Reviewerly/exHarmony huggingface dataset:
Decoupling calibration method of long focal length camera using vanishing point

Optical measurement and perception technology is widely used in the field of smart energy. The accurate calibration of the internal and external parameters of the camera in the optical system is very important for the application of the system in three-dimensional (3D) reconstruction and geometric measurement. At present, the mainstream camera calibration methods include Zhang’s calibration method and Tsai’s calibration method. These methods all choose to calculate the distortion parameters together with the camera’s internal parameters. For long focal length and narrow-field-of-view cameras with smaller perspective distortion, the coupling calculation of parameters may cause inaccurate calibration parameters and more time-consuming problems. To improve the calibration accuracy of the long focal length camera, we propose an efficient noniterative camera calibration method, based on the equation relationship between the vanishing point coordinates and the first-order single-parameter division lens distortion coefficient, and based on the radial distortion separation model as well as the corner subpixel coordinates and checkerboard 3D space points. The spatial point correspondence is solved to obtain the homography matrix to complete the calculation of the internal parameters of the camera. Our work has potential applications in photovoltaic troubleshooting and intelligent inspection. It may also contribute to the practical application of the sensor in intelligent energy.
Defending against adversarial examples using perceptual image hashing

Conventional deep neural networks (DNNs) have been shown to be vulnerable to images with adversarial perturbations, referred to as adversarial examples. In this study, we propose a method to protect neural networks against adversarial examples using perceptual image hashing. Because perceptual hashing is robust to adversarial perturbations, we combine hash sequences of input images with the parameters of a neural network in an image-hash processing network. Thus, outputs of the neural network are affected by image hashes, which render the model robust to adversarial examples to some extent. Thus, the proposed approach provides a defense against adversarial examples. The experiment was conducted on the CIFAR-10 dataset, and we used ResNet-18 as our target network. To verify our method, we tested our defense network using several common white-box attacks and black-box attacks. The results show that it achieved a significant improvement in the classification accuracy for adversarial examples.
Polish Contribution to Software Engineering Research 1992–2021

Abstract Since the collapse of the Soviet Bloc, Polish researchers in the software engineering area made a large e ort to close the gap developed during the times of the Iron Curtain and the ensuing limited access to Western technology. The question arises as to whether they eventually managed to attain – or maybe even to surpass the scientific output in this area of the developed Western countries. In this paper, we perform analysis on papers published in 16 high-quality software engineering journals within the last 30 years to measure the Polish contribution to the global scientific output in the area of software engineering. We observe how the Polish contribution changed in quantity in relation to the global scientific output in subsequent years. We also identify the journals that attracted the Polish authors the most, the institutions located in Poland that contributed the most, as well as the most productive Polish authors and the most acknowledged works (in terms of citation number) written by Polish authors. Moreover, we investigate the topics most often chosen by them.
Fractional Chebyshev Kernel Functions: Theory and Application

Orthogonal functions have many useful properties and can be used for different purposes in machine learning. One of the main applications of the orthogonal functions is producing powerful kernel functions for the support vector machine algorithm. Maybe the simplest orthogonal function that can be used for producing kernel functions is the Chebyshev polynomials. In this chapter, after reviewing some essential properties of Chebyshev polynomials and fractional Chebyshev functions, various Chebyshev kernel functions are presented, and fractional Chebyshev kernel functions are introduced. Finally, the performance of the various Chebyshev kernel functions is illustrated on two sample datasets.
Introduction to SVM

In this chapter, a review of the machine learning (ML) and pattern recognition concepts is given, and basic ML techniques (supervised, unsupervised, and reinforcement learning) are described. Also, a brief history of ML development from the primary works before the 1950s (including Bayesian theory) up to the most recent approaches (including deep learning) is presented. Then, an introduction to the support vector machine (SVM) with a geometric interpretation is given, and its basic concepts and formulations are described. A history of SVM progress (from Vapnik’s primary works in the 1960s up to now) is also reviewed. Finally, various ML applications of SVM in several fields such as medical, text classification, and image classification are presented.
Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework

