Perfect Ground Research Articles

Strategies for Intervention and Prevention in Online Child Sexual Exploitation

Online child sexual exploitation (OCSE) is a significant societal and technological problem that ought to be addressed immediately. This article broadly deals with the intricate complexity of OCSE, laying bare the identities and behaviors of online predators, the characteristics of virtual playgrounds, and the adverse consequences for victims. Virtual offenders, by taking advantage of the anonymity of internet spaces, mostly use a variety of tactics to approach vulnerable children. These tactics demand thorough assessment to work out and deliver effective interventions. Virtual environments, such as game communities and chat rooms, have become the perfect hunting grounds for predators, which accentuates the crucial need for preventive measures. Nonetheless, technology gives reason for optimism about the elimination of OCSE despite the challenges of scale and precision. This article, therefore, conducts an in-depth analysis of the issue of online child sexual exploitation and offers comprehensive interventions as a way of increasing knowledge and mobilizing action to shield children from online child sexual exploitation.

Quantum Tunneling Instability in Pericyclic Reactions: The Cheletropic, Coarctate, and Ene Cases.

Some retro-pericyclic reactions, as a result of their high exothermicity and short trajectories, are the perfect ground for heavy atom tunneling molecular decompositions, also known as "quantum tunneling instability" (QTI). Considering this effect, in our first installment [Frenklach, A.; Amlani, H.; Kozuch, S. Quantum Tunneling Instability in Pericyclic Reactions. J. Am. Chem. Soc. 2024, 146 (17), 11823-11834, DOI: 10.1021/jacs.4c00608], we computed several retro-Diels-Alder reactions, predicting that many studied reactants cannot be isolated. Herein, we will explore the QTI of retro-cheletropic, coarctate, and ene exemplars, where again we hypothesize the impossibility to detect their reactants.

Text-Critical Edition of the List of Solomon’s Officials (3 Kgdms 4:8–19)

Abstract The List of Solomon’s officials (1 Kgs/3 Kgdms 4:8–19) presents many textual problems both in the MT and the LXX texts. It constitutes then a perfect testing ground for the critical edition of 3–4 Kingdoms. This paper intends to revise the Rahlfs’ approach and conclusions, in particular regarding the correspondence εἷς = ‮אֶחָד‬‎ attested differently in the different text forms, and presents a commented preliminary edition of the passage, explaining the critical choices of the editors.

Optoelectronic and thermoelectric properties of spinel chalcogenides HgLa2X4 (X=S and Se): A first-principles study

We investigated spinel chalcogenides HgLa2(S/Se)4 by using density function theory, and scrutinized the structural, optical, electrical, mechanical and thermoelectric transport characteristics of HgLa2(S/Se)4 spinel chalcogenides. The enthalpy of formation, energy of cohesion, and energy volume optimum plots were used to obtain the stability as well as the perfect ground state of these materials. The ductility of these materials was the best illustrated with the help of Poisson's and Pugh's ratios. The band gap results were obtained using Trans and Blaha modified Becke-Johanson potential (TB-mBJ). Both the materials present semi-conducting nature with direct band gap equal to 1.449 and 0.892 eV respectively for HgLa2S4 and HgLa2Se4. Calculations were also made for optical characteristics with the values of dielectric function, absorption coefficient, optical conductivity, reflectivity, and refractive indices. According to the findings, both of these materials are suitable for infrared optoelectronic applications. These materials were found to have promising optoelectronic and thermoelectric applications after their optical properties and transport aspects were evaluated. Despite tiny levels of temperature conductivities, substantial amounts of power conductivities, the figure of merit (more than unity), as well as Seebeck coefficients all point to the potential use of both the materials in thermoelectric power generators.

Tree based regression methods for gap reconstruction of motion capture sequences

In the article, we study application of tree based regression methods for reconstruction of gaps in marker trajectories in optical motion capture systems. Having perfect ground truth sequences, we artificially introduced gaps of lengths between 0.1 and 2 s, then reconstructed them using various tree based regression methods – CART, LS boost, bagging, random forests, M5P (model tree). We also tested several methods known from the literature – neural networks (NN), interpolation, and matrix completion. We found out that in general, tree based methods perform poorly, but M5P model tree performs very well, especially for longer gaps, with results on average on par or better than neural networks. Finally, we compared the outcomes of M5P and NN, they appear to be uncorrelated, so they offer the possibility to create an ensemble predictor based on both methods.

