Reviewerly/exHarmony huggingface dataset:
Worst-Case Morphs Using Wasserstein ALI and Improved MIPGAN

A morph is a combination of two separate facial images and contains the identity information of two different people. When used in an identity document, both people can be authenticated by a biometric face recognition (FR) system. Morphs can be generated using either a landmark-based approach or approaches based on deep learning, such as generative adversarial networks (GANs). In a recent paper, we introduced a worst-case upper bound on how challenging morphing attacks can be for an FR system. The closer morphs are to this upper bound, the bigger the challenge they pose to FR. We introduced an approach with which it was possible to generate morphs that approximate this upper bound for a known FR system (white box) but not for unknown (black box) FR systems. In this paper, we introduce a morph generation method that can approximate worst-case morphs even when the FR system is not known. A key contribution is that we include the goal of generating difficult morphs during training. Our method is based on adversarially learned inference (ALI) and uses concepts from Wasserstein GANs trained with gradient penalty, which were introduced to stabilise the training of GANs. We include these concepts to achieve a similar improvement in training stability and call the resulting method Wasserstein ALI (WALI). We finetune WALI using loss functions designed specifically to improve the ability to manipulate identity information in facial images and show how it can generate morphs that are more challenging for FR systems than landmark- or GAN-based morphs. We also show how our findings can be used to improve MIPGAN, an existing StyleGAN-based morph generator.
Students’ attitude towards distance teaching of studio-based and virtual reality-based fashion design courses

In this study, studio-based and virtual reality-based courses were taught online to fashion design students during a semester. Students’ satisfaction with distance learning in these two courses was measured by relating to their computer self-efficacy and spatial learning capability. The perceived usefulness of distance learning and students’ online engagement were tested as the mediator and moderator, respectively. This research showed that spatial ability skills play a significant role in students’ satisfaction with the distance learning of a studio-based course. In contrast, it is not significant for the virtual reality-based course.
“Hey, Alexa” “Hey, Siri”, “OK Google” ….” exploring teenagers’ interaction with artificial intelligence (AI)-enabled voice assistants during the COVID-19 pandemic

AI-enabled virtual voice assistants have eased user interaction and user experience. The current study aims to understand how teenagers interact and engage with voice assistants. The rationale of usage, perceptions, patterns and expectations of teenagers from smart speakers has been explored within the framework of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. The study uses an interpretive qualitative research method to gain in-depth insights, empirical details, and narrative aspects of user experiences of smart speakers. Semi-structured interviews were conducted with 36 teenagers pursuing high school in the age group 13–15 years during the COVID-19 pandemic. The results indicated that all seven variables (performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivations, habit, and privacy concerns) impact the usage of smart speakers. The study also ascertains that hedonic motivation is a significant driving factor in the UTAUT2 model.
Short Paper: Accelerating Hyperparameter Optimization Algorithms with Mixed Precision

Hyperparameter Optimization (HPO) of Neural Networks (NNs) is a computationally expensive procedure. On accelerators, such as NVIDIA Graphics Processing Units (GPUs) equipped with Tensor Cores, it is possible to speed-up the NN training by reducing the precision of some of the NN parameters, also referred to as mixed precision training. This paper investigates the performance of three popular HPO algorithms in terms of the achieved speed-up and model accuracy, utilizing early stopping, Bayesian, and genetic optimization approaches, in combination with mixed precision functionalities. The benchmarks are performed on 64 GPUs in parallel on three datasets: two from the vision and one from the Computational Fluid Dynamics domain. The results show that larger speed-ups can be achieved for mixed compared to full precision HPO if the checkpoint frequency is kept low. In addition to the reduced runtime, small gains in generalization performance on the test set are observed.
ETNAS: An energy consumption task-driven neural architecture search

Neural Architecture Search (NAS) is crucial in the field of sustainable computing as it facilitates the development of highly efficient and effective neural networks. However, it cannot automate the deployment of neural networks to accommodate specific hardware resources and task requirements. This paper introduces ETNAS, which is a hardware-aware multi-objective optimal neural network architecture search algorithm based on the differentiable neural network architecture search method (DARTS). The algorithm searches for a lower-power neural network architecture with guaranteed inference accuracy by modifying the loss function of the differentiable neural network architecture search. We modify the dense network in DARTS to simultaneously search for networks with a lower memory footprint, enabling them to run on memory-constrained edge-end devices. We collected data on the power consumption and time consumption of numerous common operators on FPGA and Domain-Specific Architectures (DSA). The experimental results demonstrate that ETNAS achieves comparable accuracy performance and time efficiency while consuming less power compared to state-of-the-art algorithms, thereby validating its effectiveness in practical applications and contributing to the reduction of carbon emissions in intelligent cyber–physical systems (ICPS) edge computing inference.
Case-based decision making for diversion scenarios

