Reviewerly/exHarmony huggingface dataset:
Transformation of Plants into Polka Dot Arts: Kusama Yayoi as an Inspiration for Deep Learning

Museums and galleries are places that stimulate the imagination and creativity of people. However, against the backdrop of the COVID-19 pandemic, many museums have been forced to close. As a result, more visitors have had to start visiting online exhibitions. Under this trend, the quality of the online exhibition experience is facing unprecedented challenges, and visitors need to be provided with a high-quality online experience. With the rapid development of emerging technologies, museums and galleries have started to use technology to create “digitalization” experiences, such as 3D virtual exhibitions and VR interactive experiences. The current online exhibition format focuses more on improving the visitor’s experience of viewing and browsing works and related information and less on how visitors create their artworks. In order to remedy the experience of visitors actively creating artworks, this study uses Yayoi Kusama’s works as a case study to help form an utterly interactive process between visitors and artworks. We propose a Polka Dot Arts Transfer Network (PDAT-net), mixing realistic-looking and Polka Dot Art-style reference images. The output image looks like an actual natural image “drawn” in the polka dot art style. The PDAT-net is trained to compose an image in the style of Polka Dot Arts, based on neural style transfer. Convolutional networks implemented the training. Plant images performed the final test. The results showed that the generated polka dot images have similar styles to the style images, proving that our PDAT-network has good transfer performance of Polka Dot Arts.
DDG-Based Optimization Metrics for Defect Prediction

Software defect prediction helps improve software quality and allocate software test resources reasonably. Many defect prediction models based on software metrics have been proposed. However, the existing software metrics are mainly focused on structure information of source code, and the semantic information is lacking. Compilation optimization is the result of deep analysis of program semantics, and intuitively we believe that it should reflect the semantic information of the program in some ways to help defect prediction. Based on the optimization options widely used in the current compiler, this paper extracts 40 compilation optimization metrics based on DDG of program, and proposes seven types of metrics models that designed by different metrics sets. The relationship between compilation optimization metrics and software defect predictions was evaluated by 10 commonly used classifiers. Experimental results show: a) Compilation optimization metrics have a significant impact on the recall rate of software defect prediction. b) Static code metrics combined with compilation optimization metrics can improve the performance of software defect prediction in most classifiers. c) Code size optimization metrics and performance optimization metrics have their characteristics, combined both of them can get better performance in software defect prediction.
The Optimization of Warhead and QRCS Simulation for A Stealth Missile

In this study, the analytical expression of quantum radar cross section (QRCS) for three-dimensional (3D) convex targets is presented. The QRCS simplified calculation equation is derived numerically after the introduction of the correlation photon parameters and vector dot product, and the computing efficiency of the proposed equation increases significantly. In this study, two types of the 3D convex target were selected to verify the accuracy and efficiency of the equation, and an optimization approach is presented to analyze QRCS of the stealth missile based on the genetic algorithm (GA) unprecedentedly. The aim of this study is to achieve the minimum forward QRCS of the stealth missile through GA and to adjust the geometry design variables on the head of the missile. As revealed by the optimized results, the proposed technique is confirmed to be effective and acceptable.
Hourly Global Horizontal Irradiance Over West Africa: Satellite- and Reanalysis-Derived Estimates vs. In Situ Measurements

Estimates of global horizontal irradiance (GHI) from reanalysis and satellite-based data are the most important information for the design and monitoring of PV systems in Africa, but their quality is unknown due to the lack of in situ measurements. In this study, we evaluate the performance of hourly GHI from state-of-the-art reanalysis and satellite-based products (ERA5, CAMS, MERRA-2 and SARAH -2) with 51 quality-controlled in situ measurements from novel meteorological networks established in Burkina Faso and Ghana under different weather conditions. The effects of clouds and aerosols are also considered in the analysis by using common performance measures for the main quality attributes and a new overall performance value for the joint assessment. The results show that SARAH-2 performs better under cloudy sky (RMSE=237 W/m2 (24.51%)), clear sky (113 W/m2 (10.65%)) and all sky conditions (167 W/m2 (15.18%)), while ERA5 performs poorly under all sky conditions. The poor performance of all datasets under cloudy sky conditions is due to a large bias observed during the Harmattan period. The new measure of overall performance clearly shows that the hourly GHI derived from SARAH-2 is the best alternative for assessing solar energy in the different climatic zones of West Africa.
Turning Machine Translation Metrics into Confidence Measures

