Hardware-based tracing, being efficient, can be a good alternative to the computationally-expensive software-based instrumentation in binary-only greybox fuzzing. However, it only records all branches within a specified address range, lacking the flexibility to re-filter them. To overcome these limitations, this paper introduces Tatoo, a hardware platform that employs tagged architectures and hardware tracing to enable users to perform instruction-level tagging, which can significantly reduce the volume of traced data and improve fuzzing efficiency. Tatoo also supports recording the dataflow information for smart mutations. Implemented on a real hardware FPGA platform, Tatoo demonstrates a mere 8.7% performance overhead.

This paper describes SRCB-LUL team’s person identification system submitted to track 1 of the ICASSP 2023 Person Identification and Relapse Detection from Continuous Recordings of Biosignals (e-Prevention) challenge, which aims to identify the wearer of the smartwatch. At first, abnormal values of the multi-channel physiological signals are replaced, and the valid data are divided into multiple fine-grained fragments. Then, these fragments are fed into a 1D-CNN, and the user ID of a day is predicted through the voting of fragment results. Based on this framework, we train multiple base networks with different input lengths or different signal channels and aggregate these base networks for ensemble learning. The accuracy of our system is 96.16/95.00% on the validation/test set.

Dynamic Information Flow Tracking (DIFT) is a fundamental computer security technique that tracks the data flow of interest at runtime, overcoming the limitations of discovering data dependencies statically at compilation time. However, software-based DIFT tools often suffer from unbearably high runtime overhead due to dynamic binary instrumentation or virtual machine, limiting the usefulness of DIFT. Even though hardware-assisted DIFT frameworks cut down the performance overhead effectively, it is still unacceptable for applications under rigorous time constraints.

Early depression detection research employs machine learning models trained on crowd-sourcing data. The training data easily suffer from label noise due to weak self-perception of people and uncontrollability of collection process. The noise issue is seldom discussed in the previous depression detection work. In this work, we firstly introduce the influence-based relabeling method in the depression detection task to revise noise labels, and further move one step forward to propose a threshold ratio function to control the relabeling sample size. The relabeling sample size is usually ignored in the previous influence function, so that the relabeling is sometimes overwhelming, leading to great change on the distribution of the training data, and model performance decline. Our proposed method aims at avoiding giant change on the training data. To achieve this, we design an adjustable ratio threshold for the samples to be relabeled. The ratio is adjusted according to the trained model performance. If the model has good performance on the validation set, the relabeling ratio tends mild, otherwise, the relabeling can be aggressive. In the experiments, we recruited 205 participants and collect the usage data from smartphones and wearable bands, including participants’ response to the questionnaire Depression Anxiety Stress Scale-21. We discuss several main stream denoising methods and compare four most recent methods in the depression detection task. The proposed model achieves a best testing F1 score of 86.3%.

Excessive stress will have a negative impact on people’s physical and mental health, especially for some special occupations. Because stressful stimuli can trigger a variety of physiological responses, analyzing physiological signals collected by wearable devices has become an important way to evaluate the stress state in recent years. However, the number of available subjects of a target group may be small, and collecting a large amount of data when the target group changes is costly and time-consuming. To solve this problem, we propose a stress detection framework for a small target group which uses adversarial transfer learning method to learn shared knowledge about stress between different groups. In order to verify the performance of the framework, we establish a dataset consisting of 264 ordinary college students and 32 police school students, aiming to evaluate the acute stress state of police school students under video stimuli for psychological training in the future. Comprehensive experiments show that our algorithm has achieved a significant improvement in the target group compared with the baseline methods.

Hand gesture recognition plays a significant role in human-computer interaction for understanding various human gestures and their intent. However, most prior works can only recognize gestures of limited labeled classes and fail to adapt to new categories. The task of Generalized Zero-Shot Learning (GZSL) for hand gesture recognition aims to address the above issue by leveraging semantic representations and detecting both seen and unseen class samples. In this paper, we propose an end-to-end prototype-based GZSL framework for hand gesture recognition which consists of two branches. The first branch is a prototype-based detector that learns gesture representations and determines whether an input sample belongs to a seen or unseen category. The second branch is a zero-shot label predictor which takes the features of unseen classes as input and outputs predictions through a learned mapping mechanism between the feature and the semantic space. We further establish a hand gesture dataset that specifically targets this GZSL task, and comprehensive experiments on this dataset demonstrate the effectiveness of our proposed approach on recognizing both seen and unseen gestures.