Reviewerly/exHarmony huggingface dataset:
EOG Signal Classification Based on Blink-to-Speak Language

Given the need for a universal communication method for people who cannot communicate using oral language (as speech disability, limited or no movement in the upper limbs avoiding the use of sign language, among others), the electrooculogram (EOG) is proposed as a tool for recording eye patterns. Therefore, a total of 10 patterns derived from the Blink-to-speak language, which contains essential needs, were carefully chosen to be recorded by 20 participants. From these recordings, various statistical and signal measurements were extracted as distinctive features. Subsequently, a comprehensive database was constructed using these features in order to train a machine-learning algorithm, specifically a decision tree model. The primary objective of this model was to accurately detect and predict the specific eye-movement patterns that corresponded to a given word or communicative concept, all while considering the underlying intention behind each movement. An 83.4% of accuracy were achieved for this multiclass task, the patterns that achieved 100% correct predictions were “I want to sleep” and “danger”, while the patterns with lower performance were “yes” and “toilet”. Hence, our proposal represent a viable communication alternative for those who cannot communicate due to speech and limb movement limitations.
A regression tree method for longitudinal and clustered data with multivariate responses

In this paper, we propose a tree-based method called Multivariate RE-EM tree, which combines the regression tree and the linear mixed effects model for modeling multivariate response longitudinal or clustered data. The Multivariate RE-EM tree method estimates a population-level single tree structure that is driven by the multiple responses simultaneously and object-level random effects for each response variable, where correlation between the response variables and between the associated random effects are each allowed. Through simulation studies, we verify the advantage of the Multivariate RE-EM tree over the use of multiple univariate RE-EM trees and the Multivariate Regression Tree. We apply the Multivariate RE-EM tree to analyze a real data set that contains multidimensional nonfinancial characteristics of poverty of different countries as responses, and various potential causes of poverty as predictors.
A Comparative Study of Cloud Databases

The adoption of cloud-based services has been on the rise in recent years. In this study, a comparative study of three major cloud database platforms, namely Microsoft Azure, IBM DB2, and Oracle Cloud, was conducted to analyze their features, performance, and suitability for different use cases. This chapter explores the architectures of these platforms, examines their database management tools, and evaluates their support for scalability, security, and data integration. In the experiment section, Oracle performed the best with an average execution time of 3.35ms, which was the fastest of the three databases. In the survey section, the results show that MS Azure is the most available, secure, cost effective, and efficient. Oracle can be seen from the survey responses as the most reliable and also most cost effective. This chapter gives insightful information to organizations and people who want to pick the finest cloud database platform for their requirements.
Robust RGB-T Tracking via Adaptive Modality Weight Correlation Filters and Cross-modality Learning

RGBT tracking is gaining popularity due to its ability to provide effective tracking results in a variety of weather conditions. However, feature specificity and complementarity have not been fully used in existing models that directly fuse the correlation filtering response, which leads to poor tracker performance. In this article, we propose correlation filters with adaptive modality weight and cross-modality learning (AWCM) ability to solve multimodality tracking tasks. First, we use weighted activation to fuse thermal infrared and visible modalities, and the fusion modality is used as an auxiliary modality to suppress noise and increase the learning ability of shared modal features. Second, we design modal weights through average peak-to-correlation energy coefficients to improve model reliability. Third, we propose consistency in using the fusion modality as an intermediate variable for joint learning consistency, thereby increasing tracker robustness via interactive cross-modal learning. Finally, we use the alternating direction method of multipliers algorithm to produce a closed solution and conduct extensive experiments on the RGBT234, VOT-TIR2019, and GTOT tracking benchmark datasets to demonstrate the superior performance of the proposed AWCM against compared to existing tracking algorithms. The code developed in this study is available at the following website. 1
Designing Feminist Methodologies: Foregrounding Gender, Positionality, and Justice in Communication Design Research

While several studies from ACM-SIGDOC have used feminist research methodology as a frame, there exists no clear definition of what constitutes feminist methodology for the organization's constituencies, or how it can be applied across its disciplines (i.e., communication design and technical/professional communication [TPC]). To generate a baseline understanding of the field's understanding of feminist research methodologies, I conducted a survey of TPC and communication design scholars, which asked four questions about how they defined these types of research frameworks, and whether they used such frameworks in their own scholarship. I report the results of this survey and use participants’ responses, alongside previously published scholarship in the field, to create a taxonomy of feminist methodological values that can guide research in TPC and communication design. I conclude with a description of practical tactics that researchers can use to align their projects with these values and integrate feminist methodology into their work.
Predictive geochemical mapping using machine learning in western Kenya