Background In health care, diagnosis codes in claims data and electronic health records (EHRs) play an important role in data-driven decision making. Any analysis that uses a patient’s diagnosis codes to predict future outcomes or describe morbidity requires a numerical representation of this diagnosis profile made up of string-based diagnosis codes. These numerical representations are especially important for machine learning models. Most commonly, binary-encoded representations have been used, usually for a subset of diagnoses. In real-world health care applications, several issues arise: patient profiles show high variability even when the underlying diseases are the same, they may have gaps and not contain all available information, and a large number of appropriate diagnoses must be considered. Objective We herein present Pat2Vec, a self-supervised machine learning framework inspired by neural network–based natural language processing that embeds complete diagnosis profiles into a small real-valued numerical vector. Methods Based on German outpatient claims data with diagnosis codes according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10), we discovered an optimal vectorization embedding model for patient diagnosis profiles with Bayesian optimization for the hyperparameters. The calibration process ensured a robust embedding model for health care–relevant tasks by aggregating the metrics of different regression and classification tasks using different machine learning algorithms (linear and logistic regression as well as gradient-boosted trees). The models were tested against a baseline model that binary encodes the most common diagnoses. The study used diagnosis profiles and supplementary data from more than 10 million patients from 2016 to 2019 and was based on the largest German ambulatory claims data set. To describe subpopulations in health care, we identified clusters (via density-based clustering) and visualized patient vectors in 2D (via dimensionality reduction with uniform manifold approximation). Furthermore, we applied our vectorization model to predict prospective drug prescription costs based on patients’ diagnoses. Results Our final models outperform the baseline model (binary encoding) with equal dimensions. They are more robust to missing data and show large performance gains, particularly in lower dimensions, demonstrating the embedding model’s compression of nonlinear information. In the future, other sources of health care data can be integrated into the current diagnosis-based framework. Other researchers can apply our publicly shared embedding model to their own diagnosis data. Conclusions We envision a wide range of applications for Pat2Vec that will improve health care quality, including personalized prevention and signal detection in patient surveillance as well as health care resource planning based on subcohorts identified by our data-driven machine learning framework.
Decentralized Energy Management of Microgrid Based on Blockchain-Empowered Consensus Algorithm With Collusion Prevention

The concern for privacy and scalability has motivated a paradigm shift to decentralized energy management methods in microgrids. The absence of a central authority brings significant challenges to promote trusted collaboration and avoid collusion. To address these issues, this paper proposes a blockchain-empowered microgrid energy management framework, which adopts a novel consensus-based algorithm with a collusion prevention mechanism. Aiming at social welfare maximization, the energy management problem is formulated into a convex and decomposable form, which can be solved in a decentralized manner. To prevent the collusion between malicious agents, we propose a random information transmission mechanism empowered by the blockchain smart contract to replace the time-invariant communication topology. The consensus-based algorithm is extended to obtain the optimal solution of the energy management problem on the random and time-varying communication topology. We theoretically proved that the proposed algorithm converges to the global optimal solution with a probability of 1, without violating the physical constraints of individual agents. The effectiveness of the proposed method was validated by multiple experiments, both within the simulation environment and on a hardware system.
CONTINUITY AS A BASIS FOR CONTINUOUS SOCIAL AND HUMANITARIAN EDUCATION AT UNIVERSITY

The article analyzes the problems and features of the system of continuous social and humanitarian education at the university, which implies the unity of all the formation stages of universal competencies among university students. Foreign policy change causes the need to transform the multi-level training of university students developed under the Bologna Agreement. It is necessary to strengthen the educational and cultural components of education, aimed to form traditional spiritual and moral values and civil and patriotic consciousness of students. The integration of competency-based and cultural approaches to social and humanitarian education allows the author to consider social and humanitarian education not only as a pedagogical, but also as a socio-cultural process aimed to ensure the continuity of socialization and the development of the student's personality as a person of culture and morality. The author defines continuity as a methodological principle of designing and organizing a modern integrative educational environment which is an integral system of multi-level training of students. This system includes a complex of interrelated components (methodological, methodical, content, organizational, diagnostic) that ensure the effectiveness of a continuous educational process. The novelty of the study lies in the development of a model of continuous social and humanitarian education of students of a higher educational institution of physical culture based on the principles of continuity, complementarity, interdisciplinarity and integrativity as well as in the substantiation of the pedagogical conditions for its effective implementation. The defined conditions contribute to the comprehensive intellectual, socio-cultural, spiritual and moral development of the individual in the process of gradual formation of universal competencies.
TECHNOLOGY FOR DEVELOPING THE CREATIVITY OF FUTURE ENGINEERS USING MOODLE

