Event quantification provides frequency information for runtime events and is widely used to build fast, secure, and reliable software and systems. It is usually achieved through instrumentation that introduces new instructions into programs but has significant runtime overhead. A key challenge in developing efficient instrumentation is that the instrumentation can barely benefit from modern compiler optimizations because the additional instructions introduce side effects that complicate the optimization process.

As AI agents act on behalf of users, designers increasingly combine explainability (XAI) and anthropomorphism to build trust. Yet, whether these cues create synergy or interference remains a critical, open question. Our online experiment (N=900) revealed a counterintuitive interference effect: anthropomorphism reduced trust in an explainable agent. A preregistered lab study with eye-tracking (N=57) reversed this finding: under controlled conditions, the combined design elicited the highest trust. Eye-tracking reveals the mechanism: XAI promotes deeper cognitive engagement (e.g., longer fixations), which primes users to allocate attention to social cues (e.g., avatars). Our findings show that trust depends on cognitive engagement moderating social cue processing, yielding a critical design insight: effectively pairing explanatory and anthropomorphic interfaces requires first securing the user’s cognitive engagement to avoid undermining trust.

Runtime feedback is at the heart of efficient greybox fuzzing, and the collection of runtime feedback is the most important infrastructure for greybox fuzzing. However, existing fuzzers have difficulty collecting runtime feedback for the memory, which is the most important and vulnerable component of a running program. The operating system does not support associative queries between arbitrary pointers and runtime objects. Therefore, existing works only capture aggregate statistics (e.g., memory usage) or random quantities (e.g., the random addresses stored in pointers) to provide low-precision memory-related feedback. This article presents Spinel , a greybox fuzzer equipped with a brand-new infrastructure for memory feedback collection. It introduces an almost zero-overhead runtime system for associating arbitrary pointers with the corresponding runtime objects and offers spatial distance information as memory-related fuzzing feedback. To avoid introducing accumulated overhead upon silent error detectors (e.g., sanitizers that are used to detect memory safety violations), we introduce the post-execution validation technique to remove the expensive runtime safety checks while maintaining the same error detection ability. Our experiments on 33 real-world programs show that Spinel detects 1.30×–2.33× unique bugs compared to state-of-the-art fuzzers. Furthermore, according to the restricted mean survival time, Spinel achieves 1.56×–8.21× speed up in triggering ground-truth bugs collected by the Magma benchmark.

Memory safety sanitizers, the sharp weapon for detecting invalid memory operations during execution, employ runtime metadata to model the memory and help find memory errors hidden in the programs. However, location-based methods, the most widely deployed memory sanitization methods thanks to their high compatibility, face the low protection density issue: the number of bytes safeguarded by one metadata is limited. As a result, numerous memory accesses require loading excessive metadata, leading to a high runtime overhead.

Static binary bug detection has been a prominent approach for ensuring the security of binaries used in our daily lives. However, the type information lost in binaries prevents the improvement opportunity for a static analyzer to utilize type information to prune away infeasible facts and increase analysis precision. To make binary bug detection more practical with higher precision, in this work, we propose the first hybrid-sensitive type inference, Manta, that combines data-flow analysis with different sensitivities to complement each other and infer precise types for many variables. The inferred types are then used to assist with bug detection by pruning infeasible indirect call targets and data dependencies. Our experiments indicate Manta outperforms prior work by inferring types with 78.7% precision and 97.2% recall. Based on the inferred types, we can prune away 63.9% more infeasible indirect-call targets compared to existing type analysis techniques and perform program slicing on binaries with 61.1% similarity to that on source code. Moreover, Manta has led to 86 new developer-confirmed vulnerabilities in many popular IoT firmware, with 64 CVE/PSV IDs assigned.