Digital soil mapping techniques represent a cost-effective method for obtaining detailed information regarding the spatial distribution of chemical elements in soils. Machine learning (ML) algorithms using random forest (RF) models have been developed for classification, pattern recognition and regression tasks, they are capable of modelling non-linear relationships using a range of datasets, identifying hierarchical relationships, and determining the importance of predictor variables. In this study, we describe a framework for spatial prediction based on RF modelling where inverse distance weighted (IDW) predictors are used in conjunction with ancillary environmental covariates. The model was applied to predict the total concentration (mg kg−1) and assess the prediction uncertainty of 56 elements, soil pH and organic matter content using 466 soil samples in western Kenya; the results of iodine (I), selenium (Se), zinc (Zn) and soil pH are highlighted in this work. These elements were selected due to contrasting biogeochemical cycles and widespread dietary deficiencies in sub-Saharan Africa, whilst soil pH is an important parameter controlling soil chemical reactions. Algorithm performance was evaluated determining the relative importance of each predictor variable and the model's response using partial dependence profiles. The accuracy and precision of each RF model were assessed by evaluating out-of-bag predicted values. The models R2 values range from 0.31 to 0.64 whilst CCC values range from 0.51 to 0.77. The IDW predictor variables had the greatest impact on assessing the distribution of soil properties in the study area, however, the inclusion of ancillary environmental data improved model performance for all soil properties. The results presented in this paper highlight the benefits of ML algorithms which can incorporate multiple layers of data for spatial prediction, uncertainty assessment and attributing variable importance. Additional research is now required to ensure health practitioners and the agri-community utilise the geochemical maps presented here for assessing the relationship between environmental geochemistry, endemic diseases and preventable micronutrient deficiency.
Analyzing YouTube Content: Methodological Considerations for Technical Communication Research

The YouTube platform plays a vital role in contemporary technical communication as a central warehouse of technical instructions. Though several studies have explored the importance of specific videos, creators, and communities, it is also necessary to study the platform with a wider, data-driven lens in order to consider rhetorical strategies across channels and communities. But while this relationship between close and distant reading has been applied successfully to text-focused platforms, YouTube's multimodality leads to challenges of scale and categorization for researchers taking a data-driven approach. This paper presents methodological insights from a study sampling and analyzing 300 YouTube videos. The authors reflect on the methodological challenges and propose potential solutions.
Universal approximation property of a continuous neural network based on a nonlinear diffusion equation

Abstract Recently, differential equation-based neural networks have been actively studied. This paper discusses the universal approximation property of a neural network that is based on a nonlinear partial differential equation (PDE) of the parabolic type. Based on the assumption that the activation function is non-polynomial and Lipschitz continuous, and applying the theory of the difference method, we show that an arbitrary continuous function on any compact set can be approximated using the output of the network with arbitrary precision. Additionally, we present an estimate of the order of accuracy with respect to △ t and △ x .
FarmIns: Blockchain Leveraged Secure and Reliable Crop Insurance Management System

Farmer uses traditional crop insurance to protect their farms against crop loss and natural risks. However, farmers are concerned about crop insurance claims due to delays in processing claims that cost significantly. Insurance fraud is another problem in crop insurance which costs significantly for insurance companies. The proposed FarmIns framework uses blockchain technology, allowing farmers to create and manage insurance agreements with insurance providers through smart contracts and creating a verifiable log of farm monitoring parameters to help insurance providers verify and approve claims promptly. FarmIns uses the Internet of Agro-Things (IoAT), and video surveillance technologies like Closed-Circuit Television (CCTV) to monitor and provide reliable farm data to process claims. FarmIns also acts as Decision Support Tool (DST) for both the insurer and the insured.
Introduction to Privacy Preservation and Secure Data Storage in Cloud Computing

Cloud computing has swiftly emerged as the dominant IT paradigm, revolutionizing data storage and processing with scalable, cost-effective solutions for individuals and businesses. However, concerns over data security and privacy have arisen due to the migration of sensitive data to remote servers. This chapter introduces privacy and secure data storage principles in the cloud context. It covers threats, emphasizing robust security measures like encryption, access control, and anonymization. The chapter also addresses legal frameworks, urging alignment with regulations. It presents best practices for secure data storage, including encryption and emerging tech like homomorphic encryption. Ultimately, the chapter aims to equip readers with a comprehensive understanding of privacy preservation and secure data handling in the cloud, enabling informed decisions that uphold data privacy.
Improving Cloud Security Using Distributed Ledger Technology

