Reviewerly/exHarmony huggingface dataset:
Metaheuristics Guided by the Apriori Principle for Association Rule Mining

Association rule mining (ARM), one of the most known tasks in data mining, is considered as an optimization problem. The ARM problem can be solved either by exact methods or by metaheuristics. Exact methods such as Apriori algorithm are very efficient to deal with small and medium datasets. However, when dealing with large size datasets, these methods suffer from time complexity. Metaheuristics are proven faster but most of them suffer from accuracy. To deal with these two challenging issues, this work investigates to enhance metaheuristics and proposes hybrid approaches, which combine metaheuristics and the Apriori principle to intelligently explore the association rules space. To validate the proposed approaches the chemical reaction optimization metaheuristic (CRO) was used. Intensive experiments have been carried out and the first results are very promising in terms of accuracy and processing time.
Teacher Self-Efficacy and Reflection as Predictors of Teacher Burnout: An Investigation of Iranian English Language Teachers

In modern education, teachers are regarded as a central and focal part of educational systems and are responsible in the development of education. It should be mentioned that teachers have an influential role in planning and offering an effective and significant educational program. The significant role of teacher-related variables in affecting teachers’ performance and learning outcomes of students has been widely acknowledged in various educational contexts. Therefore, the investigation of teacher variables has received research attention in English as a Foreign Language (EFL) context. To contribute to this line of research, the current study was set to investigate the role of teacher reflection and self-efficacy in predicting burnout among Iranian EFL teachers. To this end, three validated scales measuring these variables were administered to a number of 171 male and female teachers. As for the data analysis, Structural Equation Modeling was utilized to test the hypothesized model of the constructs. The results indicated that teacher reflection accounted for 12.1% of the variance, and teacher self-efficacy accounted for 25.2% of the variance in burnout. Although both variables had a unique effect on teaching burnout, teacher self-efficacy turned out to be a stronger predictor of burnout. Concerning the implications, teacher education programs may pay more serious attention to teacher self-efficacy and reflection as they proved to play a significant role in reducing teacher burnout.
The Reform of Teaching Management Mode Based on Artificial Intelligence in the Era of Big Data

Abstract To integrate AI into teaching management can optimize teaching tools, improve teaching efficiency, and ultimately improve teaching quality, so as to cultivate more excellent talents in line with market demand. The purpose of this paper is to study the reform of teaching management mode based on artificial intelligence in the era of big data. After understanding the characteristics of teaching management in the era of big data, this paper studies the reform of management mode of intelligent teaching and intelligent learning. Taking a comprehensive university as the research sample, this paper discusses the impact of MOOC on university teaching management through questionnaire survey and focus interview, and puts forward new ideas on how to better adapt to the development of education in the new era. The results show that the number of dissatisfied students is generally higher than the number of satisfied students in the freedom of major choice, curriculum choice, teacher choice and learning style choice. Only 16.09% of the students think that they have a high degree of freedom in these aspects. MOOC relies on advanced information technology to form a vivid information collection, which can help the management of college students change from unified management to personalized management.
Problems in FAIRifying Medical Datasets.

Despite their young age, the FAIR principles are recognised as important guidelines for research data management. Their generic design, however, leaves much room for interpretation in domain-specific application. Based on practical experience in the operation of a data repository, this article addresses problems in FAIR provisioning of medical data for research purposes in the use case of the Leipzig Health Atlas project and shows necessary future developments.
Benchmarking Neural Machine Translation for Southern African Languages

Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared, meaning researchers struggle to reproduce reported results, and almost no publicly available benchmarks or leaderboards for African machine translation models exist. To start to address these problems, we trained neural machine translation models for a subset of Southern African languages on publicly-available datasets. We provide the code for training the models and evaluate the models on a newly released evaluation set, with the aim of starting a leaderboard for Southern African languages and spur future research in the field.
Hypothesis testing interpretations and Renyi differential privacy

Differential privacy is a de facto standard in data privacy, with applications in the public and private sectors. A way to explain differential privacy, which is particularly appealing to statistician and social scientists is by means of its statistical hypothesis testing interpretation. Informally, one cannot effectively test whether a specific individual has contributed her data by observing the output of a private mechanism---any test cannot have both high significance and high power. In this paper, we identify some conditions under which a privacy definition given in terms of a statistical divergence satisfies a similar interpretation. These conditions are useful to analyze the distinguishability power of divergences and we use them to study the hypothesis testing interpretation of some relaxations of differential privacy based on Renyi divergence. This analysis also results in an improved conversion rule between these definitions and differential privacy.
MTAdam: Automatic Balancing of Multiple Training Loss Terms

