Reviewerly/exHarmony huggingface dataset:
Estimation and Interpretation of Machine Learning Models with Customized Surrogate Model

Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust in them; thus, highly accurate machine learning models require reason. Such models are black boxes for end-users. Therefore, the concept of interpretability plays the role if assisting users in a couple of ways. Interpretable models are models that possess the quality of explaining predictions. Different strategies have been proposed for the aforementioned concept but some of these require an excessive amount of effort, lack generalization, are not agnostic and are computationally expensive. Thus, in this work, we propose a strategy that can tackle the aforementioned issues. A surrogate model assisted us in building interpretable models. Moreover, it helped us achieve results with accuracy close to that of the black box model but with less processing time. Thus, the proposed technique is computationally cheaper than traditional methods. The significance of such a novel technique is that data science developers will not have to perform strenuous hands-on activities to undertake feature engineering tasks and end-users will have the graphical-based explanation of complex models in a comprehensive way—consequently building trust in a machine.
Wireless Image Transmission Interference Signal Recognition System Based on Deep Learning

In the process of wireless image transmission, there are a large number of interference signals, but the traditional interference signal recognition system is limited by various modulation modes, it is difficult to accurately identify the target signal, and the reliability of the system needs to be further improved. In order to solve this problem, a wireless image transmission interference signal recognition system based on deep learning is designed in this paper. In the hardware part, STM32F107VT and SI4463 are used to form a wireless controller to control the execution of each instruction. In the software part, aiming at the time-domain characteristics of the interference signal, the feature vector of the interference signal is extracted. With the support of GAP-CNN model, the interference signal is recognized through the training and learning of feature vector. The experimental results show that the packet loss rate of the designed system is less than 0.5%, the recognition performance is good, and the reliability of the system is improved.
Data Quality and Network Considerations for Mobile Contact Tracing and Health Monitoring

Machine Learning (ML) has been a useful tool for scientific advancement during the COVID-19 pandemic. Contact tracing apps are just one area reaping the benefits, as ML can use location and health data from these apps to forecast virus spread, predict “hotspots,” and identify vulnerable groups. However, to do so, it is first important to ensure that the dataset these apps yield is accurate, free of biases, and reliable, as any flaw can directly influence ML predictions. Given the lack of criteria to help ensure this, we present two requirements for those exploring using ML to follow. The requirements we presented work to uphold international data quality standards put forth for ML. We then identify where our requirements can be met, as countries have varying contact tracing apps and smartphone usages. Lastly, the advantages, limitations, and ethical considerations of our approach are discussed.
Adaptive Branching Quests Based on Automated Planning and Story Arcs

Interactive storytelling in games is a powerful tool to create immersive and engaging experiences for players. In this context, the adherence to a predefined story arc, coupled with adaptation to individual personality traits, are essential to ensure, at the same time, thematic consistency and player involvement in story-driven games. One promising way to meet such requirements is to treat plot composition as an interactive plan-generation problem, and develop a method whereby branching quests can be adequately handled and adapted in real-time. A key feature of the method is the ability to, after evaluating the effects of player decisions, perform the adaptations needed to keep the current story arc in close approximation to the predefined story arc. The underlying player preference model uses a set of artificial neural networks trained, on the basis of the players' responses to a brief Big Five personality test, to classify their preferences for specific quest decisions. This paper presents our quest adaptation method and summarizes the results we obtained through the application of our method in a fully implemented game prototype.
Word Sequential Using Deep LSTM and Matrix Factorization to Handle Rating Sparse Data for E-Commerce Recommender System

Recommender systems are essential engines to deliver product recommendations for e-commerce businesses. Successful adoption of recommender systems could significantly influence the growth of marketing targets. Collaborative filtering is a type of recommender system model that uses customers' activities in the past, such as ratings. Unfortunately, the number of ratings collected from customers is sparse, amounting to less than 4%. The latent factor model is a kind of collaborative filtering that involves matrix factorization to generate rating predictions. However, using only matrix factorization would result in an inaccurate recommendation. Several models include product review documents to increase the effectiveness of their rating prediction. Most of them use methods such as TF-IDF and LDA to interpret product review documents. However, traditional models such as LDA and TF-IDF face some shortcomings, in that they show a less contextual understanding of the document. This research integrated matrix factorization and novel models to interpret and understand product review documents using LSTM and word embedding. According to the experiment report, this model significantly outperformed the traditional latent factor model by more than 16% on an average and achieved 1% on an average based on RMSE evaluation metrics, compared to the previous best performance. Contextual insight of the product review document is an important aspect to improve performance in a sparse rating matrix. In the future work, generating contextual insight using bidirectional word sequential is required to increase the performance of e-commerce recommender systems with sparse data issues.
Evaluation of Automatic Speech Recognition Systems

