Reviewerly/exHarmony huggingface dataset:
Improving Automated Hyperparameter Optimization with Case-Based Reasoning

The hyperparameter configuration of machine learning models has a great influence on their performance. These hyperparameters are often set either manually w. r. t. to the experience of an expert or by an Automated Hyperparameter Optimization (HPO) method. However, integrating experience knowledge into HPO methods is challenging. Therefore, we propose the approach HypOCBR (Hyperparameter Optimization with Case-Based Reasoning) that uses Case-Based Reasoning (CBR) to improve the optimization of hyperparameters. HypOCBR is used as an addition to HPO methods and builds up a case base of sampled hyperparameter vectors with their loss values. The case base is then used to retrieve hyperparameter vectors given a query vector and to make decisions whether to proceed trialing with this query or abort and sample another vector. The experimental evaluation investigates the suitability of HypOCBR for two deep learning setups of varying complexity. It shows its potential to improve the optimization results, especially in complex scenarios with limited optimization time.
TREAM: A Tool for Evaluating Error Resilience of Tree-Based Models Using Approximate Memory

Approximate memories reduce the resource demand of machine learning (ML) systems at the cost of bit errors. ML models have an intrinsic error resilience and are therefore suitable candidates to use with approximate memories. Although the error resilience of neural networks has been considered in many studies, tree-based applications have received less attention. In addition, there is no tool available to specifically evaluate the error resilience of tree-based models. In this work, we present TREAM, a general tool built upon the sklearn framework for injecting bit flips during the inference of tree-based models. TREAM is capable of injecting bit flips into the tree and input parameters, i.e. feature and split values, in addition to feature and children indices. It can also be used for both floating point and integer values. Furthermore, we provide an abstract assessment of bit flip injection into the aforementioned parameters. Based on this, we construct a set of experiments using TREAM for different random forest structures and datasets over a set of bit error rates, where the relation between accuracy and bit error rate is considered for error resilience. The results demonstrate that child indices have the highest deviations in accuracy under bit errors. Moreover, the results give us insights into how varying numbers of trees, depth and different datasets affect the resilience.
Exploring the Effect of Recipe Representation on Critique-Based Conversational Recommendation

Diet diversification can facilitate both positive health outcomes and greater enjoyment in food consumption. Case-based recommendation can play an important role to promote a more diverse diet by connecting users with more diverse options in meal planning. Our research investigates conversational CBR approaches to support greater diversity in recommendations for recipes and meal planning. This paper presents and evaluates a critique based conversational recommender approach to support diet diversification. The approach incorporates both (1) dynamic generation of diversity driven critique for conversational interaction, and (2) identification of key recipe features in contexts that promote greater diversity of results through dynamic critique. Our approach is evaluated using an initial offline simulation study, followed by a full online user study. Results show that meal diversity outcomes for users can be increased using dynamic critique, and that recipe representation plays an important role in the diversity of recommended recipes and has a direct impact on user choices and outcomes.
Character Education in the Poetry Collection of Kidung Cisadane by Rini Intama: A Study of Literary Reception

The method used in this study is descriptive qualitative. Data were collected by distributing questionnaires to 20 respondents. The objectives of this study were to determine the understanding and responses of readers, namely junior high school teachers in Pasar Rebo District about the collection of Kidung Cisadane poems by Rini Intama and to increase teachers' knowledge about literary assessment. From 704 data on the value of character education which is the result of the literary reception of the teachers to the collection of Kidung Cisadane poems, the results obtained are the religious aspect totaling 119 data or about 16.9%, the cultural aspect totaling 212 data or about 30.1%, the environmental aspect amounting to 203 data or approx. 28.8% and aspects of self-potential amounted to 170 data or about 24.1%. So, the dominant aspects of character education are cultural aspects, environmental aspects, self-potential aspects, and religious aspects.Keywords: Poetry, character education, literary reception.
Group anomaly detection in mobile app usages: A spatiotemporal convex hull methodology

