Reviewerly/exHarmony huggingface dataset:
Cloud-Computed Solar Tracking System

Nations in the contemporary world choose renewable energy sources since it is cheaper, cleaner, and more plentiful than fossil fuels. According to recent studies, international airports like CIAL, which operate only on renewable energy, are transforming India into a solar powerhouse. And in the Indian state of Tamil Nadu, one of the giant solar power plants has recently been built. The project's goal is to develop a test device for usage before a solar power plant is created, which will cost a lot of money. Hardware and software are the two components of the project. Arduino, LDR sensors, and servo motors make up the hardware, while a mobile application accesses the graph on the go. Amazon's Web server is used for cloud computing. The LDR sensors provide data to the cloud, where computer operations are carried out. Finally, a graph will be generated as a result of the processing. Using the smartphone app, you may see the chart. We can determine whether the site we are considering for a large project like a solar farm is viable by looking at the graph. With this initiative, we can anticipate the future performance of the solar farm and prepare accordingly. As a result, it aids in avoiding significant investments before the project's viability has been determined.
A load-balanced hybrid heuristic for allocation of batch of tasks in cloud computing environment

Purpose Cloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be done optimally to achieve proficient results in a cloud computing environment. While satisfying the user’s requirements in a cloud environment, scheduling has been proven an NP-hard problem. Therefore, it leaves scope to develop new allocation models for the problem. The aim of the study is to develop load balancing method to maximize the resource utilization in cloud environment. Design/methodology/approach In this paper, the parallelized task allocation with load balancing (PTAL) hybrid heuristic is proposed for jobs coming from various users. These jobs are allocated on the resources one by one in a parallelized manner as they arrive in the cloud system. The novel algorithm works in three phases: parallelization, task allocation and task reallocation. The proposed model is designed for efficient task allocation, reallocation of resources and adequate load balancing to achieve better quality of service (QoS) results. Findings The acquired empirical results show that PTAL performs better than other scheduling strategies under various cases for different QoS parameters under study. Originality/value The outcome has been examined for the real data set to evaluate it with different state-of-the-art heuristics having comparable objective parameters.
Cryptoeconomics: Designing Effective Incentives and Governance Models for Blockchain Networks Using Insights from Economics

This research is an authoritative guide to the emerging field of cryptoeconomics, the study of individual and collective choices in blockchain-based environments. These digital environments are more difficult to change than other software ecosystems, and so their creators must design them very thoughtfully. The authors provide a framework for economic design and walk readers through the phases of the design process. They analyze the challenges that stakeholders face in governing blockchain projects, and they explain why many projects fail to achieve their goals.
SOME ASPECTS OF DESIGNING DIGITAL EDUCATIONAL RESOURCES IN THE WORKS OF DOMESTIC SCIENTISTS

The article examines the problem of creating and using digital educational resources, as recent years have been years of trials for Ukraine and educational institutions: first a global pandemic, now the war has become a key factor in the development of digital learning tools and educational resources. The article considers different approaches to the use and development of electronic educational resources. In particular, different approaches of scientists to this problem were analyzed, who studied: informatization of education in Ukraine in the context of research on the quality of ICT, the use of the Internet and electronic educational resources in the educational process of higher education, the development of criteria for evaluating electronic educational resources textbooks and electronic teaching aids in the context of digitalization of general secondary education in Ukraine, etc. The article aims to review some aspects of designing digital educational resources for various purposes in the works of domestic scientists. Thus, this study analyzes some approaches to the design of some digital educational resources in the domestic literature. It has been found that researchers consider different approaches to both the design and use of digital educational resources. It was found that pedagogical design primarily involves modeling. As a result, different approaches to modeling different types of digital educational resources are analyzed. In general, researchers have considered many problems in the design and use of various digital educational resources, which include e-learning tools, mass open online courses, cloud-based learning environments, and more. They all agree that to better understand the functioning of a tool, you should first model a specific tool, and consider its use.
Enhancing the MR Neuroimaging by Using the Deep Super-Resolution Reconstruction

