Reviewerly/exHarmony huggingface dataset:
Agnostic deep neural network approach to the estimation of hydrogen production for solar-powered systems

Countries' abilities to meet their own energy needs using renewable and clean energy regardless of external sources is an important option in terms of sustainability. Moreover, very low emission systems can be developed using a combination of hydrogen energy technologies and renewable energy systems. However, renewable energy shows certain irregularities as a fact of its very nature. However, an energy source that is not affected by irregularities is quite possible using electricity produced from renewable energy in production and storage of hydrogen. At this point, the predictability of a country's energy sources is vital when investing in stable energy systems. In this study, the hydrogen production values of a PEM electrolyser (PEM-E), as supported by GaAs Photovoltaic (GaAsPV) technology, are estimated using data from a meteorological station with high solar energy potential. The observed and predicted solar radiation values constitute the main dataset. Daily radiation estimations are achieved via an Artificial Neural Network (ANN), Adaptive Network-Based Fuzzy Logic Inference System (ANFIS), Multiple Linear Regression (MLR), Empirical Angström-Prescott (EAP), and Agnostic Deep Learning (DL) models. The system, which consists of a 10 kW (68.10 m2) GaAsPV and 4688 W PEM-E, is considered for the determination of HP values. The root mean square error (RMSE) and the coefficient of determination (R2) are used as performance criteria in comparing hydrogen production based on estimated solar radiation with observed values falling on inclined surfaces. The DL model is highly compatible with the observed data and is the most successful model of those considered with a performance value of 96.26% (R2).
Exploring Chemistry with Wireless, PC-Less Portable Virtual Reality Laboratories

Although virtual reality (VR) has been around for decades, the primary market has been video game entertainment. Because VR allows for realistic interaction with computer-generated 3-dimensional (3D) hologram-like learning objects with handheld controllers, it is inherently versatile, and we have therefore asked: Can we extend the documented benefits of such immersive experiences to an undergraduate chemistry lab at scale? This activity describes an in-class (i.e., portable) virtual reality chemistry lab, applied in an undergraduate biochemistry class, with over 200 students enrolled in 8 lab sessions, which took place at Harvard University in the spring of 2020. What made this deployment unique is that each student had the opportunity to individually explore the shapes of proteins and drug compounds and to learn how these structures explain function using the latest, tetherless VR technology (Oculus Quest). Students completed in-class, VR-based work with the aid of peer-to-peer support (pairs), and students engaged with this active learning module to observe, manipulate, and build molecules in an immersive virtual environment. The goal of the curricular integration is to improve students’ spatial recognition of protein structures and engagement in 3D chemistry experiments. This particular VR lab has the advantages of being green (less hazardous, eco-friendly, low-cost) and portable (wireless, no computer needed, convenient to transport), both features that are easily applied to future chemistry classes, including remote courses that will undoubtedly become more common in the era of e-learning. Finally, we conducted survey research accessing students’ perception of this portable VR chemistry lab and their self-reported learning outcomes. This paper covers the quantitative and qualitative analysis of this survey-based assessment as well as highlights limitations and potential ways forward with these technologies used in chemistry curricula.
RA: Hunting for Re-Entrancy Attacks in Ethereum Smart Contracts via Static Analysis

Ethereum smart contracts are programs that are deployed and executed in a consensus-based blockchain managed by a peer-to-peer network. Several re-entrancy attacks that aim to steal Ether, the cryptocurrency used in Ethereum, stored in deployed smart contracts were found in the recent years. A countermeasure to such attacks is based on dynamic analysis that executes the smart contracts themselves, but it requires the spending of Ether and knowledge of attack patterns for analysis in advance. In this paper, we present a static analysis tool named RA (Re-entrancy Analyzer), a combination of symbolic execution and equivalence checking by a satisfiability modulo theories solver to analyze smart contract vulnerabilities against re-entrancy attacks. In contrast to existing tools, RA supports analysis of inter-contract behaviors by using only the Ethereum Virtual Machine bytecodes of target smart contracts, i.e., even without prior knowledge of attack patterns and without spending Ether. Furthermore, RA can verify existence of vulnerabilities against re-entrancy attacks without execution of smart contracts and it does not provide false positives and false negatives. We also present an implementation of RA to evaluate its performance in analyzing the vulnerability of deployed smart contracts against re-entrancy attacks and show that RA can precisely determine which smart contracts are vulnerable.
A Blockchain Based Decentralized Computing And NFT Infrastructure For Game Networks