Testing the performance, adequacy, and applicability of an artificial intelligence model for pediatric pneumonia diagnosis

BackgroundCommunity-acquired Pneumonia (CAP) is a common childhood infectious disease. Deep learning models show promise in X-ray interpretation and diagnosis, but their validation should be extended due to limitations in the current validation workflow. To extend the standard validation workflow we propose doing a pilot test with the next characteristics. First, the assumption of perfect ground truth (100% sensitive and specific) is unrealistic, as high intra and inter-observer variability have been reported. To address this, we propose using Bayesian latent class models (BLCA) to estimate accuracy during the pilot. Additionally, assessing only the performance of a model without considering its applicability and acceptance by physicians is insufficient if we hope to integrate AI systems into day-to-day clinical practice. Therefore, we propose employing explainable artificial intelligence (XAI) methods during the pilot test to involve physicians and evaluate how well a Deep Learning model is accepted and how helpful it is for routine decisions as well as analyze its limitations by assessing the etiology. This study aims to apply the proposed pilot to test a deep Convolutional Neural Network (CNN)-based model for identifying consolidation in pediatric chest-X-ray (CXR) images already validated using the standard workflow. MethodsFor the standard validation workflow, a total of 5856 public CXRs and 950 private CXRs were used to train and validate the performance of the CNN model. The performance of the model was estimated assuming a perfect ground truth. For the pilot test proposed in this article, a total of 190 pediatric chest-X-ray (CXRs) images were used to test the CNN model support decision tool (SDT). The performance of the model on the pilot test was estimated using extensions of the two-test Bayesian Latent-Class model (BLCA). The sensitivity, specificity, and accuracy of the model were also assessed. The clinical characteristics of the patients were compared according to the model performance. The adequacy and applicability of the SDT was tested using XAI techniques. The adequacy of the SDT was assessed by asking two senior physicians the agreement rate with the SDT. The applicability was tested by asking three medical residents before and after using the SDT and the agreement between experts was calculated using the kappa index. ResultsThe CRXs of the pilot test were labeled by the panel of experts into consolidation (124/176, 70.4%) and no-consolidation/other infiltrates (52/176, 29.5%). A total of 31/176 (17.6%) discrepancies were found between the model and the panel of experts with a kappa index of 0.6. The sensitivity and specificity reached a median of 90.9 (95% Credible Interval (CrI), 81.2–99.9) and 77.7 (95% CrI, 63.3–98.1), respectively. The senior physicians reported a high agreement rate (70%) with the system in identifying logical consolidation patterns. The three medical residents reached a higher agreement using SDT than alone with experts (0.66±0.1 vs. 0.75±0.2). ConclusionsThrough the pilot test, we have successfully verified that the deep learning model was underestimated when a perfect ground truth was considered. Furthermore, by conducting adequacy and applicability tests, we can ensure that the model is able to identify logical patterns within the CXRs and that augmenting clinicians with automated preliminary read assistants could accelerate their workflows and enhance accuracy in identifying consolidation in pediatric CXR images.

Malicious uses of blockchains by malware: from the analysis to Smart-Zephyrus

The permanent availability and relative obscurity of blockchains is the perfect ground for using them for malicious purposes. However, the use of blockchains by malwares has not been characterized yet. This paper analyses the current state of the art in this area. One of the lessons learned is that covert communications for malware have received little attention. To foster further defence-oriented research, a novel mechanism (dubbed Smart-Zephyrus) is built leveraging smart contracts written in Solidity. Our results show that it is possible to hide 4 Kb of secret in 41 s. While being expensive (around USD 1.82 per bit), the provided stealthiness might be worth the price for attackers.

Tackling the Fake News Epidemic using Machine Learning Algorithm

Abstract: Fake news has been a problem since the internet boom. Websites that keep us up to date with what's going on in the world are the perfect breeding ground for bad news and fake news. Fighting fake news is important because the world is knowledge-based. People do not make important decisions based on information; they also form their own ideas. Incorrect information can cause serious damage. It is not possible to identify all messages from a contact. This article attempts to speed up the fake news detection process by recommending a reliable fake news classification method. Machine learning contains different algorithms like naive Bayes, passive-aggressive classifiers and deep neural networks used eight different datasets from different sources. The text also includes the analysis and results of each model. With the right standards and the right tools, the task of detecting fake news will not be trivial.