The Interactive Machine Translation (IMT) systems produce translations combining the knowledge of professional translators with the generation speed of the Machine Translation (MT) models. Both interact with the finality of generating error-free translations. The main goal of research in the IMT field is to reduce the effort that the professional translators have to perform during the IMT session. There are very different techniques to reduce this effort, from changing the display used to perform the corrections to changing the feedback signal that the user sends to the MT model. This article propose a method to reduce the effort performed by applying Confidence Measures (CMs) that give us a score for each translation and only let the user translate those that obtained a low score. We have trained for Recurrent Neural Network (RNN) models to approximate the scores from four of the most used metrics in MT: Bleu, Meteor, Chr-F, and Ter. We have simulated the user interaction with an Interactive-Predictive Neural Machine Translation (IPNMT) system to study the effort reduction that we can obtain while getting high-quality translations from the system. We have tested different thresholds values to consider that a translation has a low score, which gives us a transition between a convention IPNMT system where the system has to correct all the translations to an unsupervised MT system. The results showed that this method obtains very good translations – 70 points of Bleu – and reduces the human effort by 60%.
Using Multi-modal Machine Learning for User Behavior Prediction in Simulated Smart Home for Extended Reality

We propose a multi-modal approach to manipulating smart home devices in a smart home environment simulated in virtual reality. Our multi-modal approach seeks to determine the user’s intent in the form of the user’s target smart home device and the desired action for that device to perform. We do this by examining information from two main modalities: spoken utterance and spatial information (such as gestures, positions, hand interactions, etc.). Our approach makes use of spoken utterance, spatial information, and additional information such as the device’s state to predict the user’s intent. Since the information contained in the user’s utterance and the spatial information can be disjoint or complementary to one another, we process the two sources of information in parallel using multiple machine learning models to determine intent. The results of these models are ensembled to produce our final prediction results. Aside from the proposed approach, we also discuss our prototype and discuss our initial findings.
An Adaptative Active Approach with Monitoring Quality of Stream Modeling in the Presence of Concept Drift

Monitoring the quality of statistical models for stream big data processing in nonstationary environments is a practical approach to provide the adaptability of the modeling process in the face of change and drift. It is cost-effective to offer a well-functional calibrated processing system to maintain high-level accuracy with minimum variance in adaptive modeling. The study of drifting concepts and continuous learning has helped predict the behavior of various real-time big data stream adaptations. A concept drift detection method provides an adaptive mechanism in nonstationary characteristics of providing triggers to adaptation design when the drift is detected. Stream processing is considered a production system with prediction and adaptation output from a quality control perspective. This research provides a concept drift detection method that monitors non-defective production proportion as a critical quality performance indicator. An active adaptive framework with explicit concept drift detection to govern the incremental learning process utilizing triggering mechanisms is an issue that has been focused on in this study. The estimation of the quality indicator originates from the concept of Bayesian analysis in predicting changes in the model parameters. The estimate is updated sequentially then a detection control design is used to trigger the possible evolution of distribution parameters to the adaptation module, providing minor adjustments to passive adaptation. Among the key contributions of this research, the algorithmic method described herein provides superior adaptation among common triggering methods in active detection alongside partial fit in Gaussian Naive Bayes as incremental learning when tested on electricity datasets, the most commonly used in concept drift detector studies. The general framework of this research is a variety of data stream applications in the data stream that have concept drift.
A Novel Hybrid Algorithm Based on Lion Swarm Optimization and Differential Evolution Algorithm

