Can Few Lines of Code Change Society ? Beyond fack-checking and moderation : how recommender systems toxifies social networking sites

As the last few years have seen an increase in online hostility and polarization both, we need to move beyond the fack-checking reflex or the praise for better moderation on social networking sites (SNS) and investigate their impact on social structures and social cohesion. In particular, the role of recommender systems deployed at large scale by digital platforms such as Facebook or Twitter has been overlooked. This paper draws on the literature on cognitive science, digital media, and opinion dynamics to propose a faithful replica of the entanglement between recommender systems, opinion dynamics and users' cognitive biais on SNSs like Twitter that is calibrated over a large scale longitudinal database of tweets from political activists. This model makes it possible to compare the consequences of various recommendation algorithms on the social fabric and to quantify their interaction with some major cognitive bias. In particular, we demonstrate that the recommender systems that seek to solely maximize users' engagement necessarily lead to an overexposure of users to negative content (up to 300\% for some of them), a phenomenon called algorithmic negativity bias, to a polarization of the opinion landscape, and to a concentration of social power in the hands of the most toxic users. The latter are more than twice as numerous in the top 1\% of the most influential users than in the overall population. Overall, our findings highlight the urgency to identify harmful implementations of recommender systems to individuals and society in order better regulate their deployment on systemic SNSs

Bayesian changepoint models motivated by cyber-security applications

Changepoint detection has an important role to play in the next generation of cyber security defenses. A cyber attack typically changes the behaviour of the target network. Therefore, to detect the presence of a network intrusion, it can be informative to monitor for changes in the high-volume data sources that are collected inside an enterprise computer network. However, most traditional changepoint detection methods are not adapted to characterise what cyber security analysts mean by a change, and consequently raise too many false alerts but also overlook weak signals that are suggestive of a real attack. This thesis will present three novel Bayesian changepoint models that address some challenges raised by cyber data: the first model combines evidence across a graph of time series to identify patterns of changepoints that are a priori more likely to correspond to an attack; the second model offers robustness to non-exchangeable data within segments so that normal dynamic phenomena observed in cyber data can be captured; and, the third model relaxes the standard assumption that changes are instantaneous, so that time intervals where cyber data may be subject to non-instantaneous changes can be identified.Open Acces

Networks in cognitive science

Networks of interconnected nodes have long played a key role in Cognitive Science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions, and collaborations among scientists. Today, the inclusion of network theory into Cognitive Sciences, and the expansion of complex-systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the Cognitive Sciences.Postprint (author's final draft

Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception

Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) as an essential component to achieve V2X can overcome the inherent limitations of individual perception, including occlusion and long-range perception. In this survey, we provide a comprehensive review of CP methods for V2X scenarios, bringing a profound and in-depth understanding to the community. Specifically, we first introduce the architecture and workflow of typical V2X systems, which affords a broader perspective to understand the entire V2X system and the role of CP within it. Then, we thoroughly summarize and analyze existing V2X perception datasets and CP methods. Particularly, we introduce numerous CP methods from various crucial perspectives, including collaboration stages, roadside sensors placement, latency compensation, performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover, we conduct extensive experimental analyses to compare and examine current CP methods, revealing some essential and unexplored insights. Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue. Also, we examine methods under different LiDAR ranges. To study the model robustness, we further investigate the effects of various simulated real-world noises on the performance of different CP methods, covering communication latency, lossy communication, localization errors, and mixed noises. In addition, we look into the sim-to-real generalization ability of existing CP methods. At last, we thoroughly discuss issues and challenges, highlighting promising directions for future efforts. Our codes for experimental analysis will be public at https://github.com/memberRE/Collaborative-Perception.Comment: 19 page

Mapping connection, disconnection and power within the social news media network: a case study of the Great Barrier Reef UNESCO 2021 'In Danger' recommendation on Twitter, YouTube and Facebook

This research investigates how power is asserted and contested in the ‘social news media network’ (Bruns, 2018) in relation to environmental protection. The integrated mixed methods research design combines social network analysis, framing analysis and close reading to identify dominant actors, information flows and frames within the contemporary news-sharing spaces of Twitter, YouTube and Facebook. This includes the tactics that actors embedded in these networks use to further certain frames and information flows, or to re-frame and redirect information to suit their own goals. Applying Manuel Castells’ network theory of power (2011) and drawing upon the mediatised and politicised Great Barrier Reef (GBR) as a case study, this work addresses several gaps in understanding about how the protection of the GBR is being contested online and the power dynamics in decentralised contemporary communication spaces in relation to environmental protection

Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design

Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design