Reviewerly/exHarmony huggingface dataset:
Ontological modelling of welding processes

Abstract In this work, an ontological model for welding processes is proposed and the need to manage the information and knowledge associated with such complex discipline is justified. First, an overview of the meaning and use of the models for this purpose and the concept of interoperability applied to the information contained in the welding standards is given. Next, an approach to the concept of ontology is made, the ontology applications for engineering, basic functionalities of ontologies and the way in which they are implemented. The work concludes showing some entities of the proposed ontology, focusing on the definition and classification of welding processes and knowledge inference based on the information contained in the ontology.
Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

Developing successful software with no defects is one of the main goals of software projects. In order to provide a software project with the anticipated software quality, the prediction of software defects plays a vital role. Machine learning, and particularly deep learning, have been advocated for predicting software defects, however both suffer from inadequate accuracy, overfitting, and complicated structure. In this paper, we aim to address such issues in predicting software defects. We propose a novel structure of 1-Dimensional Convolutional Neural Network (1D-CNN), a deep learning architecture to extract useful knowledge, identifying and modelling the knowledge in the data sequence, reduce overfitting, and finally, predict whether the units of code are defects prone. We design large-scale empirical studies to reveal the proposed model's effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA datasets. The experimental results demonstrate that in terms of f-measure, an optimal and modest 1D-CNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79% and 23.88%, respectively, in ways that minimize overfitting and improving prediction performance for software defects. According to the results, 1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software engineering. This, in turn, could lead to saving software development resources and producing more reliable software.
Improved Automated CASH Optimization with Tree Parzen Estimators for Class Imbalance Problems

The imbalanced classification problem is very relevant in both academic and industrial applications. The task of finding the best machine learning model to use for a specific imbalanced dataset is complicated due to a large number of existing algorithms, each with its own hyperparameters. The Combined Algorithm Selection and Hyperparameter optimization (CASH) has been introduced to tackle both aspects at the same time. However, CASH has not been studied in detail in the class imbalance domain, where the best combination of resampling technique and classification algorithm is searched for, together with their optimized hyperparameters. Thus, we target the CASH problem for imbalanced classification. We experiment with a search space of 5 classification algorithms, 21 resampling approaches and 64 relevant hyperparameters in total. Moreover, we investigate performance of 2 well-known optimization approaches: Random search and Tree Parzen Estimators approach which is a kind of Bayesian optimization. For comparison, we also perform grid search on all combinations of resampling techniques and classification algorithms with their default hyperparameters. Our experimental results show that a Bayesian optimization approach outperforms the other approaches for CASH in this application domain.
Response to commentaries to Luck et al. (2021). Progress toward resolving the attentional capture debate

In a review paper, Luck et al. [(2021). Progress toward resolving the attentional capture debate. Visual Cognition, 29(1), 1–21. https://doi.org/10.1080/13506285.2020.1848949] discussed multiple perspectives on the attentional capture debate. In response to this review paper, several commentaries were written. Here, I respond to these commentaries and discuss those issues that seem to be the most pressing in the discussion on whether progress is made. I particularly focus on issues that are relevant from my point of view. I have organized the reply to the commentators into separate sections and addressed the issues that were commonly raised.
Style Transfer based Coronary Artery Segmentation in X-ray Angiogram

X-ray coronary angiography (XCA) is a principal approach employed for identifying coronary disorders. Deep learning-based networks have recently shown tremendous promise in the diagnosis of coronary disorder from XCA scans. A deep learning-based edge adaptive instance normalization style transfer technique for segmenting the coronary arteries, is presented in this paper. The proposed technique combines adaptive instance normalization style transfer with the dense extreme inception network and convolution block attention module to get the best artery segmentation performance. We tested the proposed method on two publicly available XCA datasets, and achieved a segmentation accuracy of 0.9658 and Dice coefficient of 0.71. We believe that the proposed method shows that the prediction can be completed in the fastest time with training on the natural images, and can be reliably used to diagnose and detect coronary disorders.
Learning architecture for the recognition of walking and prediction of gait period using wearable sensors

