Reviewerly/exHarmony huggingface dataset:
An Empirical Analysis of Range for 3D Object Detection

LiDAR-based 3D detection plays a vital role in autonomous navigation. Surprisingly, although autonomous vehicles (AVs) must detect both near-field objects (for collision avoidance) and far-field objects (for longer-term planning), contemporary benchmarks focus only on near-field 3D detection. However, AVs must detect far-field objects for safe navigation. In this paper, we present an empirical analysis of far-field 3D detection using the long-range detection dataset Argoverse 2.0 to better understand the problem, and share the following insight: near-field LiDAR measurements are dense and optimally encoded by small voxels, while far-field measurements are sparse and are better encoded with large voxels. We exploit this observation to build a collection of range experts tuned for near-vs-far field detection, and propose simple techniques to efficiently ensemble models for long-range detection that improve efficiency by 33% and boost accuracy by 3.2% CDS.
Evaluating the Weather Forecasting Models and the Impact to PV Generation Forecasting

Accurate photovoltaic (PV) generation forecasting is an important feature that can assist utilities and plant operators in the direction of energy management and dispatchability planning. However, the accuracy of the PV forecasting is directly related to the quality of the weather forecasts. In this work, an evaluation of the various weather forecasting models is performed. Additionally, an investigation of the effect of the quality of the weather forecasts on PV generation forecasting will be examined. Finally, this study focused to propose an approach to improve the quality of the weather forecasts and eventually the PV production forecasting accuracy by employing machine learning and linear regression models that could record the behaviour of the local weather.
Electrical Grid Anomaly Detection via Tensor Decomposition

Supervisory Control and Data Acquisition (SCADA) systems often serve as the nervous system for substations within power grids. These systems facilitate real-time monitoring, data acquisition, control of equipment, and ensure smooth and efficient operation of the substation and its connected devices. As the dependence on these SCADA systems grows, so does the risk of potential malicious intrusions that could lead to significant outages or even permanent damage to the grid. Previous work has shown that dimensionality reduction-based approaches, such as Principal Component Analysis (PCA), can be used for accurate identification of anomalies in SCADA systems. While not specifically applied to SCADA, non-negative matrix factorization (NMF) has shown strong results at detecting anomalies in wireless sensor networks. These unsupervised approaches model the normal or expected behavior and detect the unseen types of attacks or anomalies by identifying the events that deviate from the expected behavior. These approaches; however, do not model the complex and multi-dimensional interactions that are naturally present in SCADA systems. Differently, non-negative tensor decomposition is a powerful unsupervised machine learning (ML) method that can model the complex and multi-faceted activity details of SCADA events. In this work, we novelly apply the tensor decomposition method Canonical Polyadic Alternating Poisson Regression (CP-APR) with a probabilistic framework, which has previously shown state-of-the-art anomaly detection results on cyber network data, to identify anomalies in SCADA systems. We showcase that the use of statistical behavior analysis of SCADA communication with tensor decomposition improves the specificity and accuracy of identifying anomalies in electrical grid systems. In our experiments, we model real-world SCADA system data collected from the electrical grid operated by Los Alamos National Laboratory (LANL) which provides transmission and distribution service through a partnership with Los Alamos County, and detect synthetically generated anomalies.
DUBIOUS: Detecting Unknown Backdoored Input by Observing Unusual Signatures

Trojan / backdoor attacks represent a serious threat when using machine learning models that are either supplied by unreliable 3rd parties, or trained on data that contain deliberately poisoned samples. For such situations, a malicious actor can control outputs of the model on specially crafted inputs. It is not yet possible to accurately detect if a model was poisoned, so security considerations require augmenting Trojan detection methods with mitigation strategies. Mitigation measures should reduce the model’s vulnerability to poisoning attacks while maintaining its accuracy. Our goal is thus to determine if an input is triggering Trojan behavior, and if so, reject the model’s prediction for that input. We do this by first perturbing clean samples multiple times, and then gathering special statistics on the outputs of these perturbations. These statistics are referred to as a sample’s signature. The signature is built across multiple perturbation magnitudes to better capture the perturbations’ effects. Given a novel sample at test time, we build its signature in the same way, and compare it to those of the known clean examples. We can then apply outlier detection to determine if the input was indeed poisoned. In this paper, we examine Trojan attack mitigation using standard datasets in multiple modalities, namely image classification, natural language processing, and malware detection.
Image Guided Inpainting with Parameter Efficient Learning

