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Social robot navigation (SRN) is a relevant problem that involves navigating a pedestrian-rich environment in a socially acceptable manner. It is an essential part of making social robots effective in pedestrian-rich settings. The use cases of such robots could vary from companion robots to warehouse robots to autonomous wheelchairs. In recent years, deep reinforcement learning has been increasingly used in research on social robot navigation. Our work introduces CAMRL (Context-Aware Mamba-based Reinforcement Learning). Mamba is a new deep learning-based State Space Model (SSM) that has achieved results comparable to transformers in sequencing tasks. CAMRL uses Mamba to determine the robot's next action, which maximizes the value of the next state predicted by the neural network, enabling the robot to navigate effectively based on the rewards assigned. We evaluate CAMRL alongside existing solutions (CADRL, LSTM-RL, SARL) using a rigorous testing dataset which involves a variety of densities and environment behaviors based on ORCA and SFM, thus, demonstrating that CAMRL achieves higher success rates, minimizes collisions, and maintains safer distances from pedestrians. This work introduces a new SRN planner, showcasing the potential for deep-state space models for robot navigation.

Open Domain Question Answering (ODQA) within natural language processing involves building systems that answer factual questions using large-scale knowledge corpora. Recent advances stem from the confluence of several factors, such as large-scale training datasets, deep learning techniques, and the rise of large language models. High-quality datasets are used to train models on realistic scenarios and enable the evaluation of the system on potentially unseen data. Standardized metrics facilitate comparisons between different ODQA systems, allowing researchers to objectively track advancements in the field. Our study presents a thorough examination of the current landscape of ODQA benchmarking by reviewing 52 datasets and 20 evaluation techniques across textual and multimodal modalities. We introduce a novel taxonomy for ODQA datasets that incorporates both the modality and difficulty of the question types. Additionally, we present a structured organization of ODQA evaluation metrics along with a critical analysis of their inherent trade-offs. Our study aims to empower researchers by providing a framework for the robust evaluation of modern question-answering systems. We conclude by identifying the current challenges and outlining promising avenues for future research and development., Comment: 22 pages, 13 tables, 7 figures

We demonstrate absorber-free passive and hybrid mode-locking at sub-GHz repetition rates using a hybrid integrated extended cavity diode laser around 1550 nm. The laser is based on InP as gain medium and a long Si$_3$N$_4$ feedback circuit, with three highly frequency selective microring resonators. The feedback resonators not only increases the cavity length up to 0.6 m to achieve sub-GHz repetition rates but also serve as a dispersive narrowband mirror for sharp spectral filtering, which enables Fourier domain mode-locking. We observe passive mode-locking with repetition rates below 500 MHz, with $\approx$ 15 comb lines at around 0.2 mW total power. To stabilize the repetition rate, hybrid mode-locking is demonstrated by weak RF modulation of the diode current at frequencies around 500 MHz. The RF injection reduces the Lorentzian linewidth component from 8.9 kHz to a detection limited value around 300 mHz. To measure the locking range of the repetition rate, the injected RF frequency is tuned with regard to the passive mode-locking frequency and the injected RF power is varied. The locking range increases approximately as a square-root function of the injected RF power. At 1 mW injection a wide locking range of about 80 MHz is obtained. We observe the laser maintaining stable mode-locking also when the DC diode pump current is increased from 40 mA to 190 mA, provided that the cavity length is maintained constant with thermo-refractive tuning., Comment: This manuscript is under review for publication with optics express

Through a large-scale study over diverse face images, we show that facial attribute editing using modern generative AI models can severely degrade automated face recognition systems. This degradation persists even with identity-preserving generative models. To mitigate this issue, we propose two novel techniques for local and global attribute editing. We empirically ablate twenty-six facial semantic, demographic and expression-based attributes that have been edited using state-of-the-art generative models, and evaluate them using ArcFace and AdaFace matchers on CelebA, CelebAMaskHQ and LFW datasets. Finally, we use LLaVA, an emerging visual question-answering framework for attribute prediction to validate our editing techniques. Our methods outperform the current state-of-the-art at facial editing (BLIP, InstantID) while improving identity retention by a significant extent., Comment: Under review

