Bengaluru, India – On January 23, an icon of Indian cinema and politics, M Karunanidhi appeared before a live audience on a large projected screen, to congratulate his 82-year-old friend and fellow politician TR Baalu on the launch of his autobiographical book. Dressed in his trademark black sunglasses, white shirt, and a yellow shawl around his shoulders -- Karunanidhi's style was spot on. In his eight-minute speech, the veteran poet-turned-politician congratulated the book's author but was also effusive in his praise for the able leadership of MK Stalin, his son and the current leader of the state. Karunanidhi has been dead since 2018. This was the third time, in the past six months, that the iconic leader of the Dravida Munnetra Kazhagam (DMK) party was resurrected using artificial intelligence (AI) for such public events.

Subsea exploration, inspection, and intervention operations heavily rely on remotely operated vehicles (ROVs). However, the inherent complexity of the underwater environment presents significant challenges to the operators of these vehicles. This paper delves into the challenges associated with navigation and maneuvering tasks in the teleoperation of ROVs, such as reduced situational awareness and heightened teleoperator workload. To address these challenges, we introduce an underwater Digital Twin (DT) system designed to enhance underwater teleoperation, enable autonomous navigation, support system monitoring, and facilitate system testing through simulation. Our approach involves a dynamic representation of the underwater robot and its environment using desktop virtual reality, as well as the integration of mapping, localization, path planning and simulation capabilities within the DT system. Our research demonstrates the system's adaptability, versatility and feasibility, highlighting significant challenges and, in turn, improving the teleoperators' situational awareness and reducing their workload.

This paper describes a temporal-spatial model for video processing with special applications to processing event camera videos. We propose to study a conjecture motivated by our previous study of video processing with delay loop reservoir (DLR) neural network, which we call Temporal-Spatial Conjecture (TSC). The TSC postulates that there is significant information content carried in the temporal representation of a video signal and that machine learning algorithms would benefit from separate optimization of the spatial and temporal components for intelligent processing. To verify or refute the TSC, we propose a Visual Markov Model (VMM) which decompose the video into spatial and temporal components and estimate the mutual information (MI) of these components. Since computation of video mutual information is complex and time consuming, we use a Mutual Information Neural Network to estimate the bounds of the mutual information. Our result shows that the temporal component carries significant MI compared to that of the spatial component. This finding has often been overlooked in neural network literature. In this paper, we will exploit this new finding to guide our design of a delay-loop reservoir neural network for event camera classification, which results in a 18% improvement on classification accuracy.

The acceleration in the adoption of AI-based automated decision-making systems poses a challenge for evaluating the fairness of algorithmic decisions, especially in the absence of ground truth. When designing interventions, uplift modeling is used extensively to identify candidates that are likely to benefit from treatment. However, these models remain particularly susceptible to fairness evaluation due to the lack of ground truth on the outcome measure since a candidate cannot be in both treatment and control simultaneously. In this article, we propose a framework that overcomes the missing ground truth problem by generating surrogates to serve as a proxy for counterfactual labels of uplift modeling campaigns. We then leverage the surrogate ground truth to conduct a more comprehensive binary fairness evaluation. We show how to apply the approach in a comprehensive study from a real-world marketing campaign for promotional offers and demonstrate its enhancement for fairness evaluation.

We propose and analyze an adaptive adversary that can retrain a Trojaned DNN and is also aware of SOTA output-based Trojaned model detectors. We show that such an adversary can ensure (1) high accuracy on both trigger-embedded and clean samples and (2) bypass detection. Our approach is based on an observation that the high dimensionality of the DNN parameters provides sufficient degrees of freedom to simultaneously achieve these objectives. We also enable SOTA detectors to be adaptive by allowing retraining to recalibrate their parameters, thus modeling a co-evolution of parameters of a Trojaned model and detectors. We then show that this co-evolution can be modeled as an iterative game, and prove that the resulting (optimal) solution of this interactive game leads to the adversary successfully achieving the above objectives. In addition, we provide a greedy algorithm for the adversary to select a minimum number of input samples for embedding triggers. We show that for cross-entropy or log-likelihood loss functions used by the DNNs, the greedy algorithm provides provable guarantees on the needed number of trigger-embedded input samples. Extensive experiments on four diverse datasets -- MNIST, CIFAR-10, CIFAR-100, and SpeechCommand -- reveal that the adversary effectively evades four SOTA output-based Trojaned model detectors: MNTD, NeuralCleanse, STRIP, and TABOR.