When training neural models, it is common to combine multiple loss terms.The balancing of these terms requires considerable human effort and is computationally demanding.Moreover, the optimal trade-off between the loss terms can change as training progresses, e.g., for adversarial terms.In this work, we generalize the Adam optimization algorithm to handle multiple loss terms.The guiding principle is that for every layer, the gradient magnitude of the terms should be balanced.To this end, the Multi-Term Adam (MTAdam) computes the derivative of each loss term separately, infers the first and second moments per parameter and loss term, and calculates a first moment for the magnitude per layer of the gradients arising from each loss.This magnitude is used to continuously balance the gradients across all layers, in a manner that both varies from one layer to the next and dynamically changes over time.Our results show that training with the new method leads to fast recovery from suboptimal initial loss weighting and to training outcomes that match or improve conventional training with the prescribed hyperparameters of each method.
An Evaluation of the Next-Generation Image Coding Standard AVIF

This paper presents a comparative performance evaluation of the newly proposed AV1 Image File Format (AVIF) vs. other state-of-the art image codecs, for natural, synthetic and gaming images. The codecs are compared in terms of Rate-quality curves and BD-Rate savings considering different quality metrics. AVIF results in the best overall performance considering both 4:2:0 and 4:4:4 chroma sub-sampling encoded images.
Performance optimization of a reservoir computing system based on a solitary semiconductor laser under electrical-message injection

A simple reservoir computing (RC) system based on a solitary semiconductor laser under an electrical message injection is proposed, and the performances of the RC are numerically investigated. Considering the lack of memory capacity (MC) in such a system, some auxiliary methods are introduced to enhance the MC and optimize the performances for processing complex tasks. In the pre-existing method, the input information is the current input data combined with some past input data in a weighted sum in the input layer (named as M-input). Another auxiliary method (named as M-output) is proposed to introduce the output layer for optimizing the performances of the RC system. The simulated results demonstrate that the MC of the system can be improved after adopting the auxiliary methods, and the effectiveness under adopting the M-input integrated with the M-output (named as M-both) is the most significant. Furthermore, we analyze the system performances for processing the Santa Fe time series prediction task and the nonlinear channel equalization (NCE) task after adopting the above three auxiliary methods. Results show that the M-input is the most suitable for the prediction task while the M-both is the most appropriate for the NCE task.
CryptoRNN - Privacy-Preserving Recurrent Neural Networks Using Homomorphic Encryption

Recurrent Neural Networks (RNNs) are used extensively for mining sequential datasets. However, performing inference over an RNN model requires the data owner to expose his or her raw data to the machine learning service provider. Homomorphic encryption allows calculations to be performed on ciphertexts, where the decrypted result is the same as if the calculation has been made directly on the plaintext. In this research, we suggest a Privacy-Preserving RNN–based inference system using homomorphic encryption. We preserve the functionality of RNN and its ability to make the same predictions on sequential data, within the limitations of homomorphic encryption, as those obtained for plaintext on the same RNN model. In order to achieve this goal, we need to address two main issues. First, the noise increase between successive calculations and second, the inability of homomorphic encryption to work with the most popular activation functions for neural networks (sigmoid, ReLU and tanh). In this paper, we suggest several methods to handle both issues and discuss the trade-offs between the proposed methods. We use several benchmark datasets to compare the encrypted and unencrypted versions of the same RNN in terms of accuracy, performance, and data traffic.
An Enhanced Butterfly Optimization Algorithm for Function Optimization

ButterflySharma, Sushmita optimizationSaha, Apu Kumar algorithmNama, Sukanta (BOA), a newly developed optimization algorithm, has captured the researcher’s interest in solving real-life and engineering optimization problems due to its simplicity, efficiency and robustness. On the other hand, symbiosis organisms search algorithm has proved its efficiency in solving complex real-life optimization problem. In this paper, a new combinatorial optimization approach has been introduced combining the explorative advantages of BOA with the exploitative advantage of SOS to eliminate weak exploitation ability of BOA and weak exploration ability of SOS. Efficiency and robustness of the algorithm have been evaluated using twenty-five classical benchmark problems and compared with some state-of-the-art optimization algorithms. The compared results show that the proposed method to be superior and reliable than the other algorithms.
Simulated Annealing Based Algorithm for Tuning LDA Hyper Parameters