A 325-GHz Balanced Frequency Tripler Based on Novel Three-Port E-Probe With Individual Groundings for RF and DC

This letter reports a balanced frequency tripler with novel configuration based on Schottky diodes featuring great conversion efficiency. A novel three-port E-probe has been proposed to achieve virtual grounding for radio frequency (RF) signals and perfect grounding for dc. As a result, this architecture could provide inherent suppressions on even-order harmonics even without the lossy on-chip capacitor necessary in conventional balanced approaches. An example of the frequency tripler, operating up to 330 GHz, has been demonstrated based on this configuration. At room temperature, the fabricated circuit exhibits an 8% 3-dB bandwidth and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 14.17$ </tex-math></inline-formula> % peak conversion efficiency (at 325.8 GHz) for a driven power of 125 mW. At the same time, this module could deliver maximum power over 18 mW with the maximum input power level.

Comment: The Essential Role of Policy Evaluation for the 2020 Census DisclosureAvoidance System

In "Differential Perspectives: Epistemic Disconnects Surrounding the US Census Bureau's Use of Differential Privacy," boyd and Sarathy argue that empirical evaluations of the Census Disclosure Avoidance System (DAS), including our published analysis, failed to recognize how the benchmark data against which the 2020 DAS was evaluated is never a ground truth of population counts. In this commentary, we explain why policy evaluation, which was the main goal of our analysis, is still meaningful without access to a perfect ground truth. We also point out that our evaluation leveraged features specific to the decennial Census and redistricting data, such as block-level population invariance under swapping and voter file racial identification, better approximating a comparison with the ground truth. Lastly, we show that accurate statistical predictions of individual race based on the Bayesian Improved Surname Geocoding, while not a violation of differential privacy, substantially increases the disclosure risk of private information the Census Bureau sought to protect. We conclude by arguing that policy makers must confront a key trade-off between data utility and privacy protection, and an epistemic disconnect alone is insufficient to explain disagreements between policy choices.

AcousticIA, a deep neural network for multi-species fish detection using multiple models of acoustic cameras

Acoustic cameras, or imaging sonars, are high-potential devices for many applications in aquatic ecology, notably for fisheries management and population monitoring. However, how to extract such data into high-value information without a time-consuming entire data set reading by an operator is still a challenge. Moreover, the analysis of acoustic imaging, due to its low signal-to-noise ratio, is a perfect training ground for experimenting with new approaches, especially concerning deep learning techniques. We present hereby a novel approach that takes advantage of both convolutional neural network (CNN) and classical computer vision (CV) techniques, able to detect fish passages in acoustic video streams. The pipeline pre-treats the acoustic images to localise the signals of interest and to improve the detection performances. The YOLOv3-based model was trained with fish data from multiple species recorded by the two most frequently used models of acoustic cameras, the DIDSON and ARIS, including species of high ecological interest, as Atlantic salmon or European eels. The pre-treatment of images greatly improves the model performance, increasing its F1-score from 0.52 to 0.69. The model we developed provides satisfying results detecting almost 80% of fish passages and minimising the false-positive rate. On a validation data set, 40 h of videos and around 1 800 fish passages, the efficiency increases with the fish sizes, notably reaching a recall higher than 95% for Atlantic salmon. Conversely, the model appears much less efficient for eel detections on ARIS videos than on DIDSON data (31% recall vs 75%).

Review of stance detection for rumor verification in social media

Social media is a perfect breeding ground for false rumors due to the simplicity of sharing information, which may have negative implications in a variety of domains, including economics, healthcare, and politics. Previous research indicates that public reactions to false rumors are a critical indicator for determining the truthfulness of news. In social media, substantial effort has been invested in detecting and debunking rumors based on crowd stance, given that stance is a vital part of automatic news verification. This paper presents a review of recent approaches in the area of rumor verification using stance detection, which attempts to determine a given document’s stance with respect to a given piece of news. The review offers a detailed list of datasets, as well as a summary of relevant experiments and methods employed, as well as analysis of helpful features for addressing this issue. Finally, we highlight the main challenges and future directions in this field by utilizing stance detection.

