ethicalabs/Research-Intent-Collab huggingface dataset:
Hierarchically sinergistical integration of Social Media Analytics/Social CRM with Business Intelligence and with the Geographic Information System

Показана актуальність об’єднання Social Media Analytics/Social CRM з Decision Support Systems на основі Business Intelligence (DSS/BI) і з Geographic Information Systems (GIS). На основі їх об’єднання запропоновано новий тип DSS – Social Media Spatial DSS/BI. Показано варіант реалізації цієї системи на програмній платформі Social Media Analytics компанії SemanticForce, яка має власний семантичний аналізатор Blueberry. Обґрунтована доцільність введення Social Media Analytics/Social CRM до Business Analytics Taxonomy, яка визначає систему програмних додатків для типових сфер бізнесу. Застосування розробленої об’єднаної системи може забезпечити підвищення ефективності засобів автоматизації у різних сферах Social CRM на національному і міжнаціональному рівнях.
SRE-Llama - Fine-Tuned Meta’s Llama LLM, Federated Learning, Blockchain and NFT Enabled Site Reliability Engineering(SRE) Platform for Communication and Networking Software Services

Software services are crucial for reliable communication and networking; therefore, Site Reliability Engineering (SRE) is important to ensure these systems stay reliable and perform well in cloud-native environments. SRE leverages tools like Prometheus and Grafana to monitor system metrics, defining critical Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for maintaining high service standards. However, a significant challenge arises as many developers often lack in-depth understanding of these tools and the intricacies involved in defining appropriate SLIs and SLOs. To bridge this gap, we propose a novel SRE platform, called SRE-Llama, enhanced by Generative-AI, Federated Learning, Blockchain, and Non-Fungible Tokens (NFTs). This platform aims to automate and simplify the process of monitoring, SLI/SLO generation, and alert management, offering ease in accessibility and efficy for developers. The system operates by capturing metrics from cloud-native services and storing them in a time-series database, like Prometheus and Mimir. Utilizing this stored data, our platform employs Federated Learning models to identify the most relevant and impactful SLI metrics for different services and SLOs, addressing concerns around data privacy. Subsequently, fine-tuned Meta's Llama-3 LLM is adopted to intelligently generate SLIs, SLOs, error budgets, and associated alerting mechanisms based on these identified SLI metrics. A unique aspect of our platform is the encoding of generated SLIs and SLOs as NFT objects, which are then stored on a Blockchain. This feature provides immutable record-keeping and facilitates easy verification and auditing of the SRE metrics and objectives. The automation of the proposed platform is governed by the blockchain smart contracts. The proposed SRE-Llama platform prototype has been implemented with a use case featuring a customized Open5GS 5G Core.
Altering Microbiomes with Hydroxyapatite Nanoparticles: A Metagenomic Analysis

Hydroxyapatite (HAp), the most abundant biological material among mammals, has been recently demonstrated to possess moderate antibacterial properties. Metagenomics provides a series of tools for analyzing the simultaneous interaction of materials with larger communities of microbes, which may aid in optimizing the antibacterial activity of a material such as HAp. Here, a microbiome intrinsic to the sample of sandy soil collected from the base of an African Natal plum (Carissa macrocarpa) shrub surrounding the children’s sandbox at the Arrowhead Park in Irvine, California was challenged with HAp nanoparticles and analyzed with next-generation sequencing for hypervariable 16S ribosomal DNA base pair homologies. HAp nanoparticles overwhelmingly reduced the presence of Gram-negative phyla, classes, orders, families, genera and species, and consequently elevated the relative presence of their Gram-positive counterparts. Thermodynamic, electrostatic and chemical bonding arguments were combined in a model proposed to explain this selective affinity. The ability of amphiphilic surface protrusions of lipoteichoic acid in Gram-positive bacteria and mycolic acid in mycobacteria to increase the dispersibility of the bacterial cells and assist in their resistance to capture by the solid phase is highlighted. Within the Gram-negative group, the variability of the distal, O-antigen portion of the membrane lipopolysaccharide was shown to be excessive and the variability of its proximal, lipid A portion insufficient to explain the selectivity based on chemical sequence arguments. Instead, flagella-driven motility proves to be a factor favoring the evasion of binding to HAp. HAp displayed a preference toward binding to less pathogenic bacteria than those causative of disease in humans, while taxa having a positive agricultural effect were largely captured by HAp, indicating an evolutionary advantage this may have given it as a biological material. The capacity to selectively sequester Gram-negative microorganisms and correspondingly alter the composition of the microbiome may open up a new avenue in environmental and biomedical applications of HAp.
CI/CD Pipeline Automation for Enterprise Data Artifacts Using Azure DevOps