Conditional inpainting is the challenging task of generating images that fill in specific regions of an image while preserving the surrounding details, based on an arbitrary binary mask and a specified condition (e.g., text or image). Existing methods for conditional inpainting often struggle to preserve the appearance of the user’s subject in the input images and can be computationally expensive to tune for each new condition. In this paper, we propose a novel approach to conditional inpainting that combines an Image Guided Inpainting model with a Denoising Diffusion Probabilistic Model (DDPM). Our approach trains the DDPM model using a small number of user-provided images, enabling users to insert a subject into any scene even if the poses and views were not present in the tuning data. We also propose a parameter-efficient method for training the DDPM that preserves its core performance while reducing the number of retrained parameters. Our experimental results demonstrate that our proposed approach outperforms existing methods in terms of both reconstruction quality and computational efficiency, making it well-suited for use in low-resource environments. Overall, our approach offers a valuable baseline for future research on guided inpainting and personalization.
Evaluation of Irradiance Variability Adjustments for Subhourly Clipping Correction

Subhourly changes in solar irradiance can lead to energy models being biased high if realistic distributions of irradiance values are not reflected in the resource data and model. This is particularly true in solar facility designs with high inverter loading ratios (ILRs). When resource data with sufficient temporal and spatial resolution is not available for a site, synthetic variability can be added to the data that is available in an attempt to address this issue. In this work, we demonstrate the use of anonymized commercial resource datasets with synthetic variability and compare results with previous estimates of model bias due to inverter clipping and increasing ILR.
A Robust Approach for Daily Solar Irradiance Clustering

Variability in solar irradiance is a major concern for power utilities and island grids. Clustering of historical irradiance data can help develop a better understanding of the patterns in a given location. This work uses a simple, yet effective scalar metric named Daily Irradiance Index (DII) obtained by comparing the measured irradiance level with the clear sky irradiance level. We explore a two-layered clustering approach for a data set from NREL's Solar Radiation Research Laboratory for Golden, Colorado. The first layer is designed to identify the seasons and in the second layer seasonal days are clustered based on the irradiance levels as measured by DII. For the first layer of clustering, we find the use of attributes from clear sky irradiance is sufficient to clearly identify the seasons. In the second layer of clustering, based on the cloud activities, days are clustered into high, medium and low irradiance levels corresponding to clear, intermittent, and cloudy days. The quality of the clusters is monitored through the well-known Silhouette score, Calinski-Harabasz score and Davies-Bouldin score. The effectiveness of employing clear sky irradiance, specifically sunshine duration, as a means of identifying solar seasons was demonstrated.
Guiding Video Prediction with Explicit Procedural Knowledge

We propose a general way to integrate procedural knowledge of a domain into deep learning models. We apply it to the case of video prediction, building on top of object-centric deep models and show that this leads to a better performance than using data-driven models alone. We develop an architecture that facilitates latent space disentanglement in order to use the integrated procedural knowledge, and establish a setup that allows the model to learn the procedural interface in the latent space using the downstream task of video prediction. We contrast the performance to a state-of-the-art data-driven approach and show that problems where purely data-driven approaches struggle can be handled by using knowledge about the domain, providing an alternative to simply collecting more data.
Resilience of Industrial Control Systems Using Signal Temporal Logic And Autotuning Mechanism

Building robust, reliable, and resilient safety critical systems is a major challenge facing nations and states today. The reason is that the complexity and technological advancement of these systems and their control from remote locations have raised various security threats, leading to a high increase in successful attacks witnessed over the years. To address these challenges, timely detection and recovery of the system under attacks or failures are needed. To this end, we proposed an STL-Autotuning framework that integrates the concept of Signal Temporal Logic (STL) formalism and an autotuning mechanism to reason about system resilience. In this work, we focus on the study of critical processes whose changes have notable impacts on the overall system. Our STL-based approach implemented in breach provides real-time monitoring of these processes for the timely detection of violations capable of crippling the system. The auto-tuning mechanism adopts a Simple Internal Model Control (SIMC) PID rule for the quick recovery of the system under attacks or faults. We implemented an STL quantitative semantics to measure the degree of violations of system safety constraints necessary for triggering the autotuning mechanism. The reason is to avoid triggering the autotuning mechanism for minor violations that do not impact the system's safety and availability. We tested our proposed approach at the Tennessee Eastman Plant, a complex model explicitly designed for the study of industrial processes and control. The findings derived from our experiment demonstrate the efficacy of our approach in promptly detecting violations and effectuating timely and efficient plant recovery. As a result, our framework is highly suitable for building robust, reliable, and resilient systems.
Preface 27th International Conference on Circuits, Systems, Communications and Computers