As brain investigation progresses, the need has become urgent from acquiring the higher resolution neuroimaging data to give a more detailed interpretation. In particular, the technological development and innovation of the Magnetic Resonance Imaging (MRI) machine, through increasing the magnetic field from low (such as 3T) to high (such as 7T), has revealed significant advantages regarding the image quality enhancement, etc. Currently, due to the limitations of hardware, physics and physiology, the large-scale acquisition of the high-resolution MRI neuroimages is still running on the road. Hence, enhancing the quality of the low-field MRI data is critical by using the advanced artificial intelligence technology. In this study, we propose a novel image enhancement framework, namely SR-MRI, trying to improve the quality of the low-resolution neuroimage: (1) combining with the Real-ESRGAN deep learning model; (2) bridging the 3T-MRI and the 7T-MRI within the same analysis scale; and (3) systematically comparing multiple evaluation indicators, including Brenner, SMD, SMD2, Variance, Vollath, Entropy, and NIQE. The experimental results suggest that the edge, fineness and texture features of the low-resolution neuroimages are restored to a great extent from the SR-MRI framework. In addition, the evaluation results of multiple indicators show that the processed 3T-MRI can achieve the similar effect from the 7T-MRI machine.
The Effect of Role Assignment on Students’ Collaborative Inquiry-based Learning in Augmented Reality Environment

Augmented Reality (AR) has great potential in science education, and Collaborative Inquiry-based Learning (CIBL) in the AR environment is of great significance. However, there is a problem of low collaborative performance in technology-based CIBL. This study applied the strategy of role assignment to AR-based CIBL, aiming to explore the effect of role assignment on students’ collaboration. Forty-seven sixth-grade students in elementary school were randomly divided into Group A (without role assignment) and Group B (with role assignment) to participate in AR-based collaborative scientific inquiry activities. Data on students’ scientific knowledge achievement, attitudes toward science learning, cognitive load, and flow experience were collected. In addition, interviews were conducted to investigate students’ opinions on role assignments. It is found that the strategy of role assignment could significantly improve students’ science knowledge achievement. The interview results revealed how role assignments facilitate students’ collaboration from three aspects.
Work-to-Home Cybersecurity Spillover: Construct Development and Validation

The COVID-19 pandemic has forced employees to work from home using enterprise systems outside traditional organizational boundaries. Thus, organizations cannot enforce security policies for safe IT use behaviors but depend on continuance intentions of work policies at home. We study the transfer of IT security behaviors from work to home by developing the construct of Work-to-Home Cybersecurity Spillover and empirically testing and validating it. Our findings provide practical implications for researchers and practitioners in cybersecurity.
Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN)

In principle, a video codec is built by implementing various algorithms and their development. The next generation of codecs involves more artificial intelligence applications and their development. DCNN (Deep Convolutional Neural Network) is a multi-layer NN concept with a deep learning approach in the field of artificial intelligence development. This study has proposed a DCNN with three hidden layers for intra-frame-based video compression. DCT and fractal methods were used to compare the performance of the proposed method.Â The training image (obtained from the average of all down-sampled frames) is divided into several square blocks using the square block shift operation until all parts of the image are fulfilled. All pixels in each block act as input data patterns. After the training process, the trained proposed DCNN was then used to construct the feature and sub-feature image obtained through the max function operation in the feature bank and sub-feature bank. These feature and sub-feature images were then a spatial redundancy minimizer with specific manipulation techniques and simultaneously a quantizer without converting the frame's pixels to a bit-stream. The result of this process is a compressed image. Experiments on the entire dataset resulted in AAPR (Average Approximate Performance Ratio) of 147.71%, or an average of 1.5 times better than other methods. For further studies, the performance improvement of the proposed DCNN is performed by modifying its structure so that the output is direct in the form of feature and sub-feature images. Another way is to combine it with the DCT or fractal method to improve the performance of the result.
Debugging Support for Machine Learning Applications in Bioengineering Text Corpora

Modeling in machine learning (ML) is becoming an essential part of software systems in practice. Validating ML applications is a challenging and time-consuming process for developers since the accuracy of prediction heavily relies on generated models. ML applications are written by relatively more data-driven programming based on the blackbox of ML frameworks. If all of the datasets and the ML application need to be individually investigated, the ML debugging tasks would take a lot of time and effort. To address this limitation, we present a novel debugging technique for machine learning applications, called MLDBUG that helps ML application developers inspect the training data and the generated features for the ML model. Inspired by software debugging for reproducing the potential reported bugs, MLDBUG takes as input an ML application and its training datasets to build the ML models, helping ML application developers easily reproduce and understand anomalies on the ML application. We have implemented an Eclipse plugin for MLDBUG which allows developers to validate the prediction behavior of their ML applications, the ML model, and the training data on the Eclipse IDE. In our evaluation, we used 23,500 documents in the bioengineering research domain. We assessed the MLDBUG's capability of how effectively our debugging technique can help ML application developers investi-gate the connection between the produced features and the labels in the training model and the relationship between the training instances and the instances the model predicts.
A Metrological Perspective on Reproducibility in NLP*

