True Information Research Articles

Machine learning-based amide proton transfer imaging using partially synthetic training data.

Machine learning (ML) has been increasingly used to quantify CEST effect. ML models are typically trained using either measured data or fully simulated data. However, training with measured data often lacks sufficient training data, whereas training with fully simulated data may introduce bias because of limited simulations pools. This study introduces a new platform that combines simulated and measured components to generate partially synthetic CEST data, and to evaluate its feasibility for training ML models to predict amide proton transfer (APT) effect. Partially synthetic CEST signals were created using an inverse summation of APT effects from simulations and the other components from measurements. Training data were generated by varying APT simulation parameters and applying scaling factors to adjust the measured components, achieving a balance between simulation flexibility and fidelity. First, tissue-mimicking CEST signals along with ground truth information were created using multiple-pool model simulations to validate this method. Second, an ML model was trained individually on partially synthetic data, in vivo data, and fully simulated data, to predict APT effect in rat brains bearing 9 L tumors. Experiments on tissue-mimicking data suggest that the ML method using the partially synthetic data is accurate in predicting APT. In vivo experiments suggest that our method provides more accurate and robust prediction than the training using in vivo data and fully synthetic data. Partially synthetic CEST data can address the challenges in conventional ML methods.

Accurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA

Cancers develop and progress as mutations accumulate, and with the advent of single-cell DNA and RNA sequencing, researchers can observe these mutations and their transcriptomic effects and predict proteomic changes with remarkable temporal and spatial precision. However, to connect genomic mutations with their transcriptomic and proteomic consequences, cells with either only DNA data or only RNA data must be mapped to a common domain. For this purpose, we present MaCroDNA, a method that uses maximum weighted bipartite matching of per-gene read counts from single-cell DNA and RNA-seq data. Using ground truth information from colorectal cancer data, we demonstrate the advantage of MaCroDNA over existing methods in accuracy and speed. Exemplifying the utility of single-cell data integration in cancer research, we suggest, based on results derived using MaCroDNA, that genomic mutations of large effect size increasingly contribute to differential expression between cells as Barrett’s esophagus progresses to esophageal cancer, reaffirming the findings of the previous studies.

Social Media Fake Information Identification Method Based on LSTM

False information is spreading like wildfire on social media platforms, causing unnecessary confusion and mistrust. Most people do not have the ability to judge for themselves whether information is true or false that is why it is important to use machine learning models to help people identify false information on the web. LSTM model is a good choice for dealing with identifying social media disinformation. The key steps in identifying social media disinformation using LSTM include data preparation and preprocessing, building a model structure adapted to LSTM, model training and evaluation, and subsequent hyper-parameter tuning and model deployment. These steps constitute a complete process that enables LSTM to effectively identify false information and provide credible prediction results through the processing of text data and model training. After model training and validation, LSTM has an accuracy of 99%, AUC of 100% and successfully identifies false information on social media, providing reliable classification results for both true and false information.

PH-shape: an adaptive persistent homology-based approach for building outline extraction from ALS point cloud data

ABSTRACT Building outline extraction from segmented point clouds is a critical step of building footprint generation. Existing methods for this task are often based on the convex hull and α-shape algorithm. There are also some methods using grids and Delaunay triangulation. The common challenge of these methods is the determination of proper parameters. While deep learning-based methods have shown promise in reducing the impact and dependence on parameter selection, their reliance on datasets with ground truth information limits the generalization of these methods. In this study, a novel unsupervised approach, called PH-shape, is proposed to address the aforementioned challenge. The methods of Persistence Homology (PH) and Fourier descriptor are introduced into the task of building outline extraction. The PH from the theory of topological data analysis supports the automatic and adaptive determination of proper buffer radius, thus enabling the parameter-adaptive extraction of building outlines through buffering and “inverse” buffering. The quantitative and qualitative experiment results on two datasets with different point densities demonstrate the effectiveness of the proposed approach in the face of various building types, interior boundaries, and the density variation in the point cloud data of one building. The PH-supported parameter adaptivity helps the proposed approach overcome the challenge of parameter determination and data variations and achieve reliable extraction of building outlines.