Data and artificial intelligence (AI) models lack the governance and protective mechanisms that traditional applications enjoy through software engineering (DevOps) practices in industry. The complexities of the enterprise CI/CD pipelines, which span across source control, continuous integration, continuous delivery deployments, testing, monitoring for observability, blocking and telemetry for production, enterprise dashboards with pipelines and project health, for AI models and Data in Enterprise environments is presented. Data at Rest, Metadata and Smart data through automation that provide lineage and tracking automation for ML models are discussed. Cloud DevOps ecosystem built with Azure DevOps repository and Pipelines with extensions, which enables orchestration creation and data pipeline YAML templates that embrace best CI/CD practices guide is discussed with detailed indices for enterprise reference.     The enterprise ecosystem provides seamless package management integrated with other Azure DevOps extensions and third-party services availability. The pipelines provide the ability to deploy to any environments read from the pipeline YAML config. The development of pipeline YAML is driven as per the extensions installed and project structure for easy consumption by developers with integrated documentation and additional metadata to help in easy maintainability. These pipelines templates can be used to build, test, and publish data artifacts in enterprise data pipeline, enterprise database automation.
Presenting a Structural Model of Virtual Leadership to Predict Job Desire, Job Satisfaction, and Innovative Behaviors Based on Organizational Commitment, Organizational Climate, and Management Strategies

The purpose of this study was to present a structural model of virtuous leadership in order to predict job aspiration, job satisfaction and innovative behaviors based on organizational commitment, organizational climate and management strategies in 1398. The research method was applied in terms of purpose and descriptive-survey in terms of data collection. The statistical population of the study consisted of 2522 faculty members of Azad University units in Tehran province. Due to the different nature of Azad University, using the Krejcie and Morgan table and initially by stratified sampling, 350 individuals were considered as sample size. Data were collected using standard questionnaire from Cheraghi et al. (1998). After collecting the questionnaires, data were analysed by mean, standard deviation, etc. using Spss-21 software and statistical inference through structural equation modelling using LISREL-v8.80. The technical characteristics of the questionnaire including reliability, convergent validity, and divergent validity were investigated. In addition, path coefficients and software significant coefficients were used to investigate the research hypotheses. Finally, the findings of the study confirmed the impact of virtuous leadership in predicting job aspiration, job satisfaction and innovative behaviors based on organizational commitment, organizational climate and management strategies.
Improved Gibbs samplers for cosmic microwave background power spectrum estimation

We study different variants of the Gibbs sampler algorithm from the perspective of their applicability to the estimation of power spectra of the cosmic microwave background (CMB) anisotropies. These include approaches studied earlier in the CMB literature as well as new ones which are proposed in this work. We demonstrate all these variants on full and cut sky simulations and compare their performance, assessing both their computational and statistical efficiency. For this we employ a consistent comparison metric, an effective sample size (ESS) per second, commonly used in this context in the statistical literature. We show that one of the proposed approaches, referred to as Centered overrelax, which capitalizes on additional, auxiliary variables to minimize computational time needed per sample, and uses overrelaxation to decorrelate subsequent samples, performs better than the standard Gibbs sampler by a factor between one and two orders of magnitude in the nearly full-sky, satellite-like cases. It therefore potentially provides an interesting alternative to the currently favored approaches.
What Is Public Engagement, and What Is It for? A Study of Scientists’ and Science Communicators’ Views

The “Open Air Laboratories” (OPAL) is a large, England-wide environmental public engagement (PE) project based on the “citizen science” model. It is designed to involve people of all backgrounds and abilities in the production of environmental science and in the process to educate and raise awareness and enthusiasm about nature and its importance. This article draws on a series of interviews with scientists and science communicators involved in the project to explore their motivations and aims for the project and what they see as the goals of public engagement generally. We find a varied and nuanced array of motivations and aims that interviewees cite for taking part in the project, pointing toward a reevaluation of traditional ways of understanding the value of public engagement, policy relevance, and dialogue within public engagement. Especially relevant in relation to thinking about the policy relevance of PE is our conclusion that there are many different ways of thinking about the value of PE, characterized in this article as “the neglected middle.”
Celeb-FBI: A Benchmark Dataset on Human Full Body Images and Age, Gender, Height and Weight Estimation using Deep Learning Approach