Problems with decision-making have been pointed out as contributing factors to the occurrence of aeronautical incidents and accidents. In this work, we tackled the decision-making process related to diversion scenarios. Diversion is a deviation or alternative from the original flight plan, usually due to some problem caused in the aircraft, such as leakage of fuel, of engine and in aerodynamic surfaces, among other problems of the aircraft that can make it impossible to arrive at the previously defined destination airport. We propose a case-based reasoning (CBR) model for aiding the pilot to deal with diversion situations, chosen as adequate scenarios from the results of a survey conducted with pilots to identify those for which an AI-based approach might be helpful regarding decision making. A CBR system basically uses a memory of recorded cases for generating new solutions to similar problems, it thus contains a "memory" of cases with descriptions of specific situations that occurred in the past, solutions of these described situations, and the evaluation of their results. The reasoner solves new problems by adapting in-memory solutions to it, via some similarity criteria. Specific similarity measures for comparing cases and retrieving the ones most similar to the diversion situation at hand were defined, and a database for 26 diversion cases was built for conducting the experiments on decision making. Case retrieval experiments over 20 diversion test situations produced a success rate (that is, a diversion suggestion compatible with pilot decisions) of 85%. We intend to perform as future work an extension of the cases database by incorporating real diversion situations, dynamic attributes related to weather and traffic intensity at the diversion airport, and a more comprehensive assessment by pilots, possibly towards a learnable automatic weight adjustment for the attributes that compose the case similarity metric.
Applying the Assessment List for Trustworthy Artificial Intelligence on the development of AI supported Air Traffic Controller Operations

The advance of AI in safety- and security-critical domains such as aviation needs high standards for its trustworthy development. In this context the EASA introduced the Assessment List for Trustworthy AI (ALTAI) as an helpful tool. This paper presents an approach for applying the ALTAI in the development of an AI-based digital Air Traffic Control Operator (ATCO). Specifically, the aim was using the ALTAI to derive a set of high-level requirements for the AI-based system to guarantee trustworthiness in the early development stages. The focus is thus given on how the ALTAI questions can be processed in order to yield system requirements. However, the necessity for a structured approach becomes apparent when confronted with the abundance of diverse perspectives within the ALTAI. Accordingly, various filtering, prioritization, and grouping methods were implemented in an usable framework. Consequently, the applicability of the ALTAI is analyzed, discovering a divergence between technical and ethical requirements. It is illustrated that technical questions often lead to highly applicable specific requirements, compared to ethical questions. Especially due to their importance, the challenges of deriving specific requirements for certain ethical aspects are emphasized and discussed. Additionally, suggestions on future versions of the ALTAI are given in order to strengthen its application during the development of AI-based systems. By showcasing our method and specific requirements obtained for the digital ATCO system, the objective is to highlight the necessity of the ALTAI and to provide a basis for its wider use.
Democratizing HPC Access and Use with Knowledge Graphs

The field of High-Performance Computing (HPC) is undergoing rapid evolution, with an expanding and diverse user base harnessing its unparalleled computational capabilities. As the range of HPC applications grows, newcomers to the field are faced with the daunting task of optimizing their applications for efficient execution on HPC systems. Traditional documentation, often spanning dozens of pages, is cumbersome for finding answers and ill-suited for integration with emerging conversational AI-powered user interfaces like chatbots. Addressing this challenge, we propose a novel HPC ontology crafted to encapsulate HPC runtime relations in a scalable fashion. Our proposed ontology not only facilitates the transfer and querying of this knowledge but also serves as a foundational pillar for our AI-powered Speech Assistant Interface (SAI)[13]. This ensures reproducibility, reliability, and optimal performance when executing tasks. In this paper, we elucidate the relationships and properties underpinning our ontology and showcase how users can interact with knowledge graphs based on our proposed ontology to derive insights.
Multi-Sensor Placement and Information Fusion Analysis to Enable Beyond Visual Line of Sight Operations for Small Uncrewed Aerial Vehicles

This paper describes the development of a multi-sensor placement and information fusion testbed for detection and tracking of small uncrewed aerial vehicles (sUAV). This testbed is called Mason’s UAV Systems and Cyber Analysis Testbed (MUSCAT) and combines JDL Data Fusion model theoretic foundations with systems engineering principles applied to sUAV corridors design. Recent results are focused on analyzing sensor coverage with terrain models as a function of drone altitude, with a goal to identify optimal placement of multiple sensors and provide cost-performance trade-offs. The methodology is composed of four main phases: setting up the computational environment, defining sensor attributes and region of interest, computing sensor coverage for individual sensor types and respective combinations, and displaying results. An instance considering a drone corridor is used to analyze the optimal placement of short-range radars and remote identification (RID) receivers to maximize the total coverage and improve the system’s resilience by track fusion with varied sensors. Results show that MUSCAT is able to generate a wide range of metrics and spatial insights, important to identify the most suitable solution based on user requirements.
Modular System Management Software Reference Architecture (EWSPMan) for Airborne Electronic Warfare Self-Protection Systems