Adversarial Attacks on Deep Reinforcement Learning-based Traffic Signal Control Systems with Colluding Vehicles

The rapid advancements of Internet of Things (IoT) and Artificial Intelligence (AI) have catalyzed the development of adaptive traffic control systems (ATCS) for smart cities. In particular, deep reinforcement learning (DRL) models produce state-of-the-art performance and have great potential for practical applications. In the existing DRL-based ATCS, the controlled signals collect traffic state information from nearby vehicles, and then optimal actions (e.g., switching phases) can be determined based on the collected information. The DRL models fully “trust” that vehicles are sending the true information to the traffic signals, making the ATCS vulnerable to adversarial attacks with falsified information. In view of this, this article first time formulates a novel task in which a group of vehicles can cooperatively send falsified information to “cheat” DRL-based ATCS in order to save their total travel time. To solve the proposed task, we develop CollusionVeh , a generic and effective vehicle-colluding framework composed of a road situation encoder, a vehicle interpreter, and a communication mechanism. We employ our framework to attack established DRL-based ATCS and demonstrate that the total travel time for the colluding vehicles can be significantly reduced with a reasonable number of learning episodes, and the colluding effect will decrease if the number of colluding vehicles increases. Additionally, insights and suggestions for the real-world deployment of DRL-based ATCS are provided. The research outcomes could help improve the reliability and robustness of the ATCS and better protect the smart mobility systems.

IMPACT OF FALSE FIRST AID INFORMATION AND MYTHS ON PARAMEDIC EDUCATION

Aim: The purpose of this study was to evaluate the effect of false information and myths related to first aid on paramedic education that have been formed in society.
 Method: A two-level qualitative approach was employed. Focus group interviews with paramedic students were conducted at the beginning of their paramedic training and subsequently, at the end of the second semester, when they had completed their basic paramedic training. An independent qualitative researcher conducted thematic analysis in two stages. Furthermore, to complete the analysis and triangulation of data, paramedic educators involved in the research team reviewed and interpreted the presented themes independently.
 Findings: Ten themes emerged from the first focus group interviews, including misinformation about first aid and practices that may be considered myths. The participants also revealed that the media and family elders disseminated false information and myths related to first aid. Furthermore, while false information could be corrected in subjects such as foreign body aspiration and hemorrhages that are taught mainly in practice in the curriculum, there was resistance when attempting to rectify false information in more theoretical subjects such as poisoning and epilepsy.
 Conclusion: Paramedic students are affected by false information related to first aid. Despite the academic education they received to become professional emergency medical service (EMS) employees, they appeared to resist replacing some incorrect first aid information with that which is correct. It is deemed that the media is the most important means to replace false information with true information and spread scientific information.

Are you sure? Examining the potential benefits of truth-checking as a learning activity.

Learners may be uncertain about whether encountered information is true. Uncertainty may encourage people to critically assess information's accuracy, serving as a kind of desirable difficulty that benefits learning. Uncertainty may also have negative effects, however, leading people to mistrust true information or to later misremember false information as true. In three experiments, participants read history statements. In one condition, all statements were true, and the participants knew it. In the other two conditions, some statements were true, and others were false. Participants were either told the statements' accuracy or they guessed the statements' accuracy prior to feedback, a manipulation we refer to as truth-checking. All participants were then tested on recalling the true information and on recognising true versus false statements. We observed a significant benefit of truth-checking in one of the three experiments, suggesting that truth-checking may have some potential to enhance learning, perhaps by inducing people to encode to-be-learned information more deeply than they would otherwise. Even so, the benefit may come at a cost-truth-checking took significantly longer than study alone, and it led to a greater likelihood of thinking false information was true, suggesting costs of truth-checking may tend to outweigh benefits.

Reconfigurable dual-mode optical encryption enabled by block copolymer photonic crystal with micro-imprinted holographic metasurface

Dual-mode optical encryption based on holographic metasurfaces and color components is of great attraction because of their enhanced information security and storage; however, the realization of independently as well as reversibly encodable holographic metasurfaces and color components remains unreported. Herein, we present reconfigurable dual-mode encryptions of structural colors (SC) and holograms, achieved through stimuli-responsive block copolymer (BCP) photonic crystals (PCs) with micro-imprinted holographic metasurfaces. Holographic images appear when the micro-imprinted BCP PCs, consisting of self-assembled alternating lamellae of two dielectrics, are exposed to an incident laser. A characteristic SC develops in the visible range when the imprinted film is immersed in a liquid agent that can swell one of the dielectrics, allowing for dual-mode holographic and SC encodings in the solid and liquid states, respectively. The dual-mode optical encoding is reconfigured. The holographic image can be erased and replaced with another micropattern, while preserving the SC. Moreover, an SC, set by crosslinking of the swellable lamellae, is reset by chemical de-crosslinking and subsequent transient re-crosslinking, enabling the SC reconfigurability of the BCP PC film. A prototype of a high-security reconfigurable dual encryption has been developed, wherein true information is decrypted when holographic passwords are confirmed with full-color visible SC passwords.