This work presents a novel learning architecture for the recognition and prediction of walking activity and gait period, respectively, using wearable sensors. This approach is composed of a Convolutional Neural Network (CNN), a Predicted Information Gain (PIG) module and an adaptive combination of information sources. The CNN provides the recognition of walking and gait periods. This information is used by the proposed PIG method to estimate the next most probable gait period along the gait cycle. The outputs from the CNN and PIG modules are combined by a proposed adaptive process, which relies on data from the source that shows to be more reliable. This adaptive combination ensures that the learning architecture provides accurate recognition and prediction of walking activity and gait periods over time. The learning architecture uses data from an array of three inertial measurement units attached to the lower limbs of individuals. The validation of this work is performed by the recognition of level-ground walking, ramp ascent and ramp descent, and the prediction of gait periods. The recognition of walking activity and gait period is 100% and 98.63%, respectively, when the CNN model is employed alone. The recognition of gait periods achieves a 99.9% accuracy, when the PIG method and adaptive combination are also used. These results demonstrate the benefit of having a system capable of predicting or anticipating the next information or event over time. Overall, the learning architecture offers an alternative approach for accurate activity recognition, which is essential for the development of wearable robots capable of reliably and safely assisting humans in activities of daily living.
Elastic and Stable Compaction for LSM-tree

LSM-tree is widely used as a write-optimized storage engine in many NoSQL systems. However, the periodical compaction operations in LSM-tree cost many I/O bandwidths and CPU resources of the local server, resulting in throughput drops of the system. To address this issue, this paper proposes a new compaction scheme based on the FaaS (Functions as a Service) architecture, which is called FaaS Compaction. It utilizes the elastic computing capability of FaaS and always pushes compactions to a FaaS cluster. The FaaS cluster will perform actual compaction operations, which will not affect the processing of the local server. Therefore, we can maintain stable performance even when periodical compactions are triggered. We also present a Parallel Slight Compaction method to solve the timeout problem caused by heavy compactions. We implement the FaaS Compaction based on TerarkDB and a real FaaS cluster and experimentally compare the FaaS Compaction with the RocksDB's local compaction scheme and the state-of-the-art offloading compaction policy. The results suggest the efficiency, stability, and elasticity of our proposal.
Using the ANOVA F-Statistic to Rapidly Identify Near-Field Vulnerabilities of Cryptographic Modules

The analysis of variance (ANOVA) F-statistic is proposed as an indicator to accelerate the identification of nearfield vulnerabilities of cryptographic modules to electromagnetic side-channel analysis (EM SCA) attacks. It is hypothesized that optimal measurement configurations that require collecting the fewest measurements to disclosure have high F-values; i.e., in these configurations, the measured signals exhibit high variability when the encryption changes and low variability when the encryption is repeated. The concept is demonstrated for an EM SCA attack to disclose the secret key used in an open-source implementation of the Advanced Encryption Standard (AES). It is shown that the F-statistic reduces the search space to identify optimal measurement configurations 6× to 17× depending on the probe height and orientation.
QUALITY OF HIGHER EDUCATION AS A COMPONENT OF HUMAN CAPITAL FORMATION: CHALLENGES FOR UKRAINE

The research actualizes problem of ensuring the quality of higher education for the preparation of innovative human capital, which contributes to increasing the global competitiveness of Ukraine. The purpose of the article is to identify areas for quality assurance in higher education to create effective mechanisms for improving the quality of human capital in the university environment. The paper pays attention to global challenges, that determined by the development of the digital economy, the COVID-19 pandemic, and the internationalization of the educational space. Author analyzes the problems of inconsistency between the system of higher education and the labor market in Ukraine, the reasons for unemployment of persons with higher education, peculiarities labor and educational migrations. The need for systematic modernization and advanced development of university education is pointed out. Emphasis is placed on the need for lifelong learning and the formation of new competencies and non-specialized, interprofessional skills (soft skills) to increase the competitiveness of professionals and the quality of human capital. Author proposes the directions of ensuring the quality of higher education in order to optimize the contribution of human capital to the intellectual and innovative potential of the country.
A scalable AutoML approach based on graph neural networks

AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each learner. Many AutoML systems use meta-learning to guide search for optimal pipelines. In this work, we present a novel meta-learning system called KGpip which (1) builds a database of datasets and corresponding pipelines by mining thousands of scripts with program analysis, (2) uses dataset embeddings to find similar datasets in the database based on its content instead of metadata-based features, (3) models AutoML pipeline creation as a graph generation problem, to succinctly characterize the diverse pipelines seen for a single dataset. KGpip's meta-learning is a sub-component for AutoML systems. We demonstrate this by integrating KGpip with two AutoML systems. Our comprehensive evaluation using 121 datasets, including those used by the state-of-the-art systems, shows that KGpip significantly outperforms these systems.
A novel parameters correction and multivariable decision tree method for edge computing enabled HGR system

