OpenRaiser/Intern-Atlas huggingface dataset:
Towards more accurate and useful data anonymity vulnerability measures
Venue: arXiv cs.CR 2024
Year: 2024

The purpose of anonymizing structured data is to protect the privacy of individuals in the data while retaining the statistical properties of the data. There is a large body of work that examines anonymization vulnerabilities. Focusing on strong anonymization mechanisms, this paper examines a number of prominent attack papers and finds several problems, all of which lead to overstating risk. First, some papers fail to establish a correct statistical inference baseline (or any at all), leading to incorrect measures. Notably, the reconstruction attack from the US Census Bureau that led to a redesign of its disclosure method made this mistake. We propose the non-member framework, an improved method for how to compute a more accurate inference baseline, and give examples of its operation. Second, some papers don't use a realistic membership base rate, leading to incorrect precision measures if precision is reported. Third, some papers unnecessarily report measures in such a way that it is difficult or impossible to assess risk. Virtually the entire literature on membership inference attacks, dozens of papers, make one or both of these errors. We propose that membership inference papers report precision/recall values using a representative range of base rates.
Balanced Substructures in Bicolored Graphs
Venue: arXiv cs.DS 2024
Year: 2024

An edge-colored graph is said to be balanced if it has an equal number of edges of each color. Given a graph $G$ whose edges are colored using two colors and a positive integer $k$, the objective in the Edge Balanced Connected Subgraph problem is to determine if $G$ has a balanced connected subgraph containing at least $k$ edges. We first show that this problem is NP-complete and remains so even if the solution is required to be a tree or a path. Then, we focus on the parameterized complexity of Edge Balanced Connected Subgraph and its variants (where the balanced subgraph is required to be a path/tree) with respect to $k$ as the parameter. Towards this, we show that if a graph has a balanced connected subgraph/tree/path of size at least $k$, then it has one of size at least $k$ and at most $f(k)$ where $f$ is a linear function. We use this result combined with dynamic programming algorithms based on color coding and representative sets to show that Edge Balanced Connected Subgraph and its variants are FPT. Further, using polynomial-time reductions to the Multilinear Monomial Detection problem, we give faster randomized FPT algorithms for the problems. In order to describe these reductions, we define a combinatorial object called relaxed-subgraph. We define this object in such a way that balanced connected subgraphs, trees and paths are relaxed-subgraphs with certain properties. This object is defined in the spirit of branching walks known for the Steiner Tree problem and may be of independent interest.
Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings
Venue: arXiv cs.LG 2024
Year: 2024

In the ever-evolving era of Artificial Intelligence (AI), model performance has constituted a key metric driving innovation, leading to an exponential growth in model size and complexity. However, sustainability and energy efficiency have been critical requirements during deployment in contemporary industrial settings, necessitating the use of data-efficient approaches such as few-shot learning. In this paper, to alleviate the burden of lengthy model training and minimize energy consumption, a finetuning approach to adapt standard object detection models to downstream tasks is examined. Subsequently, a thorough case study and evaluation of the energy demands of the developed models, applied in object detection benchmark datasets from volatile industrial environments is presented. Specifically, different finetuning strategies as well as utilization of ancillary evaluation data during training are examined, and the trade-off between performance and efficiency is highlighted in this low-data regime. Finally, this paper introduces a novel way to quantify this trade-off through a customized Efficiency Factor metric.
Towards Zero-Shot Interpretable Human Recognition: A 2D-3D Registration Framework
Venue: arXiv cs.CV 2024
Year: 2024

Large vision models based in deep learning architectures have been consistently advancing the state-of-the-art in biometric recognition. However, three weaknesses are commonly reported for such kind of approaches: 1) their extreme demands in terms of learning data; 2) the difficulties in generalising between different domains; and 3) the lack of interpretability/explainability, with biometrics being of particular interest, as it is important to provide evidence able to be used for forensics/legal purposes (e.g., in courts). To the best of our knowledge, this paper describes the first recognition framework/strategy that aims at addressing the three weaknesses simultaneously. At first, it relies exclusively in synthetic samples for learning purposes. Instead of requiring a large amount and variety of samples for each subject, the idea is to exclusively enroll a 3D point cloud per identity. Then, using generative strategies, we synthesize a very large (potentially infinite) number of samples, containing all the desired covariates (poses, clothing, distances, perspectives, lighting, occlusions,...). Upon the synthesizing method used, it is possible to adapt precisely to different kind of domains, which accounts for generalization purposes. Such data are then used to learn a model that performs local registration between image pairs, establishing positive correspondences between body parts that are the key, not only to recognition (according to cardinality and distribution), but also to provide an interpretable description of the response (e.g.: "both samples are from the same person, as they have similar facial shape, hair color and legs thickness").
Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment
Venue: arXiv cs.CV 2024
Year: 2024

