Reviewerly/exHarmony huggingface dataset:
Compositional genetic programming for symbolic regression

In genetic programming, candidate solutions are compositional structures that can be easily decomposed into constituent parts and assembled from them. This property is extensively used in search operators, but rarely exploited in other stages of evolutionary search. We propose an approach to symbolic regression that augments the search state by maintaining, apart from the population of candidate solutions, a library of subprograms and a library of program contexts, i.e. partial programs that need to be supplemented by a subprogram to form a complete program. This allows us to identify the promising program components and guide search using two mechanisms in parallel: the conventional fitness-based selection pressure, and matching contexts with subprograms using a gradient-based mechanism. In experimental assessment, the approach significantly outperforms the control setups and the conventional GP. Maintaining subprograms and contexts in efficient data structures prevents redundancy and lessens the demand for computational resources, in particular memory.
Discovery of implicit relationships from data using linear programming and mixed integer linear programming

Symbolic regression is widely used to discover underlying relationships within observed data in the form of equations. Most studies in the existing literature have focused on explicit equations, i.e., y = f (x). However, in a number of scenarios, such as in complex manifolds and partial differential equations, the relationship among the variables may conform to implicit equation, i.e., f (x) = 0. In this paper, we are interested in uncovering implicit equations that have the form of a general linear model. In particular, we construct improvised formulations of linear programming and mixed-integer linear programming to uncover implicit equations while avoiding trivial solutions. The strengths and limitations of both these approaches are assessed on 23 simulated benchmarks. The results are promising and establish the suitability of the proposed approach for integration with genetic programming frameworks to expedite the search for accurate implicit expressions.
DEvS

The development of machine learning solutions often relies on training using large labeled datasets. This raises challenges in terms of data storage, data privacy protection, and longer model training time. One of the possible solutions to overcome these problems is called dataset distillation - a process of creating a smaller dataset while maximizing the preservation of its task-related information. In this paper, a new dataset distillation algorithm is proposed, called DEvS, which uses an evolutionary strategy approach to condense the training samples initially available for an image classification task, while minimizing the loss of classification accuracy. Experiments on CIFAR-10 demonstrate the competitiveness of the proposed approach. Also, contrary to recent trends, DEvS is derivative-free image generation, and therefore has greater scalability on larger input image sizes.
Human activity recognition using grammar-based genetic programming

Smart devices provide a way of acquiring useful data for human activity recognition (HAR). The identification of activities is a task applicable to a wide range of situations, such as automatically providing aid to someone in need. Machine learning techniques can solve this problem, but their capacity in providing understanding regarding the classification is usually limited. Here, we propose a Grammar-based Genetic Programming (GGP) to generate interpretable models for HAR. A Context-free Grammar defines a language that the models belong to, providing a way to read and extract knowledge. The results show that the proposed GGP generates results better than another Genetic Programming method and machine learning approaches. Also, the models created provided an understanding of the features associated with the activities.
Evaluation of inverse selection operators on maximum flow test generation problem

The choice of selection operators in evolutionary algorithms can greatly affect their performance, as it can be hard to balance maintaining population diversity with providing enough selection pressure. Recently Corus et al. have shown that reverse tournaments of fixed size can be a good default choice for steady-state evolutionary algorithms.
Representations for evolutionary algorithms

tutorial Share on Representations for evolutionary algorithms Author: Franz Rothlauf Johannes Gutenberg-Universität Mainz, Mainz, Germany Johannes Gutenberg-Universität Mainz, Mainz, GermanyView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 1046–1066https://doi.org/10.1145/3520304.3533661Published:19 July 2022Publication History 0citation32DownloadsMetricsTotal Citations0Total Downloads32Last 12 Months32Last 6 weeks1 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
Using domain knowledge in coevolution and reinforcement learning to simulate a logistics enterprise

We demonstrate a framework (CoEv-Soar-RL) for a logistics enterprise to improve readiness, sustainment, and reduce operational risk. The CoEv-Soar-RL uses reinforcement learning and coevolutionary algorithms to improve the functions of a logistics enterprise value chain. We address: (1) holistic prediction, optimization, and simulation for the logistics enterprise readiness; (2) the uncertainty and lack of data which require large-scale systematic what-if scenarios to simulate potential new and unknown situations. In this paper, we perform four experiments to investigate how to integrate prediction and simulation to modify a logistics enterprise's demand models and generate synthetic data based. We use general domain knowledge to design simple operators for the coevolutionary search algorithm that provide realistic solutions for the simulation of the logistic enterprise. In addition, to evaluate generated solutions we learn a surrogate model of a logistic enterprise environment from historical data with Soar reinforcement learning. From our experiments we discover, and verify with subject matter experts, novel realistic solutions for the logistic enterprise. These novel solutions perform better than the historical data and where only found when we include knowledge derived from the historical data in the co-evolutionary search.
Privacy-utility trades in crowdsourced signal map obfuscation