In this paper, we present a modular reference software architecture (EWSPMan) for Electronic Warfare Self-Protection Systems which decouples platform dependencies and system requirements to bound the software complexity and increase scalability and flexibility. EWSPMan depends on FACE combined with IMA to manage the Electronic Warfare Self-Protection Systems.
Adaptable Hardware Fingerprinting for Radio Data Links and Avionics Buses in Adversarial Settings

Despite their substantially different purposes, a common issue both for many legacy data links and many onboard data buses used in aviation is a lack of authentication and thus a vulnerability to spoofing and message manipulation attacks. This problem has been discussed at length for some prominent technologies of both sorts (e.g., ADS-B, ARINC 429) but is common in many more cases (e.g., ACARS, CPDLC, MIL-STD-1553, RS-485). For ground data links, attacks can take place over-the-air, while for onboard buses an attacker requires some physical access to an aircraft. Yet, in both cases an attacker’s goal is to obtain control of a transceiver on the communication channel. As such, hardware fingerprinting methods are useful in both contexts. Prior work has applied such methods to specific protocols. We now propose a transferable fingerprinting scheme that has applicability in both contexts, describe our experiences in applying it for each and evaluate its performance in benign and malicious conditions. As replacing legacy communication links with newer, more secure protocols is practically challenging, the improvements to practical deployment offered by our system represent a meaningful benefit for real deployment efforts.
Bridging the Gap: Applying Argument to MIL-HDBK-516C Certification of a Neural Network Controller Guarded by ASIF Run Time Assurance

Recent advances in artificial intelligence and machine learning may soon yield paradigm-shifting benefits for aerospace systems. However, complexity and/or on-line learning make neural network control systems (NNCS) difficult or impossible to certify under the United States Military Airworthiness Certification Criteria defined in MIL-HDBK-516C. Run time assurance (RTA) is a control system architecture designed to maintain properties such as safety regardless of whether a primary control system is fully verifiable. This work examines how to satisfy compliance with MIL-HDBK-516C while using active set invariance filtering (ASIF), an advanced form of RTA not envisaged by the 516c committee. ASIF filters the commands from a primary controller, passing on safe commands while optimally modifying unsafe commands to ensure safety with minimal deviation. Gaps between existing 516C conformance criteria and best assurance practices for ASIF-RTA are bridged by applying the core theory of ASIF to assurance arguments explaining compliance. The result demonstrates how argument can support compliance of novel technologies with 516C and provide input for updates to the standard.
Safety Validation for Deep Reinforcement Learning Based Aircraft Separation Assurance with Adaptive Stress Testing

Ensuring safe separation of aircraft has become a major challenge given the growing demand for air transportation. Recently, deep reinforcement learning (DRL) has been successfully applied to develop aircraft separation assurance systems. Though showing promising performance, the DRL systems are trained as black boxes with no safety guarantees. Given the catastrophic impact of potential failures, validations of these systems become critical. However, existing validation approaches focus on system failures caused by single factors and only work on the cases with one validated system, which greatly limits their use in real-world high-density air traffic where numerous validated systems exist and multiple factors cause the failures together. In this paper, we introduce a multi-agent adaptive stress testing formulation for the system validation task and propose a Multi-Agent Hybrid Adaptive Stress Testing (MAHAST) framework to comprehensively validate DRL-based aircraft separation assurance systems given high-density air traffic by detecting system failures caused by unsafe system dynamics and improper environment properties. Moreover, we introduce a novel hybrid adaptive stress testing approach with a hierarchical policy, detecting the failures caused by combinations of multiple factors. We conduct extensive numerical experiments in a real-time air traffic environment. Results empirically show that MAHAST can effectively detect diverse failures in DRL-based aircraft separation assurance systems with high-density air traffic.
Nonlocal correlations of a fully entangled qubit-qutrit Bell scenario

With the aim of understanding the role that entanglement plays in the generation of EPR correlations, in this paper we address the study of a Bell scenario involving qubit-qutrit entangled pairs with only two measurement options per side. The key feature of our setup is the proposition of two non-projective POVMs to describe the Bob's measurement operators, preserving orthogonal and projective measurements for Alice. Despite the eigenvalues of the CHSH operator indicate that the correlations generated cannot exceed the local bound, their eigenvectors are used to define two families of maximally entangled states (negativity one). A consequence of the above is that in this scenario entanglement can be fully understood in terms of local resources and shared randomness. This fact is used later to design, using techniques of convex geometry, a device that reproduces the correlations generated by these families of maximally entangled states for the considered setup.
HMI Design for Explainable Machine Learning Enhanced Risk Detection in Low-Altitude UAV Operations