Most computer vision applications aim to identify pixels in a scene and use them for diverse purposes. One intriguing application is car damage detection for insurance carriers which tends to detect all car damages by comparing both pre-trip and post-trip images, even requiring two components: (i) car damage detection; (ii) image alignment. Firstly, we implemented a Mask R-CNN model to detect car damages on custom images. Whereas for the image alignment section, we especially propose a novel self-supervised Patch-to-Patch SimCLR inspired alignment approach to find perspective transformations between custom pre/post car rental images except for traditional computer vision methods.
Answering Diverse Questions via Text Attached with Key Audio-Visual Clues
Venue: arXiv cs.CV 2024
Year: 2024

Audio-visual question answering (AVQA) requires reference to video content and auditory information, followed by correlating the question to predict the most precise answer. Although mining deeper layers of audio-visual information to interact with questions facilitates the multimodal fusion process, the redundancy of audio-visual parameters tends to reduce the generalization of the inference engine to multiple question-answer pairs in a single video. Indeed, the natural heterogeneous relationship between audiovisuals and text makes the perfect fusion challenging, to prevent high-level audio-visual semantics from weakening the network's adaptability to diverse question types, we propose a framework for performing mutual correlation distillation (MCD) to aid question inference. MCD is divided into three main steps: 1) firstly, the residual structure is utilized to enhance the audio-visual soft associations based on self-attention, then key local audio-visual features relevant to the question context are captured hierarchically by shared aggregators and coupled in the form of clues with specific question vectors. 2) Secondly, knowledge distillation is enforced to align audio-visual-text pairs in a shared latent space to narrow the cross-modal semantic gap. 3) And finally, the audio-visual dependencies are decoupled by discarding the decision-level integrations. We evaluate the proposed method on two publicly available datasets containing multiple question-and-answer pairs, i.e., Music-AVQA and AVQA. Experiments show that our method outperforms other state-of-the-art methods, and one interesting finding behind is that removing deep audio-visual features during inference can effectively mitigate overfitting. The source code is released at http://github.com/rikeilong/MCD-forAVQA.
PCLD: Point Cloud Layerwise Diffusion for Adversarial Purification
Venue: arXiv cs.CV 2024
Year: 2024

Point clouds are extensively employed in a variety of real-world applications such as robotics, autonomous driving and augmented reality. Despite the recent success of point cloud neural networks, especially for safety-critical tasks, it is essential to also ensure the robustness of the model. A typical way to assess a model's robustness is through adversarial attacks, where test-time examples are generated based on gradients to deceive the model. While many different defense mechanisms are studied in 2D, studies on 3D point clouds have been relatively limited in the academic field. Inspired from PointDP, which denoises the network inputs by diffusion, we propose Point Cloud Layerwise Diffusion (PCLD), a layerwise diffusion based 3D point cloud defense strategy. Unlike PointDP, we propagated the diffusion denoising after each layer to incrementally enhance the results. We apply our defense method to different types of commonly used point cloud models and adversarial attacks to evaluate its robustness. Our experiments demonstrate that the proposed defense method achieved results that are comparable to or surpass those of existing methodologies, establishing robustness through a novel technique. Code is available at https://github.com/batuceng/diffusion-layer-robustness-pc.
On the Secrecy Rate of In-Band Full-duplex Two-way Wiretap Channel
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, we consider a two-way wiretap Multi-Input Multi-Output Multi-antenna Eve (MIMOME) channel, where both nodes (Alice and Bob) transmit and receive in an in-band full-duplex (IBFD) manner. For this system with keyless security, we provide a novel artificial noise (AN) based signal design, where the AN is injected in both signal and null spaces. We present an ergodic secrecy rate approximation to derive the power allocation algorithm. We consider scenarios where AN is known and unknown to legitimate users and include imperfect channel information effects. To maximize secrecy rates subject to the transmit power constraint, a two-step power allocation solution is proposed, where the first step is known at Eve, and the second step helps to improve the secrecy further. We also consider scenarios where partial information is known by Eve and the effects of non-ideal self-interference cancellation. The usefulness and limitations of the resulting power allocation solution are analyzed and verified via simulations. Results show that secrecy rates are less when AN is unknown to receivers or Eve has more information about legitimate users. Since the ergodic approximation only considers Eves distance, the resulting power allocation provides secrecy rates close to the actual ones.
Comparative Analysis of NMPC and Fuzzy PID Controllers for Trajectory Tracking in Omni-Drive Robots: Design, Simulation, and Performance Evaluation
Venue: arXiv cs.RO 2024
Year: 2024

