Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence: Powering Human Exploration of the Moon and Mars

The NASA Autonomous Systems and Operations (ASO) project has developed and demonstrated numerous autonomy enabling technologies employing Artificial Intelligence (AI) techniques. Our work has employed AI in three distinct ways to enable autonomous mission operations capabilities. Crew Autonomy gives astronauts tools to operate space vehicles or systems without assistance from Mission Control. Vehicle System Management uses AI techniques to turn the astronaut’s spacecraft into a robot, allowing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots to act as crew assistants or proxies when the crew are not present. In this paper, we first describe human spaceflight mission operations functions, and classify these functions as monitoring, planning, plan execution, and fault management. We describe the wide range of development and demonstration activities performed by ASO since 2011. We describe the AI techniques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learning based approaches. Finally, we conclude with an assessment of these demonstrations, motivating future development needs for AI to enable NASA’s future Exploration missions.
AuTraScale: An Automated and Transfer Learning Solution for Streaming System Auto-Scaling

The complexity and variability of streaming data have brought a great challenge to the elasticity of the data processing systems. Streaming systems, such as Flink and Storm, need to adapt to the changes of workload with auto-scaling to meet the QoS requirements while saving resources. However, the accuracy of classical models (such as a queueing model) for QoS prediction decreases with the increase of the complexity and variability of streaming data and the resource interference. On the other hand, the indirect metrics used to optimize QoS may not accurately guide resource adjustment. Those problems can easily lead to waste of resources or QoS violation in practice. To solve the above problems, we propose AuTraScale, an automated and transfer learning auto-scaling solution, to determine the appropriate parallelism and resource allocation that meet the latency and throughput targets. AuTraScale uses Bayesian optimization to adapt to the complex relationship between resources and QoS, minimizing the impact of resource interference on the prediction accuracy, and a new metric that measures the performance of operators for accurate optimization. Even when the input data rate changes, it can quickly adjust the parallelism of each operator in response, with a transfer learning algorithm. We have implemented and evaluated AuTraScale on a Flink platform. The experimental results show that, compared with the state-of-the-art method like DRS and DS2, AuTraScale can reduce 66.6% and 36.7% resource consumption respectively in the scale-down and scale-up scenarios while ensuring QoS requirements, and save 13.5% resource on average when the input data rate changes.
ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media

Adverse weather conditions, including snow, rain, and fog, pose a major challenge for both human and computer vision. Handling these environmental conditions is essential for safe decision making, especially in autonomous vehicles, robotics, and drones. Most of today’s supervised imaging and vision approaches, however, rely on training data collected in the real world that is biased towards good weather conditions, with dense fog, snow, and heavy rain as outliers in these datasets. Without training data, let alone paired data, existing autonomous vehicles often limit themselves to good conditions and stop when dense fog or snow is detected. In this work, we tackle the lack of supervised training data by combining synthetic and indirect supervision. We present ZeroScatter, a domain transfer method for converting RGB-only captures taken in adverse weather into clear daytime scenes. ZeroScatter exploits model-based, temporal, multi-view, multi-modal, and adversarial cues in a joint fashion, allowing us to train on unpaired, biased data. We assess the proposed method on in-the-wild captures, and the proposed method outperforms existing monocular descattering approaches by 2.8 dB PSNR on controlled fog chamber measurements.
Hornero: Thunderstorms Characterization using Visual Analytics

Abstract Analyzing the evolution of thunderstorms is critical in determining the potential for the development of severe weather events. Existing visualization systems for short‐term weather forecasting (nowcasting) allow for basic analysis and prediction of storm developments. However, they lack advanced visual features for efficient decision‐making. We developed a visual analytics tool for the detection of hazardous thunderstorms and their characterization, using a visual design centered on a reformulated expert task workflow that includes visual features to overview storms and quickly identify high‐impact weather events, a novel storm graph visualization to inspect and analyze the storm structure, as well as a set of interactive views for efficient identification of similar storm cells (known as analogs) in historical data and their use for nowcasting. Our tool was designed with and evaluated by meteorologists and expert forecasters working in short‐term operational weather forecasting of severe weather events. Results show that our solution suits the forecasters' workflow. Our visual design is expressive, easy to use, and effective for prompt analysis and quick decision‐making in the context of short‐range operational weather forecasting.
An earthquake alert system based on a collaborative approach using smart devices

