Reviewerly/exHarmony huggingface dataset:
A Pilot Study of the Efficiency of LSTM-Based Motion Classification Algorithms Using a Single Accelerometer

Inertial sensors are widely used for classifying the motions of daily activities. Although hierarchical classification algorithms were commonly used for defined motions, deep-learning models have been used recently to classify a greater diversity of motions. In addition, ongoing studies are actively investigating algorithm efficiency (e.g., training time and accuracy). Thus, a deep-learning model was constructed in this study for the classification of a given motion based on the raw data of inertial sensors. Furthermore, the number of epochs (150, 300, 500, 750, and 900) and hidden units (100, 150, and 200) were varied in the model to determine its efficiency based on training time and accuracy, and the optimum accuracy and training time was determined. Using a basic long short-term memory (LSTM), which is a neural network known to be suitable for sequential data, the data classification training was conducted on a common desktop PC with typical specifications. The results show that the accuracy was the highest (99.82%) with 150 hidden units and 300 epochs, while the training time was also relatively short (78.15 min). In addition, the model accuracy did not always increase even when the model complexity was increased (by increasing the number of epochs and hidden units) and the training time increased as a consequence. Hence, through suitable combinations of the two factors that constitute deep-learning models according to the data, the potential development and use of efficient models have been verified. From the perspective of training optimization, this study is significant in having determined the importance of the conditions for hidden units and epochs that are suitable for the given data and the adverse effects of overtraining.
Double-image compression and encryption scheme based on chaotic system and real-preserving multi-order discrete fractional Fourier transform

In order to reduce the amount of data transmission and key consumption, a double-image compression and encryption scheme based on chaotic system and real-preserving multi-order discrete fractional Fourier transform (RPMODFrFT) is proposed in this paper. It can compress and encrypt two images simultaneously, and the measurement matrix of compression sensing is controlled by 2D Logistic-Sine-Coupling map (2D-LSCM). After consolidating two compressed images into one image, the image is further encrypted by scrambling and 2D-LSCM controlled RPMODFrFT operation. At last, the bitwise XOR operation using 2D-LSCM with the initial value related to the plain image is carried out. The simulation results show that the proposed encryption-compression scheme has high security and robustness through a lot of attack analysis, i.e. differential attack analysis (NPCR > 99.6022% and UACI >33.5658%), entropy values (average 7.99710), statistical attack (correlation coefficient values are almost 0), noise attack and cropping attack.
Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning

Aiming at the scarcity of Low Earth Orbit (LEO) satellite spectrum resources, this paper proposes an algorithm of interference signal feature extraction and pattern classification based on deep learning to further improve the stability of satellite–ground communication links. The algorithm can successfully predict the interference signal pattern, start–stop time, frequency change range and other parameters, and has the advantages of excellent interference detection performance, high detection accuracy and small parameter prediction error, etc. It can be applied in the field of channel monitoring of communication satellite-to-ground communication links, and realize the repeated and efficient utilization of spectrum resources. Experiments show that the precision and recall of the algorithm for detecting five kinds of interference signals are all close to 100%, the prediction error of starting and ending time is less than 4 ms, and the prediction error of starting and ending frequency is less than 6 KHz.
ON ZARDINI’S RULES FOR MULTIPLICATIVE QUANTIFICATION AS THE SOURCE OF CONTRA(DI)CTIONS

Abstract Certain instances of contraction are provable in Zardini’s system $\mathbf {IK}^\omega $ which causes triviality once a truth predicate and suitable fixed points are available.
Spatial–temporal phase demodulation decoding superimposed ghost reflections in optical testing

We introduce a method of phase demodulation to distinguish the ghost reflection commonly observed in optical testing. The method digitally processes a sequence of fringe patterns to separate the reflections from the front and rear surfaces. First, we use a phase-shifting algorithm to compute an analytic signal with the two reflections coupled, and then we decouple them through a maximized spatial filtering. The process requires the acquisition of at least three fringe patterns, as well as fulfilling opposite signs for the magnifications of the two reflections. We demonstrate the decoding process through a real phase measuring deflectometry data processing example.
Construction and Optimization of Artificial Intelligence-Assisted Interactive College Music Performance Teaching System

