Results from experiments on three large real-world benchmark datasets demonstrate that OptFeature performs well in terms of accuracy and efficiency. Additional studies support the feasibility of our method.

PGT -Aug involves three key steps: (i) volumetric 3D instance reconstruction using a 2D-to-3D view synthesis model, (ii) object-level domain alignment with LiDAR intensity simulation, and (iii) a hybrid context-aware placement method from ground and map information. We demonstrate the superiority and generality of our method through performance improvements in extensive experiments conducted on popular benchmarks, i.e., nuScenes, KITTI, and Lyft, especially for the datasets with large domain gaps

Japanese internal affairs minister Yoshimasa Hayashi (center) poses for a photo with ministers from ASEAN member states in Hanoi on Thursday. HANOI - Japan and the Association of Southeast Asian Nations have agreed to work together on developing new artificial intelligence models and preparing related laws. The AI-sector cooperation was included in a joint statement adopted at a meeting of digital ministers from Japan and ASEAN member states in Hanoi on Thursday. The statement was proposed by Japanese communications minister Yoshimasa Hayashi, who attended the meeting. Japan and ASEAN aim to join hands at a time when the United States and China are boosting their presence in the AI sector.

Malaysia's tech regulator said on Sunday that the country suspended access to Elon Musk's chatbot Grok over AI-generated pornographic content. AFP-JIJI - Malaysia suspended access to Elon Musk's chatbot Grok over AI-generated pornographic content, the country's tech regulator said on Sunday. The decision follows global backlash after it emerged that Grok's image creation feature allowed users to sexualize pictures of women and children using simple text prompts. On Saturday Indonesia became the first country to deny all access to the tool, which has been restricted to paying subscribers elsewhere. The Malaysian Communications and Multimedia Commission said in a statement it had directed a temporary restriction on access to the Grok artificial intelligence for users in Malaysia with immediate effect. This action follows repeated misuse of Grok to generate obscene, sexually explicit, indecent, grossly offensive and non-consensual manipulated images, the regulator said.

Automated defect detection from UAV imagery of transmission lines is a challenging task due to the small size, ambiguity, and complex backgrounds of defects. This paper proposes TinyDef-DETR, a DETR-based framework designed to achieve accurate and efficient detection of transmission line defects from UAV-acquired images. The model integrates four major components: an edge-enhanced ResNet backbone to strengthen boundary-sensitive representations, a stride-free space-to-depth module to enable detail-preserving downsampling, a cross-stage dual-domain multi-scale attention mechanism to jointly model global context and local cues, and a Focaler-Wise-SIoU regression loss to improve the localization of small and difficult objects. Together, these designs effectively mitigate the limitations of conventional detectors. Extensive experiments on both public and real-world datasets demonstrate that TinyDef-DETR achieves superior detection performance and strong generalization capability, while maintaining modest computational overhead. The accuracy and efficiency of TinyDef-DETR make it a suitable method for UAV-based transmission line defect detection, particularly in scenarios involving small and ambiguous objects.

Password security plays a crucial role in cybersecurity, yet traditional password strength meters, which rely on static rules like character - type requirements, often fail . Such methods are easily bypassed by common password patterns (e.g., 'P@ssw0rd1!'), giving users a false sense of security . To address this, we implement and evaluate a password strength scoring system by comparing four machine learning models: Random Forest (RF), Support Vector Machine (SVM), a Convolutional Neural Network (CNN), and Logistic Regression with a dataset of over 660,000 real - world passwords. Our primary contribution is a novel hybrid feature engineering approach that captures nuanced vulnerabilities missed by standard metrics . We introduce features like leetspeak - normalized Shannon entropy to assess true randomness, pattern detection for keyboard walks and sequences, and character - level TF - IDF n - grams to identify frequently reused substrings from breached password datasets. Crucially, the interpretability of the Random Forest model allows for feature importance analysis, providing a clear pathway to developing security tools that offer specific, actionable feedback to users. This study bridges the gap betwee n predictive accuracy and practical usability, resulting in a high - performance scoring system that not only reduces password - based vulnerabilities but also empowers users to make more informed security decisions. Keywords - Password Security, Machine Learning, Rule - Based Attack, Brute - Force Attack, Dictionary Attack, Cybersecurity. 1. P asswords remain a cornerstone of online security, serving as the primary means of authentication for countless systems and applications . However, this reliance is a critical vulnerability; according to a report by Google Cloud, a staggering 86% of breaches involve stolen credentials, posing a significant threat to both user data and system security .[1] M any users choose weak, easily guessable passwords, which pose a serious threat to both user data and system security . Attackers frequently exploit this vulnerability in large - scale attacks, compromising user privacy and enabling financial fraud . Most traditional password strength scoring tools rely on static rules, such as requiring a mix of lowercase, uppercase, digits, and special characters (LUDS), which fail to adapt to evolving attack patterns .

The art and science of Quranic recitation (Tajweed), a discipline governed by meticulous phonetic, rhythmic, and theological principles, confronts substantial educational challenges in today's digital age. Although modern technology offers unparalleled opportunities for learning, existing automated systems for evaluating recitation have struggled to gain broad acceptance or demonstrate educational effectiveness. This literature review examines this crucial disparity, offering a thorough analysis of scholarly research, digital platforms, and commercial tools developed over the past twenty years. Our analysis uncovers a fundamental flaw in current approaches that adapt Automatic Speech Recognition (ASR) systems, which emphasize word identification over qualitative acoustic evaluation. These systems suffer from limitations such as reliance on biased datasets, demographic disparities, and an inability to deliver meaningful feedback for improvement. Challenging these data-centric methodologies, we advocate for a paradigm shift toward a knowledge-based computational framework. By leveraging the unchanging nature of the Quranic text and the well-defined rules of Tajweed, we propose that an effective evaluation system should be built upon rule-based acoustic modeling centered on canonical pronunciation principles and articulation points (Makhraj), rather than depending on statistical patterns derived from flawed or biased data. The review concludes that the future of automated Quranic recitation assessment lies in hybrid systems that combine linguistic expertise with advanced audio processing. Such an approach paves the way for developing reliable, fair, and pedagogically effective tools that can authentically assist learners across the globe.