Information Manipulation Sets
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Related Articles from SNS
Human-Centred Risk Mitigation for AI-Mediated Information Manipulation: A SOCMINT Framework Based on Information Manipulation Sets
Announce Type: new Abstract: AI-mediated information manipulation increasingly takes the form of social cyber attacks that target trust, attention, credibility, reputation, and decision-making rather than only technical infrastructures or isolated false contents. Existing defensive approaches often oscillate between incident-level analysis, which fragments campaigns into weak signals, and attribution-first analysis, which may delay mitigation until responsibility is established. This paper...
Bajaj Allianz wins mediclaim dispute after probe finds hospital records manipulated
The Delhi consumer court has set aside an order directing reimbursement of a mediclaim amount, holding that an insurer was justified in repudiating the claim after an investigation revealed multiple flaws and discrepancies in the hospital records. A bench comprising Justice Sangita Dhingra Sehgal (President) and Bimla Kumari (Member) allowed the appeal filed by Bajaj Allianz General Insurance against a district commission order that had earlier directed the insurer to pay the claim amount...
Non-obvious Manipulability in the Additively Separable Group Activity Selection Problem
new Abstract: In this work, we study the additively separable Group Activity Selection Problem (AS-GASP) in an imperfect information setting, where agents have private preferences over activities and weights over other agents. Our goal is to design mechanisms that assign agents to activities based on their declared preferences and weights, with the objective of maximizing social welfare while ensuring truthful reporting. We, therefore, focus on the notion of non-obvious manipulability (NOM),...
FingerEye: Learning Dexterous Manipulation with Continuous Vision-Tactile Sensing
arXiv:2604.20689v3 Announce Type: replace Abstract: Dexterous robotic manipulation requires perception that remains informative from pre-contact approach to contact initiation and post-contact control. We introduce FingerEye, a sensing and learning framework that strengthens robotic dexterity through continuous vision-tactile feedback throughout interaction. On the sensing side, FingerEye integrates binocular RGB cameras with a compliant contact interface to support perception both before...
Feat2Go: Visual Feature-Grounded Value Estimation for Embodied Reinforcement Learning
arXiv:2605.30795v1 Announce Type: new Abstract: Reinforcement learning is a promising approach for improving the capabilities of vision-language-action (VLA) models while avoiding the heavy data requirements of imitation learning. However, its effectiveness for VLA models is often constrained by sparse supervision and the difficulty of designing informative reward signals for long-horizon manipulation. In this work, we present Feat2Go, a fine-grained value estimation framework for embodied...
Direct Informed Sampling on Riemannian Manifolds via Loewner Order Lower Bounds
arXiv:2606.02879v1 Announce Type: new Abstract: Informed sampling techniques accelerate sampling-based motion planners by focusing the search on promising regions of the state space, yet most existing methods rely on Euclidean heuristics that become inadmissible under configuration-dependent Riemannian metrics. While scalar eigenvalue bounds restore admissibility by uniformly scaling the Euclidean distance, they discard the directional structure of the metric, producing overly conservative...
Chameleon: Control-Indexed Prospective Memory for Visuomotor Manipulation
arXiv:2603.24576v2 Announce Type: replace Abstract: Robots often observe information that determines a future action long before that action is executed. In a shell game, for example, a robot first sees which cup hides the ball, watches the cups move, and only later needs to choose the correct cup. The final observation alone is not enough for a decision: the correct action depends on an earlier event.
Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs
arXiv:2606.01072v2 Announce Type: replace Abstract: Imitation learning enables robots to learn how to execute tasks via observation. However, real-world environments like homes and offices are often severely partially observed due to their large spatial scales. In addition, many tasks involve executing a series of subtasks requiring autonomous robots to reason over extended time horizons.
Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs
Announce Type: new Abstract: Imitation learning enables robots to learn how to execute tasks via observation. However, real-world environments like homes and offices are often severely partially observed due to their large spatial scales. In addition, many tasks involve executing a series of subtasks requiring autonomous robots to reason over extended time horizons.
Spencer Pratt rides Big Tech’s rightward wave as latest Silicon Valley titan opens wallet
Google co-founder Sergey Brin just became the latest high-profile member of the tech world to throw his financial support behind a Republican. Brin gave Los Angeles mayoral candidate Spencer Pratt a maximum donation of $1,800 on May 27, according to municipal campaign finance records. Other big names in tech making contributions to the mayoral hopeful include Palantir’s chief technology officer, SpaceX’s director of solar production, a communications executive at TikTok, the co-chairman and...