Reviewerly/exHarmony huggingface dataset:
Quantification of multiple soil trace elements by combining portable X-ray fluorescence and reflectance spectroscopy

Several combinations of proximal sensors, portable x-ray fluorescence spectroscopy (pXRF), visible-near-infrared spectroscopy (vis-NIR), and mid-infrared spectroscopy (MIR) are assessed to quantify multiple soil trace elements (TE) with reliable accuracy. A total 622 topsoil samples (0–20 cm depth) collected at regular 8 km spacing across southern New Zealand were analysed for TE concentrations by a reference laboratory method and scanned with pXRF, vis-NIR, and MIR. The laboratory results and spectral information were used to develop robust models that enable prediction for soil concentrations of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn). Chemometric approaches including spectral data fusion, partial least squares – support vector machine (PLS-SVM) model fusion, and model averaging were tested to obtain optimal quantitative TE predictions in the samples. Validation of PLS-SVM based models on held-out samples showed the combination of pXRF and MIR performed optimally for predicting As, Cr, and Pb concentrations with concordance correlation coefficient (CCC) of 0.97 and root mean square error (RMSE) of 0.57 mg As/kg, 1.14 mg Cr/kg, and 1.05 mg Pb/kg respectively. Fusion of pXRF and vis-NIR data performed optimally for quantifying Cu, Ni, and Zn concentrations with CCC of 0.98 and RMSE of 1.09 mg Cu/kg, 0.63 mg Ni/kg, and 3.90 mg Zn/kg respectively. For Cd and Hg, Granger-Ramanathan model averaging of outputs from PLS-SVM based pXRF, vis-NIR, and MIR models performed optimally with CCC > 0.80 and RMSE of 0.03 mg Cd/kg and 0.01 mg Hg/kg respectively. This study showed that quantitative prediction models for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn can be successfully implemented at local- and national-scale and for long-term monitoring of soil trace elements at concentrations below pXRF detection limits and with reduced matrix interference from organic matter than from individual techniques alone.
General Type-2 Fuzzy Logic Systems Using Shadowed Sets: A New Paradigm Towards Fault-Tolerant Control

Fuzzy Inference Systems (FIS) are now widely employed to regulate a wide range of safety-critical engineering systems. The main reason for this is that FIS may be created using expert human knowledge. Furthermore, with the introduction of Type-2 Fuzzy Logic, the ability to handle uncertainty offers an alluring improvement for fault-tolerant abilities in fault-tolerant control methods, and, in fact, recent studies have shown that the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) provides better results than Type-1 Fuzzy Inference Systems (T1 FIS).The current research intends to suggest a novel strategy employing Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for significant features in fault-tolerant capability in the Passive Fault-Tolerant Control (PFTC) method based on the performance increase shown by IT2 FIS. The ST2 FIS is based on the ideas of Shadowed Fuzzy Sets and is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS). The fundamental rationale for utilising ST2 FIS instead of GT2 FIS is that the computational cost of GT2 FIS is too high for this application. The simulation results for the FTC using IT2 FIS and FTC using ST2 FIS for Coupled Conical Tank Level Control (CCTLC) system with 30 % and 40% loss of effectiveness in the main actuator are presented in this study. In addition, the article compares the proposed FTC approach using ST2 FIS to FTC using IT2 FIS in terms of fault-recovery time.
<scp>IoT</scp> incorporated deep learning model combined with <scp>SmartBin</scp> technology for real‐time solid waste management

Abstract With a view of the massive human resources and time requirements, the need for an automated, more accurate, and quicker method for handling the classification of solid wastes is felt more than ever worldwide. In this work, an attempt has been made to develop a model named SmartBin. Two different approaches have been followed to classify solid wastes as biodegradable and non‐biodegradable efficiently. The first approach is based on convolutional neural network (CNN) and Internet of Things (IoT), while the second approach adds several sensors to the model developed using the first approach. CNN‐based IoT is applied on datasets collected using three methods. The first one is Images from Kaggle; the second method adopted searches through Google and Bing, whereas the third one involved captured manually under a controlled environment. It is observed that the second approach has proved to be better, with an accuracy level of 98.57, which is a significantly improved performance over the first approach with an accuracy of 95.24%.
Person re-identification based on deep learning — An overview