We propose the first randomized algorithms for Symmetric Nonnegative Matrix Factorization (SymNMF). Nonnegative Matrix Factorization (NMF) is an important method in data analysis with applications to data visualization, text mining, feature learning, information fusion and more [28, 25, 46, 22, 11]. SymNMF is a variant of NMF where the input matrix is symmetric and the output low-rank approximation is also constrained to be symmetric [25, 49]. Applications of SymNMF include (hyper)graph clustering, image segmentation, and information fusion [44, 10, 19, 5, 6]. Several randomized algorithms for nonsymmetric NMF have been previously proposed and shown to be effective for dense and small sparse problems [43, 41, 13], but as far as we are aware there is no prior work on randomized algorithms for SymNMF. Our contributions in this work include a randomized algorithm for SymNMF we call "Low-rank Approximated Input SymNMF" (LAI-SymNMF), a randomized algorithm based on leverage score sampling for least squares problems we call LvS-SymNMF, novel theoretical analysis of leverage score sampling for the Nonnegative Least Squares problem and theoretical analysis of a hybrid sampling scheme for leverage score sampling. The rest of the paper is organized as follows. Section 2, which discusses background material including non-randomized SymNMF algorithms, reviews existing randomized NMF methods and other related work such as randomized methods for other low-rank matrix decompositions and tensor decompositions.

Background: Machine learning (ML) methods often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices in clinical settings, where data drift may cause unexpected performance that jeopardizes patient safety. Method: We propose a ML-enabled Statistical Process Control (SPC) framework for out-of-distribution (OOD) detection and drift monitoring. SPC is advantageous as it visually and statistically highlights deviations from the expected distribution. To demonstrate the utility of the proposed framework for monitoring data drift in radiological images, we investigated different design choices, including methods for extracting feature representations, drift quantification, and SPC parameter selection. Results: We demonstrate the effectiveness of our framework for two tasks: 1) differentiating axial vs. non-axial computed tomography (CT) images and 2) separating chest x-ray (CXR) from other modalities. For both tasks, we achieved high accuracy in detecting OOD inputs, with 0.913 in CT and 0.995 in CXR, and sensitivity of 0.980 in CT and 0.984 in CXR. Our framework was also adept at monitoring data streams and identifying the time a drift occurred. In a simulation with 100 daily CXR cases, we detected a drift in OOD input percentage from 0-1% to 3-5% within two days, maintaining a low false-positive rate. Through additional experimental results, we demonstrate the framework's data-agnostic nature and independence from the underlying model's structure. Conclusion: We propose a framework for OOD detection and drift monitoring that is agnostic to data, modality, and model. The framework is customizable and can be adapted for specific applications.

The advent of the Internet of Things (IoT) has brought forth additional intricacies and difficulties to computer networks. These gadgets are particularly susceptible to cyber-attacks because of their simplistic design. Therefore, it is crucial to recognise these devices inside a network for the purpose of network administration and to identify any harmful actions. Network traffic fingerprinting is a crucial technique for identifying devices and detecting anomalies. Currently, the predominant methods for this depend heavily on machine learning (ML). Nevertheless, machine learning (ML) methods need the selection of features, adjustment of hyperparameters, and retraining of models to attain optimal outcomes and provide resilience to concept drifts detected in a network. In this research, we suggest using locality-sensitive hashing (LSH) for network traffic fingerprinting as a solution to these difficulties. Our study focuses on examining several design options for the Nilsimsa LSH function. We then use this function to create unique fingerprints for network data, which may be used to identify devices. We also compared it with ML-based traffic fingerprinting and observed that our method increases the accuracy of state-of-the-art by 12% achieving around 94% accuracy in identifying devices in a network.

Early detection of network intrusions and cyber threats is one of the main pillars of cybersecurity. One of the most effective approaches for this purpose is to analyze network traffic with the help of artificial intelligence algorithms, with the aim of detecting the possible presence of an attacker by distinguishing it from a legitimate user. This is commonly done by collecting the traffic exchanged between terminals in a network and analyzing it on a per-packet or per-connection basis. In this paper, we propose instead to perform pre-processing of network traffic under analysis with the aim of extracting some new metrics on which we can perform more efficient detection and overcome some limitations of classical approaches. These new metrics are based on graph theory, and consider the network as a whole, rather than focusing on individual packets or connections. Our approach is validated through experiments performed on publicly available data sets, from which it results that it can not only overcome some of the limitations of classical approaches, but also achieve a better detection capability of cyber threats.

Understanding and adapting to climate change is paramount for professionals such as urban planners, emergency managers, and infrastructure operators, as it directly influences urban development, disaster response, and the maintenance of essential services. Nonetheless, this task presents a complex challenge that necessitates the integration of advanced technology and scientific insights. Recent advances in LLMs present an innovative solution, particularly in democratizing climate science. They possess the unique capability to interpret and explain technical aspects of climate change through conversations, making this crucial information accessible to people from all backgrounds Rillig et al. [2023], Bulian et al. [2023], Chen et al. [2023]. However, given that LLMs are generalized models, their performance can be improved by providing additional domain-specific information. Recent research has been focusing on augmenting LLMs with external tools and data sources to ensure that the information provided is scientifically accurate: for example, leveraging authoritative data sources such as ClimateWatch Kraus et al. [2023] and findings from the IPCC AR6 reports Vaghefi et al. [2023] helps in refining the LLM's outputs, ensuring that the information is grounded in the latest research.