The market value of the Gaming industry was said to be over 138 Billion USD in 2019. Competitive Gaming or eSports was already included in the Asian Games 2020, and the Olympic committee is considering including eSports into Olympics 2024. The online gaming segment amounts up to 7 quintillion bytes of internet traffic every month. Most of this data is controlled by centralized gatekeepers like cloud agencies and game creators. This is causing many issues ranging from privacy concerns to latency. The game makers have complete rights over the game to arbitrarily change the rules, set prices for the assets, and control over game servers. Even the reward mechanisms in most of the games, including eSports, are controlled by the game producers, and these rewards have no real-world value. The motivation behind this research is to create a decentralized computation and token management infrastructure for game networks. This paper focuses on using Ethereum Blockchain, IPFS and ERC - Ethereum Request for Comment 1155 architecture to build a gaming-oriented public decentralized network.
Discovering dynamic adverse behavior of policyholders in the life insurance industry

Adverse selection (AS) is one of the significant causes of market failure worldwide. Analysis and deep insights into the Australian life insurance market show the existence of adverse activities to gain financial benefits, resulting in loss to insurance companies. Understanding the behavior of policyholders is essential to improve business strategies and overcome fraudulent claims. However, policyholders’ behavior analysis is a complex process, usually involving several factors depending on their preferences and the nature of data such as data which is missing useful private information, the presence of asymmetric information of policyholders, the existence of anomalous information at the cell level rather than the data instance level and a lack of quantitative research. This study aims to analyze the life insurance policyholder’s behavior to identify adverse behavior (AB). In this study, we present a novel association rule learning-based approach ‘ARLAS’ to detect the AS behavior of policyholders. In addition to the original data, we further created a synthetic AS dataset by randomly flipping the attribute values of 10% of the records in the test set. The experiment results on 31,800 Australian life insurance users show that the proposed approach achieves significant gains in performance comparatively.
Sentiment Analysis of Social Media via Multimodal Feature Fusion

In recent years, with the popularity of social media, users are increasingly keen to express their feelings and opinions in the form of pictures and text, which makes multimodal data with text and pictures the con tent type with the most growth. Most of the information posted by users on social media has obvious sentimental aspects, and multimodal sentiment analysis has become an important research field. Previous studies on multimodal sentiment analysis have primarily focused on extracting text and image features separately and then combining them for sentiment classification. These studies often ignore the interaction between text and images. Therefore, this paper proposes a new multimodal sentiment analysis model. The model first eliminates noise interference in textual data and extracts more important image features. Then, in the feature-fusion part based on the attention mechanism, the text and images learn the internal features from each other through symmetry. Then the fusion features are applied to sentiment classification tasks. The experimental results on two common multimodal sentiment datasets demonstrate the effectiveness of the proposed model.
Deep Dehazing Network With Latent Ensembling Architecture and Adversarial Learning

Most existing dehazing algorithms recover haze-free image by solving the hazy imaging model using estimated transmission map and global atmospheric light. However, inaccurate estimation of these variables and the strong assumptions of imaging model result in unrealistic dehazing results. In this paper, we use the adversarial game between a pair of neural networks to accomplish end-to-end photo-realistic dehazing. To avoid uniform contrast enhancement, the generator learns to simultaneously restore haze-free image and capture the non-uniformity of haze. The modules for the two tasks are assembled in sequential and parallel manners to enable information sharing at different levels, and the architecture of the generator implicitly forms an ensemble of dehazing models that allows for feature selection. A multi-scale discriminator competes with the generator by learning to detect dehazing artifacts and the inconsistency between dehazed image and the spatial variation of haze. Unlike existing works that penalize dehazing artifacts via hand-crafted loss, the proposed algorithm uses the identity mapping in the space of clear-scene images to regularize data-driven dehazing. The proposed work also addresses the adaptability of data-driven dehazing to high-level computer vision task. We propose a task-driven training strategy that can optimize the object detection performance on dehazed images without updating the parameters of object detector. Performance of the proposed algorithm is assessed on the RESIDE, I-Haze, and O-Haze benchmarks. The comparison with ten state-of-the-art algorithms shows that the proposed work is the best performer in most competitions.
CSGAN: Cyclic-Synthesized Generative Adversarial Networks for image-to-image transformation