Lion swarm optimization (LSO) algorithm, a novel and efficient meta-heuristic optimization technique, has received considerable attention and also has been applied to solve science and engineering optimization problems for outstanding performance. However, like some other typical swarm optimization algorithms, LSO may also trap individuals into local regions, resulting in premature convergence. Furthermore, how to balance contradictions between exploration and exploitation is also a critical issue that must be addressed in the LSO study. Hence, to address the aforementioned problems, a novel LSO variant algorithm called “hybrid lion swarm optimization (LSO) with differential evolution (DE)”, referred to as HLSOD, is proposed in this paper. First, LSO and DE are improved, respectively. For LSO, a nonhomogeneous cuckoo search strategy based on quantum mechanism is designed to enhance the exploitation capability. In contrast, a crossover strategy “crossover operator based on covariance matrix learning”, which not only can alleviate the algorithm’s dependence upon the coordinate system to some extent but also enhances the algorithm’s ability to tackle problems with high correlation between the variables, is employed to efficiently maintain the population diversity. For DE, the mutation strategy “DE/current-to-pbest” with optional external archive, which can use historical data to provide information of progress direction, is introduced as the mutation operator. The crossover strategy “crossover operator based on covariance matrix learning” is used for the crossover operator. Moreover, the adaptive parameter settings, which can automatically update the control parameters to appropriate values and avoid a user’s prior knowledge of the relationship between the parameter settings and the optimization problems’ characteristics, are applied for the parameter settings of the mutation and crossover operators. Thus, it is helpful not only for balancing contradictions between exploration and exploitation but also for improving the algorithm’s robustness. To enhance the algorithm’s convergence speed and maintain better candidate solutions, the greedy selection method is also adopted at each iteration of LSO and DE after the population is updated. Second, the improved LSO and the improved DE are hybridized to formulate HLSOD. A series of contrastive experiments are conducted on test functions from classic functions and CEC2017 test suite. Experimental results substantiate that the proposed HLSOD could achieve competitive or even better performance compared with other state-of-the-art algorithms. In addition, the application to four engineering design optimization problems further demonstrates the practicability and performance of HLSOD in tackling complex and diverse real-world optimization problem.
Integrate Generalized Suffix Tree into Dictionary Attack

It is not rare that one person uses the same password across his/her different accounts or uses the same substring(s) to build different passwords. Similarly, different persons may use the same passwords or same substring(s) in their passwords. How do the shared passwords or substrings impact the strength of the passwords? In this research, we find that using the dictionaries with shared substrings can easily and effectively crack most hashed passwords. We used Generalized Suffix Tree to systematically analyze more than 35 million plaintext passwords. The shared substrings found by Generalized Suffix Tree are compiled into dictionaries/word lists. By using these dictionaries, the Dictionary (Combinator) Attack of Hashcat cracked $$89\%$$ , $$80\%$$ , $$90\%$$ , $$89\%$$ of English, Russian, Chinese, and French domain passwords respectively. In this work, we treat Combinator Attack as a special case of Dictionary Attack and use these two terms interchangeably.
The role of interactive platforms in the study of foreign languages by students of technical institutions of higher education

The paper studies the role and features of the use of modern interactive platforms in the study of foreign languages by students of technical free economic zones as one of the important structural elements in ensuring a quality educational process.The essence of the category "interactive educational platforms" is analyzed and their main features are investigated.It is determined that the main criteria for analysis and selection of interactive educational platforms that determine their availability and functionality in the individualization of foreign language learning by students of technical free economic zones are clear interface, methodical and reasonable selection of electronic content, multimedia learning elements, interactivity, free version variability of tasks, the ability to monitor learning outcomes and diagnose errors throughout the training, receiving feedback from users, the ability to set time limits for tasks, etc.It is established that for the successful implementation of the concept of Education 3.0 the use of modern interactive platforms and digital technologies in the study of foreign languages by students of technical free economic education in the educational process is not a whim but a necessity.The main types of modern interactive platforms and technologies for learning foreign languages by technical freelance students are analyzed, which include modular digital learning environments, mass open online courses and distance education, LMS (learning management systems) and LCMS (learning content management system).On the basis of an online survey of students of the National Technical University of Ukraine "Kyiv Polytechnic Institute named after Igor Sikorsky" established that the most interactive platforms for learning a foreign language are the following: Coursera (98.4%),Prometheus (96.9%),LearnEnglish from the British Council (93.1%),LinguaLeo (91.5%),Callan Method (88.5%).
Analysis of Graphical Causal Models with Discretized Data

In several fields, sample data are observed at discrete instead of continuous levels. For example, in psychology an individual’s disease level is typically observed as ‘mild’, ‘moderate’ or ‘strong’, while the underlying mental disorder intensity is potentially a continuous variable. Implications of such discretization in linear regression are well-known: uncertainty increases and estimated causal relations become biased and inconsistent. For more complex models, implications of discretization are not theoretically studied. This paper considers an empirical study of complex models where causal relationships are unknown, some variables are discretized and graphical causal models are used to estimate causal relationships and effects. We study the implications of discretization on the obtained results using simulations. We show that discretization affects the correct estimation of causal relations and the uncertainty of obtained causal relations between discretized variables and non-discretized variables. In addition, we show that discretization influences estimated causal effects and we relate this influence to the properties of discretized data and sample size.
Fostering Science Teaching and Learning in a Multicultural Environment Through the Culturo-Techno-Contextual Approach

