High-quality training data is critical to the performance of large language models (LLMs). Recent work has explored using LLMs to rate and select data based on a small set of human-designed criteria (rules), but these approaches often rely heavily on heuristics, lack principled metrics for rule evaluation, and generalize poorly to new tasks. We propose a novel rule-based data selection framework that introduces a metric based on the orthogonality of rule score vectors to evaluate and select complementary rules. Our automated pipeline first uses LLMs to generate diverse rules covering multiple aspects of data quality, then rates samples according to these rules and applies the determinantal point process (DPP) to select the most independent rules. These rules are then used to score the full dataset, and high-scoring samples are selected for downstream tasks such as LLM fine-tuning. We evaluate our framework in two experiment setups: (1) alignment with ground-truth ratings and (2) performance of LLMs fine-tuned on the selected data. Experiments across IMDB, Medical, Math, and Code domains demonstrate that our DPP-based rule selection consistently improves both rating accuracy and downstream model performance over strong baselines.

Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be computationally expensive in resource-constrained environments. We recently proposed Differentiable Logic Networks (DLNs) to address these issues for tabular classification based on relaxing discrete logic into a differentiable form, thereby enabling gradient-based learning of networks built from binary logic operations. DLNs offer interpretable reasoning and substantially lower inference cost. We extend the DLN framework to supervised tabular regression. We first redesign the final output layer (the SumLayer) to support continuous targets. More critically, we find the original two-phase training procedure used for classification is suboptimal for regression, and thus develop a unified, single-stage optimization procedure. We also demonstrate that temperature annealing of the network’s differentiable relaxations is decisive for achieving stable convergence and high accuracy. We evaluate the resulting model on 15 public regression benchmarks, comparing it with modern neural networks and classical regression baselines. Regression DLNs match or exceed baseline accuracy while preserving interpretability and fast inference. Our results show that DLNs are a viable, cost-effective alternative for regression tasks, especially where model transparency and computational efficiency are important.

The recent advancement of the automatic speech recognition (ASR) contributes to the voice user interface (VUI), which is broadly embedded into mobile apps. The VUI implemented on modern mobile operating systems like Android naturally involves multiple threads, and brings new race issues and challenges in defining and identifying them. Specifically, when the GUI and VUI (GV) actions both access to the same resource simultanously, the data race named GV-race may occur. GV-race can lead to wrong behavior and even crashes. However, to the best of our knowledge, this problem has not been adequately studied. In this paper, we present the first study of GV-race on Android apps. However, the involvement of the VUI complicates the concurrency model, affects the temporal relationship and brings state space explosion in global analysis. To tackle these challenges, we firstly defineprimitivesand theirhappen-beforerules to abstract GV interaction patterns. Using these primitives, we are able to characterize and formally define GV-race. We then developRoma(GV-race detectoronmobileapps) to detect both app-level and system-level GV-race automatically. Through static program analysis, Roma extracts GV related call graphs for each pair of conflicting GV actions to reduce the state space, and generates a universal GV interaction graph using our pre-defined primitives. It encodes happen-before constraints to formally specify thefreeness of GV-race, so that the detection of GV-race can be reduced to constraint solving with SMT solvers. We apply Roma to analyze 266 apps. Roma finds 52 apps with app-level GV-race and 56 apps with system-level GV-race. We confirm that 101 apps are true positives.

Given the need to evaluate the robustness of face recognition (FR) models, many efforts have focused on adversarial patch attacks that mislead FR models by introducing localized perturbations. Impersonation attacks are a significant threat because adversarial perturbations allow attackers to disguise themselves as legitimate users. This can lead to severe consequences, including data breaches, system damage, and misuse of resources. However, research on such attacks in FR remains limited. Existing adversarial patch generation methods exhibit limited efficacy in impersonation attacks due to (1) the need for high attacker capabilities, (2) low attack success rates, and (3) excessive query requirements. To address these challenges, we propose a novel method SAP-DIFF that leverages diffusion models to generate adversarial patches via semantic perturbations in the latent space rather than direct pixel manipulation. We introduce an attention disruption mechanism to generate features unrelated to the original face, facilitating the creation of adversarial samples and a directional loss function to guide perturbations toward the target identity's feature space, thereby enhancing attack effectiveness and efficiency. Extensive experiments on popular FR models and datasets demonstrate that our method outperforms state-of-the-art approaches, achieving an average attack success rate improvement of 45.66% (all exceeding 40%), and a reduction in the number of queries by about 40% compared to the SOTA approach.

Backdoor attacks have become a significant threat to deep neural networks (DNNs), whereby poisoned models perform well on benign samples but produce incorrect outputs when given specific inputs with a trigger. These attacks are usually implemented through data poisoning by injecting poisoned samples (samples patched with a trigger and mislabelled to the target label) into the dataset, and the models trained with that dataset will be infected with the backdoor. However, most current backdoor attacks lack stealthiness and robustness because of the fixed trigger patterns and mislabelling, which humans or some backdoor defense approach can easily detect. To address this issue, we propose a frequency-domain-based backdoor attack method that implements backdoor implantation without mislabeling the poisoned samples or accessing the training process. We evaluated our approach on four benchmark datasets and two popular scenarios: no-label self-supervised and clean-label supervised learning. The experimental results demonstrate that our approach achieved a high attack success rate (above 90%) on all tasks without significant performance degradation on main tasks and robust against mainstream defense approaches.

In the past decade, the security of cellular networks has been increasingly under scrutiny, leading to the discovery of numerous vulnerabilities that expose the network and its users to a wide range of security risks, from denial of service to information leak. However, most of these findings have been made through ad-hoc manual analysis, which is inadequate for fundamentally enhancing the security assurance of a system as complex as the cellular network. An important observation is that the massive amount of technical documentation of cellular network can provide key insights into the protection it puts in place and help identify potential security flaws. Particularly, we found that such documentation often contains hazard indicators (HIs) – the statement that describes a risky operation (e.g., abort an ongoing procedure) when a certain event happens at a state, which can guide a test on the system to find out whether the operation can indeed be triggered by an unauthorized party to cause harm to the cellular core or legitimate users’ equipment. Based upon this observation, we present in this paper a new framework that makes the first step toward intelligent and systematic security analysis of cellular networks. Our approach, called Atomic, utilizes natural-language processing and machine learning techniques to scan a large amount of LTE documentation for HIs. The HIs discovered are further parsed and analyzed to recover state and event information for generating test cases. These test cases are further utilized to automatically construct tests in an LTE simulation environment, which runs the tests to detect the vulnerabilities in the LTE that allow the risky operations to happen without proper protection. In our research, we implemented Atomic and ran it on the LTE NAS specification, including 549 pages with 13,598 sentences and 283,850 words. In less than 5 hours, our prototype reported 42 vulnerabilities from 192 HIs discovered, including 10 never reported before, under two threat models. All these vulnerabilities have been confirmed through end-to-end attacks, which lead to unauthorized disruption of the LTE service a legitimate user’s equipment receives. We reported our findings to authorized parties and received their confirmation that these vulnerabilities indeed exist in major commercial carriers and $2,000 USD reward from Google.