This research focuses on the relationship between artificial intelligence technology and music performance in colleges and universities and explores the application of artificial intelligence technology in music teaching. At the same time, it explores the deficiencies of existing computer-assisted language learning software and systems through questionnaires and interviews and also proposes solutions, such as further understanding of theories, enhancing learners’ confidence, and stimulating their interest in learning. The results of this research will help to improve the application of online intelligent human-computer systems in music teaching and provide dynamic support for improving the quality of music teaching and talent training in colleges and universities.
Analysis of University Education Management Based on Artificial Intelligence

With the rapid development of information technology, the process of informatization of education management has been accelerated. In this context, more and more education management information systems have been used in education management, providing a lot of data support for education decision-making. In addition, the development of artificial intelligence has greatly changed the way people work and live. Intelligence has emerged in various fields, bringing great convenience to people, especially the university education management. This study will integrate artificial intelligence and university classroom teaching and apply it in the field of education management. In particular, the proposed intelligent education management system mainly includes three submodules: preclass attendance, in-class state monitoring, and after-class online learning. The main function of the preclass attendance module is that half an hour before the class starts, the camera captures students’ video information and sends it back to the convolutional neural network (CNN) model for face recognition processing. In class, the state detection module is mainly based on face recognition to judge the state of students. The after-class module analyzes the evaluation information of students’ online learning to provide a teaching reference for the school. The system proposed in this study can improve the quality of students’ classroom learning and teachers’ monitoring of the quality of students’ classroom.
Computational Technologies for IoT Networks Application in English Classroom Teaching

The purpose of this paper is to combine digital sensing technology with English character recognition in order to improve its overall effectiveness. In addition, this paper will comprehensively analyze the functional requirements of the digital system in teaching management and will clarify the functional goals that the digital system of teaching management needs to achieve. In addition, this study does an in-depth analysis of the many functions of the system and categorizes each of those functions into a number of distinct business operations. In addition, the English classroom teaching mode is improved with the help of machine learning, deep learning, and digital technology. In addition, an English classroom teaching system that is based on IoT Networks technology is constructed in this study. In addition to this, the study investigates the algorithmic flow of the functional structure modules of the system, develops each functional module of the system in detail, and elucidates the software design schemes at all levels connected to each functional module. In conclusion, the experimental research presented in this work validates the efficiency of the algorithm model presented in this paper. The findings of the study indicate that the digital English teaching system constructed in this work is effective, which supports the findings of the study.
Quantum technology and human rights: an agenda for collaboration*

Abstract Quantum technologies have the potential to transform many aspects of our lives as they come into wider use, but how do we ensure that these technologies promote human rights instead of harming them? This article will draw lessons from our collective experience in managing the human rights impacts of internet technologies to help the quantum science and technology community build and develop technologies that respect and promote the fundamental rights of all people.
International Chinese Education Expert System Based on Artificial Intelligence and Machine Learning Algorithms

This study builds an international Chinese education expert system based on artificial intelligence and machine learning algorithms, introduces interval intuition fuzzy sets to express expert evaluation information, and uses the entropy weight method to determine the weight of evaluation attributes in order to improve the effect of international Chinese teaching and learning. The group utility value, personal regret value, and comprehensive evaluation value of each system are then calculated in this study. At the same time, this study introduces the degree of closeness and satisfaction to improve the decision-making process and finally determines the optimal solution. In addition, this study constructs an intelligent system based on the improved algorithm. The research shows that the international Chinese education expert system based on artificial intelligence and machine learning algorithm proposed in this study has a very good effect.
Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data

Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose an efficient deep lossless compression algorithm, which uses arithmetic coding to quantify the network output. This scheme compares the training effects of Bi-directional Long Short-Term Memory (Bi-LSTM) and Transformers on minute-level power data that are not sparse in the time-frequency domain. The model can automatically extract features and adapt to the quantification of the probability distribution. The results of minute-level power data show that the average compression ratio (CR) is 4.06, which has a higher compression ratio than the classical entropy coding method.
Institutional repositories and knowledge organization: A bibliographic study from Library and Information Science

