New perspectives on cognitive warfareFenstermacher, Laurie; Uzcha, David; Larson, Katie; Vitiello, Christine; Shellman, Steve
doi: 10.1117/12.2666777pmid: N/A
Cognitive warfare is not new. Weaker parties in an asymmetric conflict have manipulated information and ideas to convince stronger opponents to not fight (e.g., the Trojan Horse). What is new is the extent to which technologies enable cognitive warfare – resulting in the delegitimization of governments by sowing discord and creating division in order to compel acceptance of political will Information sharing tools enable adversaries to interfere more directly than ever with national political processes as well as citizens minds3. Cognitive warfare is considered a new domain of warfare, along with land, maritime, air, space and cyber (technical). The goal of cognitive warfare attacks is to alter or mislead the thoughts of leaders and operators, of members of entire social or professional classes, of the men and women in an army, or on a larger scale, of an entire population in a given region, country or group of countries and impact territory, influence, service interruptions, transportation, etc. The means could be social cyber, cyber technical, electronic warfare, and broadcast, etc. Senior officers and strategists in the Chinese People’s Liberation Army (PLA) claim that AI, neuroscience, and digital applications (e.g., social media) will be able to influence enemies by affecting human cognition directly, Russia’s Gerasimov doctrine talks of the “battlespace of the mind”, Pocheptsov provides examples including creation of fake events and objects and organizing protest actions in Ukraine. Dr Giordano stated, “the brain is the battlefield of the future”. This paper will highlight current examples of cognitive warfare, touch on enabling technologies and relevant social science principles of influence (cognitive and social), highlight existing analytics, introduce the “House model” which identifies pillars of relevant fields of knowledge as well as operationally relevant aspects related to the pillars as potentially helpful framework for thinking about cognitive warfare and identifying needed research.
Multimodal data fusion using signal/image processing methods for multi-class machine learningRichards, Casey J.; Valliani, Nawal; Johnson, Benjamin A.; Wong, Nelson Ka Ki; Pennati, Angelo; Saeed, Amir K.; Rodriguez, Benjamin M.
doi: 10.1117/12.2664987pmid: N/A
As the world progresses further into the digital era, we see a growing utility for combining datasets gathered on different devices and receivers as well as on varying time ranges, for use in machine learning. However, machine learning classification introduces a requirement for standardized data, which in turn hampers the ability to utilize diverse sets of data at a given timestamp. In this paper, we investigate the application of various signal pre-processing techniques (Daubecheis wavelet, discrete cosine and discrete fourier transform among others) for multi-modal, multi-class machine learning. Following the pre-processing, the multi-faceted signals are represented solely by features generated from first order statistics, eigen decomposition, and linear discriminant. Utilizing these generated features, as opposed to the signals themselves, these diverse datasets may now be combined as input to machine learning methods. Furthermore, we apply Fisher’s linear discriminant ratio and Random Forest feature importance metrics for feature ranking and feature space reduction followed by a comparison of the approaches. Our work demonstrates that dissimilar datasets with common classes may be combined using the proposed methods with a classification accuracy ≥ 95%. This paper demonstrates that the feature space may be reduced by approximately 60% with ≤ 5% loss in classification accuracy, and in some cases, a slight increase in classification accuracy.
Facial micro-expression recognition using deep spatio-temporal neural networksZheng, Yufeng; Blasch, Erik
doi: 10.1117/12.2665189pmid: N/A
In the billions of faces that are shaped by thousands of different cultures and ethnicities, one thing remains universal: the way emotions are expressed. To take the next step in human-machine interactions, a machine must be able to clarify facial emotions. Allowing machines to recognize micro-expressions gives them a deeper dive into a person’s true feelings at an instant which allows designers to create more empathetic machines that will take human emotion into account while making optimal decisions; e.g., these machines will be potentially able to detect dangerous situations, alert caregivers to challenges, and provide appropriate responses. Micro-expressions are involuntary and transient facial expressions capable of revealing genuine emotions. We propose to design and train a set of neural network (NN) models capable of micro-expression recognition in real-time applications. Different NN models are explored and compared in this study to design a hybrid deep learning model by combining a convolutional neural network (CNN), a recurrent neural network (RNN, e.g., long short-term memory [LSTM]), and a vision transformer. The CNN can extract spatial features (of a neighborhood within an image) whereas the LSTM can summarize temporal features. In addition, a transformer with an attention mechanism can capture sparse spatial relations residing an image or between frames in a video clip. The inputs of the model are short facial videos, while the outputs are the micro-expressions gleaned from the videos. The deep learning models are trained and tested with publicly available facial micro-expression datasets to recognize different micro-expressions (e.g., happiness, fear, anger, surprise, disgust, sadness). The results of our proposed models are compared with that of literature-reported methods tested on the same datasets. The proposed hybrid models perform the best.
Multi-task and multi-domain learning with tensor networksGarg, Yash; Prater-Bennette, Ashley; Asif, M. Salman
doi: 10.1117/12.2663623pmid: N/A
We propose a tensor network that can learn to perform multiple tasks by adjusting the factors of each layer. Most of the existing methods for multi-task learning train a single network to extract task-specific features and subsequent prediction. We propose to use a single network with task-specific transformations that can extract task-specific features and perform task inference with small memory overhead. In particular, we transform features using low-rank updates in the convolution kernels. We present experiments on different datasets for multi-task and multi-domain learning and demonstrate that our method achieves state-of-the-art performance with minimal memory overhead compared to existing methods.