The multicultural science classroom provides new vistas of opportunities for teachers and students to experiment and innovate. This chapter describes the emergence, after about four decades of experimentation in multicultural science classrooms, of the culturo-techno-contextual approach (CTCA) for breaking barriers to meaningful learning of science. CTCA is now being piloted in several African countries. It is a method that is set within the cultural context of the learner and recognizes the increasing use of technology in African science classrooms, a development that will pervade the environment and shape teaching and learning far into the twenty-first century. Beginning with a brief description of CTCA, the chapter provides step-by-step guide to using CTCA. A review of empirical studies which explored the impact of CTCA on students’ performance in science is reported, and a special focus was given to the indigenous knowledge and cultural practice elements of CTCA. The chapter also highlights the development, deployment, and guide to the use of the CTCA mobile app which is now available for STEM teachers and students all over the world. The concluding section forecasts the future of CTCA in the desire to improve science teaching and learning especially in multicultural classrooms.
Classification of Software Defects Using Orthogonal Defect Classification

Classification of software defects is an important task to know the type of defects. It helps to prioritize the defects, to understand the cause of defects for improving the process of software defect management system by taking the appropriate action. In this paper, we evaluate the performance of naïve Bayes, support vector machine, k nearest neighbor, random forest, and decision tree machine learning algorithm to classify the software defect based on orthogonal defect classification by selecting the relevant features using chi-square score. Standard metrics accuracy, precision, and recall are calculated separately for Cassandra, HBase, and MongoDB datasets. The proposed method improves the existing approach in terms of accuracy by 5%, 20%, 6%, 27%, and 26% for activity, defect impact, target, type, and qualifier respectively, and shows the enhanced performance.
Intelligent Information Design of Tunnel Engineering Based on Deep Learning

At present, the commonly used tunnel monitoring in the process of tunnel construction in China mainly includes advanced geological prediction and settlement displacement monitoring in the process of tunnel construction. This kind of monitoring data can reflect the quality of tunnel construction and play a guiding role to a certain extent. However, such monitoring methods have obvious shortcomings. The monitoring data can not guide the tunnel construction in real time, and can not predict and optimize the highway construction in the next stage. It can be seen that at this stage, there are still large deficiencies in the information monitoring and processing of tunnel construction in China. Therefore, this paper studies the intelligent information design of tunnel engineering based on deep learning.
Orchestration of Ml/ai Models Using MLOps/AIOps Frameworks

Companies require an ML/AI process that is automated and streamlined. The strategy is to set up a common architecture and development methods so that the company can get the most out of its machine learning and artificial intelligence investments. This is where ML/AIOps derived from continuous integration & continuous development CI/CD culture, may play an important role in helping enterprises increase team cooperation, improve the stability and scalability of ML/AI systems and shorten development cycles. As a result, enterprises may reap the benefits of being at the vanguard of innovation while also assisting in the realization of total business value from ML/AI investments. The ML/AIOps framework is an ML/AI engineering technique that combines the development of ML/AI systems with operation features. Organizations can benefit from shorter ML/AI system development cycles, faster product to market, improved cooperation amongst their teams, increased dependability, performance, scalability, and security of their ML/AI systems by implementing ML/AIOps framework principles. Operational and governance processes have been simplified.
A Certificateless Homomorphic Encryption Scheme for Protecting Transaction Data Privacy of Post-Quantum Blockchain

Blockchain has a profound impact on all areas of society by virtue of its immutability, decentralization and other characteristics.However, blockchain faces the problem of data privacy leakage during the application process, and the rapid development of quantum computing also brings the threat of quantum attack to blockchain.In this paper, we propose a lattice-based certificateless fully homomorphic encryption (LCFHE) algorithm based on approximate eigenvector firstly.And we use the lattice-based delegate algorithm and preimage sampling algorithm to extract part of the private key based on certificateless scheme, which is composed of the private key together with the secret value selected by the user, thus effectively avoiding the problems of certificate management and key escrow.Secondly, we propose a post-quantum blockchain transaction privacy protection scheme based on LCFHE algorithm, which uses the ciphertext calculation characteristic of homomorphic encryption to encrypt the account balance and transaction amount, effectively protecting the transaction privacy of users and having the ability to resist quantum attacks.Finally, we analyze the correctness and security of LCFHE algorithm, and the security of the algorithm reduces to the hardness of learning with errors (LWE) hypothesis.