This research presents an exploratory and descriptive study on the use of knowledge organization processes and systems in the context of repositories, published in journals indexed in databases between 2015 and 2020. The authors of these papers do not necessarily publish in specific events and journals in the Knowledge Organization area, but rather in the Library and Information Science arena. The study has been carried out in four steps: 1. Search, retrieval and selection of articles; 2. Development of a data codebook; 3. Identification and codification of topics; and, 4. Analysis of the data extracted. A final sample of 33 articles was defined. The methodology applied to determine the theme of journal articles is presented in detail, including the use of the Classification System for Knowledge Organization Literature (CSKOL). The illustrative data of disciplinarity and interdisciplinarity present in the articles object of this study are shown and discussed, regarding the predominance of certain ranges of CSKOL and regarding the diversity of the representative themes of the contents. It is possible to conclude that the use of CSKOL proves to be a suitable lens for analysing and understanding the literature on the field of knowledge organization in institutional repositories. It is shown that, in these 33 articles the themes of knowledge organization are combined with interdisciplinary themes from other areas of knowledge. In our opinion, this enriches and improves theoretical support for research development.
The pole balancing problem from the viewpoint of system flexibility

Whereas evolutionary computation usually solves problems from scratch, organisms evolve under changing environments and possess flexibility, adapting from being good at one task to being good at a related task. There is abundant evidence that there are general properties that promote flexibility in nature, such as hierarchy, modularity, exploratory behavior, and degeneracys or neutrality.
Dynamic perturbation for population diversity management in differential evolution

The performance of Differential Evolution (DE) is closely related to the population diversity since its mechanism of generating offspring depends wholly on the differences between individuals. This paper presents a simple perturbation technique to maintain the population diversity in which the noise intensity is adjusted dynamically during the search. A modification of the well-known L-SHADE adaptation method is also introduced to manipulate the convergence behaviour of DE. By incorporating these techniques, we develop a new variant of DE called S-LSHADE-DP. Experiment results conducted on the benchmark suite of CEC '22 competition show that S-LSHADE-DP is highly competitive with current state-of-the-art DE-based algorithms. The implementation of S-LSHADE-DP is available at https://github.com/cuonglvsoict/S-LSHADE-DP.
Genetic programming with external memory in sequence recall tasks

Partially observable tasks imply that a learning agent has to recall previous state in order to make a decision in the present. Recent research with neural networks have investigated both internal and external memory mechanisms for this purpose, as well as proposing benchmarks to measure their effectiveness. These developments motivate our investigation using genetic programming and an external linked list memory model. A thorough empirical evaluation using a scalable sequence recall benchmark establishes the underlying strength of the approach. In addition, we assess the impact of decisions made regarding the instruction set and characterize the sensitivity to noise / obfuscation in the definition of the benchmarks. Compared to neural solutions to these benchmarks, GP extends the state-of-the-art to greater task depths than previously possible.
KDE-GAN

Generative Adversarial Networks (GAN) is a powerful algorithm to reconstruct artificial data that is similar to a set of given data. Evolutionary GAN (E-GAN) is a state-of-the-art variant of GAN. E-GAN combines evolutionary elements such as mutation, fitness evaluation and selection to address vanishing gradient and mode collapse problems of classic GAN. This paper further improves E-GAN by proposing Knowledge Distillation and Knowledge Transfer. The new method, known as Knowledge Distillation E-GAN (KDE-GAN), incorporates the student-teacher architecture and fine-tuning of transfer learning to help the evolutionary GAN training process. Our experiments on benchmark data sets show that KDE-GAN can improve performance and efficiency. Knowledge distillation and knowledge transfer can indeed accelerate the learning process yet reach better FIDs (Frechet Inception Distance).
Phenotypic duplication and inversion in cartesian genetic programming applied to boolean function learning

The search performance of Cartesian Genetic Programming (CGP) relies to a large extent on the sole use of genotypic point mutation in combination with extremely large redundant genotypes. Over the last years, steps have been taken to extend CGP's variation mechanisms by the introduction of advanced methods for recombination and mutation. One branch of these contributions addresses phenotypic variation in CGP. However, recent studies have demonstrated the limitations of phenotypic recombination in Boolean function learning and highlighted the effectiveness of the mutation-only approach. Therefore, in this work, we further explore phenotypic mutation in CGP by the introduction and evaluation of two phenotypic mutation operators that are inspired by chromosomal rearrangement. Our initial findings show that the proposed methods can significantly improve the search performance of CGP on various single- and multiple-output Boolean function benchmarks.
Regulatory genotype-to-phenotype mappings improve evolvability in genetic programming