SQCS: A sustainable quality control system for spatial crowdsourcing via three-party evolutionary game: Theory and practice

Quality control of sensing data poses a fundamental challenge in Spatial Crowdsourcing (SC) applications, and various incentive mechanisms have been proposed to effectively motivate workers. However, real-world SC systems involve multiple stakeholders, namely workers, platforms, and requesters, who exhibit bounded rationality. Moreover, prior research has often neglected the practical aspect of platforms needing to verify sensing data against ground truth information, which incurs a cost. Therefore, platforms face the constraint of verification cost and must judiciously select the verification rate. In this paper, a Sustainable Quality Control System (SQCS) is proposed, which employs a three-party evolutionary game to model the dynamic interaction among workers, platforms, and requesters in practical SC scenarios. Based on this dynamic model: (1) In theory, we derive the minimum verification rate required for effective worker monitoring and the maximum verification cost that platforms can sustainably bear via the analysis of evolutionary stable strategies. These findings serve as guidelines for achieving sustainable quality control within the SQCS framework; (2) In practice, we apply the SQCS framework to the SC scenario of urban air pollution monitoring. The optimal quality control strategy that maximizes overall social benefits is analyzed via numerical methods. Extensive experimental validation is conducted to demonstrate the effectiveness of the proposed theory and practical approach. These findings provide valuable insights for the sustainable development of SC systems.

Synthetic modelling of railway trackbed for improved understanding of ground penetrating radar responses due to varying conditions

AbstractGround penetrating radar (GPR) is a commonly used tool for railway trackbed inspection due to its ability to collect information about subsurface materials at high resolution and high speed. Although GPR recording systems allow for the collection of vast quantities of data (hundreds of kilometres per day), accurate ground truth information is difficult to obtain. Models of trackbed can be used to generate synthetic radargrams to provide a better understanding and predictability of GPR responses to a wide range of trackbed conditions. In this research, we produced models of ballast using randomly shaped 3D particles, with a range of particle size distributions to represent various stages of ballast breakdown. Additionally, void spaces are partially filled with a constant dielectric material to represent ballast contamination. We used gprMax to simulate the GPR response for a 2 GHz horn antenna over the trackbed models. These simulations resulted in radargrams that are visually indistinct from real recorded data in known conditions. These radargrams, along with their formative models, have provided valuable insights into how variations in trackbed conditions can impact GPR data.

Design, Implementation, and Evaluation of an External Pose-Tracking System for Underwater Cameras

To advance underwater computer vision and robotics from lab environments and clear water scenarios to the deep dark ocean or murky coastal waters, representative benchmarks and realistic datasets with ground truth information are required. In particular, determining the camera pose is essential for many underwater robotic or photogrammetric applications and known ground truth is mandatory to evaluate the performance of, e.g., simultaneous localization and mapping approaches in such extreme environments. This paper presents the conception, calibration, and implementation of an external reference system for determining the underwater camera pose in real time. The approach, based on an HTC Vive tracking system in air, calculates the underwater camera pose by fusing the poses of two controllers tracked above the water surface of a tank. It is shown that the mean deviation of this approach to an optical marker-based reference in air is less than 3 mm and 0.3∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}. Finally, the usability of the system for underwater applications is demonstrated.

Privacy preserving distributed online projected residual feedback optimization over unbalanced directed graphs

The distributed online optimization (DOO) problem with privacy guarantees over an unbalanced directed graph is considered in this paper, where the cost function is explicitly unknown. To solve this problem, a distributed online one-point residual feedback optimization algorithm based on differential privacy is designed. For the objective function explicitly unknown, this algorithm employs the residual feedback to estimate the true gradient information. In addition, only the row stochastic adjacency matrix utilized releases the requirement of double stochastic weighting matrices, making the algorithm easier to implement on directed graphs. Theoretical results show that the algorithm not only protects the privacy information of the nodes but also achieves the same sublinear rate regret as the DOO algorithm based on two-point feedback. Moreover, our regret bound depends on weaker assumptions than the traditional DOO algorithm based on one-point feedback. Finally, the simulation results verify the effectiveness of the algorithm.

