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Abstract The on-ground operation of Mars rovers is a complex task that requires comprehensive planning in which path planning plays a fundamental role. The selection of paths has to be carefully chosen considering the scientific objectives, terrain, energy, and safety. In this regard, operators are assisted by path-planning algorithms that generate candidate paths based on cost functions. Distance traveled has always been considered one of the primary criteria when comparing paths. Other metrics such as the run-time to generate the solution or the number of expanded nodes are common measures considered in the literature. However, we want to analyze if those metrics provide useful information in challenging and partially known terrain. In this paper, we will review those metrics using two-path planning algorithms on real Mars maps. Based on our experience operating Mars rovers, we propose new metrics for assessing paths in real-world applications.

Abstract Democracies around the world face the threat of manipulation of their electorates via coordinated online influence campaigns. Researchers have responded by developing valuable methods for finding automated accounts and identifying false information, but these valiant efforts often fall into a cat-and-mouse game with perpetrators who constantly change their behavior. This has forced several researchers to go beyond the detection of individual malicious actors by instead identifying the coordinated activity that propels potent information operations. In this vein, we provide rigorous quantitative evidence for the notion that sudden increases in Twitter account creations may provide early warnings of online information operations. Analysis of fourteen months of tweets discussing the 2020 U.S. elections revealed that accounts created during bursts exhibited more similar behavior, showed more agreement on mail-in voting and mask wearing, and were more likely to be bots and share links to low-credibility sites. In concert with other techniques for detecting nefarious activity, social media platforms could temporarily limit the influence of accounts created during these bursts. Given the advantages of combining multiple anti-misinformation methods, we join others in presenting a case for the need to develop more integrable methods for countering online influence campaigns.

This study expands upon prior analysis of the case citation network of the International Court of Justice (ICJ) by demonstrating that reverse page-rank outperforms hub and authority score in identifying significant sources of precedent. Application of reverse page-rank reveals that though ICJ decisions that are well grounded in prior cases tend to become more important sources of precedent, this relationship is weak when compared to the US Supreme Court. This tempers past conclusions that the ICJ has become more similar to common law systems but supports a view of the Court as an institution that balances consistency in its application of international law with the statutory limits on using its decisions as sources of such law. In addition, a novel method for constructing agreement networks between judges is leveraged to identify ICJ judges with influential positions. This provides evidence that certain judges may be more indicative of which decisions become important in the future. Though the strength of this influence is also limited, it nevertheless suggests that some ICJ judges may better represent the development of international legal precedent than others. • The International Court of Justice takes a balanced approach to citing its precedent. • Reverse page-rank better identified future precedents than other centralities. • Agreement networks can highlight judges with more influential legal stances. [ABSTRACT FROM AUTHOR]

Online talk about racism has been salient throughout the COVID-19 pandemic. Yet while such social media conversations reflect existing tensions in the offline world, the same discourse has also become a target for information operations aiming to heighten social divisions. This article examines Twitter discussions of racism in the first and sixth months since COVID-19 was accorded pandemic status by the World Health Organization and uncovers dynamic associations with bot activity and hate speech. Humans initially constituted the most hateful accounts in online conversations about racism in March, but in August, bots dominated hate speech. Over time, greater bot activity likewise amplified levels of hate speech a week later. Moreover, while discourse about racism in March primarily featured an organic focus on racial identities like Asian and Chinese , we further observed a bot-dominated focus in August toward political identities like president , Democrat , and Republican . Although hate speech targeting Asian groups remained present among racism discussions in August, these findings suggest a bot-fueled redirection from focusing on racial groups at the onset of the pandemic to targeting politics closer to the 2020 US elections. This work enhances understanding of the complexity of racism discussions during the pandemic, its vulnerability to manipulation through information operations, and the large-scale quantitative study of inorganic hate campaigns in online social networks.

BackgroundDuring the time surrounding the approval and initial distribution of Pfizer-BioNTech’s COVID-19 vaccine, large numbers of social media users took to using their platforms to voice opinions on the vaccine. They formed pro- and anti-vaccination groups toward the purpose of influencing behaviors to vaccinate or not to vaccinate. The methods of persuasion and manipulation for convincing audiences online can be characterized under a framework for social-cyber maneuvers known as the BEND maneuvers. Previous studies have been conducted on the spread of COVID-19 vaccine disinformation. However, these previous studies lacked comparative analyses over time on both community stances and the competing techniques of manipulating both the narrative and network structure to persuade target audiences. ObjectiveThis study aimed to understand community response to vaccination by dividing Twitter data from the initial Pfizer-BioNTech COVID-19 vaccine rollout into pro-vaccine and anti-vaccine stances, identifying key actors and groups, and evaluating how the different communities use social-cyber maneuvers, or BEND maneuvers, to influence their target audiences and the network as a whole. MethodsCOVID-19 Twitter vaccine data were collected using the Twitter application programming interface (API) for 1-week periods before, during, and 6 weeks after the initial Pfizer-BioNTech rollout (December 2020 to January 2021). Bot identifications and linguistic cues were derived for users and tweets, respectively, to use as metrics for evaluating social-cyber maneuvers. Organization Risk Analyzer (ORA)-PRO software was then used to separate the vaccine data into pro-vaccine and anti-vaccine communities and to facilitate identification of key actors, groups, and BEND maneuvers for a comparative analysis between each community and the entire network. ResultsBoth the pro-vaccine and anti-vaccine communities used combinations of the 16 BEND maneuvers to persuade their target audiences of their particular stances. Our analysis showed how each side attempted to build its own community while simultaneously narrowing and neglecting the opposing community. Pro-vaccine users primarily used positive maneuvers such as excite and explain messages to encourage vaccination and backed leaders within their group. In contrast, anti-vaccine users relied on negative maneuvers to dismay and distort messages with narratives on side effects and death and attempted to neutralize the effectiveness of the leaders within the pro-vaccine community. Furthermore, nuking through platform policies showed to be effective in reducing the size of the anti-vaccine online community and the quantity of anti-vaccine messages. ConclusionsSocial media continues to be a domain for manipulating beliefs and ideas. These conversations can ultimately lead to real-world actions such as to vaccinate or not to vaccinate against COVID-19. Moreover, social media policies should be further explored as an effective means for curbing disinformation and misinformation online.