The primary motivation of image-to-image transformation is to convert an image of one domain to another domain. The Generative Adversarial Network (GAN) is the recent trend for image-to-image transformation. The existing GAN models suffer due to the lack of utilization of proper synthesization objectives. In this paper, we propose a new Cyclic-Synthesized Generative Adversarial Networks (CSGAN) for the development of expert and intelligent systems for image-to-image transformation. The proposed CSGAN uses a new objective function based on the proposed cyclic-synthesized loss between the synthesized image of one domain and cycled image of another domain. The proposed CSGAN enforces the mapping from one domain to another domain more accurately by limiting the scope of redundant transformation with the help of the cyclic-synthesized loss. The performance of the proposed CSGAN is evaluated on four benchmark image-to-image transformation datasets, including CUHK Face dataset, WHU-IIP Thermal-Visible Face Dataset, CMP Facades dataset, and NYU-Depth Dataset. The results are computed using the widely used evaluation metrics such as MSE, SSIM, PSNR, and LPIPS. The experimental results of the proposed CSGAN approach are compared with the latest state-of-the-art approaches, such as GAN, Pix2Pix, DualGAN, CycleGAN, and PS2GAN. The proposed CSGAN technique outperforms all the methods over CUHK dataset, WHU-IIP dataset, NYU-Depth dataset, and exhibits promising and comparable performance over Facades dataset in terms of both qualitative and quantitative measures. The code is available at https://github.com/KishanKancharagunta/CSGAN.
A2S-Det: Efficiency Anchor Matching in Aerial Image Oriented Object Detection

Object detection is a challenging task in aerial images, where many objects have large aspect ratios and are densely arranged. Most anchor-based rotating detectors assign anchors for ground-truth objects by a fixed restriction of the rotation Intersection-over-Unit (IoU) between anchors and objects, which directly follow horizontal detectors. Due to many directional objects with a large aspect ratio, the object-anchor IoU is heavily influenced by the angle, which may cause few anchors assigned for some ground-truth objects. In this study, we propose an anchor selection method based on sample balance assigning anchors adaptively, which we name the Self-Adaptive Anchor Selection (A2S-Det) method. For each ground-truth object, A2S-Det selects a set of candidate anchors by horizontal IoU. Then, an adaptive threshold module is adopted on the set of candidate anchors, which calculates a boundary of these candidate anchors aiming to keep a balance between positive and negative anchors. In addition, we propose a coordinate regression of relative reference (CR3) module to precisely regress the rotating bounding box. We test our method on a public aerial image dataset, and prove better performance than many other one-stage detectors and two-stage detectors, achieving the mAP of 70.64. An efficiency anchor matching method helps the detector achieve better performance for objects with large aspect ratios.
Automated detection of cognitive engagement to inform the art of staying engaged in problem-solving

In the present paper, we used supervised machine learning algorithms to predict students' cognitive engagement states from their facial behaviors as 61 students solved a clinical reasoning problem in an intelligent tutoring system. We also examined how high and low performers differed in cognitive engagement levels when performing surface and deep learning behaviors. We found that students' facial behaviors were powerful predictors of their cognitive engagement states. In particular, we found that the SVM (Support Vector Machine) model demonstrated excellent capacity for distinguishing engaged and less engaged states when 17 informative facial features were added into the model. In addition, the results suggested that high performers did not differ significantly in the general level of cognitive engagement with low performers. There was also no difference in cognitive engagement levels between high and low performers when they performed shallow learning behaviors. However, high performers showed a significantly higher level of cognitive engagement than low performers when conducting deep learning behaviors. This study advances our understanding of how students regulate their engagement to succeed in problem-solving. This study also has significant methodological implications for the automated measurement of cognitive engagement.
Browsing with Alexa: Interrogating the impact of voice assistants as web interfaces

Voice assistants such as Siri, Alexa, and Google Assistant have recently been the subject of lively debates in regard to issues such as artificial intelligence, surveillance, gender stereotypes, and privacy. Less attention, however, has been given to the fact that voice assistants are also web interfaces that might impact on how the web is accessed, understood and employed by users. This article aims to advance work in this context by identifying a range of issues that should spark additional reflections and discussions within communication and media studies and related fields. In particular, the article focuses on three key issues that have to do with long-standing discussions about the social and political impact of the internet: the role of web platforms in shaping information access, the relationship between production and consumption online, and the role of affect in informing engagement with web resources. Considering these issues in regard to voice assistants not only helps contextualize these technologies within existing debates in communication and media studies, but also highlights that voice assistants pose novel questions to internet research, challenging assumptions of what the web looks like as speech becomes one of the key ways to access resources and information online.
Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware

We propose an approximation of echo state networks (ESNs) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing. The reservoir of the proposed integer ESN (intESN) is a vector containing only n -bits integers (where is normally sufficient for a satisfactory performance). The recurrent matrix multiplication is replaced with an efficient cyclic shift operation. The proposed intESN approach is verified with typical tasks in reservoir computing: memorizing of a sequence of inputs, classifying time series, and learning dynamic processes. Such architecture results in dramatic improvements in memory footprint and computational efficiency, with minimal performance loss. The experiments on a field-programmable gate array confirm that the proposed intESN approach is much more energy efficient than the conventional ESN.
SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media

