STR's Analytics division researches and develops advanced analytics and machine learning-based solutions to solve challenging problems related to national security. Our team consists of passionate and motivated engineers with advanced degrees in engineering, computer science, mathematics, and data science, who are seeking opportunities to use their deep technical knowledge and creativity to tackle some of the hardest problems that our customers face. Our projects span multiple different data modalities and incorporate advanced algorithms, deep learning, and statistical techniques to uncover patterns in social media, structured and unstructured text, time series, geospatial, and imagery data, and must operate under challenging constraints not typically found in the commercial world. The tools and technologies we develop have real world impact and US Government analysts use them to extract and enrich intelligence information around the globe. As a Data Scientist, you will analyze a diverse of collection of interesting and challenging datasets to develop, implement, and evaluate statistical machine learning algorithms to discover interesting trends and form valuable intelligence insights.

In June 2021, Dave Zvenyach, director of a group tasked with improving digital access to US government services, sent a Slack message to his team. He'd decided that Login.gov, which provides a secure way to access dozens of government apps and websites, wouldn't use selfies and face recognition to verify the identity of people creating new accounts. "The benefits of liveness/selfie do not outweigh any discriminatory impact," he wrote, referring to the process of asking users to upload a selfie and photo of their ID so that algorithms can compare the two. Face recognition technology has become more accurate, but many systems have been found to work less reliably for women with dark skin, people who identify as Asian, or people with a nonbinary gender identity. Yet Zvenyach's pronouncement also put Login.gov and US agencies using the service at odds with federal security guidelines.

America's top minds are apparently putting their all into developing space technology -- but we've gotta admit, we wouldn't really have had this in mind. As the Massachusetts Institute of Technology revealed in a blog post, the Walking Oligomeric Robotic Mobility System (WORMS) modular lunar robot is intended to help NASA and other space agencies build and establish permanent Moon colonies by being able to do a bunch of different types of grunt work. "Robots could potentially do the heavy lifting [on a lunar colony] by laying cables, deploying solar panels, erecting communications towers, and building habitats," the press release reads. "But if each robot is designed for a specific action or task, a moon base could become overrun by a zoo of machines, each with its own unique parts and protocols." WORMS would head off that potential eventuality, MIT notes, by having mix-and-match components that can be traded in and off for whatever task is at hand -- and it's about as weird-looking as one could imagine a mix-and-match lunar robot could look, too.

When we think of artificial intelligence, we might think of robots like the ones in "Ex Machina," who are scarily smarter, closer to humans, and more perceptive than we think. The reality is that artificial intelligence is an innovative, growing field that offers creative opportunities for those who want to revolutionize the way we use technology. So, what is artificial intelligence, and what does a career in the field look like? Artificial intelligence (AI) is a branch of computer science concerning machines that can synthesize and process information to problem-solve. The concept first came into public view with Alan Turing's 1950 paper, "Computing Machinery and Intelligence," which explored whether we could train machines to think like humans.

Digital marketing firm Voro is using ChatGPT, the popular new artificial intelligence program, to "supercharge" content creation for clients. Before ChatGPT, content had been "incredibly expensive" to create -- especially individualized content necessary for search engine visibility, said Chris Gauron, partner and CEO at the Minneapolis firm. Voro has created an artificial intelligence-assisted, but human-edited, process that increases speed at the same time it lowers cost. ChatGPT's power and potential have fueled explosive growth, reaching 100 million users in just two months. Reports that it passed exams in four University of Minnesota law courses, at the Wharton School of Business and the exam to become a licensed physician have only heightened interest globally.

This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process presented in the paper aims to challenge asymmetric power dynamics in the fairness decision making within ML design and support ML development teams to identify, mitigate and monitor bias at each step of ML systems development. The process also provides guidance on how to explain the always imperfect trade-offs in terms of bias to users.

Considering a conversation thread, stance classification aims to identify the opinion (e.g. agree or disagree) of replies towards a given target. The target of the stance is expected to be an essential component in this task, being one of the main factors that make it different from sentiment analysis. However, a recent study shows that a target-oblivious model outperforms target-aware models, suggesting that targets are not useful when predicting stance. This paper re-examines this phenomenon for rumour stance classification (RSC) on social media, where a target is a rumour story implied by the source tweet in the conversation. We propose adversarial attacks in the test data, aiming to assess the models robustness and evaluate the role of the data in the models performance. Results show that state-of-the-art models, including approaches that use the entire conversation thread, overly relying on superficial signals. Our hypothesis is that the naturally high occurrence of target-independent direct replies in RSC (e.g. "this is fake" or just "fake") results in the impressive performance of target-oblivious models, highlighting the risk of target instances being treated as noise during training.

Many defenses have emerged with the development of adversarial attacks. Models must be objectively evaluated accordingly. This paper systematically tackles this concern by proposing a new parameter-free benchmark we coin RoBIC. RoBIC fairly evaluates the robustness of image classifiers using a new half-distortion measure. It gauges the robustness of the network against white and black box attacks, independently of its accuracy. RoBIC is faster than the other available benchmarks. We present the significant differences in the robustness of 16 recent models as assessed by RoBIC.

Crash data of autonomous vehicles (AV) or vehicles equipped with advanced driver assistance systems (ADAS) are the key information to understand the crash nature and to enhance the automation systems. However, most of the existing crash data sources are either limited by the sample size or suffer from missing or unverified data. To contribute to the AV safety research community, we introduce AVOID: an open AV crash dataset. Three types of vehicles are considered: Advanced Driving System (ADS) vehicles, Advanced Driver Assistance Systems (ADAS) vehicles, and low-speed autonomous shuttles. The crash data are collected from the National Highway Traffic Safety Administration (NHTSA), California Department of Motor Vehicles (CA DMV) and incident news worldwide, and the data are manually verified and summarized in ready-to-use format. In addition, land use, weather, and geometry information are also provided. The dataset is expected to accelerate the research on AV crash analysis and potential risk identification by providing the research community with data of rich samples, diverse data sources, clear data structure, and high data quality.

Human-machine interaction (HMI) and human-robot interaction (HRI) can assist structural monitoring and structural dynamics testing in the laboratory and field. In vibratory experimentation, one mode of generating vibration is to use electrodynamic exciters. Manual control is a common way of setting the input of the exciter by the operator. To measure the structural responses to these generated vibrations sensors are attached to the structure. These sensors can be deployed by repeatable robots with high endurance, which require on-the-fly control. If the interface between operators and the controls was augmented, then operators can visualize the experiments, exciter levels, and define robot input with a better awareness of the area of interest. Robots can provide better aid to humans if intelligent on-the-fly control of the robot is: (1) quantified and presented to the human; (2) conducted in real-time for human feedback informed by data. Information provided by the new interface would be used to change the control input based on their understanding of real-time parameters. This research proposes using Augmented Reality (AR) applications to provide humans with sensor feedback and control of actuators and robots. This method improves cognition by allowing the operator to maintain awareness of structures while adjusting conditions accordingly with the assistance of the new real-time interface. One interface application is developed to plot sensor data in addition to voltage, frequency, and duration controls for vibration generation. Two more applications are developed under similar framework, one to control the position of a mediating robot and one to control the frequency of the robot movement. This paper presents the proposed model for the new control loop and then compares the new approach with a traditional method by measuring time delay in control input and user efficiency.