Deep learning models for natural language processing rely heavily on high-quality labeled datasets. However, existing labeling approaches often struggle to balance label quality with labeling cost. To address this challenge, we propose DALL, a text labeling framework that integrates data programming, active learning, and large language models. DALL introduces a structured specification that allows users and large language models to define labeling functions via configuration, rather than code. Active learning identifies informative instances for review, and the large language model analyzes these instances to help users correct labels and to refine or suggest labeling functions. We implement DALL as an interactive labeling system for text labeling tasks. Comparative, ablation, and usability studies demonstrate DALL’s efficiency, the effectiveness of its modules, and its usability.

Vision-based physiological monitoring is an emerging technology that enables a more convenient access of cardiovascular health status in many medical industrial applications. This article aims to achieve efficient and accurate identification of pulse waveforms by proposing a weighted temporally consistent 3-D (WTC3D) convolution, in which a spatial weight template is incorporated between the spatial and temporal kernels as a constraint for the temporal kernel. WTC3D employs a temporal kernel to keep temporal consistency and a spatial weight template to impose spatial diversity during the remote photoplethysmography (rPPG) feature learning. A WTC3D-based network with a hybrid loss function is designed for pulse prediction. Experiments on three datasets demonstrate the effectiveness of the proposed approach. By considering the temporal propagation characteristics of the pulse signal in the video, WTC3D convolution not only enables efficient pulse feature learning, but also advances the deployment of rPPG networks on source-limited Internet of medical things devices.

Hybrid fuzzing that combines fuzzing and concolic execution has become an advanced technique for software vulnerability detection. Based on the observation that fuzzing and concolic execution are complementary in nature, state-of-the-art hybrid fuzzing systems deploy “optimal concolic testing” and “demand launch” strategies. Although these ideas sound intriguing, we point out several fundamental limitations in them, due to unrealistic or oversimplified assumptions. Further, we propose a novel “discriminative dispatch” strategy and design a probabilistic hybrid fuzzing system to better utilize the capability of concolic execution. Specifically, we design a Monte Carlo-based probabilistic path prioritization model to quantify each path’s difficulty, and then prioritize them for concolic execution. Our model assigns the most difficult paths to concolic execution. We implement a prototype named${\sf DigFuzz}$and evaluate our system with two representative datasets and real-world programs. Results show that the concolic execution in${\sf DigFuzz}$outperforms than those in state-of-the-art hybrid fuzzing systems in every major aspect. In particular, the concolic execution in${\sf DigFuzz}$contributes to discovering more vulnerabilities (12 versus 5) and producing more code coverage (18.9 versus 3.8 percent) on the CQE dataset than the concolic execution in Driller.

Wireless covert channel is an emerging covert communication technique which conceals the very existence of secret information in wireless signal including GSM, CDMA, and LTE. The secret message bits are always modulated into artificial noise superposed with cover signal, which is then demodulated with the shared codebook at the receiver. In this paper, we first extend the traditional KS test and regularity test in covert timing channel detection into wireless covert channel, which can be used to reveal the very existence of secret data in wireless covert channel from the aspect of multiorder statistics. In order to improve the undetectability, a wireless covert channel for OFDM-based communication system based on constellation shaping modulation is proposed, which generates additional constellation points around the standard points in normal constellations. The carrier signal is then modulated with the dirty constellation and the secret message bits are represented by the selection mode of the additional constellation points; shaping modulation is employed to keep the distribution of constellation errors unchanged. Experimental results show that the proposed wireless covert channel scheme can resist various statistical detections. The communication reliability under typical interference is also proved.