Extended datasets for multi-scale mixed modality microstructure assessment of titaniumWertz, J.; Cherry, Matt; Homa, Laura; Flournoy, Chenoa; Blasch, Erik
doi: 10.1117/12.2661338pmid: N/A
The sensors used for nondestructive evaluation (NDE) play a crucial role in ensuring aircraft availability. The current NDE paradigm often relies on mono-modal testing and signal-over-threshold criteria to provide robust defect or damage detection, not characterization. This approach works for go/no-go inspections of critical flaws that respond strongly to specific physical stimuli; for example, the electromagnetic method of eddy current testing (ECT) is sensitive enough to the abrupt change in conductivity of surface-breaking cracks in metals that it can be used exclusively for certain practical safety inspections. Yet, there are cases where this approach proves insufficient. Consider characterization of problematic microtexture regions (MTR) in certain titanium alloys, which exceeds the capabilities of any one NDE technique. In this work, a data fusion-based solution to MTR characterization is explored. The material problem and potential inspection methods are discussed. Registered datasets from these methods are presented and made available to the community.
Waveforms revisited for new-generation ELF-LF systemsAhmed, Mohiuddin "Mohin"
doi: 10.1117/12.2666899pmid: N/A
Radio frequency (RF) communication and radar applications in the low frequency ranges (3 Hz – 300 kHz ELF-LF range and beyond) form an important subclass of RF use cases that have to utilize waveforms that are inherently limited in bandwidth and hence information throughput capability. Thus, the challenge is maximizing the performance of suitable classes of waveforms in terms of throughput, noise and interference suppression, power and practical realizability. In this paper, we summarize the limitations of the traditional approach using sinusoidal waveforms, and briefly describe alternative and modified approaches using OFDM, wavelets, filter bank-based methods and optimized prolate spheroidal waveforms.
Augmented multi-head classification network: MHATTCayce, Garrett I.; Depoian, Arthur C.; Bailey, Colleen P.; Guturu, Parthasarathy
doi: 10.1117/12.2664123pmid: N/A
Classification of one-dimensional (1D) data is important for a variety of complex problems. From the finance industry to audio processing to the medical field, there are many industries that utilize 1D data. Machine learning techniques have excelled at solving these classification problems, but there is still room for improvement because the techniques have not been perfected. This paper proposes a novel architecture called Multi-Head Augmented Temporal Transformer (MHATT) for 1D classification of time-series data. Highly modified vision transformers were used to improve performance while keeping the network exceptionally efficient. To showcase its efficacy, the network is applied to heartbeat classification using the MIT-BIH OSCAR dataset. This dataset was ethically-split to ensure a fair and intensive test for networks. The novel architecture is 94.6% more efficient and had a peak accuracy of 91.79%, which was a 13.6% reduction in error over a recent state-of-the-art network. The impressive performance and efficiency of the MHATT architecture can be exploited by edge devices for unmatched performance and flexibility of deployment.
Relative navigation of UAV swarm in a GPS-denied environmentBelfadel, Djedjiga; Haessig, David; Chibane, Cherif
doi: 10.1117/12.2664917pmid: N/A
A precise relative localization system is a crucial necessity for a swarm of Unmanned Aerial Vehicles (UAVs), particularly when collaborating on a task. This paper aims to provide an alternative navigation system to enable a swarm of UAVs to conduct autonomous missions in a Global Positioning System (GPS)-denied environment. To achieve this goal, this paper proposes a relative navigation system using an Extended Kalman Filter (EKF) fusing observations from the on-board Inertial Measurement Unit (IMU) with ranging measurements obtained from the on-board ranging sensors. To ensure secure and high data communication rates, the system employs two waveforms and a low-cost beam-switching phased array. This system thus enables drone operations even in GPS-denied environments. We demonstrate the effectiveness of our approach through simulation experiments involving a swarm of six drones, which includes three fixed and three moving drones in a challenging Blue-Angel scenario. The evaluation of the statistical tests on the results of the simulations shows that this method is efficient.
Wildfire early detection systemMedina Molina, Joseph; Bravo, Berny; Melendez, Randall; Goenaga-Buelvas, Santiago; Ortiz-Rodriguez, Samira; Castillo-Charris, Eduardo; Goenaga-Jimenez, Miguel; Alvear-Suarez, Alcides
doi: 10.1117/12.2664146pmid: N/A
According to studies, today, we have seen an exponential increase in forest fires, on the island, in the United States and other places in the world. Due to climate change and extreme weather conditions, wildfires have started to appear in parts of the world that have never experienced this before. Many solutions are already being used to monitor forest fires such as: GPS, weather balloons, aerial drones, and many others. Since all these approaches use imaging sensors, they must wait until wildfires are large enough to be detected. This situation becomes a problem because forest fires would be too large to be easily controlled. This research proposes a solution that aims to attack this problem by using a microphone in the device located on the ground. Additional sensors will be incorporated, such as a digital thermometer to register humidity and temperature, a gas sensor to detect different types of gases, and long-range communications that would help our device to communicate with a network of other similar devices. Also, Internet of Things (IoT) will be implemented to send live sensor data to a central command. By focusing on the detection of forest fires, we can not only detect their occurrence in a timelier manner, but also the proposed system will have the ability to predict fires by monitoring meteorological data through smart networks.