Covid-19: A breeding ground for academic dishonesty

The initial response to the early stages of the lockdown precipitated by Covid-19 was for institutional legal education to shift to an online teaching and learning platform, which exposed many barriers to online teaching and learning within the socio-economic fabric of South Africa as well as the rise of academic dishonesty in online assessments. Three broad groups of academic dishonesty in assessments are highlighted. The first being test-cheating and collusion (referred to as “crowdsourcing”), in which two or more students work together to complete an assessment. The second takes the form of plagiarism which is advanced by the practice of “copying-and-pasting” in assessments. Finally, academic dishonesty can occur in the form of “contract cheating” where the assistance of third parties is solicited in completing an assessment. Although academic dishonesty is not something new, Covid-19 created the perfect breeding ground for academic misconduct in which not only the online teaching and learning environment was a situational temptation ready for exploitation but the pandemic itself impacted students’ day-to-day lives and academic success largely due to the socio-economic conditions in which students found themselves. The contribution considers potential responses to academic dishonesty and takes a moralist stance to addressing academic dishonesty by drawing on principles from Aristotelian value-based ethics. In doing so, it is found that a moralist stance is not necessarily out of step with the South African Qualification Authority’s learning outcomes for the Bachelor of Laws degree (LLB) and that of the values and requirements for successful participation in the legal profession. In this, ethics is practiced and reinforced by past conduct, which is reflective of future conduct. Therefore, institutional legal education should respond decisively and effectively to academic dishonesty which may include applying traditional disciplinary actions but should be precipitated by a pervasive and integrative approach to ethics education within the curriculum, which includes general ethics (or value-based ethics), legal ethics (a sub-branch of general ethics and reflective of professional accountability) and finally also techno-ethics (which relates to the ethical conduct expected of students within an online environment). It is only by equipping students with the necessary competencies and ethical courage that they will have the moral fortitude to overcome the temptation of academic misconduct in one small individual victory at a time.

An ITS-G5 V2X solution in C-ROADS Portugal"

The C-ROADS Portugal initiative created the perfect breeding ground for Portugal to shift from a nation of intelligent but unconnected roads to a Cooperative Intelligent Transport Systems (C-ITS) nationA-to-Be coordinated C-ROADS Portugal Pilot #2 and supported Brisa's massive deployment in Pilot #3. This participation was leveraged by over a decade of experience with V2X.This paper presents A-to-Be's solution architecture, its evolution and motivation for pursuing it over emerging commercial implementations. It summarizes the C-ROADS Portugal trials where A-to-Be participated, results, and the deployment of C-ITS services in more than 486 km, 44% of Brisa's core motorway network. The final remarks focus on the road user perspective, limitations and requirements for future massive adoption and on our most recent efforts, made possible by having our own V2X technology.

Hybrid Cloud Solutions for Balancing On-Premise and Cloud Infrastructure

This paper aims to analyze hybrid cloud, as this is a Cloud and on-premise arrangement. The paper also examines the advantages and disadvantages of this approach, considering the different issues that organizations face when managing I.T. assets. This paper offers a brief about hybrid cloud solutions, how they have influenced and been used and utilized in the current business environment, and most importantly, how they can benefit companies by expanding and protecting their information. The study establishes that hybrid cloud models are a perfect middle ground of the modern I.T. environment because they let organizations benefit from cloud computing while retaining the security and data control of the on-premise facilities.

Synthetic Micrographs of Bacteria (SyMBac) allows accurate segmentation of bacterial cells using deep neural networks

BackgroundDeep-learning–based image segmentation models are required for accurate processing of high-throughput timelapse imaging data of bacterial cells. However, the performance of any such model strictly depends on the quality and quantity of training data, which is difficult to generate for bacterial cell images. Here, we present a novel method of bacterial image segmentation using machine learning models trained with Synthetic Micrographs of Bacteria (SyMBac).ResultsWe have developed SyMBac, a tool that allows for rapid, automatic creation of arbitrary amounts of training data, combining detailed models of cell growth, physical interactions, and microscope optics to create synthetic images which closely resemble real micrographs, and is capable of training accurate image segmentation models. The major advantages of our approach are as follows: (1) synthetic training data can be generated virtually instantly and on demand; (2) these synthetic images are accompanied by perfect ground truth positions of cells, meaning no data curation is required; (3) different biological conditions, imaging platforms, and imaging modalities can be rapidly simulated, meaning any change in one’s experimental setup no longer requires the laborious process of manually generating new training data for each change. Deep-learning models trained with SyMBac data are capable of analysing data from various imaging platforms and are robust to drastic changes in cell size and morphology. Our benchmarking results demonstrate that models trained on SyMBac data generate more accurate cell identifications and precise cell masks than those trained on human-annotated data, because the model learns the true position of the cell irrespective of imaging artefacts. We illustrate the approach by analysing the growth and size regulation of bacterial cells during entry and exit from dormancy, which revealed novel insights about the physiological dynamics of cells under various growth conditions.ConclusionsThe SyMBac approach will help to adapt and improve the performance of deep-learning–based image segmentation models for accurate processing of high-throughput timelapse image data.