The article investigates the problem to develop creativity of future engineers capable of working in complex high-tech conditions. Creative skills as the ability to use imagination to generate new ideas are the fundamental ones for engineers. The aim of the article is to offer a pedagogical technology to develop creativity of engineering students at university using Moodle learning platform. The humancentered system-synergetic methodology and system-information approaches were chosen as the methodological bases. These approaches enable to consider this technology as a system for collecting, storing and transferring educational content at university with the support of humanism in achieving system-synergetic community of education process participants. The authors offer a pedagogical technology to develop creativity of students of engineering specialties characterized by integrity, consistency, phasing, and focus on results. The authors evaluated the effectiveness of applying the technology for developing the creativity of future engineers using the comparative analysis of the results of a stage-by-stage monitoring that showed an increase in the level of creativity of students. 96 engineering bachelor students from South Ural State University took part in the study. This technology can be used in the educational process of the university to improve other soft skills.
Smart Health Care System using Blockchain and Machine Learning

Blockchain and Machine Learning together can be beneficial on a large scale. The greatest answers for carrying out diverse duties in a smart healthcare system come from combining them. Whenever we enter any hospital, document verification is the first thing which a patient has to go through. But since it is a very hectic and time-consuming process, it becomes very risky at the time of emergencies. In the past two decades the health care industry is one of the most targeted industries in terms of data breaches. The consensus in blockchain technology can make sure that the data is legitimate and the transactions are secure. Real-time patient conditions can be recognised or diagnosed using machine learning. It can be used in the healthcare system as a result of this ability.
Capsule Network Extension Based on Metric Learning

A capsule network (CapsNet) is a deep learning model for image classification that provides robustness to changes in the poses of objects in the images. A capsule is a vector whose direction represents the presence, position, size, and pose of an object. However, with CapsNet, the distribution of capsules is concentrated in a class, and the number of capsules increases with the number of classes. In addition, learning is computationally expensive for a CapsNet. We proposed a method to increase the diversity of capsule directions and decrease the computational cost of CapsNet training by allowing a single capsule to represent multiple object classes. To determine the distance between classes, we used an additive angular margin loss called ArcFace. To validate the proposed method, the distribution of the capsules was determined using principal component analysis to validate the proposed method. In addition, using the MNIST, fashion-MNIST, EMNIST, SVHN, and CIFAR-10 datasets, as well as the corresponding affine-transformed datasets, we determined the accuracy and training time of the proposed method and original CapsNet. The accuracy of the proposed method improved by 8.91% on the CIFAR-10 dataset, and the training time reduced by more than 19% for each dataset compared with those of the original CapsNets.
An Early Forecast of Long‐Period Ground Motions of Large Earthquakes Based on Deep Learning

Abstract Long‐period (LP; approximately 2–10 s) ground motions generated by large earthquakes are amplified in large basins and threaten high‐rise buildings in modern cities. In this study, we accomplished an early forecast of LP ground motions in distant basins based on deep learning technology using waveforms observed near the epicenter. A Temporal Convolutional Network was first trained using waveform data from past large earthquakes in the Japan Trench. LP ground motions of recent large earthquakes, including the 2011 Off the Pacific coast of Tohoku earthquake (Mw 9.0), were forecasted in the Kanto (Tokyo) and Osaka basins. This study effectively forecasted LP ground motions of large earthquakes regarding amplitude, waveform envelope shape, spectral characteristics, and duration. Faster forecasts (in 0.05 s or less) allow for updating forecasts as data is acquired, improving forecast accuracies and ensuring 1–2 min of lead time before large and prolonged shakes occur.
EEFDet: an efficient and effective face detector for lightweight, fast, and accurate face detection

In practical applications, it is urgent to develop lightweight, fast, and accurate face detectors. Although the performance of face detection has made tremendous progress with the rapid development of deep learning, they still struggle to meet the requirements of both efficiency and effectiveness. This is because current face detectors either use large networks or cumbersome processes oriented for accuracy but leading to heavy model complexity and slow inference speed or use various simple designs oriented for efficiency but greatly sacrificing accuracy or not actually efficient. In this paper, we conduct an in-depth study of the network structure and anchor setting and present an efficient and effective face detector named EEFDet, which can detect faces with lightweight model, fast inference speed, and excellent accuracy. First, a lightweight, fast, and robust network (LFRNet) is constructed by carefully designing the backbone network, detection neck, and detection head of the network. LFRNet not only has small model complexity and low latency but also possesses robust feature extraction ability. Then, a sparse anchor strategy is proposed by rationally setting anchors associated with the detection head to avoid using too many redundant anchors and get the most effective anchors. This strategy enables the detector to process multiple anchor-related parts at a fast speed while still guaranteeing detection accuracy. Extensive experiments and detailed ablation analysis are conducted on several widely used face detection benchmarks based on multiple metrics and different platforms. The results show that our EEFDet is lightweight (1.195 M parameters and 0.719 G FLOPs), fast (38.780 ms on GPU and 141.787 ms on CPU), and accurate [90.8%/89.6% (easy), 88.1%/87.2% (medium), 77.9%/77.4% (hard) APs on Val/Test set of WIDER FACE, 96.3% discrete score and 79.7% continuous score on face detection data set and benchmark (FDDB), 97.7% AP on PASCAL FACE, and 98.2% AP on annotated face in-the-wild (AFW)], achieving an optimal trade-off between efficiency and effectiveness compared to state-of-the-art face detectors.
Technology Factors Influencing Saudi Higher Education Institutions’ Adoption of Blockchain Technology: A Qualitative Study

