True Information Research Articles

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.

In vivo labeling and quantitative imaging of neuronal populations using MRI

The study of neural circuits, which underlies perception, cognition, emotion, and behavior, is essential for understanding the mammalian brain, a complex organ consisting of billions of neurons. To study the structure and function of the brain, in vivo neuronal labeling and imaging techniques are crucial as they provide true physiological information that ex vivo methods cannot offer. In this paper, we present a new strategy for in vivo neuronal labeling and quantification using MRI. We demonstrate the efficacy of this method by delivering the oatp1a1 gene to the target neurons using rAAV2-retro virus. OATP1A1 protein expression on the neuronal membrane increased the uptake of a specific MRI contrast agent (Gd-EOB-DTPA), leading to hyperintense signals on T1W images of labeled neuronal populations. We also used dynamic contrast enhancement-based methods to obtain quantitative information on labeled neuronal populations in vivo.

Diffusion unit: Interpretable edge enhancement and suppression learning for 3D point cloud segmentation

3D point clouds are discrete samples of continuous surfaces which can be used for various applications. However, the lack of true connectivity information, i.e., edge information, makes point cloud recognition challenging. Recent edge-aware methods incorporate edge modeling into network designs to better describe local structures. Although these methods show that incorporating edge information is beneficial, how edge information helps remains unclear, making it difficult for users to analyze its usefulness. To shed light on this issue, in this study, we propose a new algorithm called Diffusion Unit (DU) that handles edge information in a principled and interpretable manner while providing decent improvement. First, we theoretically show that DU learns to perform task-beneficial edge enhancement and suppression. Second, we experimentally observe and verify the edge enhancement and suppression behavior. Third, we empirically demonstrate that this behavior contributes to performance improvement. Extensive experiments and analyses performed on challenging benchmarks verify the effectiveness of DU. Specifically, our method achieves state-of-the-art performance in object part segmentation using ShapeNet part and scene segmentation using S3DIS. Our source code is available at https://github.com/martianxiu/DiffusionUnit.

Information and communication activities and analytics during the Russian-Ukrainian war (on the example of social networks)

The article analyzes the importance of information and communication activities during the war. In particular, it is determined that today there is a battle between Ukraine and the Russian Federation for a narrative, where both countries are looking for legitimacy and success in spreading correct information for themselves, because the perception of these narratives directly affects the course of hostilities. The article outlines the main narratives promoted by Ukraine and Russia.
 The research hypothesis is that the world's first war of social networks is taking place in Ukraine. The interdependence between the spread of the mobile Internet, access to modern gadgets and the activity of propaganda activities in social networks is analyzed.
 The main attention in intelligence is paid to the Internet as one of the most influential channels of communication, which is capable of reaching the maximum number of recipients in the shortest possible time, who then distribute the necessary information independently. The article proves that it is social networks that contain a large share of disinformation, which is "thrown in" by information scraps and fake pages. It is noted that fake or untrue content has a much higher chance of spreading than true information.
 The article reveals the communication and technological specificity of the Internet environment in terms of propaganda influence, for which even the leading Ukrainian broadcasters (STUDIYA "1+1", "UA: First", "Rada", "ICTV", "STB" and "Ukraine 24 ") united in the joint telethon "Edyni Novyni" to show the official position of the state, sometimes use unverified information from the Internet and, accordingly, resort to spreading fakes or disinformation. In this context, the activity of the single news marathon against the background of the single information pool of Russia, which is under the control of the Kremlin, is analyzed.

P13.05.B INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASET

Abstract BACKGROUND Translation of AI algorithms into clinical practice is significantly limited by lack of large individual hospital-based datasets with expert annotations. Current methods for generation of annotated imaging data are significantly limited due to inefficient imaging data transfer, complicated annotation software, and time required for experts to generate ground truth information. We incorporated AI tools for auto-segmentation of gliomas into PACS that is used at our institution for reading clinical studies and developed a workflow for annotation of images and development of volumetric segmentations in neuroradiology clinical workflow. MATERIAL AND METHODS 1990 patients from Yale Radiation Oncology Registry (2012-2019) were screened. Segmentations were performed using a UNETR algorithm trained on BRaTS 2021 and an internal dataset of manually segmented tumors. AI generated segmentation can be revised and confirmed in PACS. Segmentations were validated by a board-certified neuro-radiologist, after which natively embedded PyRadiomics in PACS was used for direct feature extraction. RESULTS In 7 Months (05/2021 - 08/2021, 03/2022 - 05/2022) segmentations and annotations were performed in 1033 patients (429 female, 604 male, mean age 53 yrs). Dataset includes 595 Grade 4 Gliomas (96 Grade 3, 105 Grade 2, 45 Grade 1, 192 unknown). Molecular subtypes include IDH (129 mutated, 651 wildtype, 253 unknown), 1p/19q (94 deleted or co-deleted, 135 intact, 804 unknown), MGMT promotor (216 methylated, 110 partially methylated, 321 unmethylated, 383 unknown), EGFR (125 amplified, 248 not amplified, 660 unknown), ATRX (41 mutated, 238 retained, 754 unknown), Ki-67 (726 known, 307 unknown) and p53 (639 known, 394 unknown). Classification of gliomas between grade 3/4 and grade 1/2, yielded AUC of 0.85. CONCLUSION We developed a method for incorporation of volumetric segmentation, feature extraction, and classification that is easily incorporated into neuroradiology workflow. These tools allowed us to annotate over 100 gliomas per month, thus establishing a proof of concept for rapid development of annotated imaging database for AI applications.

Kalman filter based sensor fault detection in wireless sensor network for smart irrigation

Ensuring the reliability of data obtained from Wireless Sensor Networks (WSNs) deployed in farmland to monitor soil parameters is crucial for optimizing smart irrigation. These distributed sensor nodes encounter various challenges and are vulnerable to sensor faults that can significantly degrade the network's service quality. In this article, we present an innovative approach for detecting sensor faults within WSNs for smart irrigation by combining an autoregressive model with a Kalman filter. Integrating the Kalman filter and the autoregressive model combines their strengths in a synergistic manner. The algorithm is developed with consideration for the resource constraints of the sensor nodes and addresses the challenge of lacking ground truth information for the monitored area. The primary advantage of this proposed technique lies in its simplicity of implementation, requiring minimal computational complexity while enhancing the application's reliability. Through experimentation and validation, we demonstrate the effectiveness of this combined approach in detecting sensor fault detection in real-world WSNs scenarios.

Large-scale variation in lakebed properties interpreted from single-beam sonar in two Laurentian Great Lakes

Acoustic seabed classification (ASC) is an important method for understanding landscape-level physical and biological patterns in the aquatic environment. Bottom habitats in the Laurentian Great Lakes are poorly mapped to date, and will require a variety of contributors and data sources to complete. We repurposed a long-term split-beam echosounder dataset gathered for purposes of fisheries assessment to estimate lakebed properties utilizing unsupervised classification of echo return data. We interpreted first echo properties representing lakebed hardness and roughness to define and map three statistically supported lakebed classes revealed through cluster analysis. Our results indicate coherent and logical class boundaries and suggest that the dataset has promise for expanded use in ASC. To improve inferences using repeated measures, future work should focus on collecting ground truth information for areas previously surveyed and on collecting concurrent ground truth information when sampling acoustic data moving forward.