Reviewerly/exHarmony huggingface dataset:
Virtual reality in biology: could we become virtual naturalists?

Abstract The technological revolution of past decades has led teaching and learning of evolutionary biology to move away from its naturalist origins. As a result, students’ learning experiences and training on the science of natural history—which entails careful observations and meticulous data curation to generate insight—have been compromised compared with the times of the pioneers in the field. But will technology cause the extinction of natural history in its traditional form? In this essay, we provide a visionary—albeit not yet possible—perspective of the future of natural history in the technological era. We review the main concepts and applications of key state-state-of-the-art technologies to the teaching and learning of Biology including Virtual and Mixed Reality (VMR). Next, we review the current knowledge in artificial life, and describe our visionary model for the future of natural history voyages—the BioVR—which is an immersive world where students can experience evolution in action, and also shape how evolution can occur in virtual worlds. We finish the essay with a cautionary tale as to the known negative sides of using VMR technologies, and why future applications should be designed with care to protect the intended learning outcomes and students’ experience. Our aim is to stimulate debates on how new technologies can revolutionise teaching and learning across scenarios, which can be useful for improving learning outcomes of biological concepts in face-to-face, blended, and distance learning programmes.
When Comparing to Ground Truth is Wrong

We study the evaluation of graph explanation methods. The state of the art to evaluate explanation methods is to first train a GNN, then generate explanations, and finally compare those explanations with the ground truth. We show five pitfalls that sabotage this pipeline because the GNN does not use the ground-truth edges. Thus, the explanation method cannot detect the ground truth. We propose three novel benchmarks: (i) pattern detection, (ii) community detection, and (iii) handling negative evidence and gradient saturation. In a re-evaluation of state-of-the-art explanation methods, we show paths for improving existing methods and highlight further paths for GNN explanation research.
Machine Learning–Based Analysis of Encrypted Medical Data in the Cloud: Qualitative Study of Expert Stakeholders’ Perspectives

Third-party cloud-based data analysis applications are proliferating in electronic health (eHealth) because of the expertise offered and their monetary advantage. However, privacy and security are critical concerns when handling sensitive medical data in the cloud. Technical advances based on "crypto magic" in privacy-preserving machine learning (ML) enable data analysis in encrypted form for maintaining confidentiality. Such privacy-enhancing technologies (PETs) could be counterintuitive to relevant stakeholders in eHealth, which could in turn hinder adoption; thus, more attention is needed on human factors for establishing trust and transparency.The aim of this study was to analyze eHealth expert stakeholders' perspectives and the perceived tradeoffs in regard to data analysis on encrypted medical data in the cloud, and to derive user requirements for development of a privacy-preserving data analysis tool.We used semistructured interviews and report on 14 interviews with individuals having medical, technical, or research expertise in eHealth. We used thematic analysis for analyzing interview data. In addition, we conducted a workshop for eliciting requirements.Our results show differences in the understanding of and in trusting the technology; caution is advised by technical experts, whereas patient safety assurances are required by medical experts. Themes were identified with general perspectives on data privacy and practices (eg, acceptance of using external services), as well as themes highlighting specific perspectives (eg, data protection drawbacks and concerns of the data analysis on encrypted data). The latter themes result in requiring assurances and conformance testing for trusting tools such as the proposed ML-based tool. Communicating privacy, and utility benefits and tradeoffs with stakeholders is essential for trust. Furthermore, stakeholders and their organizations share accountability of patient data. Finally, stakeholders stressed the importance of informing patients about the privacy of their data.Understanding the benefits and risks of using eHealth PETs is crucial, and collaboration among diverse stakeholders is essential. Assurances of the tool's privacy, accuracy, and patient safety should be in place for establishing trust of ML-based PETs, especially if used in the cloud.
Using ontology to guide reinforcement learning agents in unseen situations

Abstract In multi-agent systems, goal achievement is challenging when agents operate in ever-changing environments and face unseen situations, where not all the goals are known or predefined. In such cases, agents need to identify the changes and adapt their behaviour, by evolving their goals or even generating new goals to address the emerging requirements. Learning and practical reasoning techniques have been used to enable agents with limited knowledge to adapt to new circumstances. However, they depend on the availability of large amounts of data, require long exploration periods, and cannot help agents to set new goals. Furthermore, the accuracy of agents’ actions is improved by introducing added intelligence through integrating conceptual features extracted from ontologies. However, the concerns related to taking suitable actions when unseen situations occur are not addressed. This paper proposes a new Automatic Goal Generation Model (AGGM) that enables agents to create new goals to handle unseen situations and to adapt to their ever-changing environment on a real-time basis. AGGM is compared to Q-learning, SARSA, and Deep Q Network in a Traffic Signal Control System case study. The results show that AGGM outperforms the baseline algorithms in unseen situations while handling the seen situations as well as the baseline algorithms.
Prospective on Eye-Tracking-based Studies in Immersive Virtual Reality