With the rapid development of cloud computing, Internet of things and artificial intelligence, human–computer interaction (HCI) is playing an increasingly important role in the daily life. As an important component of HCI, hand gesture recognition (HGR) system is usually combined with edge computing server, utilizing machine learning, including neural network, decision tree, integrated learning, to achieve low latency and high reliability service. High precision HGR with low computational complexity is prerequisite for the commercialization of gesture recognition. Therefore, this paper proposed a high-precision parameter correction algorithm based on the established scattered-point model and the outlier detection scheme, and a recognition algorithm with multivariable decision tree is then presented for the dynamic hand gestures. The experimental results show that the proposed algorithms can improve the recognition accuracy and effectively reduce the running time, which is conducive to algorithm transplantation and model deployment in edge servers.
Does Adversarial Oversampling Help us?

Traditional oversampling methods are generally employed to handle class imbalance in datasets. This oversampling approach is independent of the classifier; thus, it does not offer an end-to-end solution. To overcome this, we propose a three-player adversarial game-based end-to-end method, where a domain-constraints mixture of generators, a discriminator, and a multi-class classifier are used. Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach. In AO, the generator updates by fooling both the classifier and discriminator, however, in DO, it updates by favoring the classifier and fooling the discriminator. While updating the classifier, it considers both the real and synthetically generated samples in AO. But, in DO, it favors the real samples and fools the subset class-specific generated samples. To mitigate the biases of a classifier towards the majority class, minority samples are over-sampled at a fractional rate. Such implementation is shown to provide more robust classification boundaries. The effectiveness of our proposed method has been validated with high-dimensional, highly imbalanced and large-scale multi-class tabular datasets. The results as measured by average class specific accuracy (ACSA) clearly indicate that the proposed method provides better classification accuracy (improvement in the range of 0.7% to 49.27%) as compared to the baseline classifier
Optimization of Intelligent Logistics Supply Chain Management System Based on Wireless Sensor Network and RFID Technology

Based on the in-depth study of two technologies, wireless sensor network, and RFID technology, this paper proposes a corresponding solution to the urgent storage positioning problem and the safety of goods in transit in the logistics supply chain process. And on this basis, it broadens the whole logistics process of warehousing and distribution and designs a framework of intelligent logistics supply chain management system with wireless sensor network and RFID technology to realize the management process of logistics main business. For the problems of complicated hardware wiring, the excessive number of positioning nodes and low effective coverage rate of traditional RFID-based positioning methods in large-scale warehousing environment, the integrated RFID and wireless sensor network fusion technology constructs a new movable anchor node, divides the nodes in the traditional positioning process into fixed anchors and mobile anchors, proposes a secondary positioning method, improves positioning accuracy and effective coverage rate, and provides a new positioning method for large-scale storage environment. For the transportation of goods in transit, especially precious and dangerous goods, a six-axis acceleration sensor-based method for detecting the behavioral attitude of goods in transit is proposed. Time window slicing of cargo motion data stream is performed to reduce data processing and based on the statistical features of a large amount of experimental data, and feature vectors and classifiers are analyzed and designed to achieve behavioral attitude classification of goods in transit. The experiment proves that this method has high recognition accuracy and has certain practical application value. Finally, the whole storage and distribution logistics process is integrated, the framework of intelligent logistics supply chain management system combining WSN and RFID is built based on wireless sensor network software and hardware platform, and the detailed design of logistics supply chain management system business process is mainly carried out. Combined with the two key methods proposed in this paper, the information management process of traditional logistics business such as access, storage positioning and monitoring, and distribution monitoring and management is realized, and the management system is tested on this basis. The testing effect proves that this system has certain deployment reference significance and practical application value and to a certain extent can help improve the intelligence level of logistics supply chain management and achieve the expected goal of this study.
Performance Analysis of MD5 and SHA-256 Algorithms to Maintain Data Integrity