Person re-identification(ReID) is an intelligent video surveillance technology that retrieves the same person from different cameras. This task is extremely challenging due to changes in person poses, different camera views, and occlusion. In recent years, person ReID based on deep learning technology has received widespread attention due to the rapid development and excellent performance of deep learning. In this paper, we first divide person ReID based on deep learning approaches into seven types, i.e., fused hand-crafted features deep model, representation learning model, metric learning model, part-based deep model, video-based model, GAN-based model, unsupervised model. Furthermore, we launched a brief overview of the seven types. Then, we introduce some examples of commonly used datasets, compare the performance of some algorithms on image and video datasets in recent years, and analyze the advantages and disadvantages of various methods. Finally, we summarize the possible future research directions of person ReID technology.
Imperfect ImaGANation: Implications of GANs exacerbating biases on facial data augmentation and snapchat face lenses

In this paper, we show that popular Generative Adversarial Network (GAN) variants exacerbate biases along the axes of gender and skin tone in the generated data. The use of synthetic data generated by GANs is widely used for a variety of tasks ranging from data augmentation to stylizing images. While practitioners celebrate this method as an economical way to obtain synthetic data to train data-hungry machine learning models or provide new features to users of mobile applications, it is unclear whether they recognize the perils of such techniques when applied to real world datasets biased along latent dimensions. Although one expects GANs to replicate the distribution of the original data, in real-world settings with limited data and finite network capacity, GANs suffer from mode collapse. First, we show readily-accessible GAN variants such as DCGANs ‘imagine’ faces of synthetic engineering professors that have masculine facial features and fair skin tones. When using popular GAN architectures that attempt to address mode-collapse, we observe that these variants either provide a false sense of security or suffer from other inherent limitations due to their design choice. Second, we show that a conditional GAN variant transforms input images of female and nonwhite faces to have more masculine features and lighter skin when asked to generate faces of engineering professors. Worse yet, prevalent filters on Snapchat end up consistently lightening the skin tones in people of color when trying to make face images appear more feminine. Thus, our study is meant to serve as a cautionary tale for practitioners and educate them about the side-effect of bias amplification when applying GAN-based techniques.
Analysis of data errors in the solar photovoltaic monitoring system database: An overview of nationwide power plants in Korea

Management services for solar photovoltaic power plants (PVs) have become an important issue in today's era of increasing PV installations. The existence of error-free data is necessary for data-driven PV management; however, real-world data contain an unignorable portion of errors, thereby reducing the reliability of corresponding analyses. A report on the plausible errors within the data is necessary to help PV administrators configure proper data cleaning algorithms. This study provides an error case report derived from an analysis of nearly 2000 PVs distributed throughout Korea. Real error cases are categorized according to their causes and symptoms. The effect of typical errors on the data-driven PV analysis is particularly evaluated for the day-ahead hourly PV power forecast. Errors in the system specification data significantly decrease the forecast accuracy, thereby addressing the impact of human errors. Regression imputation in the time domain demonstrates acceptable results as a simple ad-hoc method in most error situations.
Analysis of minute-scale variability for enhanced separation of direct and diffuse solar irradiance components using machine learning algorithms

With the development of solar power generation, the importance of direct normal irradiance is being emphasized. Therefore, previous studies have examined numerous separation models for calculating direct and diffuse irradiance from global horizontal irradiance. These models have recently been applied to data in 1-min units. These have more information than the previous hourly data. This study proposes an advanced separation model for direct irradiance using machine learning (ML) techniques with 1-min time-series variabilities. Three ML models were proposed and trained using data from over 20 stations in temperate climate zone regions. The models were objectively compared and analyzed using untrained period data and validated using cross-validation. The results show that the proposed time-series ML models outperform the previous models in all the stations; moreover, the neural networks show the best results with a root mean squared error (RMSE) of 13%. This shows that an ML with enough data and variables is suitable for the separation model; especially, the influence of additional variables (i.e., variability) was found to be significant in reducing the RMSE to 30%. This study can be used as a base for separation models using ML techniques and variability.
A Systematic Review of Literature on the Effectiveness of Intelligent Tutoring Systems in STEM

Intelligent tutoring systems (ITS) have shown to be useful learning aids for helping students learn STEM subjects. Previous studies on ITS tend to focus on developmental aspects of the system, such as system design, programming architecture, and dialogue moves. In this systemic literature review, we focus on pedagogical aspects of ITS within STEM domains. Specifically, we identified the implemented scaffolding approach and the grounding on learning theories of ITS implementations. Specific research questions were: (1) what types of knowledge (i.e., conceptual learning, problem-solving, and model building) are delivered via an ITS within STEM domains? (2) what pedagogies or scaffolding methods are used to guide the ITS learning experiences? (3) what are the characteristics of the research designs and specific learning outcomes when learning with the ITS? The steps followed for performing this systematic literature review were: (1) identifying the scope and research questions, (2) defining the inclusion and exclusion search criteria of literature, and (3) classifying and cataloging the literature sources that use ITS for STEM in classroom research. The final data set is comprised of a total of 22 papers that meet our criteria. We found a lack of fine-grained research on the effectiveness of using ITS to improve the three major learning modes: conceptual learning, problem-solving, and model building, particularly in STEM domains. In addition, we recommend that research conducted on ITS and other learning technology aids should emphasize the utilization of well-established learning theories and pedagogical scaffolding methods so that ITS will be more accessible to STEM educators for introducing ITS to their students to better learn STEM subjects.
Two-color optically addressed spatial light modulator as a generic spatiotemporal system