LDA is a very popular unsupervised model used to find thematic information about the documents. The performance of the LDA greatly depends on its hyper parameter values. If its parameters are tuned properly then LDA may produce much better results. This paper mainly focused on finding good LDA configurations for finding improved LDA output as compare to the traditional LDA model. We have proposed and implemented SA-LDA algorithm that uses Simulated Annealing (SA) to find optimal values of LDA parameters. An empirical evaluation using customer review datasets from three different domains namely Mobile, Hotel and Movie is conducted. The experiment results show that SA-LDA gives better performance when it is evaluated by the coherence score.
Deep Learning Approach for Vietnamese Consonant Misspell Correction

Vietnamese words are combinations of consonants, vowels, and diacritics. Previous studies on Vietnamese spelling correction often focused on mistyped errors. Misspelled errors are more common and difficult to detect. Based on our literature review, there is no direct study to address this issue. A misspelled Vietnamese word can become another word does exist in the vocabulary but make the sentence a different meaning or meaningless. While mistyped errors are typographical errors, misspelled errors may appear in any type of text including typed documents and handwritten text. Compared to mistyped errors, misspelled errors are harder to detect, especially by people who type it out. This error comes from the wrong understanding about the spelling of the word. For that reason, checking a sentence with a vocabulary filter does not guarantee that the sentence is spelled correctly. Checking Vietnamese spelling errors is a difficult problem. There have been many articles trying to solve this problem with different approaches but they have their own limitations. In this paper, we propose a deep learning approach focusing on consonant misspell errors with superior accuracy compared to the existing methods. The accuracy of our model makes a significant gap compared to the current state-of-the-art model.
Addressing Human Factors in the Design of Cyber Hygiene Self-assessment Tools

As cybersecurity (CS) threats become more sophisticated and diversified, organizations are urged to constantly adopt and update measures for contrasting different types of attacks. Particularly, as novel techniques (e.g., social engineering and phishing) are aimed at leveraging individual users’ vulnerabilities to attack and breach a larger system or an entire company, user awareness and behavior have become key factors in preventing adverse events, mitigating their damage, and responding appropriately. As a result, the concept of Cyber Hygiene (CH) is becoming increasingly relevant to address the risk associated to an individual’s CS practices. Consequently, self-assessment tools are becoming more important for evaluating user’s literacy, implementing measures (e.g., training), and studying the effectiveness of interventions. In this paper, we propose a framework for including human factors in the design of self-assessment tools and for accurately modeling CH aspects that the root cause in CS issues.
Leveraging conditional linkage models in gray-box optimization with the real-valued gene-pool optimal mixing evolutionary algorithm

Often, real-world problems are of the gray-box type. It has been shown that the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) is in principle capable of exploiting such a Gray-Box Optimization (GBO) setting using linkage models that capture dependencies between problem variables, resulting in excellent performance and scalability on both benchmark and real-world problems that allow for partial evaluations. However, linkage models proposed for RV-GOMEA so far cannot explicitly capture overlapping dependencies. Consequently, performance degrades if such dependencies exist. In this paper, we therefore introduce various ways of using conditional linkage models in RV-GOMEA. Their use is compared to that of non-conditional models, and to VkD-CMA, whose performance is among the state of the art, on various benchmark problems with overlapping dependencies. We find that RV-GOMEA with conditional linkage models achieves the best scalability on most problems, with conditional models leading to similar or better performance than non-conditional models. We conclude that the introduction of conditional linkage models to RV-GOMEA is an important contribution, as it expands the set of problems for which optimization in a GBO setting results in substantially improved performance and scalability. In future work, conditional linkage models may prove to benefit the optimization of real-world problems.
Entropy of Fourier coefficients of periodic musical objects

There are many ways to define and measure organization, or complexity, in music, most using the notion of informational entropy, as the opposite of organization. Some researchers prompted me to study whether it could be done from the magnitudes of Fourier coefficients of musical objects (pc-sets or rhythms) instead of addressing their atomic elements (pitches, rhythmic onsets). Indeed I found that it could be a promising new approach to measuring organization of musical material. This note only purports to expose this novel idea, leaving for future research the task of comparing it with the numerous other definitions. I also sketch the study of one relevant basis for such comparisons which has been little explored, the asymptotics of entropy of arithmetic sequences modulo n.
Investigating the Benefits of Haptic Feedback During In-Car Interactions in Virtual Reality