Recently, mobile devices such as smartphones and IoT devices have been successfully used for detecting earthquakes using their built-in accelerometers. To utilize such devices as a seismic sensor, there are two approaches including a standalone or a client-server approach. The client-server approach is more accurate than the stand-alone approach but requires high-performance servers and network infrastructures to process acceleration data captured from many client devices. In the standalone approach, straightforward earthquake detection algorithms can be easily implemented on less powerful mobile devices, but there is a possibility of false alarms. To address this limitation, in this paper, we present a collaborative approach that detects an earthquake using multiple smartphones located in a near area so as to improve the earthquake detection ability of the standalone approach without system and network infrastructures. In our approach, smartphones located in a near area construct a seismic network to detect earthquakes together and then wait for any shakings caused by human activities, mechanical vibrations, earthquakes, etc. When a smartphone detects an earthquakelike motion using an earthquake detection algorithm based on a simple neural network, it broadcasts the detection result to nearby smartphones in a multi-hop manner. Upon receipt of the detection results from nearby smartphones, each smartphone participating in the seismic network performs a decision-making process and confirms an earthquake and then instantiates an alert. The experimental results show the effectiveness of the proposed approach, so we believe that our approach can be effectively used in high seismic countries that lack an earthquake early warning system (EEWS).
Analysis of Combination Algorithm Data Encryption Standard (DES) and Blum-Blum-Shub (BBS)

Abstract Data Encryption Standard (DES) has a weakness in the key that is vulnerable to security threats, but it is quite popular to use because of the fast encryption and decryption process. Combining the DES algorithm with the Pseudo-random number generator Blum-Blum-Shub (BBS) in generating external keys in the encryption and decryption process of messages, produces a unique key and a good level of security. The longer the selected key and seed, the higher the security level of the DES encryption and decryption key. The use of BBS as an external key generator in the DES algorithm does not significantly affect the encryption (key and seed generated is around 0,001 – 0,003 seconds for the 2 – 4 digits experiment) processing time, meanwhile, the time in the decryption process has no effect because the BBS key and seed are no longer subject to feasibility testing or number requirements. The combination of DES and BBS algorithms can increase security in terms of encryption and decryption keys because the random number of DES keys generated by the BBS algorithm is unique and does not burden the user in determining the keys used in the DES algorithm because the keys are generated from the programming language.
Real-Time Violence Detection in Videos Using Dynamic Images

The problem of violence detection consists of identifying scenes that characterize violence in a video stream. The violent actions in question can be of the most diverse, from fights, pushes, and robberies to shots and explosions. Detecting the presence of violence is useful for classifying videos and films, blocking inappropriate content for specific audiences, and improving security personnel's performance responsible for areas under surveillance. This work proposes an approach based on the Dynamic Images method, using handcrafted and CNN features the Bag of Visual Words paradigm and a SVM classifier to detect violent actions that involve corporal struggle in the video streams of databases of literature. The proposed methods can achieve an average accuracy of 97.50% for the Hockey dataset, 99.80% for the Movies dataset, and 93.40% for the Crowd dataset. Besides, the identification of violence in each video was performed in of hundredths of a second. Also, the techniques proposed in this work have the advantage that they can be applied even in environments where computational resources are limited, and technologies such as GPU or parallel processing are not available.
A survival model generalized to regression learning algorithms

Survival prediction is an important problem that is encountered widely in industry and medicine. Despite the explosion of artificial intelligence technologies, no uniformed method allows the application of any type of regression learning algorithm to a survival prediction problem. Here, we present a statistical modeling method that is generalized to all types of regression learning algorithm, including deep learning. We present its empirical advantage when it is applied to traditional survival problems. We demonstrate its expanded applications in different types of regression learning algorithm, such as gradient boosted trees, convolutional neural networks and recurrent neural networks. Additionally, we demonstrate its application in clinical informatic data, pathological images and the hardware industry. We expect that this algorithm will be widely applicable for diverse types of survival data, including discrete data types and those suitable for deep learning such as those with time or spatial continuity.
UniNet: Scalable Network Representation Learning with Metropolis-Hastings Sampling

Network representation learning (NRL) has been successfully adopted in various data mining and machine learning applications. Random walk based NRL is one popular paradigm, which uses a set of random walks to capture the network structural information, and then employs word2vec models to learn the low-dimensional representations. However, until now there is lack of a framework, which unifies existing random walk based NRL models and efficiently learns from large networks. The main obstacle comes from the diverse random walk models and the inefficient sampling method for the random walk generation. In this paper, we first introduce a new and efficient edge sampler based on Metropolis-Hastings sampling technique, and theoretically show the convergence property of the edge sampler to arbitrary discrete probability distributions. Then we propose a random walk model abstraction, in which users can easily define different transition probability by specifying dynamic edge weights and random walk states. The abstraction is efficiently supported by our edge sampler, since our sampler can draw samples from unnormalized probability distribution in constant time complexity. Finally, with the new edge sampler and random walk model abstraction, we carefully implement a scalable NRL framework called UniNet. We conduct extensive experiments with five random walk based NRL models over eleven real-world datasets, and the results verify the efficiency of UniNet over billion-edge networks.
Tax Evasion Detection with FBNE-PU Algorithm Based on PnCGCN and PU Learning

