Reviewerly/exHarmony huggingface dataset:
Testing Effect on High-Level Cognitive Skills

The testing effect is one of the strongest learning techniques documented to date. Although the effects of testing on high-level learning are promising, fewer studies on this have been done. In this classroom application of the testing effect, we aimed to 1) determine whether a testing effect exists on high-level testing; 2) determine whether higher-level testing has an effect on low-level content retention; and 3) determine whether content knowledge, cognitive skill, or additional components are responsible for this effect. Through a series of two experiments, we confirmed a testing effect on high-level items. However, improved content retention due to testing was not observed. We suggest that this high-level testing effect is due to a better ability to apply specific skills to specific content when this application process has appeared on a previous exam.
Graph Regularized Encoder-Decoder Networks for Image Representation Learning

Image representation learning with encoder-decoder networks plays a fundamental role in multimedia processing. Recent findings show that traditional encoder-decoders can be negatively affected by small visual perturbations. The learned non-smooth feature embedding cannot guarantee to capture semantic-meaningful geometric distance between visually-similar image samples. Inspired by manifold learning, we propose a graph regularized encoder-decoder network, which can preserve local geometric information of the code embedding space. More discriminative feature embedding is learnt to attain both high-level image semantic and neighbor relationship of image clusters. The proposed graph regularizer is formulated upon multi-layer perceptions. It uses the local invariance principle to explicitly reconstruct the geometric similarity graph. Theoretical analysis is provided to show the connection between our deep regularizer and traditional graph Laplacian regularizer. Practically, the network complexity is alleviated by anchor based bipartite graph, and this leverages our method into large scale scenario. Experimental evaluations show the comparable results of the proposed method with state-of-the-art models on different tasks.
Unsupervised 3D End-to-end Deformable Network for Brain MRI Registration

Volumetric medical image registration has important clinical significance. Traditional registration methods may be time-consuming when processing large volumetric data due to their iterative optimizations. In contrast, existing deep learning-based networks can obtain the registration quickly. However, most of them require independent rigid alignment before deformable registration; these two steps are often performed separately and cannot be end-to-end. Moreover, registration ground-truth is difficult to obtain for supervised learning methods. To tackle the above issues, we propose an unsupervised 3D end-to-end deformable registration network. The proposed network cascades two subnetworks; the first one is for obtaining affine alignment, and the second one is a deformable subnetwork for achieving the non-rigid registration. The parameters of the two subnetworks are shared. The global and local similarity measures are used as loss functions for the two subnetworks, respectively. The trained network can perform end-to-end deformable registration. We conducted experiments on brain MRI datasets (LPBA40, Mindboggle101, and IXI) and experimental results demonstrate the efficacy of the proposed registration network.
Image Feature Learning with Genetic Programming

Learning features from raw data is an important topic in machine learning. This paper presents Genetic Program Feature Learner (GPFL), a novel generative GP feature learner for 2D images. GPFL executes multiple GP runs, each run generates a model that focuses on a particular high-level feature of the training images. Then, it combines the models generated by each run into a function that reconstructs the observed images. As a sanity check, we evaluated GPFL on the popular MNIST dataset of handwritten digits, and compared it with the convolutional neural network LeNet5. Our evaluation results show that when considering smaller training sets, GPFL achieves comparable/slightly-better classification accuracy than LeNet5. However, GPFL drastically outperforms LeNet5 when considering noisy images as test sets.
Digital Innovation in Industry 4.0 Era – Rebooting UAE’s Retail

The emergence of Digital Innovation in Industry 4.0 Era towards online retail industry has transformed the way the business is carried out. The e-commerce industry has the potential to boost economic growth and social development. Economic sectors such as finance, communication, labor and retail trade has larger impact by e-commerce. It holds promise in areas such as transport, education, infrastructure, employment and government. A combination of technology innovation and regulatory reforms has enabled e-commerce to evolve and thrive. Barriers in electronic commerce affecting buyers and sellers have declined compared to the past. Still it will travel towards zero barriers during Industry 4.0 Era. This paper explores the impact of e-commerce sector success on the economic growth of the UAE. The factors affecting e-commerce development will be expounded to determine the policy initiatives that need to be implemented in the UAE. This paper also proposes appropriate e-commerce approaches to boost development and address challenges that hinder economic growth. The rapid development of technology has incentivized most countries to leverage on opportunities of the digital economy. In UAE, the e-commerce sector is on the verge of a pivotal shift. It is becoming a reality by forming new customer experiences, reinvesting shopping trends, disrupting business models and harnessing the growth of new e-commerce players and SME's. The study assesses the current e-commerce market dynamics in UAE by exploring the effect of e-commerce sector on UAE's economic growth, benefits and barriers of e-commerce development and the positive contribution of the industry to economic growth. The e-commerce industry has a significant influence on the economy of a country. Therefore, the affiliation between e-commerce and economic growth is deemed important.
Integration of Well Logging and Remote Sensing Data for Detecting Potential Geothermal Sites along the Gulf of Suez, Egypt