Given the lack of empirical research on blockchain adoption in higher education, this study aims to partially fill this gap in the knowledge by focusing on the technology factors that could play a role in the process. Using Rogers’ Diffusion of Innovation Theory, five technology factors were considered: relative advantage, compatibility, observability, complexity, and trialability. The data analysis was based on ten semi-structured interviews with university mid- and senior-level administrators and IT personnel. Relative advantage emerged as the strongest perceived factor. The effect of compatibility and complexity was observed to a lesser extent. The influence of observability and trialability was unclear. The uncovered underlying mechanisms for these effects are discussed and implications for theory, practice, and future research are explored.
DCANon: Towards Distributed Certification Authority (CA) with Non-fungible Token (NFT)

Distributed applications rely on Certification Authorities (CAs) entities associated with a few institutions around the world. There are countless trusted institutions on the networks, e.g., universities, governments, banks, making effective use of CAs. Although largely used, the decentralized management of CAs is an administrative challenge. In this context, the joint emergence of digital ledgers and Non-Fungible Tokens (NFTs) compose a new opportunity to effectively decentralize single assets safely. Towards Distributed Certification Authority with Non-Fungible Token (DCANon) is a distributed Certification Authority (CA) built to trusted institutions and supported by Non-Fungible Token (NFT) concepts. DCANon changes the way revocation may be find, permitting that all consortium member crawler the blockchain for revoked certificates, independently from which CA this revocation occurs. A proof-of-concept prototype was implemented with Hyperledger Fabric demonstrating the benefits of DCANon.
iBot: Secure and Trusted Access to IoT Data with Blockchain

iBoT brings together all desirable features of current blockchain-backed IoT systems that use the blockchain to implement authorized access and control policies to data. Compared to other approaches, iBoT foresees the same high level of security and trust for all system actors including devices, users, and applications and applies multi-level authentication by means of Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and Smart Contracts. It uses Hyperledger Fabric to implement a private blockchain for all system entities (i.e. users, devices, applications and IoT data). iBoT conforms to the principles of the Web of Things (WoT) architecture model of W3C that defines a framework for integrating devices into the Web. iBoT has been deployed on the Google Cloud Platform (GCP). The experiments demonstrated that iBot is capable of responding in real-time under heavy workloads.
A learning approach towards metre-based classification of similar Hindi poems using proposed two-level data transformation

Abstract With the advancement in technology and digitalization of resources, computation of humanities problems is no exception to remain untouched. Automatic poetry classification is now a well-defined problem which can be solved using various approaches. Mood-based poetry classification is one of the popular ones. We propose a learning approach towards metre-based classification of Hindi metrical poetry. The state of art model for the metre-based poetry classification uses the rule-based approach whereas the proposed system uses learning models to perform classification. Feature extraction and classification are the two main components of text classification in natural language processing. Text is transformed into machine-readable numbers through the process of feature extraction, which is subsequently submitted to classification models. Poems, in their most natural formulation, are unfit to any learning-based algorithms. However, transforming the data into certain form and selecting a fixed number of features out of it (feature extraction) made the classification possible using machine learning approach which was yet untouched and can act as benchmark for the concerned area of research. The article deals with six popular and similar types of Hindi poems. The dataset is collected and processed to form an early dataset that undergoes two levels of data transformation and feature engineering, resulting in the pre-processed dataset. The pre-processed dataset is then fed as input to selected machine learning models (Bernoulli Naïve Bayes, k-nearest neighbour, random forest, and support vector machine) producing classification result with best accuracy of 99%, that further undergoes a post-processing step based on observed misclassifications.