The current virtual reality (VR) techniques develop immersive environments via inducing illusions to our sense. Nowadays, most of VR focuses on inducing visual illusion. Hence, visual is the most important input channel for experiencing and exploring the VR environments. Recently, extensive research efforts have been put on eye-tracking studies. However, the development and growing trends of the VR-based eye-tracking studies are unrevealed due to the lack of a systematic literature review on it. In this study, we reviewed related literature from 2000 to 2019 and summarized them into two main categories, including eye tracking methods and eye-tracking-enabled applications, such as tracking gaze points to manipulate the VR environment, measuring user states, and evaluating the usability of VR based on eye-tracking data. Based on the literature review, we can find that eye-tracking can assist in developing adaptive VR systems and enhance users experience. While comparing with 2D environments, immersive VR environment still requires more deep studies in eye-tracking.
Improved single image dehazing methods for resource-constrained platforms

Abstract Image dehazing is an increasingly widespread approach to address the degradation of images of the natural environment by low-visibility weather, dust and other phenomena. Advances in autonomous systems and platforms have increased the need for low-complexity, high-performing dehazing techniques. However, while recent learning-based image dehazing approaches have significantly increased the dehazing performance, this has often been at the expense of complexity and hence the use of prior-based approaches persists, despite their lower performance. This paper addresses both these aspects and focuses on single image dehazing, the most practical class of techniques. A new Dark Channel Prior-based single image dehazing algorithm is presented that has an improved atmospheric light estimation method and a low-complexity morphological reconstruction. In addition, a novel, lightweight end-to-end network is proposed, that avoids information loss and significant computational effort by eliminating the pooling and fully connected layers. Qualitative and quantitative evaluations show that our proposed algorithms are competitive with, or outperform, state-of-the-art techniques with significantly lower complexity, demonstrating their suitability for use in resource-constrained platforms.
A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism

The fruit fly optimization algorithm (FOA) has drawn enormous attention from researchers and practitioners in the computation intelligence domain for the benefits of simple implementation mechanism and few parameters tuning requirement of FOA. However, FOA is hard to adapt directly to address complex continuous problems. A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism (HGCLFOA) is proposed in this study. The population is divided into elitist and inferior subpopulations with the fitness of objective function. The population center is re-designed as an elitist subpopulation to maintain the diversity of the population. In the olfaction search stage, the hierarchical guidance strategy is introduced for local search according to the difference of solution qualities to assign inferior individuals to elitist individuals on different levels. Meanwhile, the inferior information is applied by the inferior solutions repairing strategy to deflect the prediction of the elitist subpopulation for preventing HGCLFOA from falling into the local optimum. In the vision search stage, a hybrid Gaussian distribution estimation strategy is adopted to extract the elitist information of previous generations to predict the distribution of potential elitist individuals in the next generation. The exploration and exploitation of the HGCLFOA are balanced by the cooperation between elitist subpopulation and inferior subpopulation. A random walk strategy is activated to assist the elitist solutions to jump out the local optimal. The parameters of the HGCLFOA are calibrated by DOE and ANOVA methods. The experimental results demonstrated that the HGCLFOA outperformed the classical FOA and state-of-arts variants of FOA.
A Deep Dive into the Impact of COVID-19 on Software Development

Context: COVID-19 pandemic has impacted different business sectors around the world. Objective. This study investigates the impact of COVID-19 on software projects and software development professionals. Method: We conducted a mining software repository study based on 100 GitHub projects developed in Java using ten different metrics. Next, we surveyed 279 software development professionals for better understanding the impact of COVID-19 on daily activities and wellbeing. Results: We identified 12 observations related to productivity, code quality, and wellbeing. Conclusions: Our findings highlight that the impact of COVID-19 is not binary (reduce productivity vs. increase productivity) but rather a spectrum. For many of our observations, substantial proportions of respondents have differing opinions from each other. We believe that more research is needed to uncover specific conditions that cause certain outcomes to be more prevalent.
Distributed multi-view multi-target tracking based on CPHD filtering