The Gulf of Suez area is one of the most favorable regions in Egypt for geothermal exploitation since it hosts an evident cluster of superficial thermal springs. Some of these thermal springs include Hammam Musa, Hammam Faraun, Sudr, Ayn Musa and Ain Sokhna, which are characterized by high temperatures ranging between 35 °C to 86.66 °C. It is this feature that makes the Gulf of Suez locality sufficient for geothermal power production. Corrected bottom hole temperature (BHT) data from 197 oil wells situated onshore and offshore of the Gulf of Suez were utilized for the present research. The results indicated that the study area has a geothermal gradient ranging from 24.9 to 86.66 °C/km, a heat flow ranging from 31 to 127.2 m W k−1, a thermal conductivity of 2.6–3.2 W m−1 k−1, and an amplitude temperature varying from 49.48 °C to 157.8 °C. The derived geothermal and geological layers were used together with the remote sensing thermal infrared and topographic data, to map relevant physiographic variables including surface elevation, fractures density, drainage density, nighttime land surface temperature and major lithological units. The nine produced variables were integrated in GIS to model the geothermal potential map (GTP) for the Gulf of Suez region. The model identifies the northeastern and the southwestern areas as equally two sites for high geothermal potential. Findings of this study demonstrate that integration of well logging and space data with the adopted geospatial techniques is a practical method for geothermal prospecting in similarly geologic and tectonic setting in Egypt and East Africa.
A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking

This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms. This method was tested on the games in the General Video Game AI (GVGAI) framework, allowing us to identify a smaller set of games that still gives a large amount of information about the abilities of different game-playing agents. This approach can be used to make agent testing more efficient. We can achieve almost as good discriminatory accuracy when testing on only a handful of games as when testing on more than a hundred games, something which is often computationally infeasible. Furthermore, this method can be extended to study the dimensions of the effective variance in game design between these games, allowing us to identify which games differentiate between agents in the most complementary ways.
Multi-population Modified L-SHADE for Single Objective Bound Constrained optimization

In this paper, we extend a previous algorithm mL-SHADE by running the evolutionary process through multiple populations and adding dynamic control of mutation intensity and hyper-parameters. The whole population is partitioned into subpopulations by a random clustering method. Mutation intensity and hyper-parameters are adjusted based on the consumption of fitness function evaluations. Performance of the proposed algorithm is verified by ten benchmark functions in the CEC2020 Competition on Single Objective Bound Constrained optimization. The results show the competitiveness of the proposed algorithm.
Interviewing Roomba: A posthuman study of humans and robot vacuum cleaners

Abstract Roomba, the autonomous robotic vacuum cleaner sold by iRobot since 2002, is now a taken-for-granted household helper in many homes. In this study that cross-cuts phenomenology, postphenomenology, actor–network theory and media ecology, I utilize four heuristics for interviewing digital objects. I interview Roomba and utilize qualitative research methods to theorize about the complexities of the entanglements and relationships between human beings and their robot vacuum cleaners. Conclusions connect to critical theory and feminism and also question justifications of anthropocentrism in a posthuman world.
Improved Resource State for Verifiable Blind Quantum Computation

Recent advances in theoretical and experimental quantum computing raise the problem of verifying the outcome of these quantum computations. The recent verification protocols using blind quantum computing are fruitful for addressing this problem. Unfortunately, all known schemes have relatively high overhead. Here we present a novel construction for the resource state of verifiable blind quantum computation. This approach achieves a better verifiability of 0.866 in the case of classical output. In addition, the number of required qubits is 2N+4cN, where N and c are the number of vertices and the maximal degree in the original computation graph, respectively. In other words, our overhead is less linear in the size of the computational scale. Finally, we utilize the method of repetition and fault-tolerant code to optimise the verifiability.
Gaming for Meaningful Interactions in Teleworking Lessons Learned during the COVID-19 Pandemic from Integrating Gaming in Virtual Meetings