Abstract Reproducibility has become an increasingly debated topic in NLP and ML over recent years, but so far, no commonly accepted definitions of even basic terms or concepts have emerged. The range of different definitions proposed within NLP/ML not only do not agree with each other, they are also not aligned with standard scientific definitions. This article examines the standard definitions of repeatability and reproducibility provided by the meta-science of metrology, and explores what they imply in terms of how to assess reproducibility, and what adopting them would mean for reproducibility assessment in NLP/ML. It turns out the standard definitions lead directly to a method for assessing reproducibility in quantified terms that renders results from reproduction studies comparable across multiple reproductions of the same original study, as well as reproductions of different original studies. The article considers where this method sits in relation to other aspects of NLP work one might wish to assess in the context of reproducibility.
Cellular KPI Anomaly Detection with GAN and Time Series Decomposition

Detecting anomalies in key performance indicator (KPI) is crucial for cellular network management. With the 5G deployment, the network is becoming much more complicated, making manual anomaly detection tedious or even impossible. In this paper, we propose LSTM-GAN-G for anomaly detection in cellular KPI time series. We build a deep generative adversarial network (GAN) with long short term memory (LSTM) cells to better capture temporal characteristics of the input data. The LSTM-GAN is trained with purely normal data so that the trained generator learns the distribution of the normal cellular KPI time series. During anomaly detection, the trained generator (G) is exploited to detect anomalies in testing samples by computing the difference between the testing sample and the generator reconstructed samples. Additionally, we propose to decompose the input time series and feed the de-seasoned data to the LSTM-GAN to further improve the anomaly detection performance. The proposed algorithm is evaluated on a real-world cellular KPI dataset. Our results show that the proposed method is able to detect both point anomaly and segment anomaly accurately, and significantly outperforms benchmark algorithms.
H.264 Video Encoding-based Edge-assisted Mobile AR Systems: Network and Energy Issues

Edge-assisted mobile augmented reality (Edge-MAR) systems have emerged as effective ways to support computation-intensive and latency-sensitive applications for mobile devices due to the offloading capability of heavy computational burdens. However, the network- and energy-resource utilization of such systems is high. Video encoding schemes like H.264 can help Edge-MAR systems reduce latency and bandwidth utilization but at the cost of increased energy consumption. In this paper, we present a comprehensive study of Edge-MAR using H.264 video encoding with a focus on network condition, resource utilization, detection accuracy, and energy consumption of various mobile devices. We collect latency, energy, transmitted data size, and accuracy data for each segment of an object detection pipeline measured through experiments with testbeds, and analyze the non-linear behaviors of Edge-MAR. Following this, we demonstrate the challenges associated with the experiments conducted to test the system as well as the ways to overcome them. Finally, we propose regression-based models to analytically compute different Edge-MAR parameters to achieve desired outcomes. This extensive study provides essential guidelines to network- and energy-aware H.264 video encoding-based Edge-MAR system design.
Participatory co-design approach for Greencoin educational tool shaping urban green behaviors

Our main goal is to prepare assumptions of the Greencoin (GC) cybernetic system, implying pro-ecological attitudes and behavior of city residents. We used qualitative methods, including a literature review and action research -workshops attended by academics, representatives of private and business sectors, urban movements, municipal institutions’ partners, and residents. Our results defined functionalities of the GC, identified main climate challenges, and confronted city’s possibilities and expectations of its residents. Application modules were proposed to help shape pro-ecological attitudes and behaviors of city residents. The modules include educational solutions fitting into the circular economy, and metabolic approach, enabling broader inclusion in the community. Our studies contribute to and fill the gap in the stream of research and knowledge on implementations in the co-creation of application solutions that promote pro-environmental attitudes and behavior.
What do artificial neural networks learn? A study for analysis of RBS spectra