Automatic Speech Recognition (ASR) is an essential task for many applications like automatic caption generation for videos, voice search, voice commands for smart homes, and chatbots. Due to the increasing popularity of these applications and the advances in deep learning models for transcribing speech into text, this work aims to evaluate the performance of commercial solutions for ASR that use deep learning models, such as Facebook Wit.ai, Microsoft Azure Speech, and Google Cloud Speech-to-Text. The results demonstrate that the evaluated solutions slightly differ. However, Microsoft Azure Speech outperformed the other analyzed APIs.
Evaluation of 12 Transposition Models Using Observations of Solar Radiation and Power Generation

This article evaluates the performance of 12 solar transposition models according to the measured data in Zhangbei of Northern China, including four isotropic models, Liu&Jordan, Koronakis, Badescu, and Tian, and eight anisotropic models, Willmot, Bugler, Hay, Skartveit−Olseth, Stevenand−Unsworth, Reindl, Temps−Coulson, and Klucher. We measured global horizontal irradiance, beam global horizontal irradiance, and diffuse horizontal irradiance. Open circuit voltage and short circuit current data of tilted photovoltaic panels are also surveyed. The radiation data of tilted surface estimated by the transposition models are fitted with the power generation data calculated by open circuit voltage and short circuit current. The different transposition models are evaluated by three error indicators, which are, respectively, root mean square error RMBE, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The relationship between radiation and power generation is explored. In Zhangbei, the best performing anisotropic model is Reindl model. The unit area power generation of PV panels in this region is about 0.03−0.05 times of the value of unit area received radiation. We compare measured power generation with power generation estimated by the Reindl model. For most of data, the absolute errors of power generation are less than 0.1 kWh/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and relative errors of power generation are not more than 20%.
Teaching and online learning practices used in different universities during the Covid-19 crisis: Findings and Challenges

In 2020, the Covid-19 pandemic created chaos in public and private life. Education institutions worldwide had to migrate online on very short notice. Online teaching and learning has been a prominent research topic in recent years, but with the rapid change in recent months, progress and interest has been greater. A bibliographic review is carried out, with the aim of showing the experiences of some universities, professors and students about emergency online teaching and learning more specifically, and the implication that technology has in higher education. We continue with some initial reflections on online teaching and learning in higher education in general. Where it can be seen that the challenge most mentioned and highlighted by many authors is accessibility, affordability, online learning pedagogy and basic requirements such as regulatory processes and communication platforms as potential teams to provide teaching and learning. online learning.
TinyML-Based Fall Detection for Connected Personal Mobility Vehicles

A new wave of electric vehicles for personal mobility is currently crowding public spaces. They offer a sustainable and efficient way of getting around in urban environments, however, these devices bring additional safety issues, including serious accidents for riders. Thereby, taking advantage of a connected personal mobility vehicle, we present a novel on-device Machine Learning (ML)-based fall detection system that analyzes data captured from a range of sensors integrated on an on-board unit (OBU) prototype. Given the typical processing limitations of these elements, we exploit the potential of the TinyML paradigm, which enables embedding powerful ML algorithms in constrained units. We have generated and publicly released a large dataset, including real riding measurements and realistically simulated falling events, which has been employed to produce different TinyML models. The attained results show the good operation of the system to detect falls efficiently using embedded OBUs. The considered algorithms have been successfully tested on mass-market low-power units, implying reduced energy consumption, flash footprints and running times, enabling new possibilities for this kind of vehicles.
Covid-19 and its relationship with kidney diseases: a scope review

Background: COVID-19 is an acute respiratory disease originally from China that emerged in December 2019 and quickly spread around the world, affecting 230,418.415 people, and causing 4,724,876 deaths. Coming from the coronavirus family, SARS-CoV-2 is a new subtype of virus that affects the respiratory tract in different levels and can spread and affect other vital structures in the body. Objective: To identify the risk factors that lead patients infected by the new coronavirus to develop kidney disease. Methods: This is a systematic review of the Scoping Review type (scope review), according to the method proposed by the Joanna Briggs Institute, with the implementation of a checklist structured by PRISMA-ScR that contains 22 mandatory items. The following descriptors were used: coronavirus infection, acute kidney injury and risk factors in five databases, namely PudMed, Scopus, Embase, Virtual Health Library and Web of Science. Results: While reading the studies, it was concluded that Acute Kidney Injury was the main renal finding in patients contaminated by SARS-CoV-2. The risk factors for developing renal worsening in patients with COVID-19 were the extremes of age, race, sex, pre-existing diseases, and the disease evolution. Conclusion: It is assumed that renal involvement does not occur only for an exclusive reason, but as a set of factors. It is up to the health team to pay constant attention to the warning signs by monitoring the contaminated patient.
E-VM: An Elastic Virtual Machine Scheduling Algorithm to Minimize the Total Cost of Ownership in a Hybrid Cloud