The scarcity of comprehensive datasets in surveillance, identification, image retrieval systems, and healthcare poses a significant challenge for researchers in exploring new methodologies and advancing knowledge in these respective fields. Furthermore, the need for full-body image datasets with detailed attributes like height, weight, age, and gender is particularly significant in areas such as fashion industry analytics, ergonomic design assessment, virtual reality avatar creation, and sports performance analysis. To address this gap, we have created the 'Celeb-FBI' dataset which contains 7,211 full-body images of individuals accompanied by detailed information on their height, age, weight, and gender. Following the dataset creation, we proceed with the preprocessing stages, including image cleaning, scaling, and the application of Synthetic Minority Oversampling Technique (SMOTE). Subsequently, utilizing this prepared dataset, we employed three deep learning approaches: Convolutional Neural Network (CNN), 50-layer ResNet, and 16-layer VGG, which are used for estimating height, weight, age, and gender from human full-body images. From the results obtained, ResNet-50 performed best for the system with an accuracy rate of 79.18% for age, 95.43% for gender, 85.60% for height and 81.91% for weight.
Skill Recommendation System Using User-Based Collaborative Filtering Method

The rapidly evolving job landscape in the digital era requires job seekers to continuously adapt to emerging skills driven by technological advancements across various industries. However, many job seekers struggle to keep up with these changing skill demands, while existing job portals often lack features that recommend relevant skills. To address this issue, this study proposes a skill recommendation system based on the User-Based Collaborative Filtering approach, which considers similarities between users’ preferences. Two similarity measurement methods, Log-Likelihood Similarity and Cosine Similarity, are applied and compared to evaluate their effectiveness. The system matches user skill profiles with skill requirements extracted from job vacancy data, where job postings are also treated as user representations. The dataset was collected from the Jobstreet job portal using web scraping techniques, ensuring relevance to real-world job market conditions. The system performance was evaluated using the Hit Rate Matrix. The results show that the Log-Likelihood Similarity method achieved a hit rate of 0.73, outperforming the Cosine Similarity method, which obtained a score of 0.51. This indicates that Log-Likelihood Similarity provides more accurate and relevant skill recommendations. Overall, the proposed system demonstrates the potential to assist job seekers in identifying relevant skills aligned with current market demands, thereby supporting better career decision-making in a competitive and dynamic job environment.
Autonomous Robotic Pepper Harvesting: Imitation Learning in Unstructured Agricultural Environments

8 pages, 11 figures
Determinant Factors of Corporate Social Responsibility Reports’ Readability in Spanish Language: The Role of GRI Compliance and Corporate Governance

Tesis Doctoral leída en la Universidad Rey Juan Carlos de Madrid en 2021. Directora de la Tesis: María Asunción Sacristán Navarro
The Structuring and Institutionalizing of Discourses on Climate Change and Security in the United Nations Security Council

This thesis aims to understand how the United Nations Security Council (UNSC) structured climate change and security discourses and institutionalized them in its practices between 2007-2021. In this regard, Maarten Hajer’s argumentative discourse analysis has been employed to assess how the UNSC structured and institutionalized these discourses. The study additionally formed a multilevel security framework to employ over both the processes of discourse structuring and institutionalizing in order to strengthen Hajer’s analysis and make the discourses on climate change and security more meaningful. Based on the analytical literature review, the multilevel security framework establishes causal chains among climate security, human security, national security, and international security. This research understands the UNSC’s process of structuring discourses on climate change and security to still be in the developmental phase. In the context of a multilevel security framework, the discourses on whether the discourses on climate change as an international security issue or not were observed to have not been structured yet. However, climate change was observed to have been structured as a security problem by establishing causal links between climate security, human security, and national security. The second part focuses on whether or not the UNSC has institutionalized climate change and security discourses in its practices. The findings show the UNSC to have partially institutionalized discourses on climate change and security. Despite the increase in the frequency of the relevant outputs and the number of members defending climate change and the UNSC’s relationship with it, the UNSC was found to have institutionalized the discourses on climate change and security in line with the discourses of the Russian Federation. In the context of the multilevel security framework, climate change was found to have been institutionalized as a security problem by establishing causal links between human security and national security. The answer to the question at the beginning of the thesis of whether the realist security-based nature of the UNSC as problematized has changed with regard to the level of global security is that it has not changed yet.
Application investigations of force fields for molecular dynamic simulations of medical aluminum particles