Analysing mobile apps communications can unleash significant information on both the communication infrastructure state and the operations of mobile computing services. A wide variety of events can engender unusual mobile communication patterns possibly interesting for pervasive computing applications, e.g., in smart cities. For instance, local events, national events, and network outages can produce spatiotemporal load anomalies that could be taken into consideration by both mobile applications and infrastructure providers, especially with the emergence of edge computing frameworks where the two environments merge. Being able to detect and timely treat these anomalies is therefore a desirable feature for next-generation cellular and edge computing networks, with regards to security, network and application performance, and public safety. We focus on the detection of mobile access spatiotemporal anomalies by decomposing, aggregating and grouping cellular data usage features time series. We propose a methodology to detect first raw anomalies, and group them in a spatiotemporal convex hull, further refining the anomaly detection logic, with a novel algorithmic framework. We show how with the proposed framework we can unveil details about broad-category mobile events timeline, their spatiotemporal spreading, and their impacted apps. We apply our technique to extensive real-world data and open source our code. By linkage with ground-truth special events that happened in the observed period, we show how our methodology is able to detect them. We also evidence the existence of five main categories of anomalies, finely characterising them. Finally, we identify global patterns in the anomalies and assess their level of unpredictability, based on the nature of the impacted mobile applications.
Producing maximally entangled Bell type states in an efficient way for quantum teleportation

In quantum teleportation, the sender must interact her/his qubit to one of the qubits in a maximally entangled Bell/Bell-type state and as a result, a joint state is obtained. This joint state has to be rearranged in terms of the appropriate number of Bell/Bell-type states. Then the sender measures her/his qubits and sends the results via a classical channel to the receiver. According to the outcome of the measurement results, the receiver applies appropriate gates to the qubits in her/his share to recover the teleported state. Perhaps the most important step in this protocol is to be able to rewrite the joint state in terms of the appropriate number of Bell-type states. An efficient way to produce Bell-type states for quantum teleportation of an arbitrary n-qubit state is proposed. Quantum teleportation of an arbitrary three-qubit state is studied as an example.
Mitigating viewpoint sensitivity of self-supervised one-class classifiers

Recent studies on one-class classification have achieved a remarkable performance by employing the self-supervised classifier that predicts the type of pre-defined geometric transformations applied on in-class images. However, they cannot correctly identify in-class images as in-class at all when the input images have various viewpoints (e.g., translated or rotated images), because their classification-based in-class scores assume that in-class images always have a fixed viewpoint. Pointing out that humans can easily recognize such images having various viewpoints as the same class, in this work, we aim to propose a one-class classifier robust to geometrically-transformed inputs, named as GROC. To this end, we remark that in-class images match better with the in-class transformations than out-of-class images do. We introduce a conformity score indicating how strongly an input image agrees with one of the predefined in-class transformations, then utilize the conformity score with our proposed agreement measures for one-class classification. Our extensive experiments demonstrate that GROC is able to accurately distinguish in-class images from out-of-class images regardless of whether the inputs are geometrically-transformed or not, whereas the existing methods fail.
Distance learning opportunities of chemistry in a higher educational institution under a state of war conditions

Випуск 86 '2022 Серія 5. Педагогічні науки: реалії та перспективи Andriichuk V. V. Pedagogical conditions of preparation of future teachers for the formation of financial literacy of primary school studentsThe article reveals the peculiarities of preparing future teachers for the formation of financial literacy of primary school students in the context of educational reforms.The focus is on the legal framework, the State Standard of Primary Education.The content of recent research and publications on the problem is analyzed.Scientists emphasize the importance of financial literacy, its role in shaping the personality.The purpose of the article is to reveal the essence of pedagogical conditions for training future teachers to form financial literacy.The tasks to be solved included: a) defining the essence of the structure and content of the concepts of "pedagogical conditions", b) defining and justifying the pedagogical conditions for training future teachers to form financial literacy of primary school students.A content analysis of scientific works on the training of future primary school teachers, which provided an opportunity to demonstrate the need to identify pedagogical conditions in the training of future teachers in modern realities.Researchers' approaches to the separation of pedagogical conditions are clarified.The concept of "pedagogical condition" is considered as a system of certain forms, methods, real situations that are objectively formed or subjectively created and necessary to achieve a specific pedagogical goal.Based on the theoretical analysis of psychological and pedagogical literature on the research problem, the pedagogical conditions for the formation of financial literacy of primary school students were determined as a set of interrelated activities necessary to create a purposeful educational process using the latest learning technologies.It is concluded that financial literacy is an integral part of human social and life experience, the quality of which determines the effectiveness of protection of the rights of consumers of financial services and the development of Ukraine's economy.
Decomp2d: An R solution for image decomposition