The confidentiality, integrity, and availability triad is a fundamental principle for information security. The objective of this chapter is to examine this triad where blockchain technology can be applied to enhance security in cloud computing. The current pasture of cloud security is explored to identify the gaps and approaches for using blockchain technology to reduce those gaps are discussed in this chapter. Blockchain technology is well-recognized for its ability to ensure data integrity. The largest gap that can be reduced is in this area. With the decentralized and distributed architecture of blockchain, there are ways to leverage it to improve the availability of cloud security. Confidentiality, where encryption plays a significant role, has the least gap in cloud computing; however, since the responsibility for assuring confidentiality is on the user, blockchain offers alternative and additional strengths. This chapter also covers a detailed discussion of avoiding prevalent security threats by implementing blockchain technology in cloud computing for making it more secure.
Human Centered (Re)Design: Revising a Rhetoric and Writing PhD Program

In October 2022, the Department of Writing, Rhetoric, and Cultures at Michigan State University proposed an admissions pause to our Rhetoric and Writing PhD program to reimagine the possibilities of the program based on disciplinary trends and the needs and contexts of current students in the program. As part of a team of three WRAC PhD students who chose to join the research committee, we provide insight into the human-centered methodologies and mixed methods relevant to evaluating and restructuring a doctoral humanities program. This experience report describes the methodological rationale, the current status of the project, and reflections on the research teams’ experiences as graduate students working towards large-scale programmatic change.
Enhanced Throttled Load Balancing Algorithm for Optimizing Cloud Computing Efficiency

Facilitated by high-speed internet connectivity, the adoption of cloud computing has surged dramatically due to its distinctive attributes such as pay-as-you-go billing, ubiquitous accessibility, and minimal support requirements in the realm of service and resource delivery. While cloud computing draws inspiration from distributed, parallel, and grid computing paradigms, it also inherits their associated complexities and concerns. Among these challenges, optimizing performance stands out prominently. Enhancing performance can be achieved through the development of efficient load balancing algorithms. Efficient algorithms satisfy users and make optimal uses of resources of the cloud and hence improve performance. Several load balancing algorithms were designed to respond to performance problems. In this chapter, three well-established load balancing algorithms, namely round robin, equally spread current execution, and throttled, underwent a comprehensive analysis. This examination sought to unveil both the strengths and weaknesses inherent to these algorithms.
The Human Side of XAI: Bridging the Gap between AI and Non-expert Audiences

Machine Learning is widely used by practitioners to solve complex challenges. However, despite being trusted by 76% of the public, scientists struggle to explain the rationale behind machine learning-based decisions. This is concerning because research has shown that people often rely on inaccurate machine learning recommendations, even when the system is not confident or they have prior knowledge. To address these issues, there is a crucial need for greater transparency and education around machine learning decision making. In this work, we propose a set of guidelines and design implications to communicate eXplainable Artificial Intelligence models to the general audience. We do this through a literature review of the latest and eXplainable Artificial Intelligence methods and validate these insights through a user study encompassing the participants’ interpretations of the eXplainable Artificial Intelligence solutions. Combining the insights from this mixed-method study, we identify seven main communication guidelines for improving machine learning models understanding. This study contributes to the broader discussion of ethical implications surrounding opaque machine learning models in decision-making. Through the development of guidelines, we hope to bridge the gap between machine learning experts and the public, enabling a better common understanding of its increasing importance in our lives.
A Load Balancing Algorithm for Enhanced Active Monitoring in a Cloud Environment

Several strategies have been put forth to efficiently assign the accessible cloud nodes to the client's request and the goal is to improve the cloud's overall performance while also giving users better services and a higher level of customer happiness. An extensive investigation and analysis are done into the idea of using a load balancing method with active monitoring as well as the modification of such an algorithm. There are several ways to accomplish this. The results of the study suggest that when adopting a load balancing method with active monitoring methodology in a cloud computing environment, a more effective active monitoring load balancing method should be used. The load balancing technique gets increasingly challenging as a virtual machine's demand increases. The authors were able to improve throughput while also streamlining the load balancing procedure by using a buffer. Using this information as a foundation, the authors enhanced the load balancing method with active monitoring technique to take into account the growing demand of a virtual machine.
Knowledge representation and acquisition in the era of large language models: Reflections on learning to reason via PAC-Semantics