Abstract The massive analysis of Rutherford Backscattering Spectrometry (RBS) data is complex. When the data is processed manually, it requires a long time of an experienced person. Artificial Neural Networks (ANN) can analyze, speed up, and automate data processing. In fact, after training, the ANN processes one RBS spectrum in a fraction of a second with the advantage of keeping the consistency over the whole set of spectra. Our group used ANN to process a large set of RBS spectra from the inner walls of the vacuum chamber of the W7-X fusion reactor. In this work, we used a perturbation-based method to study the local explanations of the neural network predictions. In this method, we apply small perturbations to the inputs. Then, the outputs’ variations are evaluated. Thereby activation maps were created to visualize how sensitive the ANN is to perturbations. The activation maps enable the identification of the parts of the spectrum the neural network is getting information to make predictions. Therefore, we can better understand the behavior of the machine learning model and verify if the neural network learned the features of the spectra similarly to humans.
From developing to developed: Mechanisms of health inequalities among seniors in China and Japan under macro-field control

The behavioral characteristics, health statuses, and survival times of seniors in China and Japan using the fixed cohort method and constructed relationship models among capital, habitus, and health based on Pierre Bourdieu's social theory of practice. It was first found that capital, habitus, and health have a capital-based triangle generative structural relationship. Second, basic sources of health inequalities include the direct effect of capital and the indirect effect of capital through habitus, i.e., class habitus controlled by capital has class attributes and is also one of the sources of health inequalities. Third, time-space conversion of the field is not just the change in the total amount or composition of an individual's capital but also includes the development and improvement of the macro-social environment, causing altered intensities of the impacts of capital and habitus on health. Fourth, the macro-social structures of developing countries significantly differ. The direct effect of capital on health is far greater than the indirect effect of capital on health through habitus, and health inequalities are mainly derived from the direct role of capital. Fifth, with socioeconomic development and improvements in social welfare systems, health inequalities have been generally reduced but have not been eliminated, and the mechanism of health inequalities in developed countries has gradually shifted from the direct effect of capital to class habitus.
Dynamics of a dispersively coupled transmon qubit in the presence of a noise source embedded in the control line

We describe transmon qubit dynamics in the presence of noise introduced by an impedance-matched resistor ($50\,\mathrm{\Omega}$) that is embedded in the qubit control line. To obtain the time evolution, we rigorously derive the circuit Hamiltonian of the qubit, readout resonator and resistor by describing the latter as an infinite collection of bosonic modes through the Caldeira-Leggett model. Starting from this Jaynes-Cummings Hamiltonian with inductive coupling to the remote bath comprised of the resistor, we consistently obtain the Lindblad master equation for the qubit and resonator in the dispersive regime. We exploit the underlying symmetries of the master equation to transform the Liouvillian superoperator into a block diagonal matrix. The block diagonalization method reveals that the rate of exponential decoherence of the qubit is well-captured by the slowest decaying eigenmode of a single block of the Liouvillian superoperator, which can be easily computed. The model captures the often used dispersive strong limit approximation of the qubit decoherence rate being linearly proportional to the number of thermal photons in the readout resonator but predicts remarkably better decoherence rates when the dissipation rate of the resonator is increased beyond the dispersive strong regime. Our work provides a full quantitative description of the contribution to the qubit decoherence rate coming from the control line in chips that are currently employed in circuit QED laboratories, and suggests different possible ways to reduce this source of noise.
Dynamic 3D measurement based on orthogonal fringe projection and geometric constraints

Geometric constraint algorithms can solve phase ambiguity for fringe projection profilometry (FPP). However, they either require multiple cameras or suffer from a small measurement depth range. To overcome these limitations, this Letter proposes an algorithm combining orthogonal fringe projection and geometric constraints. A novel, to the best of our knowledge, scheme is developed to assess the reliabilities of the potential homologous points, which works with depth segmentation to determine the final HPs. With full consideration of lens distortions, the algorithm reconstructs two 3D results from every set of patterns. Experimental results verify that it can effectively and robustly measure discontinuous objects with complex motion over a large depth range.
Order-preserving pattern matching with scaling

Given a text T and a pattern P, the order-preserving pattern matching (OPPM for short) problem is to find all substrings of T which have the same relative orders as P. Recently, approximate OPPM that allows errors have been studied such as OPPM with k-mismatches. In this paper we define the OPPM with scaling which is a novel criteria for approximate OPPM by considering the relative orders of cusps and the scale of lengths of strings between them. Also we present an algorithm to solve the OPPM problem with scaling in O(n+mlog⁡m) time, which is the same time bound as the best known exact OPPM algorithm.