A hybrid cloud that combines both public and private clouds is becoming more and more popular due to the advantages of improved security, scalability, and guaranteed SLA (Service-Level Agreement) at a lower cost than a separate private or public cloud. The existing studies rarely consider VM migrations in a hybrid cloud environment with dynamically changed VM workloads. From an enterprise’s perspective, these migrations are necessary to minimize the cost of utilizing public clouds and guarantee SLAs of VMs in a hybrid cloud environment. In this paper, we propose an elastic VM allocation and migration algorithm for a hybrid cloud, called E-VM, to fully utilize the resources in a private cloud and to minimize the cost of using a public cloud while guaranteeing the SLAs of all VMs. The E-VM considers the bi-direction migration between private and public clouds. Two components, VM-predictor and VM-selector, are designed and implemented in E-VM to determine if a migration has to be triggered between private and public clouds and which VMs will be migrated to the opposite cloud, respectively. Moreover, E-VM is designed based on the existing public cloud pricing models and can be easily adapted to any cloud service provider. According to simulator results based on a set of captured industrial VM traces/workloads and additional experiments directly on a real-world hybrid cloud, the proposed E-VM can significantly reduce the total cost of using the public cloud compared to the existing VM migration schemes.
Texture Reformer: Towards Fast and Universal Interactive Texture Transfer

In this paper, we present the texture reformer, a fast and universal neural-based framework for interactive texture transfer with user-specified guidance. The challenges lie in three aspects: 1) the diversity of tasks, 2) the simplicity of guidance maps, and 3) the execution efficiency. To address these challenges, our key idea is to use a novel feed-forward multi-view and multi-stage synthesis procedure consisting of I) a global view structure alignment stage, II) a local view texture refinement stage, and III) a holistic effect enhancement stage to synthesize high-quality results with coherent structures and fine texture details in a coarse-to-fine fashion. In addition, we also introduce a novel learning-free view-specific texture reformation (VSTR) operation with a new semantic map guidance strategy to achieve more accurate semantic-guided and structure-preserved texture transfer. The experimental results on a variety of application scenarios demonstrate the effectiveness and superiority of our framework. And compared with the state-of-the-art interactive texture transfer algorithms, it not only achieves higher quality results but, more remarkably, also is 2-5 orders of magnitude faster.
Beyond Task-technology Fit: Exploring Network Value of Blockchain Technology Based on Two Supply Chain Cases

Despite the popularity of blockchain technology in the supply chain domain, cases with adoption beyond the pilot phase are limited.Even though technology fit is essential for blockchain adoption, we find network fit to be equally important for participating companies in a network.This research explores how the network affects value creation beyond a technology fit.Studying two cases, one from the gemstone industry and another from the shipping industry, we use the task technology fit model, network effects, and structural embeddedness as theoretical lenses to explore the fit that leads to the success of blockchain adoption.Our investigation reveals the task technology fit as a prerequisite and shows central organizations acting as initiators in the early phase, trying to extend the network in subsequent phases.Our investigation indicates that the network fit, autonomy, and equivalence of the organizations contributed to the successful adoption of blockchains.
Digital Euro: A (Digital) Symbol of Progress and Integration in Europe

The emergence of crypto assets such as Bitcoin and Ether exposed a number of advantages that these digital assets based on distributed ledger technology (DLTs) can offer. As cash is becoming less and less popular in the eurozone, the European Central Bank (ECB) is currently looking at the scenario of creating a digital euro as a kind of central bank money that may be used by the general public. DLT may be used to tokenize central bank money via digital currencies (CBDCs) issued by central banks, as well as to digitally represent bank deposits. The purpose of this article is to analyse what are the solutions for the future digitization of the monetary and financial systems and if current CBDC projects and prototypes, including those by the Chinese and Swedish central banks and the attempts of the ECB, have the chance to succeed with or without DLT.
Financial Opinion Mining