This paper addresses distributed multi-target tracking over a network of sensors having different fields-of-view (FoVs). Specifically, a cardinalized probability hypothesis density (CPHD) filter is run at each sensor node. Due to the fact that each sensor node has limited FoV, the standard fusion methods need to be suitably modified. In fact, the monitored area of multiple sensor nodes consists of several parts that are either exclusive of a single node, i.e. or common to multiple (at least two) nodes. In this setting, the crucial issue is how to account for these different information sets in the fusion rule. The problem is particularly challenging when the knowledge of the FoVs is unreliable, for example because of the presence of unknown occlusions, and when local sensors can have valuable information also on targets located outside the current nominal FoV, for example thanks to the diffusion of information in the network or because the FoVs are time-varying. In this context, we propose a distributed CPHD filter based on the idea of decomposing the posterior densities to be fused so as to account for the different information sets. A new decomposition method is proposed that does not rely on the nominal FoVs and, instead, uses clustering to decompose the posterior intensity function into multiple sub-intensities, and reconstructing the corresponding cardinality distribution via multi-Bernoulli approximation. Then, fusion is performed in parallel according to either the geometric average or arithmetic average rule. Simulation experiments are provided to demonstrate the effectiveness of the proposed approach.
Adjusted combination of moving averages: A forecasting system for medium-term solar irradiance

Global Horizontal Irradiation forecasts are necessary for an efficient use of fluctuating energy output from photovoltaic plants. The purpose of this paper is to provide efficient, easy-to-implement Global Horizontal Irradiation forecasts on an hourly basis for a medium-term horizon (longer than 48 h). These forecasts are essential for the strategic deployment of tasks including unit commitment, transmission management, trading, hedging, planning, asset optimization, maintenance scheduling and spinning of power units. Nonetheless, forecasting models for medium term horizons are scarce. This work intends to bridge that gap by proposing a method based on an adjusted combination of moving averages which includes the yearly cycle in addition to the day–night cycle. This method is straightforward to implement and easy to integrate into corporate computing systems. We compare our approach with several well-known alternative methods, such as deep recurrent neural networks or autoregressive integrated moving average models, among others. The results show that recurrent neural networks are 10% more accurate than the second best method, and 90% better than our proposal for very short horizons (up to 5 h ahead). However, this advantage is lost when longer horizons are tested, especially for horizons longer than 30 h ahead, for which our method produces forecasts which are 20% more accurate than those made by recurrent neural networks. The reason behind that improvement at longer horizons is the inclusion of the yearly cycle in the proposed approach. • There is a rich literature on short-term photovoltaic energy forecasting methods. • Many tasks in photovoltaic plants need forecasting methods for medium-term horizons. • A new method for medium-term photovoltaic energy forecasting is developed and tested. • Many methods produce accurate forecasts for short-term horizons, deep learning best. • The proposal outperforms many forecasting methods for medium-term horizons.
S<sup>2</sup>-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration

Previous studies dominantly target at self-supervised learning on real-valued networks and have achieved many promising results. However, on the more challenging binary neural networks (BNNs), this task has not yet been fully explored in the community. In this paper, we focus on this more difficult scenario: learning networks where both weights and activations are binary, meanwhile, without any human annotated labels. We observe that the commonly used contrastive objective is not satisfying on BNNs for competitive accuracy, since the backbone network contains relatively limited capacity and representation ability. Hence instead of directly applying existing self-supervised methods, which cause a severe decline in performance, we present a novel guided learning paradigm from real-valued to distill binary networks on the final prediction distribution, to minimize the loss and obtain desirable accuracy. Our proposed method can boost the simple contrastive learning baseline by an absolute gain of 5.5∼15% on BNNs. We further reveal that it is difficult for BNNs to recover the similar predictive distributions as real-valued models when training without labels. Thus, how to calibrate them is key to address the degradation in performance. Extensive experiments are conducted on the large-scale ImageNet and downstream datasets. Our method achieves substantial improvement over the simple contrastive learning baseline, and is even comparable to many mainstream supervised BNN methods. Code is available at https://github.com/szq0214/S2-BNN.
STEAM education: contributing evidence of validity and effectiveness (<i>Educación STEAM: aportando pruebas de validez y efectividad</i>)

While the acronym STEAM — Science, Technology, Engineering, Arts, Mathematics — has become a buzzword in educational settings, STEAM education is still at an early stage in its development. In fact, many consider the promotion of STEAM education as being based on relatively weak empirical grounds. This special issue responds to the need for additional evidence regarding STEAM’s validity and effectiveness. In the first part of this article, the STEAM construct is defined, with its origin, characteristics and various models described. The barriers that hinder or prevent its application in the classroom are also analysed. The second part presents the six articles included in the special issue. The first article analyses the theoretical bases of STEAM education. It is followed by five empirical studies: two are contextualized within primary education, two within secondary education and one final study on STEAM teacher education. We trust that this series of studies will contribute to further developing the theoretical, epistemological and practical frameworks underpinning STEAM education.
Big data classification using heterogeneous ensemble classifiers in Apache Spark based on MapReduce paradigm