Design of a high-efficient metamaterial X-band absorber composed of a dielectric cylinder combined with a thin magnetic cylinder unit

High-efficient microwave absorbers (MAs) with excellent absorption performance and lightweight characteristics have important applications in the military and civil. Here, we propose a dielectric-magnetic hybrid microwave absorber composed of a lattice of dielectric cylinders combined with thin magnetic cylinders backed by a perfect electrical conductor ground plane. The dielectric material that is designed as the dielectric cylinder is based on multi-walled carbon nanotubes-epoxy (MWCNT-EP). The MWCNT-EP is prepared and the S-parameters are measured by the standard transmission/reflection (T/R) method. The thin magnetic cylinder is based on a commercial magnetic material with a height of only 1 mm. With the coaction of magnetic loss and dielectric loss, the proposed microwave absorber’s reflection loss is less than −20 dB under normal incident with a total height of 5 mm in the X-band. Besides, the dielectric-magnetic microwave absorber can maintain good absorption performance with different incident angles and polarizations. In this work, we provide a method to combine the properties of magnetic and dielectric material to construct the MA with high-efficient absorption performance.

Post‐flood infectious disease prevention and control: Current scenario in Pakistan

Post‐flood infectious disease prevention and control: Current scenario in Pakistan

Rudolf Steiner: From Theosophy to Anthroposophy (1902-1913)

The Theosophical Society, founded in New York in 1875, was, at the turn of the Twentieth Century, a global phenomenon with 100,000 members. New Age philosopher Dr Rudolf Steiner (1861-1925) was appointed as the first Secretary General of the German Section of the Theosophical Society on 19 October 1902. The Theosophical Society offered Rudolf Steiner a platform, a ready-made audience, infrastructure, and the insider experience of the world’s leading New Age spiritual society. The success of the Theosophical Society demonstrated that there was a public appetite to hear about reincarnation, karma, maya, kamaloca, and other Eastern and alternative spiritual ideas. The Theosophical Society provided Rudolf Steiner a capable, multilingual, and determined personal assistant, Marie von Sivers (1867-1948). For Rudolf Steiner the Theosophical Society offered the perfect training ground for what would be, a decade later, his life’s work, the Anthroposophical Society. Rudolf Steiner grew the membership of the German Section of the Theosophical Society from 377 in 1905 to 3,702 in 1913. He earned cash from ticketing of his lectures and his Mystery plays, and from book sales of his personal publishing house, ‘Philosophisch-Theosophischer Verlag’. Another enterprise, the ‘Johannes-Bau-Verein’ (Johannes Building Association) was founded in 1911, independent of the Theosophical Society, to build a theatre in Munich to present Rudolf Steiner's plays. The building application was rejected by the Munich municipal authorities in 1912. The resistance to a build in Munich, provided impetus for the move to build in Dornach, Switzerland. The Anthroposophical Society was founded 28 December 1912 in Cologne, Germany. Most of the members of the German Section of the Theosophical Society members followed Rudolf Steiner into the Anthroposophical Society. The Theosophical Society expelled Rudolf Steiner from the Theosophical Society on 7 March 1913. The foundation stone for the Goetheanum (then still called the ‘Johannesbau’) was laid 20 September 1913. The Theosophical Society had served as the ideal prototype and springboard for founding and growing the Anthroposophical Society.

Designing elegant metadata

The advancement of digital asset management technologies, the need for integrated content systems and the huge proliferation of digital assets require equal advancements in the design and use of metadata to manage those assets. Alongside this, the importance of metadata within businesses is now being recognised as equal to content. To leverage their metadata, organisations must ensure the data are well organised, well managed, adopted and owned. Finding the perfect middle ground between simple systems and basic tags, through to complex multifunctional and multidimensional metadata management is challenging. The many difficulties can result in metadata being neglected and end users becoming disengaged. Creating elegant metadata means designing solutions that balance complex needs in a simple yet powerful way, thus making them more effective. This paper will discuss the ways in which metadata can be made elegant, and in turn become a valuable tool for users and business. To support the adoption and use of metadata, the paper also discusses how to avoid common pitfalls associated with the creation of metadata.