The subjects of image processing include feature extraction, compression, denoising, image enhancement, and restoration. Many softwares have been developed for these studies. However, few studies have focused on decomposition in the literature, even though image decomposition is essential for image processing. This paper reviews several two-dimensional methods that can be adapted for image decomposition. Furthermore, we provide an R solution termed Decomp2d, tailored to the viewpoint of image decomposition based on existing R packages.
Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax

We show that both an LSTM and a unitary-evolution recurrent neural network (URN) can achieve encouraging accuracy on two types of syntactic patterns: context-free long distance agreement, and mildly context-sensitive cross serial dependencies. This work extends recent experiments on deeply nested context-free long distance dependencies, with similar results. URNs differ from LSTMs in that they avoid non-linear activation functions, and they apply matrix multiplication to word embeddings encoded as unitary matrices. This permits them to retain all information in the processing of an input string over arbitrary distances. It also causes them to satisfy strict compositionality. URNs constitute a significant advance in the search for explainable models in deep learning applied to NLP.
Digital Technology of Image Acquisition System of Microscope and Its Application in Digital Protection of Grottoes Art

Grottoes are the cultural heritage left by ancient people to modern people, and murals in grottoes can leave a lot of information. In order to further improve the relevant information of grottoes and meet the needs of various applications of images, this paper studies the application of digital technology of image acquisition system of microscope in the digital protection of grottoes art. In this study, the digital technology of the image acquisition system of the microscope is used to collect the photos of the grottoes, and then, the photos without the microscope image acquisition system are compared. At the same time, the electronic equipment is used to carry out local measurement and real scene shooting to supplement the scanning data, establish the grottoes database, and then carry out a questionnaire survey on the development of the popularity of grottoes art on the social network. The results of this study show that more than 90% of the people do not know much about the art of grottoes, but they have a positive attitude towards its value and interest. In addition, this study uses different splicing methods to get the panorama of the two grottoes, with the reduction degree of 80%, 89.4%, and 78%, respectively, which better shows the art of the panorama of the grottoes sculpture. It can be seen that the digital technology of the image acquisition system of the microscope can better collect the photos of the grottoes, improve the accuracy and splicing accuracy of the photos, better restore the status quo of the grottoes, establish the art database of the grottoes, achieve the purpose of spreading and promoting the art and culture of the grottoes and digital protection of the grottoes, and inject a helping hand into the internationalization of the folk art of our country.
Generative Models over Neural Controllers for Transfer Learning

We introduce a technique that leverages the power of indirect encodings (IE) from the field of evolutionary computation to improve the speed of evolution in transfer learning control tasks. Although generative models have previously been used to construct IEs, their potential in transfer learning, specifically in reinforcement learning domains, has not yet been utilised. We train three types of generative models: an autoencoder (AE), a variational autoencoder (VAE) and a generative adversarial network (GAN) on the neural network weights of well-performing solutions of a set of paramaterised source domains. The decoder of the AE and VAE or the generator of the GAN is then used as the IE in an evolutionary run on unseen, but related, target domains. We compare against two baselines: a direct encoding (DE) and a DE starting evolution from a controller pre-trained to maximise the average fitness over the set of source domains. We show that, by using these IEs, the speed of learning on the target domains is greatly increased with respect to the baselines.
Generalization and Computation for Policy Classes of Generative Adversarial Imitation Learning

Generative adversarial imitation learning (GAIL) learns an optimal policy by expert demonstrations from the environment with unknown reward functions. Different from existing works that studied the generalization of reward function classes or discriminator classes, we focus on policy classes. This paper investigates the generalization and computation for policy classes of GAIL. Specifically, our contributions lie in: 1) We prove that the generalization is guaranteed in GAIL when the complexity of policy classes is properly controlled. 2) We provide an off-policy framework called the two-stage stochastic gradient (TSSG), which can efficiently solve GAIL based on the soft policy iteration and attain the sublinear convergence rate to a stationary solution. The comprehensive numerical simulations are illustrated in MuJoCo environments.
On the Impact of the Duration of Evaluation Episodes on the Evolution of Adaptive Robots