PTS-LSTM: Temperature prediction for fused filament fabrication using thermal image time series

In-situ thermography for Fuse Filament Fabrication (FFF) processes reveals the dynamic thermal behavior during printing. The data collected are thermal image time series. Their infrared (IR) intensity is visual evidence of heat-affected zone (HAZ) temperatures, which can be leveraged to train deep learning models, e.g., Long Short-Term Memory (LSTM), for real-time temperature prediction and process monitoring. Nonetheless, the data collection method and printing path may pose challenges for data modeling. Typically, the IR camera has a fixed position while the HAZ moves per the predetermined printing path. Consequently, the HAZ shifts in images and the features extracted from these thermal images show a “periodic” behavior over time. Such periodic patterns do not reflect any useful information about HAZ temperatures. Instead, they are noise hiding and interrupting the true temperature information, thus must be removed before using the data to train an LSTM model for temperature prediction. This study integrates a time series model, i.e., ARIMA, with Stacked LSTM to build a pTS-LSTM model that eliminates noisy patterns and predicts temperatures during FFF printing. The case study results show the outperformance of pTS-LSTM over conventional LSTM and classic Recurrent Neural Network models. pTS-LSTM is demonstrated to be promising for in-situ process monitoring with low-quality thermal images. In FFF practices, pTS-LSTM will be a preferred option over the commonly used deep learning models for thermal-image-based temperature prediction.

Ukrainian education system: war challenges

The challenges posed by war have had a significant impact on the Ukrainian education system. This article aims to provide an overview of the current stage of training specialists in higher education institutions, highlighting the development trends of specific professions. Attention is drawn to the crucial role of teachers in fostering educational competencies in students, thereby enhancing their competitiveness in the job market, particularly in the context of war. The article analyses the key components of the educational process, emphasizing the need for teachers to boost student motivation for learning. In the present circumstances, Ukraine requires professionals who can effectively adapt to changing situations, discern between true and false information, and maintain resilience in the face of stress. These specialists should not only possess knowledge acquired during their university studies but also demonstrate proficiency in applying relevant skills and competences in their professional endeavors. Notably, effective communication assumes paramount importance during times of war, serving as a powerful tool against the enemy. Consequently, integrating a communicative component into classes is of utmost significance.Parte superior do formulário

Glimpsing Indonesia’s Social Media Discourse: What Goes on During the Covid-19 Infodemic

The COVID-19 pandemic has led to an "infodemic", of false and true information circulating on social media platforms. This phenomenon has posed various challenges in implementing disaster management programs to mitigate the effects of the pandemic, both globally and specifically in Indonesia. This study seeks to investigate the public's perception of social media discourse during the COVID-19 infodemic in Indonesia. Both primary and secondary data were collected to gain a comprehensive understanding of the issue. The primary data was collected through a focused group discussion (FGD) method. Meanwhile, secondary data sources were gathered using a literature review of scholarly articles. Approximately 60 articles were selected from sources such as Google Scholar and PubMed, published between 2019 and 2022. The articles were selected based on their relevance of the topic discussion. The study used Critical Discourse Analysis by Fairclough and Wodak (1997) to analyze the data. Both of the data were then synthesized to glimpse how the infodemic has impacted various disaster management efforts across various parts of the country. The results revealed that the infodemic has worsened public perceptions of how the Indonesian government handles COVID-19 as well as disrupting various disaster management processes. The study finds that the infodemic's impact on the public's perception has resulted in misinformation hampering effective pandemic management efforts. By recognizing the severity of the infodemic and working to combat it, Indonesia can more effectively manage the COVID-19 pandemic and minimize its negative impact on the public.