The phase transition of metals is a research hotspot in molecular dynamic (MD) simulation. With the increase of the development of MD, different force fields with different requirements have emerged, which helps to select the appropriate force field and also increases the difficulty of selection. In addition, previous studies have been affected by particle size, which affects the melting point accuracy. MD simulation of the melting and condensing behavior of Aluminum (Al) particles was carried out by using EAM force fields from different sources. The thermal behavior of phase transition was studied by means of potential energy, specific heat, MSD, FCC lattice number, etc. The melting and condensation process of 2–10[Formula: see text]nm cubic without boundary under the NPT system was simulated, and the steady-state simulation was discussed by means of an isometric temperature point. It is proved that the borderless simulation can completely eliminate the boundary effect. The parameter setting of efficient use of EAM is widely discussed by using this method, and the effectiveness of the EAM force field is compared, so as to improve the efficiency of scholars engaged in related research and avoid the difficult choice of force field files. The results show that the borderless simulation has quite high advantages in terms of phase transition potential energy and melting point, while the current EAM force field is inherently deficient in this kind of discussion, and the development of new force field is urgent.
From Crowdsourcing to Extreme Citizen Science: Participatory Research for Environmental Health

Environmental health issues are becoming more challenging, and addressing them requires new approaches to research design and decision-making processes. Participatory research approaches, in which researchers and communities are involved in all aspects of a research study, can improve study outcomes and foster greater data accessibility and utility as well as increase public transparency. Here we review varied concepts of participatory research, describe how it complements and overlaps with community engagement and environmental justice, examine its intersection with emerging environmental sensor technologies, and discuss the strengths and limitations of participatory research. Although participatory research includes methodological challenges, such as biases in data collection and data quality, it has been found to increase the relevance of research questions, result in better knowledge production, and impact health policies. Improved research partnerships among government agencies, academia, and communities can increase scientific rigor, build community capacity, and produce sustainable outcomes.
A Complex-Valued Continuous-Variable Quantum Approximation Optimization Algorithm (CCV-QAOA)

Continuous-variable (CV) quantum systems offer a natural framework for continuous optimization through their infinite-dimensional Hilbert spaces. In this paper, we propose the Complex Continuous-Variable Quantum Approximate Optimization Algorithm (CCV-QAOA), a variational framework operating in the complex domain that optimizes over complex decision variables. The method efficiently solves real and complex multivariate optimization problems. To demonstrate its versatility, we apply CCV-QAOA across a broad suite of optimization use cases, including convex quadratic minimization, scaling studies with circuit depth and cutoff dimension, constrained quadratic programs using penalty constructions, and non-convex benchmarks such as the Styblinski-Tang function and complex quartic landscapes.
Supplementary Table S3 from Prediction of Stage, Grade, and Survival in Bladder Cancer Using Genome-wide Expression Data: A Validation Study

&lt;p&gt;Supplementary Table S3.&lt;/p&gt;
Interactive Sonification for Health and Energy using ChucK and Unity

Sonification can provide valuable insights about data but most existing approaches are not designed to be controlled by the user in an interactive fashion. Interactions enable the designer of the sonification to more rapidly experiment with sound design and allow the sonification to be modified in real-time by interacting with various control parameters. In this paper, we describe two case studies of interactive sonification that utilize publicly available datasets that have been described recently in the International Conference on Auditory Display (ICAD). They are from the health and energy domains: electroencephalogram (EEG) alpha wave data and air pollutant data consisting of nitrogen dioxide, sulfur dioxide, carbon monoxide, and ozone. We show how these sonfications can be recreated to support interaction utilizing a general interactive sonification framework built using ChucK, Unity, and Chunity. In addition to supporting typical sonification methods that are common in existing sonification toolkits, our framework introduces novel methods such as supporting discrete events, interleaved playback of multiple data streams for comparison, and using frequency modulation (FM) synthesis in terms of one data attribute modulating another. We also describe how these new functionalities can be used to improve the sonification experience of the two datasets we have investigated.
Aggregated Deep Local Features for Remote Sensing Image Retrieval

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g. 50% faster than the current systems.
Interdisciplinary dissemination and implementation research to advance translational science: Challenges and opportunities

IntroductionInterdisciplinary collaboration is a critical component of translation, dissemination, implementation, and improvement (TDII) science. Yet, little is known about effective frameworks and practices regarding interdisciplinary research in TDII.MethodsThis study drew on data collected from an expert panel during a regional symposium.ResultsFindings highlight facilitators and barriers to stimulating interdisciplinary TDII research in different domains: intrapersonal, interpersonal, organizational, power and hierarchy, physical environment, and communication and language.ConclusionsFindings have significant implications for TDII of clinical practices.