For high-stakes online exams, it is important to detect potential rule violations to ensure the security of the test. In this study, we investigate the task of detecting whether test takers are looking away from the screen, as such behavior could be an indication that the test taker is consulting external resources. For asynchronous proctoring, the exam videos are recorded and reviewed by the proctors. However, when the length of the exam is long, it could be tedious for proctors to watch entire exam videos to determine the exact moments when test takers look away. We present an AI-assisted gaze detection system, which allows proctors to navigate between different video frames and discover video frames where the test taker is looking in similar directions. The system enables proctors to work more effectively to identify suspicious moments in videos. An evaluation framework is proposed to evaluate the system against human-only and ML-only proctoring, and a user study is conducted to gather feedback from proctors, aiming to demonstrate the effectiveness of the system.

Quantum computers have the potential to revolutionize diverse fields, including quantum chemistry, materials science, and machine learning. However, contemporary quantum computers experience errors that often cause quantum programs run on them to fail. Until quantum computers can reliably execute large quantum programs, stakeholders will need fast and reliable methods for assessing a quantum computer's capability-i.e., the programs it can run and how well it can run them. Previously, off-the-shelf neural network architectures have been used to model quantum computers' capabilities, but with limited success, because these networks fail to learn the complex quantum physics that determines real quantum computers' errors. We address this shortcoming with a new quantum-physics-aware neural network architecture for learning capability models. Our architecture combines aspects of graph neural networks with efficient approximations to the physics of errors in quantum programs. This approach achieves up to $\sim50\%$ reductions in mean absolute error on both experimental and simulated data, over state-of-the-art models based on convolutional neural networks.

Online exams have become popular in recent years due to their accessibility. However, some concerns have been raised about the security of the online exams, particularly in the context of professional cheating services aiding malicious test takers in passing exams, forming so-called "cheating rings". In this paper, we introduce a human-in-the-loop AI cheating ring detection system designed to detect and deter these cheating rings. We outline the underlying logic of this human-in-the-loop AI system, exploring its design principles tailored to achieve its objectives of detecting cheaters. Moreover, we illustrate the methodologies used to evaluate its performance and fairness, aiming to mitigate the unintended risks associated with the AI system. The design and development of the system adhere to Responsible AI (RAI) standards, ensuring that ethical considerations are integrated throughout the entire development process.

Smart intersections have the potential to improve road safety with sensing, communication, and edge computing technologies. Perception sensors installed at a smart intersection can monitor the traffic environment in real time and send infrastructure-based warnings to nearby travelers through V2X communication. This paper investigated how infrastructure-based warnings can influence driving behaviors and improve roundabout safety through a driving-simulator study - a challenging driving scenario for human drivers. A co-simulation platform integrating Simulation of Urban Mobility (SUMO) and Webots was developed to serve as the driving simulator. A real-world roundabout in Ann Arbor, Michigan was built in the co-simulation platform as the study area, and the merging scenarios were investigated. 36 participants were recruited and asked to navigate the roundabout under three danger levels (e.g., low, medium, high) and three collision warning designs (e.g., no warning, warning issued 1 second in advance, warning issued 2 seconds in advance). Results indicated that advanced warnings can significantly enhance safety by minimizing potential risks compared to scenarios without warnings. Earlier warnings enabled smoother driver responses and reduced abrupt decelerations. In addition, a personalized intention prediction model was developed to predict drivers' stop-or-go decisions when the warning is displayed. Among all tested machine learning models, the XGBoost model achieved the highest prediction accuracy with a precision rate of 95.56% and a recall rate of 97.73%.

Holistic benchmarks for quantum computers are essential for testing and summarizing the performance of quantum hardware. However, holistic benchmarks -- such as algorithmic or randomized benchmarks -- typically do not predict a processor's performance on circuits outside the benchmark's necessarily very limited set of test circuits. In this paper, we introduce a general framework for building predictive models from benchmarking data using capability models. Capability models can be fit to many kinds of benchmarking data and used for a variety of predictive tasks. We demonstrate this flexibility with two case studies. In the first case study, we predict circuit (i) process fidelities and (ii) success probabilities by fitting error rates models to two kinds of volumetric benchmarking data. Error rates models are simple, yet versatile capability models which assign effective error rates to individual gates, or more general circuit components. In the second case study, we construct a capability model for predicting circuit success probabilities by applying transfer learning to ResNet50, a neural network trained for image classification. Our case studies use data from cloud-accessible quantum computers and simulations of noisy quantum computers.

Apple does not have a good track record in terms of letting customers repair their hardware. The last decade-plus has seen Apple's computers become essentially impossible for users to service or upgrade, and the iPhone has always been a locked box. Adventurous owners might follow guides from iFixit to try and do repairs themselves, but it's a dangerous proposition. Remember, it was just earlier this year, when we discovered that replacing the display on an iPhone 13 would disable Face ID (something Apple eventually made an about face on). So Apple's announcement earlier this week that it would start selling parts and tools directly to consumers and offer repair guides was a huge surprise, and a move immediately hailed as a victory for right-to-repair activists.

Last year, Staffordshire University broke into the UK university top 50 league table for the first time. Director of Digital Services at the university, Andrew Proctor, attributes this success to the digital transformation he has overseen and an IT strategy that has seen the organisation put AI front and centre. The university has formulated a digital vision that paves the way up to 2030 and Proctor claims that 18 months ago, Staffordshire was the first university to migrate to the cloud - an important strategic move that has fuelled digital innovation. This cloud strategy allowed the university to more effectively meet targets such as the increasing use of data to drive business, and boosting automation as a means of reducing the volume of repetitive tasks. "We have a lot of very, very talented academics," says Proctor. "It doesn't make sense for them to be buried down in painful work and really low-level work."

In a new ruling that will take effect on Sunday, the Librarian of Congress has carved out a series of exemptions that allow people to legally circumvent digital "locks" on devices they own, such as voice assistants, tablets, smartphones and vehicles, to repair them. Motherboard earlier reported on the ruling. Device manufactures currently use digital protection measures to safeguard their intellectual property. The digital locks are intended to prevent the theft of intellectual property and to keep consumers from compromising their electronics, thereby preserving the integrity and security of a device's operating system, Industry groups have argued. The exemptions permit customers to unlock their smartphones and get around restrictions built into other mainstream devices, including smart home assistants, said to Kyle Wiens, founder of iFixit.