The recent need for social-distancing caused by COVID-19 resulted in working remotely, which can cause loneliness and disconnect from the organization. The pandemic stimulated people to look for novel ways to interact and, at the same time, socialize with each other. The change to home office increased the number of people using digital video calls; however, these impose restrictions on social bonding. In this commentary, we argue that the social bonding capacity of video calls can be increased by using games. Playing games can create a common object with shared goals, which can give players the feeling of being in the same environment, belonging to the same organization, working on joint objectives, and give an enjoyable experience. All work activities will unlikely return to the office after the pandemic is over, and preparing for remote working and socialization is needed. We recommend several avenues for research, including researching the concepts of online socialization and evaluating the effectiveness of gaming.
Learning From Mislabeled Training Data Through Ambiguous Learning for In-Home Health Monitoring

Data are widely collected via the IoT for machine learning tasks in in-home health monitoring applications and mislabeled training data lead to unreliable machine learning models in in-home health monitoring. Researchers have proposed a wide arrangement of algorithms to deal with mislabeled training data, in which one straightforward and effective solution is to directly filter noise from training data so that the negative effects of mislabeled data can be minimized. In essence, noise filtering might be a suboptimal solution because the mislabeled data are not completely useless. The features and distributions of mislabeled data are still useful for learning, especially when training data are insufficient. In this work, we propose a novel framework to learn from mislabeled training data through ambiguous learning (LeMAL). LeMAL mainly consists of two parts. First, it converts the original training data to ambiguous data. Second, an ambiguous learning algorithm is applied to the ambiguous data. In this work, we propose a novel distance-based ambiguous learning algorithm so that the ambiguous data can be used in a better way. Finally, we demonstrate that LeMAL can effectively improve learning performance over existing noise filtering methods.
Particle Swarm optimization with pbest Perturbations

Restarts are a popular remedy to address (premature) convergence in metaheuristics. In Particle Swarm optimization, it has been observed that swarms often “stall” as opposed to “converge”. A stall occurs when all of the forward progress that could occur is instead rejected as failed exploration. Since the swarm is in a good region of the search space with the potential to make more progress, a (random) restart could be counter productive. We instead introduce a method to address the stall mechanism. The introduction of perturbations to the pbest positions leads to significant improvements in the performance of standard Particle Swarm optimization.
Genetic Programming for Domain Adaptation in Product Reviews

There is a large variety of products sold online and the websites are in several languages. Hence, it is desirable to train a model that can predict sentiments in different domains simultaneously. Previous authors have used deep learning to extract features from multiple domains. Here, each word is represented by a vector that is determined using co-occurrence data. Such a model requires that all sentences have the same length resulting in low accuracy. To overcome this challenge, we model the features in each sentence using a variable length tree called a Genetic Program. The polarity of clauses can be represented using mathematical operators such as '+' or '-' at internal nodes in the tree. The proposed model is evaluated on Amazon product reviews for different products and in different languages. We are able to outperform the accuracy of baseline multi-domain models in the range of 5-20%.
The Proliferation of Solar Photovoltaics: Their Impact on Widespread Deployment of Electric Vehicles

Energy systems, in general, and power systems, in particular, are undergoing a changing landscape in terms of generation and demand portfolios. These transitions, although offering new economic and labor market opportunities, bring forth a suite of unprecedented technical implications and challenges in the interim. According to experts, our planet's population will reach 9.3 billion in the next four decades. A fundamental and granular outlook on global energy systems (prior to the COVID-19 outbreak) predicted that electricity consumption will double by 2050, with electrified transportation and higher living standards in buildings being among the principal components of this increase.
A Reversible-Logic based Architecture for Artificial Neural Network

High-performance computing beyond sub-10 nm advanced node technology allows us to explore and use complex 2.5D/3D SOC design architecture. Node scaling, heterogeneous integration, and complex design enable us to think beyond Moore's law but, at the same time, limit the scope with concerns of excessive power dissipation. The field of quantum computation and reversible logic functions has been researched in recent years in the context of low power VLSI circuit designs and nanotechnology. Reversible computation exhibits significantly reduced power dissipation in digital circuits. In this paper, we propose a novel design of Artificial Neural Network (ANN) using reversible logic gates. A thorough search of the relevant literature yielded only a few related articles. To the best of our knowledge, our proposed approach is the first attempt to implement a complete feedforward neural network circuit using only reversible logic gates. The comparative analysis demonstrates that our proposed approach has achieved an approximately 16% reduction in overall power dissipation compared to existing approaches. The proposed approach also has better scalability than the classical design approach.
Evolving Better Rerouting Surrogate Travel Costs with Grammar-Guided Genetic Programming

