Reviewerly/exHarmony huggingface dataset:
Adaptive Population Differential Evolution with Dual Control Strategy for Large-Scale Global optimization Problems

In the greedy selection operator of differential evolution (DE), the trial solution will be selected into the new population only if it is better than the original target solution. Otherwise, the generated solution is simply eliminated. However, in most cases, these eliminated solutions may still be promising, and it will waste the computing resources to directly ignore them. Especially for the large-scale global optimization (LSGO) problems, it is important to make full use of all generated solutions and to enhance the population diversity in the limited fitness evaluation budget. To address this issue, an adaptive population DE, termed as APDE, is proposed with dual control strategy. Firstly, a population increasing (pop-inc) strategy is proposed for giving the opportunity to the generated trial solutions to survive in the population even though they are not good enough. Secondly, to avoid the gradual expansion of the population due to the pop-inc strategy, a population decreasing (pop-dec) strategy is proposed based on the “degradation value” designed for solutions. In the end of every iteration, if the degradation value of a solution is large, it represents the solution has a worse fitness value or has no improvement for a long time, and this solution will be deleted. In this way, the population size can be kept within a certain range. The test functions in CEC'2013 on LSGO are used to verify the performance of APDE. The experiment shows that APDE generally outperforms the original DE and two state-of-the-art LSGO algorithms.
Which random is the best random? A study on sampling methods in Fourier surrogate modeling

Global optimization problems can be effectively solved by means of Computational Intelligence methods. However, there are several areas in which the effectiveness of these algorithms can be hampered by the computational costs of the fitness evaluations, or by specific features of the fitness landscape that can be characterized by noise and by the presence of several (even infinite) local optima. These issues bring about the necessity of defining specific techniques to replace the original problem with a surrogate representation. Fourier surrogate modeling represents a novel and effective approach to generate smoother, and possibly easier to explore, fitness landscapes, and to reduce the computational effort. Fourier surrogates require an initial sampling of the search space that must be performed to calculate the Fourier transforms. In this paper we investigate the impact on the quality of the surrogate models of the hyper-parameters of the methodology, and of several methods that can be employed for the initial sampling of the fitness landscape (i.e., pseudorandom numbers, low discrepancy sequences, a logistic map in chaotic regime, true random positions generated by a quantum computer, and point packing). Our results show that semistructured approaches like quasi-random sequences and point packing can outperform the other sampling methods.
Pedagogical Evolved Art: An Examination and Results of the Innopolis Al Art Contest

Evolutionary Algorithms (EA) capacitate a myriad of possibilities and creativity. Therefore, while teaching evolutionary programming to students of Computer Science, we decided to equip them with the technical details and then let them explore the creative aspects themselves. As part of the introductory course on Artificial Intelligence, an evolutionary generation of art is an assignment The teaching goals were to inform students of creative aspects of EA as well as invoke critical thinking and analysis in them. The students generated images via EA, and the only restriction was on the pixel size for input. The assignment was well-received, despite its complexity. Students engaged with the art generation and felt confident in expressing their perspectives of creativity and art The assignment evaluation as a contest allowed for judges from diverse domains such as Computer scientists and artists. The goals of the course are met as the students were trained not only in technical details but also realized the capacity and generative power of algorithms, as they practically simulated and expressed their creative thoughts via this contest Furthermore, the students engaged with the philosophical foundations of computational creativity effectively by the design process, and a report completed along with the work required them to develop their own artistic exegesis of the algorithm.
Evolving Simpler Constructed Features for Clustering Problems with Genetic Programming

Clustering is a widely used unsupervised learning technique. However, as the size and complexity of data increases, the performance of clustering algorithms diminishes, as well as the interpretability of the clustering partition. Genetic programming has been used to perform feature construction on data to increase clustering performance. However, existing work has not focused on encouraging simpler constructed features. In this paper, existing techniques are further developed to include parsimony pressure-a method to encourage evolution towards simpler solutions. With simpler solutions, the constructed features become easier to understand and interpret. The results of experiments using the proposed method show that parsimony pressure is an effective method for producing significantly simpler constructed features without any reduction on the performance of k-means++clustering. Evolved individuals are also analysed to demonstrate the effect of parsimony pressure on interpretability, showing the power of parsimony pressure for avoiding redundancies in individuals, and thus increasing the interpretability.
Fast Consistency Auditing for Massive Industrial Data in Untrusted Cloud Services