In this tutorial, we will show where we are and where we will be to those researchers interested in this topic. We divide this tutorial into three parts, including coarse-grained financial opinion mining, fine-grained financial opinion mining, and possible research directions. This tutorial starts by introducing the components in a financial opinion proposed in our research agenda and summarizes their related studies. We also highlight the task of mining customers’ opinions toward financial services in the FinTech industry, and compare them with usual opinions. Several potential research questions will be addressed. We hope the audiences of this tutorial will gain an overview of financial opinion mining and figure out their research directions.
FastSS: Fast and smooth segmentation of JPEG compressed printed text documents using DC and AC signal analysis

With the surge of COVID-19 pandemic, the world is moving towards digitization and automation more than it was presumed. The Internet is becoming one of the popular mediums for communication, and multimedia (image, audio, and video) combined with data compression techniques play a pivotal role in handling a huge volume of data that is being generated on a daily basis. Developing novel algorithms for automatic analysis of compressed data without decompression is the need of the present hour. JPEG is a popular compression algorithm supported in the digital electronics world that achieves compression by dividing the whole image into non-overlapping blocks of 8 × 8 pixels, and subsequently transforming each block using Discrete Cosine Transform (DCT). This research paper proposes to carry out Fast and Smooth Segmentation (FastSS) directly in JPEG compressed printed text document images at text-line and word-level using DC and AC signals. From each 8 × 8 block, DC and AC signals are analyzed for accomplishing Fast and Smooth segmentation, and subsequently, two Faster segmentation (MFastSS) algorithms are also devised using low resolution-images generated by mapping the DC signal (DC Reduced Image) and encoded DCT (ECM Image) coefficients separately. Proposed models are tested on various JPEG compressed printed text document images created with varied space and fonts. The experimental results have demonstrated that the direct analysis of compressed streams is computationally efficient, and has achieved speed gain more than 90% when compared to uncompressed domains.
Toxic Text in Personas: An Experiment on User Perceptions

When algorithms create personas from social media data, the personas can become noxious via automatically including toxic comments.To investigate how users perceive such personas, we conducted a 2 × 2 user experiment with 496 participants that showed participants toxic and non-toxic versions of data-driven personas.We found that participants gave higher credibility, likability, empathy, similarity, and willingness-to-use scores to non-toxic personas.Also, gender affected toxicity perceptions in that female toxic data-driven personas scored lower in likability, empathy, and similarity than their male counterparts.Female participants gave higher perceptions scores to non-toxic personas and lower scores to toxic personas than male participants.We discuss implications from our research for designing data-driven personas.
Calibrated Peer Reviews in Requirements Engineering Instruction: Application and Experiences

Instructing Requirements Engineering (RE) is a challenging task due to the absence of single absolute and correct solutions computer science students so often strive for.Instead, there is often a variety of compromise solutions for each RE problem.Therefore, it is essential that aspiring Software Engineers are exposed to as many solution alternatives as possible to experience the implications of RE decisions.To facilitate this, we propose a learning-by-multiple-examples process, in which we make use of a calibrated peer review grading model for assignments.Paired with a think-pair-share model of semester-long, industry-realistic, project-based lowstakes milestones, we were able to generate a rich collaborative learning atmosphere.In this paper, we report the course design and experiences from the application of calibrated peer reviews in an undergraduate RE course.Qualitative and quantitative application results show that calibrated peer reviews significantly improve students' learning outcomes.
Sources of the Terrigenous and Volcanic Ash Material in the Middle Volgian Deposits of the Eastern Russian Platform

The current research aims to reconstruct sources of the rocks and pyroclastic input in the epeiric sea of the Eastern Russian Platform in the Latest Jurassic.Nine samples of the Volgian mudrocks, black shales, and sandstones from the Tatar Shatrashany borehole section and Gorodischi outcrop were investigated using the mineralogical, ICP-MS, XRD, electron microscopy, and Raman spectroscopy analyses.Accessory minerals of various mineral associations identified in sandstones indicate the input of terrigenous material from different sources.The Paleozoic deposits of the Volga-Ural and Voronezh Uplifts, igneous and metamorphic rocks of the Voronezh Crystalline Massive (including Pavlovsk granite complex), Ural Folding Region, and Baltic Shield were found to be the most likely sources.The morphology of zircons revealed their association with felsic alkaline rocks.Volcanic glasses and "camouflaged" pyroclastic material (heulandite, smectite, and mixed-layer mineral) were identified in mudrocks and black shales.The proportion of both smectite and mixed-layer mineral varies from 20% to 27% throughout the section, whereas the amount of heulandite reaches 25% in the Promzino black shales.The Raman spectroscopy analysis showed the intermediate-acid composition of volcanic glasses from the Promzino Formation.According to the results obtained in the study, volcanic arcs of the north margin of the Tethys and the High Arctic Large Igneous Province are the major sources of the pyroclastic material.