Trajectory tracking for an Omni-drive robot presents a challenging task that demands an efficient controller design. This paper introduces a self-optimizing controller, Type-1 fuzzyPID, which leverages dynamic and static system response analysis to overcome the limitations of manual tuning. To account for system uncertainties, an Interval Type-2 fuzzyPID controller is also developed. Both controllers are designed using Matlab/Simulink and tested through trajectory tracking simulations in the CoppeliaSim environment. Additionally, a non-linear model predictive controller(NMPC) is proposed and compared against the fuzzyPID controllers. The impact of tunable parameters on NMPC tracking accuracy is thoroughly examined. We also present plots of the step-response characteristics and noise rejection experiments for each controller. Simulation results validate the precision and effectiveness of NMPC over fuzzyPID controllers while trading computational complexity. Access to code and simulation environment is available in the following link: https://github.com/love481/Omni-drive-robot-Simulation.git.
Generalising Multi-Agent Cooperation through Task-Agnostic Communication
Venue: arXiv cs.MA 2024
Year: 2024

Existing communication methods for multi-agent reinforcement learning (MARL) in cooperative multi-robot problems are almost exclusively task-specific, training new communication strategies for each unique task. We address this inefficiency by introducing a communication strategy applicable to any task within a given environment. We pre-train the communication strategy without task-specific reward guidance in a self-supervised manner using a set autoencoder. Our objective is to learn a fixed-size latent Markov state from a variable number of agent observations. Under mild assumptions, we prove that policies using our latent representations are guaranteed to converge, and upper bound the value error introduced by our Markov state approximation. Our method enables seamless adaptation to novel tasks without fine-tuning the communication strategy, gracefully supports scaling to more agents than present during training, and detects out-of-distribution events in an environment. Empirical results on diverse MARL scenarios validate the effectiveness of our approach, surpassing task-specific communication strategies in unseen tasks. Our implementation of this work is available at https://github.com/proroklab/task-agnostic-comms.
Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection
Venue: arXiv cs.CV 2024
Year: 2024

Recently, the proliferation of increasingly realistic synthetic images generated by various generative adversarial networks has increased the risk of misuse. Consequently, there is a pressing need to develop a generalizable detector for accurately recognizing fake images. The conventional methods rely on generating diverse training sources or large pretrained models. In this work, we show that, on the contrary, the small and training-free filter is sufficient to capture more general artifact representations. Due to its unbias towards both the training and test sources, we define it as Data-Independent Operator (DIO) to achieve appealing improvements on unseen sources. In our framework, handcrafted filters and the randomly-initialized convolutional layer can be used as the training-free artifact representations extractor with excellent results. With the data-independent operator of a popular classifier, such as Resnet50, one could already reach a new state-of-the-art without bells and whistles. We evaluate the effectiveness of the DIO on 33 generation models, even DALLE and Midjourney. Our detector achieves a remarkable improvement of $13.3\%$, establishing a new state-of-the-art performance. The DIO and its extension can serve as strong baselines for future methods. The code is available at \url{https://github.com/chuangchuangtan/Data-Independent-Operator}.
Deep Learning Approaches for Human Action Recognition in Video Data
Venue: arXiv cs.CV 2024
Year: 2024

Human action recognition in videos is a critical task with significant implications for numerous applications, including surveillance, sports analytics, and healthcare. The challenge lies in creating models that are both precise in their recognition capabilities and efficient enough for practical use. This study conducts an in-depth analysis of various deep learning models to address this challenge. Utilizing a subset of the UCF101 Videos dataset, we focus on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Two-Stream ConvNets. The research reveals that while CNNs effectively capture spatial features and RNNs encode temporal sequences, Two-Stream ConvNets exhibit superior performance by integrating spatial and temporal dimensions. These insights are distilled from the evaluation metrics of accuracy, precision, recall, and F1-score. The results of this study underscore the potential of composite models in achieving robust human action recognition and suggest avenues for future research in optimizing these models for real-world deployment.
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment
Venue: arXiv cs.LG 2024
Year: 2024