Background: During the time surrounding the approval and initial distribution of Pfizer-BioNTech's COVID-19 vaccine, large numbers of social media users took to using their platforms to voice opinions on the vaccine. They formed pro- and anti-vaccination groups toward the purpose of influencing behaviors to vaccinate or not to vaccinate. The methods of persuasion and manipulation for convincing audiences online can be characterized under a framework for social-cyber maneuvers known as the BEND maneuvers. Previous studies have been conducted on the spread of COVID-19 vaccine disinformation. However, these previous studies lacked comparative analyses over time on both community stances and the competing techniques of manipulating both the narrative and network structure to persuade target audiences. Objective: This study aimed to understand community response to vaccination by dividing Twitter data from the initial Pfizer-BioNTech COVID-19 vaccine rollout into pro-vaccine and anti-vaccine stances, identifying key actors and groups, and evaluating how the different communities use social-cyber maneuvers, or BEND maneuvers, to influence their target audiences and the network as a whole. Methods: COVID-19 Twitter vaccine data were collected using the Twitter application programming interface (API) for 1-week periods before, during, and 6 weeks after the initial Pfizer-BioNTech rollout (December 2020 to January 2021). Bot identifications and linguistic cues were derived for users and tweets, respectively, to use as metrics for evaluating social-cyber maneuvers. Organization Risk Analyzer (ORA)-PRO software was then used to separate the vaccine data into pro-vaccine and anti-vaccine communities and to facilitate identification of key actors, groups, and BEND maneuvers for a comparative analysis between each community and the entire network. Results: Both the pro-vaccine and anti-vaccine communities used combinations of the 16 BEND maneuvers to persuade their target audiences of their particular stances. Our analysis showed how each side attempted to build its own community while simultaneously narrowing and neglecting the opposing community. Pro-vaccine users primarily used positive maneuvers such as excite and explain messages to encourage vaccination and backed leaders within their group. In contrast, anti-vaccine users relied on negative maneuvers to dismay and distort messages with narratives on side effects and death and attempted to neutralize the effectiveness of the leaders within the pro-vaccine community. Furthermore, nuking through platform policies showed to be effective in reducing the size of the anti-vaccine online community and the quantity of anti-vaccine messages. Conclusions: Social media continues to be a domain for manipulating beliefs and ideas. These conversations can ultimately lead to real-world actions such as to vaccinate or not to vaccinate against COVID-19. Moreover, social media policies should be further explored as an effective means for curbing disinformation and misinformation online. J Med Internet Res 2022;24(3):e34040 doi:10.2196/34040 [ABSTRACT FROM AUTHOR]

The feasibility of using the Internet for remote pathology consultation was examined. We assessed the diagnostic agreement between two groups of pathologists who independently evaluated histopathological cases exchanged by Internet electronic mail. The exchange was between two different workstations using readily available software not specifically developed for telemedicine. Data and images from 76 cases were transmitted to four pathologists. An average of 4.5 images per case were transmitted at compression ratios of between 6:1 and 40:1, corresponding to 250 kByte of data per case. In two cases the remote pathologists could not make a diagnosis. Agreement was reached in 63 of the other 74 (kappa = 0.79). In 11 cases (15%) there was a misdiagnosis. However, the results are encouraging and suggest that Internet electronic mail can be used successfully for remote consultation in pathology.

Background/aims: This paper is the first of a series describing research and development performed by the SPOTS project towards automating the process of monitoring pigmented lesions for change over time. A comprehensive overview of digital image processing techniques used for the systematic approach to segmentation of large-area skin images, including the description and classification of skin features, is presented., Methods: The multiresolution hierarchical segmentation technique, which makes use of image pyramid data structures and cooperative computation with unforced linking, was used to group pixels with similar color properties. This technique was further enhanced to enforce contiguity of segments by determining connected components. The resulting segmentation was then classified by using representative properties of pyramid nodes as input to a cascade of multilayer perceptron neural networks trained using the classical backpropagation algorithm. In order to make our system more resistant to imaging distortions, a technique was developed to grow regions based upon the resulting classification., Conclusions: This system produces a segmentation at multiple resolutions based upon color, where each segment is a contiguous group of pixels on the image. The classification makes use of both spatial and color properties, as well as parent-child relationships within the pyramid. The addition of classification-driven region growing reduces the change in lesion area where the boundary fades into surrounding skin. Furthermore, the system contains parameters that can be tuned to provide desirable results.