Reviewerly/exHarmony huggingface dataset:
Reproducible Workflows for Exploring and Modeling EMA Data

Improper use of substances like cannabis may lead to physical, emotional, economic, and social problems. Therefore, it is significant to elucidate the inter-individual and intra-individual influences along with contextual influences that predict the use of cannabis. TigerAware is a mobile survey data collection platform that holds unique promise to advance research in addiction and substance use. This paper presents a novel method to support Ecological Momentary Assessment (EMA) studies. We propose to extract useful information from TigerAware survey data using data mining and machine learning methods, and structure customizable survey analyses into reproducible workflows. Through our analysis pipeline for EMA, researchers are able to discover meaningful information from survey data with minimal duplication of effort and improve the efficiency and rigor of the process.
Context-aware Resemblance Detection based Deduplication Ratio Prediction for Cloud Storage

With the prevalence of cloud storage, people prefer to outsource their data to the cloud for flexibility and reliability. Undoubtedly, there are lots of redundancy among these data. However, high-end storage with deduplication costs heavy computation and increases the data management complexity. Potential customers need the redundancy proportion information of their outsourced data to decide whether high-end storage with deduplication is worthwhile. Thus, many researchers have previously attempted to predict the redundant ratio. However, existing mechanisms ignore the redundancy proportion among similar chunks containing many duplicate data. Although resemblance detection, detecting the duplicate parts among similar data, has become a hot issue, it is hardly applied to the conventional deduplication ratio estimation because of unacceptable calculation cost. Therefore, we analyze the limitations and challenges of deduplication ratio prediction in prediction scope and response time and further propose a novel prediction scheme. By leveraging the context-aware resemblance detection, and confidence interval theory, our method can achieve faster estimation speed with higher accuracy in deduplication ratio compared with the state-of-the-art work. Finally, the results show that our method can efficiently and effectively estimate the proportion of duplicate chunks and redundant data among similar chunks by conducting experiments on real workloads.
JobViewer: Graph-based Visualization for Monitoring High-Performance Computing System

Visualization aims to strengthen data exploration and analysis, especially for complex and high-dimensional data. High-performance computing (HPC) systems are typically large and complicated instruments that generate massive performance and operation time series. Monitoring HPC systems’ performance is a daunting task for HPC admins and researchers due to their dynamic natures. This work proposes a visual design using the bipartite graph’s idea to visualize HPC clusters’ structure, metrics, and job scheduling data. We built a web-based prototype, called JobViewer, that integrates advanced methods in visualization and human-computer interaction (HCI) to demonstrate the benefits of visualization in real-time monitoring HPC centers. We also showed real use cases and a user study to validate the efficiency and highlight the current approach’s drawbacks.
Identifying Evolution of Software Metrics by Analyzing Vulnerability History in Open Source Projects

Software developers mostly focus on functioning code while developing their software paying little attention to the software security issues. Now a days, security is getting priority not only during the development phase, but also during other phases of software development life cycle (starting from requirement specification till maintenance phase). To that end, research have been expanded towards dealing with security issues in various phases. Current research mostly focused on developing different prediction models and most of them are based on software metrics. The metrics based models showed higher precision but poor recall rate in prediction. Moreover, they did not analyze the roles of individual software metric on the occurrences of vulnerabilities separately. In this paper, we target to track the evolution of metrics within the life-cycle of a vulnerability starting from its born version through the last affected version till fixed version. In particular, we studied a total of 250 files from three major releases of Apache Tomcat (8, 9 , and 10). We found that four metrics: AvgCyclomatic, AvgCyclomaticStrict, CountDeclMethod, and CountLineCodeExe show significant changes over the vulnerability history of Tomcat. In addition, we discovered that Tomcat team prioritizes in fixing threatening vulnerabilities such as Denial of Service than less severe vulnerabilities. The results of our research will potentially motivate further research on building more accurate vulnerability prediction models based on the appropriate software metrics. It will also help to assess developer’s mindset about fixing different types of vulnerabilities in open source projects.
Face Biometric Fairness Evaluation on Real vs Synthetic Cross-Spectral Images