Cellular providers and data aggregating companies crowdsource cellular signal strength measurements from user devices to generate signal maps, which can be used to improve network performance. Recognizing that this data collection may be at odds with growing awareness of privacy concerns, we consider obfuscating such data before the data leaves the mobile device. The goal is to increase privacy such that it is difficult to recover sensitive features from the obfuscated data (e.g. user ids and user whereabouts), while still allowing network providers to use the data for improving network services (i.e. create accurate signal maps). To examine this privacy-utility tradeoff, we identify privacy and utility metrics and threat models suited to signal strength measurements. We then obfuscate the measurements using several preeminent techniques, spanning differential privacy, generative adversarial privacy, and information-theoretic privacy techniques, in order to benchmark a variety of promising obfuscation approaches and provide guidance to real-world engineers who are tasked to build signal maps that protect privacy without hurting utility. Our evaluation results, based on multiple, diverse, real-world signal map datasets, demonstrate the feasibility of concurrently achieving adequate privacy and utility, with obfuscation strategies which use the structure and intended use of datasets in their design, and target average-case, rather than worst-case, guarantees.
Unsupervised learning of multi-task deep variational model

We propose a general deep variational model (reduced version, full version as well as the extension) via a comprehensive fusion approach in this paper. It is able to realize various image tasks in a completely unsupervised way without learning from samples. Technically, it can properly incorporate the CNN based deep image prior (DIP) architecture into the classic variational image processing models. The minimization problem solving strategy is transformed from iteratively minimizing the sub-problem for each variable to automatically minimizing the loss function by learning the generator network parameters. The proposed deep variational (DV) model contributes to the high order image edition and applications such as image restoration, inpainting, decomposition and texture segmentation. Experiments conducted have demonstrated significant advantages of the proposed deep variational model in comparison with several powerful techniques including variational methods and deep learning approaches.
LEARNING PACKAGE IN ARALING PANLIPUNAN GRADE 9 WITH INFOGRAPHICS

Learning package is a learning resources that is a big help not only to the students but also to the teachers.The learning package is a bunch of informative or learning materials coordinated for students.The method used in this study was descriptive method.This study aimed to develop a learning package in Araling Panlipunan 9 with infographics.Specifically, this study sought to answer the following objectives: 1. level of acceptability of learning package in Araling Panlipunan 9 with infographics in terms of parts with regards to: content, visual and knowledge; 2. level of acceptability of learning package in Araling Panlipunan 9 with infographics in terms of characteristics with regards to usefulness, accuracy, relevance and appeal to the target user; 3. significant difference between level of acceptability of learning package in Araling Panlipunan 9 with infographics in terms of parts and 4. significant difference between level of acceptability of learning package in Araling Panlipunan 9 with infographics in terms of characteristics.Based on the results and interpretation of data, the researcher found the acceptability of the learning package in Araling Panlipunan with infographics in terms of parts with regards to content, visual and knowledge are highly acceptable.On the other hand, the acceptability of the learning package in Araling Panlipunan with infographics in terms of characteristics with regards to usefulness, accuracy, relevance and appeal to the target user are also highly acceptable.The level of acceptability of the learning package in terms of parts with regards to content, visual and knowledge are not significantly different.Also, the level of acceptability of the learning package with infographics with regards to accuracy, relevance and appeal to the target user are not significantly different except the usefulness.The conclusions made from the study are: the significant difference between the acceptability of learning package in Araling Panlipunan 9 with infographics in terms of parts as rated by the group of respondents which predicts not significantly in terms of content, visual and knowledge.Therefore, the null hypothesis is accepted.The result of the analysis shows that acceptability of infographics in terms of characteristics as rated by the group of respondents respectively has no significant difference in terms of accuracy, relevance and appeal to the target user except for the usefulness.Therefore, the null hypothesis is partially rejected.For the recommendation, the researcher suggests that the learning package in Araling Panlipunan 9 with infographics is intended to be a supplemental learning material.
USING MAGIC TO TEACH COMPUTER PROGRAMMING