In this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media.The goal of this shared task is to design automatic methods for emphasis selection, i.e. choosing candidates for emphasis in textual content to enable automated design assistance in authoring.The main focus is on short text instances for social media, with a variety of examples, from social media posts to inspirational quotes.Participants were asked to model emphasis using plain text with no additional context from the user or other design considerations.SemEval-2020 Emphasis Selection shared task attracted 197 participants in the early phase and a total of 31 teams made submissions to this task.The highest-ranked submission achieved 0.823 Match m score.The analysis of systems submitted to the task indicates that BERT and RoBERTa were the most common choice of pre-trained models used, and part of speech tag (POS) was the most useful feature.Full results can be found on the task's website 1 .
User Response Models to Improve a REINFORCE Recommender System

Reinforcement Learning (RL) techniques have been sought after as the next-generation tools to further advance the field of recommendation research. Different from classic applications of RL, recommender agents, especially those deployed on commercial recommendation platforms, have to operate in extremely large state and action spaces, serving a dynamic user base in the order of billions, and a long-tail item corpus in the order of millions or billions. The (positive) user feedback available to train such agents is extremely scarce in retrospect. Improving the sample efficiency of RL algorithms is thus of paramount importance when developing RL agents for recommender systems. In this work, we present a general framework to augment the training of model-free RL agents with auxiliary tasks for improved sample efficiency. More specifically, we opt to add additional tasks that predict users' immediate responses (positive or negative) toward recommendations, i.e., user response modeling, to enhance the learning of the state and action representations for the recommender agents. We also introduce a tool based on gradient correlation analysis to guide the model design. We showcase the efficacy of our method in offline experiments, learning and evaluating agent policies over hundreds of millions of user trajectories. We also conduct live experiments on an industrial recommendation platform serving billions of users and tens of millions of items to verify its benefit.
Metaheuristic-based possibilistic fuzzy k-modes algorithms for categorical data clustering

Smart devices and technology applications are used in many fields. Much information is now recorded and collected rapidly so data analysis, especially clustering analysis, is vital to the process of analyzing and obtaining valuable information from datasets. However, data has different types of attributes: numerical, categorical, and mixed attributes. Some datasets also contain noise and outliers. An appropriate clustering is necessary to exploit the data structure. This study proposes a clustering algorithm that is called a possibilistic fuzzy k-modes (PFKM) algorithm. This combines the concept of possibility with the fuzzy k-modes (FKM) algorithm to address the effect of outliers and to improve the clustering results for categorical data. This study also implements three metaheuristics to increase clustering performance: a genetic algorithm (GA), a particle swarm optimization (PSO) and the sine-cosine algorithm (SCA). Three clustering algorithms are proposed: the GA-PFKM, PSO-PFKM, and SCA-PFKM algorithms. The performance of the algorithms is compared with that for the classical FKM algorithm using two indices: the sum-of-squared error (SSE) and the accuracy. The experimental results show that the PSO-PFKM and SCA-PFKM algorithms perform better for most datasets.
Fusion‐based simultaneous estimation of reflectance and illumination for low‐light image enhancement

Low-light image enhancement is a challenging field in image processing. Retinex-based methods perform well for low-light images. However, reflectance and illumination estimation is an ill-posed problem. This paper presents a new framework for the simultaneous estimation of reflectance and illumination for low-light image enhancement. The algorithm estimates multiple instances of illumination and reflectance and blends them to estimate the final components. The proposed approach uses multi-scale fusion for illumination estimation and naive fusion for reflectance estimation. Extensive experimentation and analysis with a large set of low-light images validates the performance of the proposed approach. The comparison shows the superiority of the proposed approach over most of the existing low-light image enhancement methods. The proposed method provides colour constancy in low-light image enhancement and preserves the naturalness of the image.
Fostering absorptive capacity and facilitating innovation in hospitality organizations through empowering leadership

Empowering leadership facilitates a high-quality exchange of knowledge among employees. Although existing research highlights important contributions of empowering leadership at an employee level, there is little understanding of organizational mechanisms that benefit from this type of direction. Specifically, this study examined the connection of empowering leadership to the abilities of hospitality organizations to acquire and apply knowledge, namely absorptive capacity. Also, the relationships between absorptive capacity and multidimensional organizational innovation have been scarcely investigated. To bridge the research gap, this study used a time-lagged data collection method and examined a proposed structural model using the R Lavaan package. The results revealed empowering leadership had a positive relationship with potential and realized absorptive capacity. Absorptive capacity had a positive relationship with the product, process, and administrative innovation. Also, empowering leadership was indirectly associated with innovation through potential and realized absorptive capacity. Accordingly, contributions and implications of this study were discussed.