Virtual Reality (VR) offers many advantages within the product development process. One of the most valuable is the opportunity to test, visualize and evaluate virtual concepts and prototypes in an early state of the development process. Furthermore, components like usability and user experience can be tested and evaluated faster and with more iterations before a physical prototype is set up. One factor to measure the quality of a virtual environment is the feeling of presence. A high feeling of presence will lead to realistic responses from the user. Contradictory, a lack of realistic sensory perception will lower the feeling of presence and will therefore influence usability and user experience ratings. Hence, haptic feedback is indispensable for virtual user evaluations. The present study focused on a novel glove for tactile and force feedback from the company HaptX. In this study forty-five participants interacted with a virtual car interior in two conditions: high sophisticated visual feedback and visuo-haptic feedback. The subjects used different types of car controls like buttons, levers or rotary switches. After each condition, the participants answered questionnaires with items regarding realism, grasping behavior and feedback quality. The results show that visuo-haptic feedback brings important advantages to virtual interactions even when there is a highly sophisticated visual feedback. Participants were able to recognize interactions much faster and precisely with visuo-haptic feedback. However, the rating of realism failed to reach significance. The results highlight the importance of haptic feedback for interactions within virtual environments. In general, this novel method for haptic feedback improves the interaction with a virtual car interior, even when using a highly sophisticated visual feedback.
Data from Model: Extracting Data from Non-robust and Robust Models.

The essence of deep learning is to exploit data to train a deep neural network (DNN) model. This work explores the reverse process of generating data from a model, attempting to reveal the relationship between the data and the model. We repeat the process of Data to Model (DtM) and Data from Model (DfM) in sequence and explore the loss of feature mapping information by measuring the accuracy drop on the original validation dataset. We perform this experiment for both a non-robust and robust origin model. Our results show that the accuracy drop is limited even after multiple sequences of DtM and DfM, especially for robust models. The success of this cycling transformation can be attributed to the shared feature mapping existing in data and model. Using the same data, we observe that different DtM processes result in models having different features, especially for different network architecture families, even though they achieve comparable performance.
The Current Situation of AI Foreign Language Education and Its Influence on College Japanese Teaching

With the rapid development of artificial intelligence (AI), the development of AI technology is bound to have an important impact on foreign language education. Therefore, under the foreign language teaching system, “AI + Education” has become a new trend of current research and can improve the teaching efficiency of college foreign language education. Therefore, AI has gradually become the focus of foreign language education. At present, AI mainly plays four roles in the world: learning partner, student, AI teacher and classroom teacher. However, through the development of “AI + Education”, Japanese education is not only applied in pre class, classroom, after class, extracurricular, examination and other teaching environments, but also brings profound changes to Japanese teaching. The purpose of this thesis is to analyze the current application of AI in foreign language teaching and its influence on College Japanese teaching, as well as the role-playing and future challenges. It can provide reference ideas and experience for College Japanese education in the development of AI teachers and the construction of dual teacher classroom. Its research methods are data search and induction, and in terms of the impact on Japanese teaching, questionnaire survey is used to analyze the relevance of its data. The results of the analysis are helpful to understand the application and current situation of students’ AI + education. Furthermore, effective improvement strategies will be provided, which will contribute to the development of foreign language teaching and Japanese education in the future.
Semiparametric Fractional Imputation Using Gaussian Mixture Models for Handling Multivariate Missing Data

Item nonresponse is frequently encountered in practice. Ignoring missing data can lose efficiency and lead to misleading inference. Fractional imputation is a frequentist approach of imputation for handling missing data. However, the parametric fractional imputation may be subject to bias under model misspecification. In this article, we propose a novel semiparametric fractional imputation (SFI) method using Gaussian mixture models. The proposed method is computationally efficient and leads to robust estimation. The proposed method is further extended to incorporate the categorical auxiliary information. The asymptotic model consistency and n-consistency of the SFI estimator are also established. Some simulation studies are presented to check the finite sample performance of the proposed method. Supplementary materials for this article are available online.