We investigate the impact of the duration of evaluation episodes and of the way in which the duration is varied during the course of the evolutionary process in evolving robots. The results obtained demonstrates that these factors can have drastic effects on the performance of the evolving robots and on the characteristics of the evolved behaviors. Indeed, the duration of the evaluation episodes do not alter simply the accuracy of the fitness estimation but also the quality of the estimation. The comparison of the results indicates that the best results are obtained by starting with short evaluation episodes and by increasing their duration during the course of the evolutionary process, or by using shorter evaluation episodes during the first part of the evolutionary process.
Attention-Based Genetic Algorithm for Adversarial Attack in Natural Language Processing

Many recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial examples. Adversarial attacks on DNNs for natural language processing tasks are notoriously more challenging than that in computer vision. This paper proposes an attention-based genetic algorithm (dubbed AGA) for generating adversarial examples under a black-box setting. In particular, the attention mechanism helps identify the relatively more important words in a given text. Based on this information, bespoke crossover and mutation operators are developed to navigate AGA to focus on exploiting relatively more important words thus leading to a save of computational resources. Experiments on three widely used datasets demonstrate that AGA achieves a higher success rate with less than $$48\%$$ of the number of queries than the peer algorithms. In addition, the underlying DNN can become more robust by using the adversarial examples obtained by AGA for adversarial training.
Robust Neural Network Pruning by Cooperative Coevolution

Convolutional neural networks have achieved success in various tasks, but often lack compactness and robustness, which are, however, required under resource-constrained and safety-critical environments. Previous works mainly focused on enhancing either compactness or robustness of neural networks, such as network pruning and adversarial training. Robust neural network pruning aims to reduce computational cost while preserving both accuracy and robustness of a network. Existing robust pruning works usually require expert experiences and trial-and-error to design proper pruning criteria or auxiliary modules, limiting their applications. Meanwhile, evolutionary algorithms (EAs) have been used to prune neural networks automatically, achieving impressive results but without considering the robustness. In this paper, we propose a novel robust pruning method CCRP by cooperative coevolution. Specifically, robust pruning is formulated as a three-objective optimization problem that optimizes accuracy, robustness and compactness simultaneously, and solved by a cooperative coevolution pruning framework, which prunes filters in each layer by EAs separately. The experiments on CIFAR-10 and SVHN show that CCRP can achieve comparable performance with state-of-the-art methods.
Vision occlusion solution for line-structured light measurement system based on a plane mirror

To solve the problem of the measurement information loss caused by occlusion of the field of view in line-structured light measurement, a vision occlusion solution of the line-structured light measurement system based on a plane mirror is proposed in this work. In the system, a symmetrical structure between real and virtual images was created by the mirror. A checkerboard with a light stripe and its mirror image were captured by the camera in an image. The centers of the light stripe and its mirror image were corrected by the vanishing point constraint, and a calibration method of the free plane target was used to calibrate the real and virtual light planes. The real and virtual point clouds of the object were obtained by scanning with a linear guide. According to the ideal reflection model of the mirror, the real and virtual point clouds of the object were transformed into a common coordinate system, and an iterative closest point (ICP) algorithm was used to realize fine registration. As a result, the occluded parts can be compensated for each other. Compared with a standard ball, the mean/maximum absolute error of the measurement was 0.2553/0.3276 mm. The experimental results revealed that the proposed method can obtain the complete surface of the object, thus effectively solving the occlusion problem in the line-structured light measurement, and reducing the loss of the measurement information, which can meet the industrial measurement requirements.
Feature Clustering of Noisy Data and Application in the Currency Market

With the increase in high-dimensional data, researchers pay more attention to dimensionality reduction techniques because there are many noisy, redundant and irrelevant features in high-dimensional data. The existence of noisy features leads to decrease performance when analyzing high-dimensional data. Also, unsupervised dimensionality reduction techniques are widely used due to the lack of available labels. Feature clustering is an unsupervised dimensionality reduction technique to partition features into clusters in which features are strongly related. In addition, the Pearson correlation coefficient is widely used as a similarity tool for feature clustering. However, the Pearson correlation coefficient is easily influenced by outliers and noises, thus leading to misleading results. This paper focuses on the influence of dissimilarity measures on the clustering of noisy features. Heavy-tailed distributions are used for modeling data with outliers and noises. Therefore, we introduce a new dissimilarity measure based on a new dependence coefficient of heavy-tailed distributions. The performance of feature clustering using the proposed dissimilarity is evaluated using ARI and internal criteria on artificial and real currency market datasets. Experiment results have demonstrated the effectiveness of the proposed feature clustering method.