In this paper, we compare the performance and fairness metrics on visual and thermal images of faces, including the synthetic images of human subjects with face masks. The comparative experiment is performed on two datasets: the SpeakingFace and Thermal-Mask dataset. We assess fairness on real images and show how the same process can be applied to synthetic images. The chosen fairness metrics include demographic parity difference and equalized odds difference. While the demographic parity difference is assessed as 1.24 for random guessing in the process of face identification, it reaches 5.0 when both the precision and recall rate approach 99.99%. These results confirm that inherently biased datasets significantly impact the fairness of any biometric system. For biometric-enabled systems, fairness is related to the adequacy of the data to represent different groups of human subjects. In this paper, we focus on three demographic groups: age, gender, and ethnicity. A primary cause of biases with respect to these groups is the class imbalance introduced through the data collection process. To address the imbalanced datasets, the classes with fewer samples can be augmented with synthetic images to generate a more balanced dataset, resulting in less bias when training a machine learning system. The study shows that fairness is correlated to the performance of the system rather than to the genesis of the images (real or synthetic). The experiment on a simple 3-Block CNN with a precision and recall rate of 99.99% using the demographic parity difference as an estimate of fairness showed that among gender, ethnicity, and age, the latter is an attribute that is the most sensitive while age is the least one.
An Effective Supplementation of Insufficient Data by Generative Adversarial Networks

Generative Adversarial Networks (GANs) can be used for data augmentation in order to improve the outcome and performance of machine learning models for automatic information retrieval. We looked into the challenge faced with limited blurry and distorted digit images from expiry dates datasets, which is required to improve digit recognition tasks on medicine, consumables, cosmetic products and tube-type ointments. For our dataset, Wasserstein GAN with a gradient norm penalty (WGAN-GP) was effective for data augmentation among the state-of-the-art GANs by visible inspection and Fréchet Inception Distance (FID) value comparison.
Artificial Reasoning in the Streetscape

Army Research Laboratory’s Content Understanding Branch, Artificial Reasoning Team research objective is to enable systems to reason given existing and future information supporting shared understanding and providing enhanced capabilities for choices and decisions. Various reasoning approaches are used to form the “best” hypothesis from multiple modalities of data generating use cases and assessing their impact on decisions given multiple criteria. The NSF Engineering Research Center for Smart Streetscapes (CS3) convergent research is inspired by potential streetscape applications. Thus, real-time understanding of complex streetscapes correspondingly requires progress in fundamental engineering knowledge and enables exciting opportunities for deploying technology: A “smart streetscape” could instantly sense human behavior and safely guide individual within the environment, amplify emergency services, and protect people against threats and dangers. The ARL and CS3 collaboration centers around the overlapping challenge for situational awareness in complex environments and how the joint research efforts can generate potential capabilities. This paper will present concepts from existing research and ideas for new research to address these common questions and challenges.
Cognitive Architectures and their Applications

Cognitive architectures are computational implementations of unified theories of cognition. The consensus of 50 years of research in cognitive architectures can be captured in the form of a Common Model of Cognition that can provide a guide for applications in neuroscience, artificial intelligence and robotics. Being able to represent human cognition in computational form enables a wide range of applications when humans and machines interact. Using cognitive models to represent common ground between deep learners and human users enables adaptive explanations. Cognitive models representing the behavior of cyber attackers can be used to optimize cyber defenses including techniques such as deceptive signaling. Cognitive models of human-automation interaction can improve robustness of human-machine teams by predicting disruptions to measures of trust under various adversarial situations.
Focused Stochastic Neighbor Embedding for Better Preserving Points of Interest

Dimensionality reduction aims to find low-dimensional embeddings of high-dimensional data such that the low-dimensional representation preserves some meaningful properties of structures in the original data. When low-dimensional space is 2- or 3-dimensional, the low-dimensional embeddings can be visualized using a scatterplot map. Most of the existing methods try to preserve the local neighborhoods of all data points. However, in general, it is impossible to retain all such information for all data points in the low-dimensional space. As a result, there could be some data points whose neighborhoods are not faithfully displayed in the visualization due to information loss. If the information loss happens around a specific set of points of interest (e.g., specific patients, or proteins under observed), this may be problematic because the withdrawn insights may not be accurate for these observed data points. Therefore, in this paper, we introduce a problem called focused dimensionality reduction where given an original high-dimensional dataset and a set of points of interest, we want to find 2- or 3-dimensional embeddings of the original data such that the information loss in the local neighborhoods surrounding the points of interest is minimized as much as possible. In other words, if the information loss is inevitable, it should not happen around the points of interest. To solve the problem, we extend the stochastic neighbor embedding method and introduce a focused objective function where we put more weight on losses that involve points of interest. Experiments on real-world datasets show that our proposed method is better in preserving the local neighborhood structure of points of interest while the generated visualizations are as good as those generated by the stochastic neighbor embedding method.
PSLotto: A Privacy-Enhanced COVID Lottery System