Magic can be used in project-based instruction to motivate students and provide a meaningful context for learning computer programming.This work describes several magic programs of the "Choose a Number" and "Pick a Card" varieties, making connections to underlying computing concepts.Magic tricks presented as demonstrations and programming assignments elicit wonder and captivate students' attention, so that students want to understand and replicate the work to show it to friends and family members.Capturing student interest and curiosity motivates them to learn the underlying programming concepts.Two "Choose a Number" programs are shown where the computer is able to identify a user's choice among many alternatives:1. Magic Letter: A table of numbers with associated random letters is displayed.Participants make choices and the computer identifies their chosen letter.2. Number Boxes: The user chooses a secret number, for example the age at which their business becomes successful and is sold.The user indicates which boxes displayed on the screen contain their age, and the computer then identifies their secret number.Two "Pick a Card" varieties are shown, where board pieces represent cards, the user selecting one of them that the computer later identifies:1. Secret Tile: A grid of black/white tiles are displayed, where the computer verifies which secret tile selected by a volunteer has been flipped.2. Twenty-one Card Magic: Twenty-one cards are selected from a shuffled deck and displayed face-up, with a volunteer choosing a secret card.After repeated stacking and re-dealing, the secret card is identified.These examples illustrate computing concepts and can be used in classroom explanations and programming assignments.Links are given to online playable versions, assignment descriptions, and magic-themed resources in computing.
Fluid boiling during high-grade gold deposition in an epithermal gold-telluride deposit, Guilaizhuang, China

Boiling is a well-established mechanism for precious metal mineralization in low-sulfidation epithermal veins, and is considered a key process in the formation of high-grade bonanza zones in those systems. In contrast, the potential role of fluid boiling in the formation of epithermal gold-telluride veins associated with alkalic porphyries has rarely been discussed. The Guilaizhuang gold-telluride deposit, Shandong Province, China, is an ideal location to investigate the possible role of fluid boiling because the deposit comprises several distinct styles of orebodies, ranging from finely-disseminated gold mineralization hosted in carbonate rocks at depth, through to very high-grade mineralized breccia zones at shallower levels. Here, we examine the relationships between ore-forming fluids and mineralization styles in this deposit, based on paragenesis, vein textures and fluid inclusions. In the deeper carbonate-replacement style orebodies, we found no evidence of fluid boiling and, as such, alternative mechanisms such as fluid mixing or fluid-rock reactions were likely occurring. In contrast, the shallower, high-grade breccias show clear evidence of fluid boiling, which we infer contributed to the formation of exceptional bonanza zones. Our results thus indicate that fluid boiling can play a key role in precious metal mineralization in gold-telluride deposits, analogous to the low-sulfidation epithermal deposits. Furthermore, our results illuminate how contrasting mechanisms of gold mineralization in these deposits (i.e., boiling versus non-boiling) can give rise to the diversity and distribution of gold grades and reserves within an epithermal gold-telluride system.
Relationship between cryptocurrency and rates

Virtual kriptovalyuta olan bitkin rəqəmsal formata malik olan, texniki olaraq blokçeyn kimi ifadə edilən əməliyyatları əhatə edən və mərkəzi pul sisteminə daxil olmayan valyutadır. Tədqiqatda kriptovalyuta Bitcoin ilə valyuta məzənnələri arasındakı əlaqəni ortaya çıxarmaq hədəflənir. ABŞ Dolları ilə Avro, Yapon Yeni, İngilis Funtu, Avstraliya Dolları, Kanada Dolları, İsveçrə Frankı, Yuan Renminbi və İsveç Kronu məzənnələri ilə Bitcoin məzənnəsi arasındakı əlaqə, 3.02.2016- 04.10.2020 tarixləri arasındakı gündəlik məzənnələrə əsaslanaraq, struktur Qreqori və Hansen kointeqrasiyasını və Qrencer səbəb-nəticə analizini pozur. Təhlil nəticəsində müəyyən edilib ki, BTC/USD məzənnəsində struktur fasilələri 2017-cİ ilin aprel və dekabr aylarında baş verib. Bundan əlavə, tədqiqatda valyuta məzənnələrinin zaman silsiləsi arasında uzunmüddətli kointeqrasiya əlaqəsi, CNY/USD məzənnəsi ilə BTC/USD məzənnəsi arasında isə birtərəfli müsbət səbəb əlaqəsi müəyyən edilmişdir. Açar sözlər: kriptovalyuta, bitcoin, valyuta məzənnəsi, struktur fasilə, zaman seriyasi analizi Gunay Samir Karimli Relationship between cryptocurrency and rates Abstract Bitcoin, a virtual and cryptocurrency, is a digital currency that encompasses transactions, technically referred to as blockchain, and is not part of the central monetary system. The study aims to uncover the link between the cryptocurrency Bitcoin and exchange rates. The relationship between the US dollar and the euro, the Japanese yen, the British pound, the Australian dollar, the Canadian dollar, the Swiss franc, the yuan renminbi and the Swedish krona and the Bitcoin exchange rate, based on the daily exchange rates between 3.02.2016 and 04.10.2020, and Grenzer violates cause-and-effect analysis. The analysis revealed that structural breaks in the BTC / USD exchange rate occurred in April and December 2017. In addition, the study identified a long-term cointegration relationship between exchange rates over time, and a one-way positive causal relationship between the CNY / USD exchange rate and the BTC / USD exchange rate. Key words: cryptocurrency, bitcoin, exchange rate, structural break, time series analysis
The Development of Mathematics E-Modules by Using Flip PDF Professional Software on Algebraic Forms of Material