Human beings are known for their remarkable ability to comprehend, analyse, and interpret common sense knowledge. This ability is critical for exhibiting intelligent behaviour, often defined as a mapping from beliefs to actions, which has led to attempts to formalize and capture explicit representations in the form of databases, knowledge bases, and ontologies in AI agents. But in the era of large language models (LLMs), this emphasis might seem unnecessary. After all, these models already capture the extent of human knowledge and can infer appropriate things from it (presumably) as per some innate logical rules. The question then is whether they can also be trained to perform mathematical computations. Although the consensus on the reliability of such models is still being studied, early results do seem to suggest they do not offer logically and mathematically consistent results. In this short summary article, we articulate the motivations for still caring about logical/symbolic artefacts and representations, and report on recent progress in learning to reason via the so-called probably approximately correct (PAC)-semantics.
Unlocking Secure Horizons

Cloud computing revolutionized business data management yet introduced security risks. This chapter assesses blockchain's impact on cloud security, highlighting the pros and cons. It introduces blockchain's role in addressing cloud security concerns via decentralization and transparency. The chapter examines how blockchain bolsters cloud security, enhancing data integrity, privacy, and encryption. Despite benefits, deployment challenges are recognized. The chapter explores blockchain's integration with AI/ML and real-world cases, equipping readers to navigate complexities. This empowers secure and resilient cloud computing. Thus, the chapter equips readers with the necessary information to navigate the complexities of deploying blockchain-based cloud security solutions, leading to a future of secure and resilient cloud computing.
Quantum Approximate Bayesian Optimization Algorithms With Two Mixers and Uncertainty Quantification

The searching efficiency of the quantum approximate optimization algorithm is dependent on both the classical and quantum sides of the algorithm. Recently, a quantum approximate Bayesian optimization algorithm (QABOA) that includes two mixers was developed, where surrogate-based Bayesian optimization is applied to improve the sampling efficiency of the classical optimizer. A continuous-time quantum walk mixer is used to enhance exploration, and the generalized Grover mixer is also applied to improve exploitation. In this article, an extension of the QABOA is proposed to further improve its searching efficiency. The searching efficiency is enhanced through two aspects. First, two mixers, including one for exploration and the other for exploitation, are applied in an alternating fashion. Second, uncertainty of the quantum circuit is quantified with a new quantum Matérn kernel based on the kurtosis of the basis state distribution, which increases the chance of obtaining the optimum. The proposed new two-mixer QABOA's with and without uncertainty quantification are compared with three single-mixer QABOA's on five discrete and four mixed-integer problems. The results show that the proposed two-mixer QABOA with uncertainty quantification has the best performance in efficiency and consistency for five out of the nine tested problems. The results also show that QABOA with the generalized Grover mixer performs the best among the single-mixer algorithms, thereby demonstrating the benefit of exploitation and the importance of dynamic exploration–exploitation balance in improving searching efficiency.
Personalized federated learning via directed acyclic graph based blockchain

Abstract Common federated learning (FL) lacks consideration of clients' personalized requirements, which performs poorly for the scenario with data and resource heterogeneity. In order to overcome the challenge of heterogeneous characteristics, this letter proposes a novel decentralized personalized federated learning (PFL) architecture that first utilizes a directed acyclic graph (DAG) blockchain technology to achieve PFL efficiently, which is called PFLDAG. Simulation results demonstrate that PFLDAG approximately improves accuracy by 80% compared with the classic Google FedAvg algorithm, and by 10% compared with IFCA cluster PFL which considers personalized requirements. In addition, the approach also substantially improves the convergence speed.
Theoretical research of the processes to reduce the concentration of chlorinated compounds with natural fibers

Disinfectants based on chlorine are the most commonly applied biocides for public spaces disinfection. Various studies show the use of sodium hypochlorite went up several times during the pandemic. Chlorinated compounds after they enter stormwater react with organic, inorganic and anthropogenic pollutants there and form harmful disinfection by-products. The article aims to present theoretical research of the processes linked to reduce the chlorinated compounds concentrations in stormwater. Research theory is based on the scientific information and the experiments’ results. The equations of mass transfers are used to describe the process. Analysis is focused on the case when chlorinated compounds concentrations in stormwater change depending on mass transfer from solution to filter fiber. Experiments present that after filtration with natural fibers the amount of disinfectants in stormwater decreased approximately 60 percent. The values of mass transfer reveal the efficiency of hemp and peat fibers to transport chlorinated compounds (C22H48ClN). Research found out the values of different filter materials are quite similar: 0.20 Def·106, cm2s–1 (hemp) and 0.19 Def.·106, cm2s-1 (peat).