This volume comprises the proceedings of the 27th International Conference on Circuits, Systems, Communications and Computers (CSCC 2023). The conference provided a platform for researchers, engineers, academicians as well as industrial professionals to discuss research results and development activities in Circuits, Systems, Control, Signal Processing, Mathematical Methods and Techniques, Analytical Methods, Simulation Methods, Advanced Algorithms, Power Systems, Energy, Communications, Information Systems, Network Systems, Intelligent Systems, Forecasting Methods, Data Analysis, Computers in Sciences and Industry, Cyber Security and Advanced Computer Applications, with participants from all over the world. The Conference was held July 19-22, 2023 in Rodos, Rhodes Island, Greece. All chapters are self-contained and peer reviewed.
Adversarial Pixel and Patch Detection Using Attribution Analysis

Next-generation warfighters will use sensors and deep learning for advanced scene recognition and situational awareness. Adversarial pixel and patch attacks can severely degrade the performance of deep neural networks (DNNs). This poses a critical security threat future combat systems. Detecting adversarial attacks has been attempted before, but recent advances in explainable artificial intelligence (XAI) have opened the door to better detection methods using attribution analysis. In particular, we observe that benign and attacked images display different characteristics in their attribution maps. Benign images tend to have dense attributions due to the network focusing on the main object of the image, while attacked images tend to have more sparse attributions due to the widespread perturbations applied. Using this intuition, we propose a framework for adversarial attack detection in the form of a binary classifier. Using three methods: Integrated Gradients (IG), Guided Backpropagation (GBP), and Integrated Decision Gradients (IDG), we propose the training of a binary classifier that can analyze an attribution map to detect attacked input data. We evaluate the detection framework for three state-of-the-art attacks with the three attribution analysis methods. We find that IDG achieves state-of-the-art pixel attack detection performance with up to 99% accuracy, and GBP manages state-of-the-art patch detection performance achieving up to 88% accuracy.
Effect of Solar Mounting Configurations on California Zero-Carbon Grid

Using solar mounting configurations that result in various daily and seasonal solar generation profiles might be impactful in designing a future renewables-driven grid. We study the effects of PV-panel tilt and tracking on California zero-carbon grid modeling and compare results for multiple target years between 2030 and 2045 and for a range of cost assumptions. Using south-facing tilt in the candidate solar projects significantly reduces the annual solar curtailment in 2045. Tilted panels can decrease the total system (grid) cost and average annual cost of delivered solar electricity over time if technological advancement can lower the cost for tilting.
Sub-Ensembles for Fast Uncertainty Estimation in Neural Networks

Fast estimates of model uncertainty are required for many robust robotics applications. Deep Ensembles provides state of the art uncertainty without requiring Bayesian methods, but still it is computationally expensive due to the use of large ensembles. In this paper we propose deep sub-ensembles, an approximation to deep ensembles where the core idea is to ensemble only a selection of layers close to the output, and not the whole model. This is motivated by feature hierarchy learned by convolutional networks that should allow for feature reuse across ensembles. With ResNet-20 on the CIFAR10 dataset, we obtain 1.5-2.5 speedup over a deep ensemble, with a small increase in error and loss, and similarly up to 5-15 speedup with a VGG-like network on the SVHN dataset. Our results show that this idea enables a trade-off between error and uncertainty quality versus computational performance as a sub-ensemble effectively works as an approximation of a deep ensemble.
A Practical Perfect Secrecy Approach for IoBT Systems

We consider an Internet of battlefield things (IoBT) system in which multiple devices want to communicate securely with each other in the presence of an adversary with unbounded storage and unbounded computational power. We provide an information-theoretically secure protocol to exchange highly confidential messages among the devices in the system. The main idea behind the protocol is to provide one-time pad secret keys to exchange messages via using a random string that is securely shared among all the devices and pair-wise random secret keys established between each pair of devices. We show that the protocol provides ever-lasting security against the adversary with unbounded storage and unbounded computational power. More specifically, the protocol guarantees that the adversary gains no information about a message, except for an exponentially small probability in a security parameter. Moreover, we prove that revealing all the pair-wise secret keys does not provide any information to the adversary. In addition, we show that the pair-wise secret keys are established once to securely exchange multiple messages between devices in the system. Besides that, we prove that even if the random string is revealed to the adversary, the provided protocol is computationally secure.
Factorized Dynamic Fully-Connected Layers for Neural Networks