In March 2020, the World Health Organization (WHO) declared the novel coronavirus (COVID-19) a global pandemic. Globally the rapid spread of COVID-19 ground economies to a halt with stay at home orders and took the lives of millions of people. Therefore when vaccines became available as a tool to slow the spread of the COVID-19 virus, governments world-wide were looking to incentivize their populations to get vaccinated. Included in this effort, the government of Georgia created a lottery initiative to monetarily reward citizens who were vaccinated and encourage participation from those who were hesitant to get vaccinated. The Georgian lottery system that developed out of this initiative included a website displaying lottery winner data leading to serious privacy leakage. In this paper, we develop of an attack framework that allows adversaries with minimal background knowledge to re-identify STOPCOV Lottery winners and deploying our system against a subpopulation vulnerable to attack. We then propose our privacy-enhanced alternative, PSLotto, which simultaneously preserves the functionalities of the existing STOPCOV Lottery system and protects the privacy of lottery winners.
Enhanced Deep Learning Super-Resolution for Bathymetry Data

Spatial resolution is critical for observing and monitoring environmental phenomena. Acquiring high-resolution bathymetry data directly from satellites is not always feasible due to limitations on equipment, so spatial data scientists and researchers turn to single image super-resolution (SISR) methods that utilize deep learning techniques as an alternative method to increase pixel density. While super resolution residual networks (e.g., SR-ResNet) are promising for this purpose, several challenges still need to be addressed: (1) Earth data such as bathymetry is expensive to obtain and relatively limited in its data record amount; (2) certain domain knowledge needs to be complied with during model training; (3) certain areas of interest require more accurate measurements than other areas. To address these challenges, following the transfer learning principle, we study how to leverage an existing pre-trained super-resolution deep learning model, namely SR-ResNet, for high-resolution bathymetry data generation. We further enhance the SR-ResNet model to add corresponding loss functions based on domain knowledge. To let the model perform better for certain spatial areas, we add additional loss functions to increase the penalty of the areas of interest. Our experiments show our approaches achieve higher accuracy than most baseline models when evaluating using metrics including MSE, PSNR, and SSIM.
Semantic Communications: A Paradigm Whose Time Has Come

Communication system design to date is predicated on principles that abstract information as digital sequences irrespective of their meanings. Such a semantic-agnostic approach leads to fundamental limits that are application and technology independent, and offers efficient engineering of communication systems. Emerging applications that involve communications however, call for going beyond the engineering problem of reliably reconstructing a digital sequence. Most current communication devices are computing devices that execute tasks. Increasingly communication is needed for a purpose and is integrated with decision making, machine learning and sensing. Further, human networks operate over machine networks, which necessitate more sophisticated human-machine communication. The goal of this vision paper is to advocate for Semantic Communications, i.e., communication system design that, at the outset, pays attention to the content, its meaning, context, and purpose. We will argue that taking into account the meanings and context of information can lead to better communication designs. In particular, we argue in favor of semantic distortion, a novel metric introduced nearly a decade ago, based upon which communications systems design aiming to convey the meaning and purpose can be designed. We review the current efforts of Semantic Communications which has recently become a popular area of 6G, and potential directions of Semantic Communications, which can explore various different directions with novel metrics including using Knowledge Graphs (KL), information theoretic approaches, and machine/Deep Learning (DL).
Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer

Development of defenses against physical world attacks such as adversarial patches is gaining traction within the research community. We contribute to the field of adversarial patch detection by introducing an uncertainty-based adversarial patch localizer which localizes adversarial patch on an image, permitting post-processing patch-avoidance or patch-reconstruction. We quantify our prediction uncertainties with the development of Detection of Uncertainties in the Exceedance of Threshold (DUET) algorithm. This algorithm provides a framework to ascertain confidence in the adversarial patch localization, which is essential for safety-sensitive applications such as self-driving cars and medical imaging. We conducted experiments on localizing adversarial patches and found our proposed DUET model outperforms baseline models. We then conduct further analyses on our choice of model priors and the adoption of Bayesian Neural Networks in different layers within our model architecture. We found that isometric gaussian priors in Bayesian Neural Networks are suitable for patch localization tasks and the presence of Bayesian layers in the earlier neural network blocks facilitates top-end localization performance, while Bayesian layers added in the later neural network blocks contribute to better model generalization. We then propose two different well-performing models to tackle different use cases.
Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning

Machine learning models with explainable predictions are increasingly sought after, especially for real-world, mission-critical applications that require bias detection and risk mitigation. Inherent interpretability, where a model is designed from the ground-up for interpretability, provides intuitive insights and transparent explanations on model prediction and performance. In this paper, we present CoLabel, an approach to build interpretable models with explanations rooted in the ground truth. We demonstrate CoLabel in a vehicle feature extraction application in the context of vehicle make-model recognition (VMMR). By construction, CoLabel performs VMMR with a composite of interpretable features such as vehicle color, type, and make, all based on interpretable annotations of the ground truth labels. First, CoLabel performs corroborative integration to join multiple datasets that each have a subset of desired annotations of color, type, and make. Then, CoLabel uses decomposable branches to extract complementary features corresponding to desired annotations. Finally, CoLabel fuses them together for final predictions. During feature fusion, CoLabel harmonizes complementary branches so that VMMR features are compatible with each other and can be projected to the same semantic space for classification. With inherent interpretability, CoLabel achieves superior performance to the state-of-the-art black-box models, with accuracy of 0.98, 0.95, and 0.94 on CompCars, Cars196, and BoxCars116K, respectively. CoLabel provides intuitive explanations due to constructive interpretability, and subsequently achieves high accuracy and usability in mission-critical situations.
Communication and Energy Efficient Edge Intelligence

Driven by the rapid advances in Artificial Intelligence of Things (AIoT), billions of mobile and IoT devices are connected to the internet, generating huge quantities of data at the network edge. Meanwhile, traditional analytics approaches such as cloud computing and centralized AI are unable to manage these massively distributed heterogeneous data primarily because 1) moving a tremendous amount of data across the network poses severe challenges to network capacity 2) cloud-based analytics can result in prohibitively high transmission delays 3) transporting data containing private information over the network poses serious concerns for the privacy and may not even be possible due to regulations like GDPR. Accelerated by the success of AI and IoT technologies, there is an urgent need to push AI to the network edge to tap the full potential of big data.
A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels

A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs). Examples of these FWGs include the engine system, transmission, fuel system, brakes, etc. Each FWG has associated sensor-channels that gauge vehicular operating conditions. This data rich environment is conducive to the development of Predictive Maintenance (PdM) technologies. Undercutting various PdM technologies is the need for robust anomaly detection models that can identify events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal vehicular operational behavior. In this paper, we introduce the Vehicle Performance, Reliability, and Operations (VePRO) dataset and use it to create a multi-phased approach to anomaly detection. Utilizing Temporal Convolution Networks (TCN), our anomaly detection system can achieve 96% detection accuracy and accurately predicts 91% of true anomalies. The performance of our anomaly detection system improves when sensor channels from multiple FWGs are utilized.
Anomaly detection of high-dimensional data based on Ensemble GANs with Dropout

An unsupervised anomaly detection approach DGANs is proposed based on ensemble GANs with Dropout. The comparisons with representative approaches on 10 public datasets show it has advantages in accuracy, recall and F1 scores. DGANs can address the overfitting problem in ensemble GANs training on high-dimensional datasets.
DP-ADA: Differentially Private Adversarial Domain Adaptation for Training Deep Learning based Network Intrusion Detection Systems

Recent work has shown that deep learning (DL) techniques are highly effective for assisting network intrusion detection systems (NIDS) in identifying attacks on networks. Training DL classification models, however, requires vast amounts of labeled data which is often expensive and time-consuming to collect. Also, DL models trained using data from one type of network may not be able to identify attacks on other types of network or identify new families of attacks discovered over time. In this paper, we introduce a differentially private adversarial DA (DP-ADA) workflow which allows organizations to share their labeled data with other organizations in a privacy preserving way. This workflow allows for more collaboration and sharing, so that more effective DL based NIDS models can be created for deployment on different types of networks and can detect newer attack families with very little effort. Our solution provides a much better performance than fine-tuning based transfer learning mechanism and almost matches the performance of adversarial DA when the actual source dataset is used, while at the same time reducing the size of data shared between the two parties. Our solution also provides privacy protection for heterogeneous DA, where source and target datasets have different feature dimensions.