Cloud service is an essential technique to provide product traceability for industrial systems by providing data integration and sharing services. However, a malicious or corrupted cloud service may prevent industrial participants from acquiring accurate and consistent traceability of products. To fix this issue, we propose Acics, a fast consistency auditing scheme for massive industrial data in untrusted cloud services. Our scheme enables industrial participants to circularly play the role of the auditor to audit data consistency of products in real-time. Additionally, we design a separated storage mechanism to improve the auditing efficiency for massive industrial data by utilizing and tailoring ORAM. The evaluation indicates that our solution audits data consistency with reasonable cost.
Improving an Optical Flow Estimator Inspired by Insect Biology using Adaptive Genetic Algorithms

Computer vision algorithms that make use of optical flow are constantly increasing in complexity, especially in the context of elaborated algorithms that are heavily inspired by biology. To develop upon and utilise these algorithms for realworld tasks, their extensive parameter sets need to be tuned. Due to algorithmic complexity, and non-linearities present throughout their parameter set, this is no small task. Using an adaptive genetic algorithm, which itself is biologically-inspired, we look at the performance and behaviour of the tuning when significant changes have been made to a low speed rotational velocity optical flow estimation algorithm. We validate that previously reported changes to the optical flow estimator yielded a fitness increase of over 30% when compared to the baseline model the changes were made to, and a 15% increase once that baseline model was also tuned. This improvement would be extremely unlikely without the aid of evolutionary computation algorithms. This shows that even an extremely complex computer vision algorithm with many parameters, 36 in this case, can be tuned to an operating point, facilitating the continued development work on the vision algorithm by allowing for the validation and quantification of algorithmic changes.
Query Optimization for Faster Deep CNN Explanations

Deep Convolutional Neural Networks (CNNs) now match human accuracy in many image prediction tasks, resulting in a growing adoption in e-commerce, radiology, and other domains. Naturally, "explaining" CNN predictions is a key concern for many users. Since the internal workings of CNNs are unintuitive for most users, occlusion-based explanations (OBE) are popular for understanding which parts of an image matter most for a prediction. One occludes a region of the image using a patch and moves it around to produce a heatmap of changes to the prediction probability. This approach is computationally expensive due to the large number of re-inference requests produced, which wastes time and raises resource costs. We tackle this issue by casting the OBE task as a new instance of the classical incremental view maintenance problem. We create a novel and comprehensive algebraic framework for incremental CNN inference combining materialized views with multi-query optimization to reduce computational costs. We then present two novel approximate inference optimizations that exploit the semantics of CNNs and the OBE task to further reduce runtimes. We prototype our ideas in a tool we call Krypton. Experiments with real data and CNNs show that Krypton reduces runtimes by up to 5x (resp. 35x) to produce exact (resp. high-quality approximate) results without raising resource requirements.
Does it matter how well I know what you’re thinking? Opponent Modelling in an RTS game

Opponent Modelling tries to predict the future actions of opponents, and is required to perform well in multiplayer games. There is a deep literature on learning an opponent model, but much less on how accurate such models must be to be useful. We investigate the sensitivity of Monte Carlo Tree Search (MCTS) and a Rolling Horizon Evolutionary Algorithm (RHEA) to the accuracy of their modelling of the opponent in a simple Real-Time Strategy game. We find that in this domain RHEA is much more sensitive to the accuracy of an opponent model than MCTS. MCTS generally does better even with an inaccurate model, while this will degrade RHEA's performance. We show that faced with an unknown opponent and a low computational budget it is better not to use any explicit model with RHEA, and to model the opponent's actions within the tree as part of the MCTS algorithm.
Analyzing the Effects of Noise and Variation on the Accuracy of Analog Neural Networks

Traditional machine learning algorithms and neural networks implemented using digital architectures such as GPUs, TPUs, and FPGAs demonstrate high performance, but the power required to train and predict is too high to be implemented in energy-constrained systems such as implants and edge devices. Although analog classifiers offer the possibility to significantly reduce power consumption by two or three orders of magnitude, the nonidealities inherent to analog circuits such as noise, drift, and process variations make it very difficult to implement accurate analog neural networks. This paper explores the effects of these nonidealities on classification performance.
A Practical Tuner based on Opposite Information

Most of the algorithms designed for problem solving have many parameters which values determine their performance. Tuning methods or calibrators are algorithms whose goal is to automate the process of selecting the parameter values of heuristic based algorithms to efficiently solve complex search problems. However, many algorithms are still tuned by-hand either because of the execution time required or the number of scenarios to define before a calibrator is executed. In this work, we propose a practical tuning method that uses a local search procedure that allows obtaining good calibrations in a reduced amount of time, compared to other well-known calibrators. Our tuner has an opposite-inspired learning component used to focus on the most promising areas of the parameter values search space and gathers useful parameter information that is provided to the user. We compare our proposal with two well-known tuners to calibrate two classical optimization problems. We also evaluate the relevance of the opposite-inspired learning component during the search process. A convergence and statistical analysis are presented to confirm that our approach is a good option especially when the user does not have enough time for tuning.
Homomorphic Model Selection for Data Analysis in an Encrypted Domain