The number of drivers using on-board systems to navigate through urban areas is increasing. Drivers get real time information regarding traffic conditions and change their routes accordingly. Adapting a route clearly enables drivers to avoid closed roads or circumvent major hotspots. However, given the non-linearity of the traffic dynamics in urban environments, choosing a route based only on current traffic load or current average vehicle speed is not a guaranty of a lower overall travel time. In this work, we design an evolutionary system to search for better surrogate travel cost that drivers could optimise in their rerouting to achieve better overall travel times. Our system uses the Grammar-Guided Genetic Programming algorithm to evolve surrogate travel cost expressions and evaluate their performances on a micro traffic simulator. Our system is able to evolve different expressions that meet characteristics of specific urban environments instead of a one size fits all expression. We have seen in our experimental study on a traffic scenario representing Dublin city centre that our system is able to evolve surrogate travel cost expressions with ~34% and ~10% improvements in average travel time over the no rerouting and the average travel speed based rerouting algorithms.
Co-Saliency Detection via a General Optimization Model and Adaptive Graph Learning

Co-saliency detection is an important research problem, and has been widely used in computer vision area. One main challenge for co-saliency detection problem is how to explore both interactive information among different images and individual salient information within each image simultaneously in co-saliency estimation. In this paper, we propose a novel general optimization framework with adaptive graph learning for co-saliency estimation problem. The proposed model integrates multiple cues including background, and foreground priors, structural information of images, and image feature representation together to obtain a uniform, and accurate co-saliency estimation. One main benefit of the proposed co-saliency method is that it conducts co-saliency propagation, and prediction across different images while maintains the individual salient information of each image, which ensures the consistency, and communication across different images effectively in co-saliency estimation. To improve the accuracy of co-saliency estimation, we adaptively learn a neighborhood, and structured graph to conduct co-saliency propagation among superpixels. An effective optimization algorithm has been designed to seek the optimal solution for the proposed co-saliency optimization model. Experimental results on several widely used datasets show that our method outperforms some other related co-saliency detection methods.
Data-Driven Regular Expressions Evolution for Medical Text Classification Using Genetic Programming

In medical fields, text classification is one of the most important tasks that can significantly reduce human work-load through structured information digitization and intelligent decision support. Despite the popularity of learning-based text classification techniques, it is hard for human to understand or manually fine-tune the classification for better precision and recall, due to the black box nature of learning. This study proposes a novel regular expression-based text classification method making use of genetic programming (GP) approaches to evolve regular expressions that can classify a given medical text inquiry with satisfaction. Given a seed population of regular expressions (randomly initialized or manually constructed by experts), our method evolves a population of regular expressions, using a novel regular expression syntax and a series of carefully chosen reproduction operators. Our method is evaluated with real-life medical text inquiries from an online healthcare provider and shows promising performance. More importantly, our method generates classifiers that can be fully understood, checked and updated by medical doctors, which are fundamentally crucial for medical related practices.
A Parametric Study of Interaction-Transformation Evolutionary Algorithm for Symbolic Regression

The balance between approximation error and model complexity is an important trade-off for Symbolic Regression algorithms. This trade-off is achieved by means of specific operators for bloat control, modified operators, limits to the size of the generated expressions and multi-objective optimization. Recently, the representation Interaction-Transformation was introduced with the goal of limiting the search space to simpler expressions, thus avoiding bloating. This representation was used in the context of an Evolutionary Algorithm in order to find concise expressions resulting in small approximation errors competitive with the literature. Particular to this algorithm, two parameters control the complexity of the generated expression. This paper investigates the influence of those parameters w.r.t. the goodness-of-fit. Through some extensive experiments, we find that the maximum number of terms is more important to control goodness-of-fit but also that there is a limit to the extent that increasing its value renders any benefits. Second, the limit to the minimum and maximum value of the exponent has a smaller influence to the results and it can be set to a default value without impacting the final results.