In this era of big data, processing large scale data efficiently and accurately has become a challenging problem. Ensemble classification is a type of supervised learning that uses multiple experts to generate the final output. It provides a way to classify data more accurately. As a result of using multiple classifiers, they are often more complicated than single classifiers, especially for big data problems. Apache Spark is a unified analytics engine for big data processing which provides a scalable framework to analyze the data. In this paper, we first extend our previous work and design a distributed heterogeneous ensemble classifier inspired by the boosting approach, which is capable of dealing with big datasets. Using heterogeneous classifiers makes it possible to have more diverse classifiers, and consequently, a more accurate classifier is obtained. Then, we present the Spark version of the proposed approach to speed up our heterogeneous ensemble classifier using the MapReduce paradigm. In order to evaluate our approach, we have applied it to seven big datasets. Extensive experimental results indicate the superiority of the proposed method over the existing ensemble algorithms implemented by Spark MLlib in terms of the classification accuracy, performance, and scalability.
Adversarial Attacks on Deep Models for Financial Transaction Records

Machine learning models using transaction records as inputs are popular among financial institutions. The most efficient models use deep-learning architectures similar to those in the NLP community, posing a challenge due to their tremendous number of parameters and limited robustness. In particular, deep-learning models are vulnerable to adversarial attacks: a little change in the input harms the model's output. In this work, we examine adversarial attacks on transaction records data and defenses from these attacks. The transaction records data have a different structure than the canonical NLP or time-series data, as neighboring records are less connected than words in sentences, and each record consists of both discrete merchant code and continuous transaction amount. We consider a black-box attack scenario, where the attack doesn't know the true decision model and pay special attention to adding transaction tokens to the end of a sequence. These limitations provide a more realistic scenario, previously unexplored in the NLP world. The proposed adversarial attacks and the respective defenses demonstrate remarkable performance using relevant datasets from the financial industry. Our results show that a couple of generated transactions are sufficient to fool a deep-learning model. Further, we improve model robustness via adversarial training or separate adversarial examples detection. This work shows that embedding protection from adversarial attacks improves model robustness, allowing a wider adoption of deep models for transaction records in banking and finance.
Ethereum Smart Contracts: Vulnerabilities and their Classifications

Smart contract (SC) is an extension of BlockChain technology. Ethereum BlockChain was the first to incorporate SC and thus started a new era of crypto-currencies and electronic transactions. Solidity helps to program the SCs. Still, soon after Solidity's emergence in 2014, Solidity-based SCs suffered many attacks that deprived the SC account holders of their precious funds. The main reason for these attacks was the presence of vulnerabilities in SC. This paper discusses SC vulnerabilities and classifies them according to the domain knowledge of the faulty operations. This classification is a source of reminding developers and software engineers that for SC's safety, each SC requires proper testing with effective tools to catch those classes' vulnerabilities.
Differentially private distributed online learning over time‐varying digraphs via dual averaging

Abstract This article investigates a distributed online learning problem with privacy preservation, in which the learning nodes in a distributed network aims to minimize the sum of local loss functions over time horizon . Based on the push‐sum protocol and the Laplace mechanism, we propose a differentially private distributed dual averaging algorithm for constrained distributed online learning problem over time‐varying digraphs. It is shown that the expectation of the regret of our algorithm achieves a sublinear rate of . Furthermore, we provide an analysis of differential privacy, which reveals a tradeoff between the accuracy and the privacy level of our algorithm. Finally, numerical examples are presented to validate the effectiveness of the algorithm.
An Investigation of Pre-School Children’s Interactions with Augmented Reality Applications

In this study, interactions of pre-school children with the augmented reality (AR) applications have been examined in terms of student-material, student-teacher and student-student dimensions. For this study, an embedded mixed design has been preferred. The sample has consisted of 25 children in the age range of 5–6. It is determined that there are significant differences in the interactions of pre-school children in entertaining and educational AR applications. However, there is no significant difference in educational AR activities with limited and flexible interaction. In addition, there are significant differences between children in student-material, student-teacher, student-student and total interaction levels. It is also revealed that children generally enjoy using AR application and love it and want to use it again. In this context, this study is expected to guide teachers or program designers in creating interactive environments or materials with AR technology.