Secure computation, a methodology of computing on encrypted data, has become a key factor in machine learning. Homomorphic encryption (HE) enables computation on encrypted data without leaking any information to untrusted servers. In machine learning, the model selection method is a crucial algorithm that determines the performance and reduces the fitting problem. Despite the importance of finding the optimal model, none of the previous studies have considered model selection when performing data analysis through the HE scheme. The HE-based model selection we proposed finds the optimal complexity that best describes given data that is encrypted and whose distribution is unknown. Since this process requires a matrix calculation, we constructed the matrix multiplication and inverse of the matrix based on the bitwise operation. Based on these, we designed the model selection of the HE cross-validation approach and the HE Bayesian approach for homomorphic machine learning. Our focus was on evidence approximation for linear models to find goodness-of-fit that maximizes the evidence. We conducted an experiment on a dataset of age and Body Mass Index (BMI) from Kaggle to compare the capabilities and our model showed that encrypted data can regress homomorphically without decrypting it.
IARPA Janus Benchmark Multi-Domain Face

Face recognition performance degrades when images are captured in difficult environments and non-ideal imaging conditions, for example in surveillance video. Heterogenous face recognition applications that require matching of images across varying distances, resolution, and visible and infrared imaging bands pose additional challenges. Deep-learning techniques have been employed to improve unconstrained face recognition performance, but they require a very large amount of data. There is a distinct need for publicly-available facial video datasets, collected with the consent of the subjects, that can be used to overcome the challenge of performing facial recognition in multi-domain, heterogenous scenarios. This paper presents a multi-domain face image dataset comprised of ground truth and operational images and video captured using a variety of cameras. These include fixed and body-worn cameras capable of imaging at visible, short-mid-, and long-wave infrared wavelengths, including images captured at distances up to 500m. Over 161 hours of video were collected from 251 subjects. This paper provides a description of the data collection, post processing, and baseline matching performance of this unique dataset.
A Study of Fitness Landscapes for Neuroevolution

Fitness landscapes are a useful concept to study the dynamics of meta-heuristics.In the last two decades, they have been applied with success to estimate the optimization power of several types of evolutionary algorithms, including genetic algorithms and genetic programming.However, so far they have never been used to study the performance of machine learning algorithms on unseen data, and they have never been applied to neuroevolution.This paper aims at filling both these gaps, applying for the first time fitness landscapes to neuroevolution and using them to infer useful information about the predictive ability of the method.More specifically, we use a grammar-based approach to generate convolutional neural networks, and we study the dynamics of three different mutations to evolve them.To characterize fitness landscapes, we study autocorrelation and entropic measure of ruggedness.The results show that these measures are appropriate for estimating both the optimization power and the generalization ability of the considered neuroevolution configurations.
Question Answering over Curated and Open Web Sources

The last few years have seen an explosion of research on the topic of automated question answering (QA), spanning the communities of information retrieval, natural language processing, and artificial intelligence. This tutorial would cover the highlights of this really active period of growth for QA to give the audience a grasp over the families of algorithms that are currently being used. We partition research contributions by the underlying source from where answers are retrieved: curated knowledge graphs, unstructured text, or hybrid corpora. We choose this dimension of partitioning as it is the most discriminative when it comes to algorithm design. Other key dimensions are covered within each sub-topic: like the complexity of questions addressed, and degrees of explainability and interactivity introduced in the systems. We would conclude the tutorial with the most promising emerging trends in the expanse of QA, that would help new entrants into this field make the best decisions to take the community forward. This tutorial was recently presented at SIGIR 2020.
Capital flight and diamond exports in the Central African Republic: The role of political governance crisis

Abstract The purpose of this paper is to examine the relationship between capital flight, diamond exports and political governance crisis in the Central African Republic. To achieve this, use is made of multiple correspondence analysis to construct a political governance crisis index, and the Autoregressive Distributed Lag Model and Bound test for co‐integration are employed to establish short‐run and long‐run relationships. The short‐run results show political governance crisis and diamond exports have a significant and negative effect on capital flight. The interaction term between diamonds exports and political governance crisis is found to be positively related to capital flight in the short run. The long‐run equilibrium shows a significant and negative relationship between political governance crisis, diamond exports, and gross domestic product growth even though the interaction between political governance crisis and diamond exports show a positive and significant relationship. The total natural resource rent is found to be positively related to capital flight but not statistically significant. It is then recommended that a combination of stable political governance associated with reducing corruption in public affairs and promoting transparency in the management of the diamond sector be strengthened to effectively reduce the flight of capital.
ScientIST: Biomedical Engineering Experiments Supported by Mobile Devices, Cloud and IoT