Graph neural networks (GNNs) have been used for optimizing resource allocation due to their potential in scalability and size generalizability. To facilitate their application to large- scale wireless systems, decentralized inference with GNNs has been investigated recently. Yet decentralized training of GNNs at wireless nodes, which can alleviate the computing load at central server and protect privacy of users, has never been studied. In this paper, we strive to train GNNs in mobile systems in a decentralized manner, by taking power control optimization for interference coordination as an example. We present a framework for decentralized training of GNNs at wireless nodes, and propose two algorithms to tackle the challenge of training GNNs over dynamic graph topology. Simulation results show that the power control policy learned by the GNN performs very close to decentralized numerical algorithms with lower signaling overhead for inference.

Medical scene graph contributes to cognitive tasks such as question answering. An automatic medical scene graph generator can annotate medical images with scene graphs conveniently. An end-to-end model is proposed, which contains an automatic medical scene graph generator based on medical images and surgery records. The novel model can extract symptom information of the images from surgery records via name entity recognition then convert it into scene graphs. The content and format of the scene graph are pre-defined according to the features of the images. A medical scene graph (MSG) dataset is also produced. The dataset contains 116 scene graphs where each graph includes 6 to 30 entities. Experiments show that our model performs well on making scene graphs of CT coronary angiography. Significantly, the scene graphs have the type, state, location, and index of arteries. To verify the practicability of the MSG dataset, We construct an intelligent model that can generate key diagnostic results. In addition, our medical scene graph generator can be applied to other medical areas to annotate different kinds of medical images intelligently. And the MSG dataset can directly be used in other cognitive visual tasks.

We investigate the use of a simple microstrip leaky wave antenna (MLWA) as a radar front end to achieve range-azimuth tracking of humans. The frequency-scanned beam of the MLWA and its frequency bandwidth during the beam dwell are exploited to achieve simultaneous bearing estimation and ranging within a single frequency sweep. As a result, 2-D range-azimuth images can be readily generated with a high refresh rate. The radar processing algorithm is developed and tested using point scatterer simulations. Stationary trihedral and human tracking measurements are carried out to demonstrate the concept. In addition, an L1-norm minimization algorithm is applied to improve the resulting resolution.

We present a simulation methodology for generating micro-Doppler radar signatures of humans moving behind walls. The method combines primitive-based modeling of humans with finite-difference time-domain (FDTD) simulation of walls. Realistic motions of humans are generated from computer animation data. The time-varying human radar cross section is simulated using the primitive-based prediction technique. The scattered returns of humans behind walls are then simulated by a hybrid of the human simulation model with the through-wall propagation data generated from FDTD. The resulting simulator is used to investigate the effects of walls of both homogeneous and inhomogeneous types on human micro-Dopplers. It is found that while through-wall propagation affects the magnitude response of the Doppler spectrogram in the form of attenuation and fading, it only introduces very minor distortions on the actual Doppler frequencies from the body parts. This is corroborated by measurement data collected using a Doppler radar, as well as by a point-scatterer analysis of refraction and multipath introduced by walls.

The feasibility of classifying different human activities based on micro-Doppler signatures is investigated. Measured data of 12 human subjects performing seven different activities are collected using a Doppler radar. The seven activities include running, walking, walking while holding a stick, crawling, boxing while moving forward, boxing while standing in place, and sitting still. Six features are extracted from the Doppler spectrogram. A support vector machine (SVM) is then trained using the measurement features to classify the activities. A multiclass classification is implemented using a decision-tree structure. Optimal parameters for the SVM are found through a fourfold cross-validation. The resulting classification accuracy is found to be more than 90%. The potentials of classifying human activities over extended time duration, through wall, and at oblique angles with respect to the radar are also investigated and discussed.

In this letter, a radar combining Doppler processing and spatial beamforming is presented for tracking humans through walls. Multiple targets are tracked by resolving the targets in the Doppler and bearing space. To overcome the high sidelobes associated with an array of limited size, the CLEAN and RELAX algorithms are implemented, and their performances are compared with standard beamforming. The radar is tested in indoor line-of-sight and through-wall scenarios for multiple loudspeakers and human subjects.

