Search: “cross-device”

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JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition

JarvisGUI benchmark tests GUI agents on cross-device workflows across Android, Windows, and Ubuntu, revealing major gaps in state transfer and long-horizon reasoning.

JarvisGUI is a dynamic benchmark that formulates GUI tasks as input-output transformations under a lightweight type system, automatically composing multi-step cross-device workflows across Android, Windows, and Ubuntu virtual environments. Evaluation shows state-of-the-art open-source GUI agents struggle with state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management, exposing a capability gap invisible to existing single-device benchmarks.

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:59 UTC · 6d agoAI research1

PoisonSeed Attack Turns Out to Be Not a FIDO Bypass After All

The Hacker NewsJul 26, 11:39 UTC · Jul 26, 2025Threat actor

McAfee and Dell’s collaboration protects consumers as threat landscape evolves

Help Net SecurityDec 15, 12:40 UTC · Dec 15, 2023Malware

Learn the weaknesses of Apple devices at Black Hat USA 2019

Help Net SecurityAug 1, 10:38 UTC · Aug 1, 2019Vulnerability CVE-2018-4322 CVE-2018-4321

Hundreds of Apps Using Ultrasonic Signals to Silently Track Smartphone Users

The Hacker NewsMay 3, 16:43 UTC · May 3, 2017Research

New Malware Jumps Air-Gapped Devices by Turning Power

The Hacker NewsDec 16, 06:16 UTC · Dec 16, 2020Malware

Lightweight Vision Transformer Compression for On-Device Plant Disease Detection in Resource-Constrained Agricultural Field Conditions

A unified ViT compression pipeline (H-BAC pruning, quantization, distillation) cuts plant-disease models 54.5x to 6.01 MB while keeping 95.13% accuracy.

Researchers combined Hessian-Balanced Adaptive Block Pruning (H-BAC), guided by second-order sensitivity estimation, with quantization and attention-based knowledge distillation to compress Vision Transformers for on-device chilli plant disease detection in India. On a 3-class cross-village, cross-device out-of-distribution dataset, the integrated pipeline reduced model size from 327.42 MB to 6.01 MB (54.5x) at 95.13 +/- 2.32% accuracy, matching the 95.13% FP32 baseline. Ablations also show a directly trained 6.01 MB INT8 student reaches 94.87% accuracy, indicating where pruning and distillation add limited value.

arXiv cs.AI / cs.LG / cs.CLSep 4, 16:40 UTC · 11d agoAI research

ChatGPT advanced account security adds passkeys and hardware keys

OpenAI launches Advanced Account Security for ChatGPT and Codex, replacing passwords with passkeys or hardware keys and disabling email/SMS recovery.

The opt-in setting disables password sign-in plus email and SMS account recovery for ChatGPT and Codex accounts, allowing only passkeys, hardware security keys, and user-held recovery keys, with shortened sessions and automatic exclusion of enrolled accounts' conversations from model training. OpenAI partnered with Yubico to offer discounted bundles of the YubiKey C Nano and C NFC, while any FIDO2/WebAuthn-compliant key or software passkey is supported, mirroring standards adopted by Google, Microsoft, and GitHub. Individual members of Trusted Access for Cyber using the most permissive models must enable the setting from June 1, 2026, or their organizations can attest to phishing-resistant authentication in their single sign-on.

Help Net SecurityAug 24, 04:21 UTC · 23d agoAI industry