Deep Brain Stimulation (DBS) stands as an effective intervention for alleviating the motor symptoms of Parkinson's disease (PD). Traditional commercial DBS devices are only able to deliver fixed-frequency periodic pulses to the basal ganglia (BG) regions of the brain, i.e., continuous DBS (cDBS). However, they in general suffer from energy inefficiency and side effects, such as speech impairment. Recent research has focused on adaptive DBS (aDBS) to resolve the limitations of cDBS. Specifically, reinforcement learning (RL) based approaches have been developed to adapt the frequencies of the stimuli in order to achieve both energy efficiency and treatment efficacy. However, RL approaches in general require significant amount of training data and computational resources, making it intractable to integrate RL policies into real-time embedded systems as needed in aDBS. In contrast, contextual multi-armed bandits (CMAB) in general lead to better sample efficiency compared to RL. In this study, we propose a CMAB solution for aDBS. Specifically, we define the context as the signals capturing irregular neuronal firing activities in the BG regions (i.e., beta-band power spectral density), while each arm signifies the (discretized) pulse frequency of the stimulation. Moreover, an ε-exploring strategy is introduced on top of the classic Thompson sampling method, leading to an algorithm called ε-Neural Thompson sampling (ε-NeuralTS), such that the learned CMAB policy can better balance exploration and exploitation of the BG environment. The ε-NeuralTS algorithm is evaluated using a computation BG model that captures the neuronal activities in PD patients' brains. The results show that our method outperforms both existing cDBS methods and CMAB baselines.
A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa
Venue: arXiv cs.LG 2024
Year: 2024

Desert locust swarms present a major threat to agriculture and food security. Addressing this challenge, our study develops an operationally-ready model for predicting locust breeding grounds, which has the potential to enhance early warning systems and targeted control measures. We curated a dataset from the United Nations Food and Agriculture Organization's (UN-FAO) locust observation records and analyzed it using two types of spatio-temporal input features: remotely-sensed environmental and climate data as well as multi-spectral earth observation images. Our approach employed custom deep learning models (three-dimensional and LSTM-based recurrent convolutional networks), along with the geospatial foundational model Prithvi recently released by Jakubik et al., 2023. These models notably outperformed existing baselines, with the Prithvi-based model, fine-tuned on multi-spectral images from NASA's Harmonized Landsat and Sentinel-2 (HLS) dataset, achieving the highest accuracy, F1 and ROC-AUC scores (83.03%, 81.53% and 87.69%, respectively). A significant finding from our research is that multi-spectral earth observation images alone are sufficient for effective locust breeding ground prediction without the need to explicitly incorporate climatic or environmental features.
Towards Incident Response Orchestration and Automation for the Advanced Metering Infrastructure
Venue: arXiv cs.CR 2024
Year: 2024

The threat landscape of industrial infrastructures has expanded exponentially over the last few years. Such infrastructures include services such as the smart meter data exchange that should have real-time availability. Smart meters constitute the main component of the Advanced Metering Infrastructure, and their measurements are also used as historical data for forecasting the energy demand to avoid load peaks that could lead to blackouts within specific areas. Hence, a comprehensive Incident Response plan must be in place to ensure high service availability in case of cyber-attacks or operational errors. Currently, utility operators execute such plans mostly manually, requiring extensive time, effort, and domain expertise, and they are prone to human errors. In this paper, we present a method to provide an orchestrated and highly automated Incident Response plan targeting specific use cases and attack scenarios in the energy sector, including steps for preparedness, detection and analysis, containment, eradication, recovery, and post-incident activity through the use of playbooks. In particular, we use the OASIS Collaborative Automated Course of Action Operations (CACAO) standard to define highly automatable workflows in support of cyber security operations for the Advanced Metering Infrastructure. The proposed method is validated through an Advanced Metering Infrastructure testbed where the most prominent cyber-attacks are emulated, and playbooks are instantiated to ensure rapid response for the containment and eradication of the threat, business continuity on the smart meter data exchange service, and compliance with incident reporting requirements.
Responsible Artificial Intelligence: A Structured Literature Review
Venue: arXiv cs.AI 2024
Year: 2024

Our research endeavors to advance the concept of responsible artificial intelligence (AI), a topic of increasing importance within EU policy discussions. The EU has recently issued several publications emphasizing the necessity of trust in AI, underscoring the dual nature of AI as both a beneficial tool and a potential weapon. This dichotomy highlights the urgent need for international regulation. Concurrently, there is a need for frameworks that guide companies in AI development, ensuring compliance with such regulations. Our research aims to assist lawmakers and machine learning practitioners in navigating the evolving landscape of AI regulation, identifying focal areas for future attention. This paper introduces a comprehensive and, to our knowledge, the first unified definition of responsible AI. Through a structured literature review, we elucidate the current understanding of responsible AI. Drawing from this analysis, we propose an approach for developing a future framework centered around this concept. Our findings advocate for a human-centric approach to Responsible AI. This approach encompasses the implementation of AI methods with a strong emphasis on ethics, model explainability, and the pillars of privacy, security, and trust.