An Honest Joker reveals stereotypical beliefs about the face of deception

Research on deception detection has mainly focused on Simple Deception, in which false information is presented as true. Relatively few studies have examined Sophisticated Deception, in which true information is presented as false. Because Sophisticated Deception incentivizes the appearance of dishonesty, it provides a window onto stereotypical beliefs about cues to deception. Here, we adapted the popular Joker Game to elicit spontaneous facial expressions under Simple Deception, Sophisticated Deception, and Plain Truth conditions, comparing facial behaviors in static, dynamic nonspeaking, and dynamic speaking presentations. Facial behaviors were analysed via machine learning using the Facial Action Coding System. Facial activations were more intense and longer lasting in the Sophisticated Deception condition than in the Simple Deception and Plain Truth conditions. More facial action units intensified in the static condition than in the dynamic speaking condition. Simple Deception involved leaked facial behaviors of which deceivers were unaware. In contrast, Sophisticated Deception involved deliberately leaked facial cues, including stereotypical cues to lying (e.g., gaze aversion). These stereotypes were inaccurate in the sense that they diverged from cues in the Simple Deception condition—the actual appearance of deception in this task. Our findings show that different modes of deception can be distinguished via facial action analysis. They also show that stereotypical beliefs concerning cues to deception can inform behavior. To facilitate future research on these topics, the multimodal stimuli developed in this study are available free for scientific use.

Evolutionary dynamics of N-player sender-receiver game in networks with community structure.

Network typology largely affects the evolutionary dynamics of collective behaviors in many real-world complex systems. As a conventional transmission model, the sender-receiver game paves the way to explore the evolution of honest signals between senders and receivers. In practice, the utilities of an agent often depend not only on pairwise interactions, but also on the group influence of players around them, and thus there is an urgent need for deeper theoretical modeling and investigations on individuals' non-pairwise interactions. Considering the underlying evolutionary game dynamics and multiple community network structures, we explore the evolution of honest behaviors by extending the sender-receiver game to multiple communities. With the new dynamical model of the multi-community system, we perform a stability analysis of the system equilibrium state. Our results reveal the condition to promote the evolution of honest behaviors and provide an effective method for enhancing collaboration behaviors in distributed complex systems. Current results help us to deeply understand how collective decision-making behaviors evolve, influenced by the spread of true information and misinformation in large dynamic systems.

Sosialisai Stop Hoax: Saring Sebelum Sharing Di SMP Negeri 2 Kandis, Kelurahan Simpang Belutu Kecamatan Kandis, Kabupaten Siak

Easy access to social media has several impacts that can be detrimental to other people. Freedom of opinion is now being misused by irresponsible people, for example by spreading fake news or hoaxes. The flood of information makes it difficult for audiences to determine true information from false information. This research aims to determine the literacy understanding of students at SMP Negeri 2 Kandis regarding hoax news and the factors that influence the development of hoax news. This program used qualitative research with descriptive and discussion methods. This research took place at SMP Negeri 2 Kandis, Simpang Belutu subdistrict. The final results of this program are hoax news can be an influence on the views of young students and the student’s ability to evaluate fake news still at the initial category. There are several influencing factors, among others: fanaticism towards news sources, emotional factors regarding the situation of an incident, factors that are easily influenced and factors that limit the ability to test the truth.)

Studying the Impact of Social Media Algorithms on the Spread of Misinformation and its Effects on Society

Social media contain with a large amount of data and this large amount of data is filled with a misinformation, which forward mislead to make unusual wrong decision by the public, creates mostly negative public emotions, and leads to serious threats belongs to public safety and social order. The spread of misinformation on social networks has belongs to the Social media algorithms to become a widespread among scholars. After the study, we see that raw misinformation spread on social media for the research product and compared it with the original information to better understanding of the characteristics of the spread misinformation on social networks. This study declares that a deep learning method to perform information content analysis and information emotion analysis on misinformation dataset with original information and to adopt an analytic process to analyze network with the difference between misinformation and original true information in terms of diffusion network characteristics. The research indicates that the spread of misinformation on social media is by feature content and different emotions with consequent different changes. The related overall research findings follows the existing research for make a certain valuable contribution to the governance of raw misinformation and the maintenance of network order responsible to original information.

Facility location games with ordinal preferences

In this paper we study a novel model of facility location games with ordinal preferences. There is a set of self-interested agents and a set of heterogeneous facilities. Each agent is located on a line and has an ordinal preference over the facilities. Our goal is to design strategyproof mechanisms that elicit true information (preferences and/or locations) from the agents and locate the facilities to minimize both maximum and total cost objectives as well as to maximize both minimum and total utility objectives. For the four possible objectives, we consider the 2-facility settings in which only preferences are private, or locations are private. For each possible combination of the objectives and settings, we provide lower and upper bounds on the approximation ratios of strategyproof mechanisms, which are asymptotically tight up to a constant. Furthermore, we extend some of the results to the multiple-facility setting. Finally, we discuss the generalization of our results when the agents can misreport both locations and preferences, and the case when the approximations are defined additively.