Unmanned aerial vehicles (UAVs) are increasingly utilized in low-altitude urban airspace for various applications. However, operating UAVs in populated areas poses safety risks, including proximity to people and property, unauthorized flight, and collisions with obstacles or hazards such as wildfires. Safety systems like geo-fencing and collision avoidance systems can detect known, well-defined risks but fail to address real-time detection of unforeseen hazards. To tackle this challenge, the paper proposes using an onboard camera on the UAV to continuously scan the environment. The captured video is analyzed in real-time using machine learning (ML) to detect, locate and classify a limited set of common hazards, determine their severity, and reroute the UAV to ensure flight safety. In situations where autonomous rerouting is deemed too risky by the system, the remote pilot is alerted, provided with detailed information on the detected hazards including the camera imagery, and is thus enabled to make an informed decision. Here, understanding the ML-model's results and decisions is crucial for effective decision-making, yet ML-models are often opaque and difficult for humans to comprehend. To address this issue, the paper suggests integrating principles of explainable artificial intelligence (XAI) into the human-machine interface (HMI) design to enhance interpretability and make the ML-models transparent and comprehensible. Specifically, principles of Class Activation Mapping (CAM) and Layerwise Relevance Propagation (LRP) are combined with a hierarchical HMI to present the information to the remote pilot. The paper describes relevant XAI-principles for HMI design and develops methods to comprehensively display ML-based information to remote pilots. These display design principles are then applied and integrated into an HMI design framework for supervising multiple UAVs. The resulting XAI-display prototype is discussed, limitations are identified, and further research steps are outlined for implementing the ML-models and testing the HMI with subject matter experts.
UAV Fault and Anomaly Detection Using Autoencoders

The popularity of Uncrewed Aerial Vehicles (UAVs) is on the rise, but these complex systems are susceptible to faults and anomalies impacting their safety and performance. To deal with emergency situations, it is necessary to monitor the status of these aircraft and report any anomalies or faults. Therefore, it is of great significance to study the anomaly detection method for UAV systems. In this study, unsupervised neural network models called Autoencoders (AE) and Variational Autoencoders (VAE) are utilized to detect UAV faults and anomalies. The key idea is to train autoencoders to learn the normal data and, after training, use them to identify the abnormal data by observing the magnitude of the reconstruction error. This serves as both an indicator of anomalies during inference and a cost function in training. Our results from publicly available real UAV sensor data called ALFA (Air Lab Failure and Anomaly) verify that the VAE-based method can effectively detect faults and anomalies with an average accuracy of 95.6%.
Formal and Practical Elements for the Certification of Machine Learning Systems

Over the past decade, machine learning has demonstrated impressive results, often surpassing human capabilities in sensing tasks relevant to autonomous flight. Unlike traditional aerospace software, the parameters of machine learning models are not hand-coded nor derived from physics but learned from data. They are automatically adjusted during a training phase, and their values do not usually correspond to physical requirements. As a result, requirements cannot be directly traced to lines of code, hindering the current bottom-up aerospace certification paradigm. This paper attempts to address this gap by 1) demystifying the inner workings and processes to build machine learning models, 2) formally establishing theoretical guarantees given by those processes, and 3) complementing these formal elements with practical considerations to develop a complete certification argument for safety-critical machine learning systems. Based on a scalable statistical verifier, our proposed framework is model-agnostic and tool-independent, making it adaptable to many use cases in the industry. We demonstrate results on a widespread application in autonomous flight: vision-based landing.
Automatic In Flight Conflict Resolution for Urban Air Mobility using Fluid Flow Vector Field based Guidance Algorithm

In this study, a vector field-based guidance algorithm is presented for tactical deconfliction in urban air mobility. The proposed guidance algorithm mimics fluid flow around obstacles to generate a vector field that guide vehicles through flight corridors while ensuring collision avoidance. The algorithm is flexible and can be tailored to fit the needs of current air traffic regulations. The conflict resolution and corridor following capability of the proposed method is evaluated through extensive flight test campaign. Flight tests are conducted in a scaled urban environment for two different scenarios: the conflict between of two medical drones in the same corridor and the conflict of an air taxi and a non-cooperative intruder. Both scenarios are tested on simple and complex shaped corridors using different corridor models, offering varying degrees of strictness and freedom of movement. Experimental results demonstrate the algorithm’s ability to resolve conflicts and ensure safe navigation within designated corridors. The proposed guidance algorithm shows promise as a tool for tactical deconfliction and autonomous navigation in urban airspace.