Currently, mobile devices such as smartphones or tablets are widespread within the student community. However, their potential to be used in classrooms is yet to be fully explored. Our work proposes an approach that benefits from the ease of access to mobile devices, and combines it with state-of-the-art software and hardware. This approach builds upon previous developments from our team on biosignal acquisition and analysis, and is designed towards the enrichment of the teaching experience for students, namely in what concerns laboratory activities in the field of biomedical engineering. The implementation of such methodology aims at involving students more actively in the learning process, using case studies and emerging educational approaches such as project-based, active and research-based learning. It also provides an effective option for remote teaching, as recently required by the COVID-19 outbreak. In our approach (ScientIST) we explore the use of the Arduino MKR WIFI 1010, a variant of the popular electronic platform, recently launched for prototyping Internet of Things (IoT) applications, and the Google Science Journal (GSJ), a digital notebook created by Google, to support laboratory activities using mobile devices. This approach has shown promising results in two case studies, namely, documenting a Histology laboratory class and a Photoplethysmography (PPG) data acquisition and processing experiment. The System Usability Scale (SUS) was used in the evaluation of the students’ experience, revealing an overall score of 78.68%.
Horst - The teaching frog

Learning environments targeting Augmented Reality (AR) visualize complex facts, can increase a learner's motivation, and allow for the application of learning contents. When using tangible user interfaces, the learning process receives a physical aspect improving the overall intuitive use. We present a tangible AR system targeting the learning of a frog's anatomy. The learning environment is based on a plushfrog containing removable markers. Detecting the markers, replaces them with 3D models of the organs. By extracting individual organs, learners can inspect them up close and learn more about their functions. Our AR frog further includes a quiz for a self-assessment of the learning progress and a gamification system to raise the overall motivation.
Optimal design of the solar tracking system of parabolic trough concentrating collectors

Abstract The present work aimed to select the optimum solar tracking mode for parabolic trough concentrating collectors using numerical simulation. The current work involved: (1) the calculation of daily solar radiation on the Earth’s surface, (2) the comparison of annual direct solar radiation received under different tracking modes and (3) the determination of optimum tilt angle for the north-south tilt tracking mode. It was found that the order of solar radiation received in Shanghai under the available tracking modes was: dual-axis tracking &amp;gt; north-south Earth’s axis tracking &amp;gt; north-south tilt tracking (β = 15°) &amp;gt; north-south tilt tracking (β = 45) &amp;gt; north-south horizontal tracking &amp;gt; east-west horizontal tracking. Single-axis solar tracking modes feature simple structures and low cost. This study also found that the solar radiation received under the north-south tilt tracking mode was higher than that of the north-south Earth’s axis tracking mode in 7 out of 12 months. Therefore, the north-south tilt tracking mode was studied separately to determine the corresponding optimum tilt angles in Haikou, Lhasa, Shanghai, Beijing and Hohhot, respectively, which were shown as follows: 18.81°, 27.29°, 28.67°, 36.21° and 37.97°.
Surrogate-Assisted Memetic Algorithm with Adaptive Patience Criterion for Computationally Expensive Optimization

Surrogate-assisted memetic algorithm (SAMA) has been recognized to be an effective tool for computationally expensive optimization. The termination criterion of the local search in SAMA determines the allocation of limited computational resources between global and local search, and has a tremendous impact on the optimization performance. The commonly used termination criterion based on setting a limit to the local search depth can lead to premature termination or excessive stagnation iterations of the local search. This paper proposes a SAMA with adaptive patience criterion (SAMA/APC) to improve the efficiency of traditional SAMA. The SAMA/APC consists of three main subprocedures, which are carried out iteratively. First, the operators of differential evolution (DE) are employed for global exploration. Then, the proposed Kriging-based patience allocation strategy (KPAS) is performed, which adaptively allocates a patience value to each individual of the population according to two basic principles. Third, the trust-region search (TRS) is carried out on each individual for local exploitation. The TRS is a process of consuming the patience, and it terminates when the patience value is reduced to zero. The local optimum obtained by the TRS is returned back to the population of DE in the spirit of Lamarckian learning. Experimental studies on the CEC' 14 expensive optimization test suite demonstrate the efficiency of the proposed SAMA/APC.