Tax evasion is an illegal activity in which individuals or entities avoid paying their true tax liability. It has always been a crucial issue for both governments and academic researchers to efficiently detect tax evasion. Recent research has proposed the use of machine learning technology to detect tax evasion and has shown good results in some specific areas. Regrettably, there are still two major obstacles to detect tax evasion. First, it is hard to extract powerful features because of the complexity of tax data. Second, due to the complicated process of tax auditing, labeled data are limited. Such obstacles motivate the contributions of this work. In this paper, we propose a novel tax evasion detection framework named FBNE-PU, a multi-stage method to detect tax evasion in real-life scenarios. In this paper, we perform an in-depth analysis of the characteristics of the transaction network and propose a novel network embedding algorithm, the PnCGCN. It significantly improves detection performance by extracting powerful features from basic features and the tax-related transaction network. Moreover, we utilize nnPU to assign pseudo labels for unlabeled data. Finally, a MLP is trained as the decision function. Experiments on three real-world datasets demonstrate that our method significantly outperforms the comparison methods in the tax evasion detection task.
Learning risk-mediated traversability maps in unstructured terrains navigation through robot-oriented models

In robotic navigation, safety and efficiency play an important role and must be evaluated together. This paper proposes a simple and efficient method to derive the traversability maps of unstructured environments and the optimal path to be followed to reach target locations based on the specific characteristics of different types of robots available. The optimal solution minimises the path length while maintaining the risk associated with that path below the maximum acceptable upper bound. The ability of each robot to traverse terrains with specific characteristics is formalised and modelled using simple and efficient neural networks and trained in a dynamic simulation environment. The proposed shallow network topology achieves results, in terms of accuracy, that are comparable with other standard classifiers and more complex deep networks. Applying this procedure to different robotic structures, the best system within the team (wheeled, legged, and hybrid) can be selected to accomplish a specific assigned task. The proposed strategy, together with the obtained simulation results is presented, carefully analysed, and then compared using real-life simulated scenarios.
Reconstruction of Orthographic Mosaics From Perspective X-Ray Images

Image stitching is a prominent challenge in medical imaging, where the limited field-of-view captured by single images prohibits holistic analysis of patient anatomy. The barrier that prevents straight-forward mosaicing of 2D images is depth mismatch due to parallax. In this work, we leverage the Fourier slice theorem to aggregate information from multiple transmission images in parallax-free domains using fundamental principles of X-ray image formation. The details of the stitched image are subsequently restored using a novel deep learning strategy that exploits similarity measures designed around frequency, as well as dense and sparse spatial image content. Our work provides evidence that reconstruction of orthographic mosaics is possible with realistic motions of the C-arm involving both translation and rotation. We also show that these orthographic mosaics enable metric measurements of clinically relevant quantities directly on the 2D image plane.
Semantic Palette: Guiding Scene Generation with Class Proportions

Despite the recent progress of generative adversarial networks (GANs) at synthesizing photo-realistic images, producing complex urban scenes remains a challenging problem. Previous works break down scene generation into two consecutive phases: unconditional semantic layout synthesis and image synthesis conditioned on layouts. In this work, we propose to condition layout generation as well for higher semantic control: given a vector of class proportions, we generate layouts with matching composition. To this end, we introduce a conditional framework with novel architecture designs and learning objectives, which effectively accommodates class proportions to guide the scene generation process. The proposed architecture also allows partial layout editing with interesting applications. Thanks to the semantic control, we can produce layouts close to the real distribution, helping enhance the whole scene generation process. On different metrics and urban scene benchmarks, our models outperform existing baselines. Moreover, we demonstrate the merit of our approach for data augmentation: semantic segmenters trained on real layout-image pairs along with additional ones generated by our approach outperform models only trained on real pairs.
Semi-Supervised Clustering for Financial Risk Analysis