The development The purpose of this research is to develop and produce Mathematics E-Modules by Using Flip PDF Professional Software on Algebraic Forms material. Research and Development model used ADDIE (Analysis, Design, Development, Implementation, Evaluation). The research subjects were class VII students, instrument validators, educational technology media validators and mathematics learning materials (lecturers and teachers). The object of research is the E-Module of Mathematics using Flip PDF Professional Software on Algebraic Forms material. The data collection instrument was in the form of a questionnaire, with qualitative and quantitative data analysis techniques. Based on the validity test, the E-Module Mathematics using Flip PDF Professional Software was declared very valid with a percentage level of validity of 87.65%, and the small group practicality test with a number of respondents of 10 students found that it was very practical with a practicality level percentage of 86.35%. The results of the study indicate that the e-module developed is suitable for use in the mathematics learning process in schools that have online facilities.
Electric vehicles rating from you tube comments using machine learning

Electric motors play the notable position inside the automobile industry. A swiftly growing technology that ambitions to lessen using fossil fuels in the automobile quarter. The electric vehicles produced in enormous scale in now a day. Now many organizations are liberating their electric powered motors and in destiny it will become the most famous one. It reduces the air pollutants and need of fossil fuels to run a vehicle is constantly reduced. The Charging cost is comparatively lower than regular vehicles. The electric vehicles run on Lithium-ion batteries which will reduce the air pollutants in an effective manner. But in producing the Lithium-Ion batteries also can cause pollution. Electric powered motor’s function which have fewer shifting parts and are therefore greater environmentally friendly. This challenge explains the people perspectives that the era of electric vehicles prompted. Perspectives and ideas that the humans think about electric powered motors via the remarks on YouTube may be explored. The positive and drawbacks of the electrical vehicles given by the people can be analysed. The peoples who work in industries can realize how electric powered cars creates an effect in surroundings. They percentage about their thoughts about electric powered motors through You tube comments.
Sentiment Analysis of Public Reaction towards Pakistan Democratic Movement

This paper performs the sentiment analysis on tweets and social media posts of general people of Pakistan about one of Pakistan’s political party i.e. Pakistan Democratic Movement (PDM). PDM is a political movement comprised of 11 political parties of Pakistan founded against the current government of Pakistan. This paper focus on analyzing the sentiments of common Pakistanis towards PDM. Sentiment analysis is also called opinion mining or text mining. It is a way to find out public opinion and their reaction towards a particular entity or a topic. In the proposed system, data is extracted from Facebook using instant data scraper, and also tweets from twitter were extracted using twitter API. The data was extracted based on the query: current situations in Pakistan i.e. Pakistan Democratic Movement. This paper focuses on mining social media comments written in different languages and mostly in English. After pre-processing, data is labelled manually using 5 emotions which are agree, disagree, neutral, sarcastic and angry. After labeling the data several algorithms are used like support vector machines, Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) to classify the tweets/ posts.
Hybrid Quantum Convolutional Neural Networks in TensorFlow Quantum

This paper presents a quantum convolutional neural network (QCNN) that classifies the happy and sad emotions from the fabled facial emotion recognition dataset (FER), FER-2013. The model uses the data re-uploading concept on the qubit(s) of the parameterized quantum circuits (PQCs) in the quantum convolutional layer of the hybrid QCNN model, which achieved a 70.25 % validation accuracy on the test set. Later, a model averaging ensemble approach was implemented which yielded results motivating further investigation.
BackboneAnalysis: Structured Insights into Compute Platforms from CNN Inference Latency

Customization of a convolutional neural network (CNN) to a specific compute platform involves finding an optimal pareto state between computational complexity of the CNN and resulting throughput in operations per second on the compute platform. However, existing inference performance benchmarks compare complete backbones that entail many differences between their CNN configurations, which do not provide insights in how fine-grade layer design choices affect this balance.We present BackboneAnalysis, a methodology for extracting structured insights into the trade-off for a chosen target compute platform. Within a one-factor-at-a-time analysis setup, CNN architectures are systematically varied and evaluated based on throughput and latency measurements irrespective of model accuracy. Thereby, we investigate the configuration factors input shape, batch size, kernel size and convolutional layer type.In our experiments, we deploy BackboneAnalysis on a Xavier iGPU and a Coral Edge TPU accelerator. The analysis reveals that the general assumption from optimal Roofline performance that higher operation density in CNNs leads to higher throughput does not always hold. These results highlight the importance for a neural network architect to be aware of platform-specific latency and throughput behavior in order to derive sensible configuration decisions for a custom CNN.