The design of neural network layers plays a crucial role in determining the efficiency and performance of various computer vision tasks. However, most existing layers compromise between fast feature extraction and reasoning abilities, resulting in suboptimal outcomes. In this paper, we propose a novel and efficient operator for representation learning that can dynamically adjust to the underlying data structure. We introduce a general Dynamic Fully-Connected (DFC) layer, a non-linear extension of a Fully-Connected layer that has a learnable receptive field, is instance-adaptive, and spatially aware. We propose to use CP decomposition to reduce the complexity of the DFC layer without compromising its expressivity. Then, we leverage Summed Area Tables and Modulation to create an adaptive receptive field that can process the input with constant complexity. We evaluate the effectiveness of our method on image classification and other downstream vision tasks using both hierarchical and isotropic architectures. Our results demonstrate that our method outperforms other commonly used layers by a significant margin while keeping a fixed computational budget, therefore establishing a new strategy to efficiently design neural architectures that can capture the multi-scale features of the input without increasing complexity.
A Comprehensive Empirical Evaluation on Online Continual Learning

Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evaluate various methods from the literature that tackle online continual learning. More specifically, we focus on the class-incremental setting in the context of image classification, where the learner must learn new classes incrementally from a stream of data. We compare these methods on the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average accuracy, forgetting, stability, and quality of the representations, to evaluate various aspects of the algorithm at the end but also during the whole training period. We find that most methods suffer from stability and underfitting issues. However, the learned representations are comparable to i.i.d. training under the same computational budget. No clear winner emerges from the results and basic experience replay, when properly tuned and implemented, is a very strong baseline. We release our modular and extensible codebase at https://github.com/AlbinSou/ocl_survey based on the avalanche framework to reproduce our results and encourage future research.
Class-aware Memory Guided Unbiased Weighting for Universal Domain Adaptive Object Detection

Cross-domain object detection aims to align the feature distributions across the source and target domains. Existing cross-domain object detectors typically rely on identical label space assumption, which, however, greatly limits their universality under class gap. This paper introduces Universal Domain Adaptive Object Detection (UDAOD) toward more practical scenarios without any prior knowledge on the category consistency. In the proposed universal setting, the category space is partially intersected (i.e., common classes) between domains. The class gap caused by source-private and target-private classes leads to serious negative transfer and degrades adaptation performance. To this end, we propose a Universal Cross-domain Faster R-CNN (UCF) with a novel unbiased weighting mechanism to effectively measure the common or private classes. Specifically, we propose a dynamic Class-aware Memory (CaM) to overcome the bias of class weights, caused by class incompleteness in a batch of UniDA. We further propose a Weight Surgery Equalization (WSE) to strengthen the polarization of the weights for common and private classes and suppress incorrect alignment. Extensive experiments under the novel UDAOD setting on multiple benchmarks including PASCAL VOC, Clipart, WaterColor, Cityscapes, and FoggyCityscapes are implemented, which shows the SOTA universality of our model.
ALFA – Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals

We propose an exhaustive methodology that leverages all levels of feature abstraction, targeting an enhancement in the generalizability of image classification to unobserved hospitals. Our approach incorporates augmentation-based self-supervision with common distribution shifts in histopathology scenarios serving as the pretext task. This enables us to derive invariant features from training images without relying on training labels, thereby covering different abstraction levels. Moving onto the subsequent abstraction level, we employ a domain alignment module to facilitate further extraction of invariant features across varying training hospitals. To represent the highly specific features of participating hospitals, an encoder is trained to classify hospital labels, independent of their diagnostic labels. The features from each of these encoders are subsequently disentangled to minimize redundancy and segregate the features. This representation, which spans a broad spectrum of semantic information, enables the development of a model demonstrating increased robustness to unseen images from disparate distributions. Experimental results from the PACS dataset (a domain generalization benchmark), a synthetic dataset created by applying histopathology-specific jitters to the MHIST dataset (defining different domains with varied distribution shifts), and a Renal Cell Carcinoma dataset derived from four image repositories from TCGA, collectively indicate that our proposed model is adept at managing varying levels of image granularity. Thus, it shows improved generalizability when faced with new, out-of-distribution hospital images. The code is available at: https://github.com/miladsikaroudi/ALFA.