Recent advancement and increase in digitization in every dimension of life has led to increase in data leaks. It is hence very important to protect data from cyber-attacks. Cryptography is an indispensable tool which is used to maintain data integrity or protect the information in all digital devices. It converts a simple plain text into its hashed form which cannot be reversed. It is especially used in storing passwords, digital advertising firms use them so that personally identifiable information is not used, and it is widely used in transmission of messages via digital medium. The application of cryptography only keeps increasing every day. This paper presents a detailed review of two important hashing techniques, MD5 and SHA-256 algorithm and which is preferred better to maintain Data Integrity. It converts the data into irreversible, fixed length text.
Full-Stack Application of Skin Cancer Diagnosis Based on CNN Model

Convolutional neural network (CNN) is a subset of deep neural networks, and it has commonly applied to analyze images. Skin cancer is a disease that can be observed without the help of expensive and professional instruments. At the same time, consulting a doctor in a hospital is not a cheap thing for many people. To tackle this issue, this paper introduced a skin cancer detection application based on CNN. In this application, the graphical user interface is implemented by Swift UI, and the backend is ExpressJs. It loads a keras CNN model through TensorFlow JS to detect and classify skin cancer. The CNN model used in the application is created through TensorFlow and trained with the HAM10000 skin cancer data set. It also integrated Natural Language Process (NLP) function, so that users can ask some questions and consult doctors online. The current version of this application can successfully operate in the IOS environment, and fully achieve those functions above. Also, the experimental result demonstrated that the accuracy of CNN model is around 75% for HAM10000 test set.
LSB Based Audio Steganography using RSA and ChaCha20 Encryption

With the evolution of the digital era and internet technology, most of our confidential information such as financial and personal data or even secret information about our assets is exposed to a huge number of breaches or risks. Matters of data security and data privacy assume great importance for the secure transmission of data. Thus, techniques such as steganography and cryptography are being employed to protect the data and to prevent it from being viewed by unauthorized users. Steganography conceals the presence of the data thereby preventing it from being discovered. It involves the technique of hiding secret information within an ordinary multimedia file. In this paper, a modified Least Significant Bit (LSB) based steganography approach for hiding data within audio files has been introduced. The method involves the incorporation of RSA and ChaCha20 encryption techniques to enhance digital data security. In the final part of this paper, the results achieved using the modified approach have been discussed.
Efficient Learning of the Parameters of Non-Linear Models Using Differentiable Resampling in Particle Filters

It has been widely documented that the sampling and resampling steps in particle filters cannot be differentiated. The {\itshape reparameterisation trick} was introduced to allow the sampling step to be reformulated into a differentiable function. We extend the {\itshape reparameterisation trick} to include the stochastic input to resampling therefore limiting the discontinuities in the gradient calculation after this step. Knowing the gradients of the prior and likelihood allows us to run particle Markov Chain Monte Carlo (p-MCMC) and use the No-U-Turn Sampler (NUTS) as the proposal when estimating parameters. We compare the Metropolis-adjusted Langevin algorithm (MALA), Hamiltonian Monte Carlo with different number of steps and NUTS. We consider two state-space models and show that NUTS improves the mixing of the Markov chain and can produce more accurate results in less computational time.
A Performance Evaluation of DRAM Access for In-Memory Databases

The latest CPUs(computer cpu processors) employ multiple cores, massively superscalar pipelines, out-of-order execution of tons of instructions, and advanced SIMD capabilities, which can hide the memory access latency.And most of recent memory-oriented data structures have already benefit from these features.However, due to the complexity of data organization, these CPUs do not always work well in memory resident database systems (MMDBs), particularly regarding storing data in dynamic randomaccess memory (DRAM).This article studies memory-efficient data structures by analyzing the run time, access latency, cache misses, instructions per cycle (IPC), and DRAM reads (bytes).Then, we design and implement two data organization schemas in the main memory database: dispersing data block organization and clustering data block organization.Using algorithmic engineering and careful attention to internal parallelism, cache alignment can hide the memory access latency.However, we find that these data structures work well in some cases, though they have been eclipsed in the face of complex access paths.To determine the reasons, we study the impact of database techniques on memory access latency, such as data partitioning, storage models, and by processing algorithms.With the specific main memory database system, we estimate the performance of each data organization schema based on DRAM DDR4 and the latest Intel Haswell microarchitecture.In conclusion, this work will make DRAM access applicable in real-world situations by implementing the schema to systems, such as in-memory databases.