Nonlinear spatiotemporal systems are the basis for countless physical phenomena in such diverse fields as ecology, optics, electronics, and neuroscience. The canonical approach to unify models originating from different fields is the normal form description, which determines the generic dynamical aspects and different bifurcation scenarios. Realizing different types of dynamical systems via one experimental platform that enables continuous transition between normal forms through tuning accessible system parameters is, therefore, highly relevant. Here, we show that a transmissive, optically addressed spatial light modulator under coherent optical illumination and optical feedback coupling allows tuning between pitchfork, transcritical, and saddle-node bifurcations of steady states. We demonstrate this by analytically deriving the system's dynamical equations in correspondence to the normal forms of the associated bifurcations and confirm these results via extensive numerical simulations. Our model describes a nematic liquid crystal device using nano-dimensional dichalcogenide (a-As 2S 3) glassy thin films as photo sensors and alignment layers, and we use device parameters obtained from experimental characterization. Optical coupling, for example, using diffraction, holography, or integrated unitary maps allows implementing a variety of system topologies of technological relevance for neural networks and potentially Ising or XY-Hamiltonian models with ultralow energy consumption.
Dissecting Cloud Gaming Performance with DECAF

Cloud gaming platforms have witnessed tremendous growth over the past two years with a number of large Internet companies including Amazon, Facebook, Google, Microsoft, and Nvidia publicly launching their own platforms. While cloud gaming platforms continue to grow, the visibility in their performance and relative comparison is lacking. This is largely due to absence of systematic measurement methodologies which can generally be applied. As such, in this paper, we implement DECAF, a methodology to systematically analyze and dissect the performance of cloud gaming platforms across different game genres and game platforms. DECAF is highly automated and requires minimum manual intervention. By applying DECAF, we measure the performance of three commercial cloud gaming platforms including Google Stadia, Amazon Luna, and Nvidia GeForceNow, and uncover a number of important findings. First, we find that processing delays in the cloud comprise majority of the total round trip delay experienced by users, accounting for as much as 73.54% of total user-perceived delay. Second, we find that video streams delivered by cloud gaming platforms are characterized by high variability of bitrate, frame rate, and resolution. Platforms struggle to consistently serve 1080p/60 frames per second streams across different game genres even when the available bandwidth is 8-20× that of platform's recommended settings. Finally, we show that game platforms exhibit performance cliffs by reacting poorly to packet losses, in some cases dramatically reducing the delivered bitrate by up to 6.6× when loss rates increase from 0.1% to 1%. Our work has important implications for cloud gaming platforms and opens the door for further research on comprehensive measurement methodologies for cloud gaming.
Data breaches in the age of surveillance capitalism: Do disclosures have a new role to play?

The rise of big data has led to profound changes to the dynamics of accumulation and profiteering. Today, data is captured, produced, and reproduced with such regularity that its collection, utility, and value can go largely unnoticed, giving rise to "surveillance capitalism" (Zuboff, 2019a). This paper explores emerging forms of exploitation within the data economy, including the rise of "instrumentarian power" (Zuboff, 2019a), opacity surrounding data collection and use, and the impact of data breaches on our capacity to function within the information economy. We consider whether new forms of extended responsibility reporting may help to disrupt the trajectory of surveillance capitalism and democratise participation in the digital economy (Crawford, 2021). We draw on the accounting literature on organisational disclosures to consider whether the disclosure of data breaches might enhance accountability by making aspects of the surveillance economy knowable to us. Empirically, our analysis considers the various rules currently governing the disclosure of data breaches in Australia, the US, the EU, and Canada, and the application of these rules in practice. While regulation of the digital economy is developing, laws governing the disclosure of data breaches are highly dependent on an organisation's judgement. As a consequence, the nature, scale, and timeliness of these disclosures vary significantly, and the lack of clear routines makes it difficult for stakeholders to assess data risks. In response, we consider whether a mandatory disclosure framework might contribute usefully to the public "naming and taming" of surveillance capitalism (Zuboff, 2019a) and the democratisation of our digital future.
An Oscillator without Linear Terms: Infinite Equilibria, Chaos, Realization, and Application