Most genotype-to-phenotype mappings in EAs are redundant, i.e., multiple genotypes can map to the same phenotype. Phenotypes are accessible from one to another through point mutations. However, these mutational connections can be unevenly distributed among phenotypes. Quantitative analysis of such connections helps better characterize the robustness and evolvability of an EA. In this study, we propose two genotype-to-phenotype mapping mechanisms for linear genetic programming (LGP), where the execution and output of a linear genetic program are varied by a regulator. We investigate how such regulatory mappings can alter the genotypic connections among different phenotypes and the robustness and evolvability of phenotypes. We also compare the search ability of LGP using the conventional mapping versus the regulatory mappings, and observe that the regulatory mappings improve the efficiency in all three search scenarios, including random walk, hill climbing, and novelty search.
Automated algorithm configuration and design

tutorial Share on Automated algorithm configuration and design Authors: Thomas Stützle ULB, Brussels, Belgium ULB, Brussels, BelgiumView Profile , Manuel López-Ibáñez University of Malaga University of MalagaView Profile , Leslie Pérez-Cáceres Pontifical Catholic University of Valparaíso Pontifical Catholic University of ValparaísoView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 997–1019https://doi.org/10.1145/3520304.3533663Published:19 July 2022Publication History 0citation43DownloadsMetricsTotal Citations0Total Downloads43Last 12 Months43Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Probabilistic reasoning as quadratic unconstrained binary optimization

Probabilistic reasoning is an important tool for using uncertainty in AI, especially for automated reasoning. Partial probability assessments are a way of expressing partial probabilistic knowledge on a set of events. These assessments contain only the information about "interesting" events (hence it can be easily assessed by an expert). On the other hand, partial assessments can cause consistency problems. In this paper we show how to formulate the main tasks of probabilistic reasoning on partial probability assessments, namely check of coherence, correction, and inference, as QUBO problems. This transformation allows to solve these problems with a quantum or a digital annealer and thus providing new computational methods to perform these tasks.
Boosting the convergence of a GA-based wrapper for feature selection problems on high-dimensional data

High-dimensional data often need techniques, such as Feature Selection (FS), in order to solve the curse of dimensionality problem. One of the most popular approaches to FS is wrapper methods, which are based on a search algorithm and a clustering technique. NSGA-II and k-NN are applied in this paper. Since NSGA-II is intended to converge to a small subset of features, the generation of the initial population is crucial to speed up the search process. The lower number of features selected by the individuals belonging to the initial population, the lower number of generations needed to achieve convergence. This work presents a novel technique to reduce the average size (number of features selected) of the individuals forming the initial population. This technique is based on a hyper-parameter, p, which controls the probability of any feature being selected by any individual of the initial population. An analysis of both convergence time and classification accuracy is performed for different values of p, concluding that p can be set to quite low values, accelerating markedly the convergence of the algorithm without affecting the quality of the solutions.
Going faster and hence further with lexicase selection

Lexicase selection is a semantic-aware parent selection method, which has demonstrated success in multiple research areas including genetic programming, symbolic regression, and recently deep learning. One potential drawback of lexicase selection and its variants is that the selection procedure requires evaluating training cases in a single data stream, making it difficult to handle tasks where the evaluation is computationally heavy or the dataset is large-scale. In this work, we investigate how the weighted shuffle methods can be employed to improve the efficiency of lexicase selection. We propose fast lexicase selection, a method that incorporates lexicase selection and weighted shuffle with partial evaluation. Experiments on both classic genetic programming and deep learning tasks demonstrate that the proposed method has superior efficiency with a significantly reduced number of evaluation steps during selection, as well as sustains equivalent performance.
A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models

The goal of Quality Diversity Optimization is to generate a collection of diverse yet high-performing solutions to a given problem at hand. Typical benchmark problems are, for example, finding a repertoire of robot arm configurations or a collection of game playing strategies. In this paper, we propose a set of Quality Diversity Optimization problems that tackle hyperparameter optimization of machine learning models - a so far underexplored application of Quality Diversity Optimization. Our benchmark problems involve novel feature functions, such as interpretability or resource usage of models. To allow for fast and efficient benchmarking, we build upon YAHPO Gym, a recently proposed open source benchmarking suite for hyperparameter optimization that makes use of high performing surrogate models and returns these surrogate model predictions instead of evaluating the true expensive black box function. We present results of an initial experimental study comparing different Quality Diversity optimizers on our benchmark problems. Furthermore, we discuss future directions and challenges of Quality Diversity Optimization in the context of hyperparameter optimization.