A new approach to adaptive downlink beamforming to combat fast Rayleigh fading is presented. In this approach, the antennas at the base transceiver station form transmit beam patterns according to the prediction of downlink channels. The channel prediction is a linear prediction based on the autoregressive model, which is downsampled to extend the memory span given fixed model order. For a wideband code-division multiple-access downlink, pre-RAKE transmission is employed to achieve the multipath diversity gain. In particular, we combine pseudoinverse directions of arrival beamforming with pre-RAKE transmission to alleviate self-interference. The beamforming weights are adjusted within a downlink frame to compensate the predicted fading. We give measures of the prediction and beamforming performance and evaluate the impact of prediction errors on the downlink. Ray tracing simulations in a three-dimensional urban physical model show that the predictive downlink beamforming outperforms the conventional beamforming over Rayleigh-fading channels.

Multiple-input multiple-output (MIMO) wireless communication systems employ multiple transmit and multiple receive antennas to obtain significant improvement in channel capacity. However, the capacity is limited by the correlation of subchannels in non-ideal scattering environments. In this paper, we investigate MIMO systems that use antennas with dissimilar radiation patterns to introduce decorrelation, hence increasing channel capacity. We develop a ray tracing model that takes into account both the propagation channel and the transmit and receive antenna patterns. Using a computational electromagnetic simulator, we show that: (1) MIMO systems that exploit antenna pattern diversity allow for improvement over dual-polarized antenna systems; and (2) the capacity increase of such MIMO systems depends on the characteristics of the scattering environment.

This paper investigates the benefit of mutual coupling compensation via a method of moments (MoM) approach in a uniform circular antenna array operating at 1.8 GHz. This mutual coupling compensation technique is applied to a direction of arrival (DOA) study of up to two cochannel mobile users. Field measurements and computer simulations are examined to explore the assumptions of the technique and verify its effect when using the Bartlett and MUSIC DOA algorithms. Computer simulations considering the application of the technique to down-link beamforming are also included. Experimental results show that the mutual coupling compensation technique improves up-link DOA algorithm performance primarily by reducing unwanted sidelobe levels. This reduction in sidelobe levels aids in down-link beamforming weight design. Specifically, simulation results show that use of the compensation technique allows DOA-based down-link beamforming algorithms to perform similarly to spatial signature-based algorithms. All field measurements were made using the smart antenna testbed at the University of Texas at Austin.

A frequency-aspect extrapolation algorithm is proposed to accelerate ISAR image simulation using fast multipole solvers. A two-dimensional (2D) multiple-arrival model based on high-frequency physics is proposed to parameterize the induced currents on the target. A 2D estimation of parameters via rotation invariance technique (ESPRIT) algorithm is developed to estimate the model parameters from a limited number of computed data samples in frequency and aspect. The model is then extrapolated to other frequencies and aspects to arrive at broadband, wide-angle radar cross section (RCS) data for inverse synthetic aperture radar (ISAR) image construction. This algorithm is tested using a canonical cylinder-plate structure to evaluate its performance. The ISAR image of the benchmark VFY-218 airplane at UHF band is then predicted using the fast multipole solver FISC and the 2D extrapolation algorithm. The resulting image compares favorably with that obtained from chamber measurement data.

Personal communication systems within office buildings are designed on the premise of slow (>

A series of fast algorithms for simulating the inverse synthetic aperture radar (ISAR) imagery of complex targets using the shooting and bouncing ray technique is presented. The image-domain ray spread function is first derived to directly account for the contribution of each ray in the image plane. The Sullivan (1990) scheme is next applied to achieve fast ray summation. Using the fast algorithms, the ISAR image simulation time can be reduced to about one hour on a Silicon Graphics Indigo workstation for a realistic airplane at X-band. This is more than two orders of magnitude less than that of the standard frequency-aspect image formation process. The simulation methodology and the simulated imagery of a helicopter with rotating blades are also presented.>