Oscillations and oscillators appear in various fields and find applications in numerous areas. We present an oscillator with infinite equilibria in this work. The oscillator includes only nonlinear elements (quadratic, absolute, and cubic ones). It is different from common oscillators, in which there are linear elements. Special features of the oscillator are suitable for secure applications. The oscillator’s dynamics have been discovered via simulations and an electronic circuit. Chaotic attractors, bifurcation diagrams, Lyapunov exponents, and the boosting feature are presented while measurements of the implemented oscillator are reported by using an oscilloscope. We introduce a random number generator using such an oscillator, which is applied in biomedical image encryption. Moreover, the security and performance analysis are considered to confirm the correctness of encryption and decryption processes.
A framework for merging ontologies in the context of smart factories

Current trends, such as individualization, increasing complexity, and specialization, require digitization in engineering and production. However, digitization by itself often leads to so-called data silos, which cannot be leveraged effectively when designing and operating smart factories due to the heterogeneity of the information available. This paper presents a framework for (semi-)automatically merging the highly reusable terminological components of production ontologies in an a posteriori way. The framework combines translations, domain-specific vocabularies, and inconsistency checks with syntactic, terminological, and structural analyses to integrate knowledge representations formalized in the Web Ontology Language. Integrating heterogeneous knowledge representations in the production domain can improve support systems for engineers, increase awareness of interdependencies, and enable unambiguous communication in smart factories.
The exclusive nature of global payments infrastructures: the significance of major banks and the role of tech-driven companies

Despite the narrative of a globalized economy, there is no effectively working global payment system. Although there is an infrastructure that allows the transmission of data about global payments, the movement of actual money is executed indirectly, making it an incalculable endeavor. The reason is that money is not simply data, but a complex bundle of rights closely tied to the nation state. In the absence of infrastructure that reliably links payments with guarantees of the nation state, intermediaries that facilitate global payments are forced to create trust in a different way. This is only possible by occupying a highly centralized and therefore powerful position. In this article, we investigate which actors were historically able to hold such a position and how these actors are challenged by digitalization. We suggest that there are three models of payment infrastructure provision. Bank-based systems were dominant until the 1980s, but in the following decades, a second model emerged: the provision of financial infrastructure by global companies. Since the early 2000s, we see a third model: the entrance of tech-driven companies in the payment sector. We conclude that digital technologies will not necessarily solve the problems, but might in fact exacerbate them.
FEDBS: Learning on Non-IID Data in Federated Learning using Batch Normalization

Federated learning (FL) is a well-established distributed machine-learning paradigm that enables training global models on massively distributed data i.e., training on multi-owner data. However, classic FL algorithms, such as Federated Averaging (FedAvg), generally underperform when faced with Non-Independent and Identically Distributed (Non-IID) data. Such a problem is aggravated for some hyperparametric methods such as optimizers, regularization, and normalization techniques. In this paper, we introduce FedBS, a new efficient strategy to handle global models having batch normalization layers, in the presence of Non-IID data. FedBS modifies FedAvg by introducing a new aggregation rule at the server-side, while also retaining full compatibility with Batch Normalization (BN). Through our evaluations, we have empirically proven that FedBS outperforms both classical FedAvg, as well as the state-of-the-art FedProx through a comprehensive set of experiments conducted on Cifar-10, Mnist, and Fashion-Mnist datasets under various Non-IID data settings. Furthermore, we observed that in some cases, FedBS can be 2× faster than other FL approaches, coupled with higher testing accuracy.
A deformable CNN-based triplet model for fine-grained sketch-based image retrieval

With the popularity of electronic touch-screen and pressure sensing devices, fine-grained sketch based image retrieval (FG-SBIR) has become a research hotspot. In this paper, we stress the core problems of FG-SBIR: a. how to reduce the difference between the non-homogenous of heterogeneous media, and b. how to improve the distinguishability of sketch features. Specifically, a sketch generation model is first proposed to replace the conventional pre-processing of roughly extracting image edges, moreover, this model can alleviate the dilemma of sketch data scarcity. We then construct a novel FG-SBIR model which takes advantage of deformable convolutional neural network while taking into consideration of semantic attributes together. In addition, we build a fine-grained clothing sketch-image dataset, which has rich attribute annotations, for the first time. Extensive experiments exhibit that our proposed model achieves a better performance in improving the retrieval accuracy over the state-of-the-art baselines.