Scammers are aggressively leveraging AI voice-cloning technology for social engineering attacks, a situation significantly worsened by the advent of audio Real-time Deepfakes (RTDFs). RTDFs can clone a target's voice in real-time over phone calls, making these interactions highly interactive and thus far more convincing. Our research confidently addresses the gap in the existing literature on deepfake detection, which has largely been ineffective against RTDF threats. We introduce a robust challenge-response-based method to detect deepfake audio calls, pioneering a comprehensive taxonomy of audio challenges. Our evaluation pitches 20 prospective challenges against a leading voice-cloning system. We have compiled a novel open-source challenge dataset with contributions from 100 smartphone and desktop users, yielding 18,600 original and 1.6 million deepfake samples. Through rigorous machine and human evaluations of this dataset, we achieved a deepfake detection rate of 86% and an 80% AUC score, respectively. Notably, utilizing a set of 11 challenges significantly enhances detection capabilities. Our findings reveal that combining human intuition with machine precision offers complementary advantages. Consequently, we have developed an innovative human-AI collaborative system that melds human discernment with algorithmic accuracy, boosting final joint accuracy to 82.9%. This system highlights the significant advantage of AI-assisted pre-screening in call verification processes. Samples can be heard at https://mittalgovind.github.io/autch-samples/, Comment: Dataset will be made public by end of March 2024

In this commentary, we discuss the evolving nature of search engines, as they begin to generate, index, and distribute content created by generative artificial intelligence (GenAI). Our discussion highlights challenges in the early stages of GenAI integration, particularly around factual inconsistencies and biases. We discuss how output from GenAI carries an unwarranted sense of credibility, while decreasing transparency and sourcing ability. Furthermore, search engines are already answering queries with error-laden, generated content, further blurring the provenance of information and impacting the integrity of the information ecosystem. We argue how all these factors could reduce the reliability of search engines. Finally, we summarize some of the active research directions and open questions., Comment: 9 pages, 5 figures

A dictionary attack in a biometric system entails the use of a small number of strategically generated images or templates to successfully match with a large number of identities, thereby compromising security. We focus on dictionary attacks at the template level, specifically the IrisCodes used in iris recognition systems. We present an hitherto unknown vulnerability wherein we mix IrisCodes using simple bitwise operators to generate alpha-mixtures - alpha-wolves (combining a set of "wolf" samples) and alpha-mammals (combining a set of users selected via search optimization) that increase false matches. We evaluate this vulnerability using the IITD, CASIA-IrisV4-Thousand and Synthetic datasets, and observe that an alpha-wolf (from two wolves) can match upto 71 identities @FMR=0.001%, while an alpha-mammal (from two identities) can match upto 133 other identities @FMR=0.01% on the IITD dataset., Comment: 8 pages, 5 figures, 13 tables, Workshop on Manipulation, Adversarial, and Presentation Attacks in Biometrics, Winter Conference on Applications of Computer Vision

The Image Data Commons (IDC) contains publicly available cancer radiology datasets that could be pertinent to the research and development of advanced imaging tools and algorithms. However, the full extent of its research capabilities is limited by the fact that these datasets have few if any, annotations associated with them. Through this study with the Artificial Intelligence (AI) AI in Medical Imaging (AIMI) initiative, we produced high-quality, AI-generated imaging annotations of tissues, organs, and/or cancers for 11 distinct medical imaging collections from the IDC. These collections have a variety of image modalities, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) imaging modalities. Furthermore, the imaging collections cover various body parities, such as the chest, breast, kidneys, prostate, and liver. Both publicly available and novel AI algorithms were adopted and further developed using open-sourced data coupled with expert annotations to create the AI-generated annotations. A portion of the AI annotations were reviewed and corrected by a radiologist to assess the AI model's performance. Both the AI's and the radiologist's annotations conformed to DICOM standards for seamless integration into the IDC collections as third-party analyses. All the models and datasets (images and annotations) are publicly accessible., Comment: 24 pages; 20 figures