Many methods have been developed for financial risk analysis. In general, the conventional unsupervised approaches lack sufficient accuracy and semantics for the clustering, and the supervised approaches rely on large amount of training data for the classification. This paper explores the semi-supervised scheme for the financial data prediction, in which accurate predictions are expected with a small amount of labeled data. Due to lack of sufficient distinguishability in financial data, it is hard for the existing semi-supervised approaches to obtain satisfactory results. In order to improve the performance, we first convert the input labeled clues to the global prior probability, and propagate the'soft' prior probability to learn the posterior probability instead of directly propagating the'hard' labeled data. A label diffusion model is then constructed to adaptively fuse the information at feature space and label space, which makes the structures of data affinity and labeling more consistent. Experiments on two public real financial datasets validate the effectiveness of the proposed method.
A Cost-Efficient Digital ESN Architecture on FPGA for OFDM Symbol Detection

The echo state network (ESN) is a recently developed machine-learning paradigm whose processing capabilities rely on the dynamical behavior of recurrent neural networks. Its performance outperforms traditional recurrent neural networks in nonlinear system identification and temporal information processing applications. We design and implement a cost-efficient ESN architecture on field-programmable gate array (FPGA) that explores the full capacity of digital signal processor blocks on low-cost and low-power FPGA hardware. Specifically, our scalable ESN architecture on FPGA exploits Xilinx DSP48E1 units to cut down the need of configurable logic blocks. The proposed architecture includes a linear combination processor with negligible deployment of configurable logic blocks and a high-accuracy nonlinear function approximator. Our work is verified with the prediction task on the classical NARMA dataset and a symbol detection task for orthogonal frequency division multiplexing systems using a wireless communication testbed built on a software-defined radio platform. Experiments and performance measurement show that the new ESN architecture is capable of processing real-world data efficiently for low-cost and low-power applications.
DeepNavNet: Automated Landmark Localization for Neuronavigation

Functional neurosurgery requires neuroimaging technologies that enable precise navigation to targeted structures. Insufficient image resolution of deep brain structures necessitates alignment to a brain atlas to indirectly locate targets within preoperative magnetic resonance imaging (MRI) scans. Indirect targeting through atlas-image registration is innately imprecise, increases preoperative planning time, and requires manual identification of anterior and posterior commissure (AC and PC) reference landmarks which is subject to human error. As such, we created a deep learning-based pipeline that consistently and automatically locates, with submillimeter accuracy, the AC and PC anatomical landmarks within MRI volumes without the need for an atlas. Our novel deep learning pipeline (DeepNavNet) regresses from MRI scans to heatmap volumes centered on AC and PC anatomical landmarks to extract their three-dimensional coordinates with submillimeter accuracy. We collated and manually labeled the location of AC and PC points in 1128 publicly available MRI volumes used for training, validation, and inference experiments. Instantiations of our DeepNavNet architecture, as well as a baseline model for reference, were evaluated based on the average 3D localization errors for the AC and PC points across 311 MRI volumes. Our DeepNavNet model significantly outperformed a baseline and achieved a mean 3D localization error of 0.79 ± 0.33 mm and 0.78 ± 0.33 mm between the ground truth and the detected AC and PC points, respectively. In conclusion, the DeepNavNet model pipeline provides submillimeter accuracy for localizing AC and PC anatomical landmarks in MRI volumes, enabling improved surgical efficiency and accuracy.
Rushmore

We present Rushmore, a system that securely displays static or animated images using TrustZone. The core functionality of Rushmore is to securely decrypt and display encrypted images (sent by a trusted party) on a mobile device. Although previous approaches have shown that it is possible to securely display encrypted images using TrustZone, they exhibit a critical limitation that significantly hampers the applicability of using TrustZone for display security. The limitation is that, when the trusted domain of TrustZone (the secure world) takes control of the display, the untrusted domain (the normal world) cannot display anything simultaneously. This limitation comes from the fact that previous approaches give the secure world exclusive access to the display hardware to preserve security. With Rushmore, we overcome this limitation by leveraging a well-known, yet overlooked hardware feature called an IPU (Image Processing Unit) that provides multiple display channels. By partitioning these channels across the normal world and the secure world, we enable the two worlds to simultaneously display pixels on the screen without sacrificing security. Furthermore, we show that with the right type of cryptographic method, we can decrypt and display encrypted animated images at 30 FPS or higher for medium-to-small images and at around 30 FPS for large images. One notable cryptographic method we adapt for Rushmore is visual cryptography, and we demonstrate that it is a light-weight alternative to other cryptographic methods for certain use cases. Our evaluation shows that in addition to providing usable frame rates, Rushmore incurs less than 5% overhead to the applications running in the normal world.