA-empowered distributed training solutions with the same speedup.
Single underwater image restoration based on color correction and optimized transmission map estimation

Abstract Underwater images often suffer from color distortion and resolution degradation due to the absorption and scattering of light. To deal with these problems, a novel underwater image restoration framework was designed to remove color deviation and background scattering successively. Firstly, an effective color correction method based on Lab color space is adopted to remove the color cast. Secondly, the color, saturation, and detail information are considered comprehensively to construct the scene depth map and edge map to estimate the transmission, and the relationship between the scattering coefficient and wavelength is used to modify the transmission map of each channel. In addition, a weighted least squares filter with adaptive smoothing parameters is introduced to estimate the local background light, which is applicable to the underwater scene with complex scattering. The experimental results show that the method performs better in color correction and detail enhancement, and the image quality is significantly improved.
Better Than Worst-Case Decoding for Quantum Error Correction

The overheads of classical decoding for quantum error correction in cryogenic quantum systems grow rapidly with the number of logical qubits and their correction code distance. Decoding at room temperature is bottlenecked by refrigerator I/O bandwidth while cryogenic on-chip decoding is limited by area/power/thermal budget.
KIT: Testing OS-Level Virtualization for Functional Interference Bugs

Container isolation is implemented through OS-level virtualization, such as Linux namespaces. Unfortunately, these mechanisms are extremely challenging to implement correctly and, in practice, suffer from functional interference bugs, which compromise container security. In particular, functional interference bugs allow an attacker to extract information from another container running on the same machine or impact its integrity by modifying kernel resources that are incorrectly isolated. Despite their impact, functional interference bugs in OS-level virtualization have received limited attention in part due to the challenges in detecting them. Instead of causing memory errors or crashes, many functional interference bugs involve hard-to-catch logic errors that silently produce semantically incorrect results.
Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression

In training of modern large natural language processing (NLP) models, it has become a common practice to split models using 3D parallelism to multiple GPUs. Such technique, however, suffers from a high overhead of inter-node communication. Compressing the communication is one way to mitigate the overhead by reducing the inter-node traffic volume; however, the existing compression techniques have critical limitations to be applied for NLP models with 3D parallelism in that 1) only the data parallelism traffic is targeted, and 2) the existing compression schemes already harm the model quality too much.
P-values and confidence intervals of linear rank tests for left-truncated data under truncated binomial design

The paper provides computations comparing the accuracy of the saddlepoint approximation approach and the normal approximation method in approximating the mid-p-value of Wilcoxon and log-rank tests for the left-truncated data using a truncated binomial design. The paper uses real data examples to apply the comparison, along with some simulated studies. Confidence intervals are provided by the inversion of the tests under consideration.
Improving biomedical named entity recognition through transfer learning and asymmetric tri-training

Today, electronic health records have turned into prime sources of information for physicians looking after their patients. EHRs and computerized patient data resources have expedited the accelerated discovery of formerly obscure biomedical and clinical information. Because of the lengthy, error-prone, non-scalable, and expensive manual abstraction process, natural language processing (NLP) procedures are being wielded more and more in biomedical and clinical fields. One of the building blocks of all NLP systems, Named Entity Recognition (NER) is considered a sub-activity of information retrieval. To extract biomedical knowledge from electronic health records, a prerequisite is the efficient recognition of biomedical entities. Deep-learning techniques have gained more and more consideration recently for the above-mentioned task. Notwithstanding, these methods are based on high-caliber, high-cost, labeled data. In this work, a biomedical-named entity recognition model based on transfer learning and asymmetric tri-training is proposed to diminish the limited annotated data problem in the biomedical-named entity recognition domain. The proposed model showed a significant improvement of more than 9% over the baseline BiLSTM-CRF model in the exact F1 scores on four different datasets considered in this work.