Quantum All-Subkeys-Recovery Attacks on 6-round Feistel-2* Structure Based on Multi-Equations Quantum Claw Finding

Exploiting quantum mechanisms, quantum attacks have the potential ability to break the cipher structure. Recently, Ito et al. proposed a quantum attack on Feistel-2* structure (Ito et al.'s attack) based onthe Q2 model. However, it is not realistic since the quantum oracle needs to be accessed by the adversary, and the data complexityis high. To solve this problem, a quantum all-subkeys-recovery (ASR) attack based on multi-equations quantum claw-finding is proposed, which takes a more realistic model, the Q1 model, as the scenario, and only requires 3 plain-ciphertext pairs to quickly crack the 6-round Feistel-2* structure. First, we proposed a multi-equations quantum claw-finding algorithm to solve the claw problem of finding multiple equations. In addition, Grover's algorithm is used to speedup the rest subkeys recovery. Compared with Ito et al.'s attack, the data complexity of our attack is reduced from O(2^n) to O(1), while the time complexity and memory complexity are also significantly reduced.Comment: 18 pages, 4 figure

Reduction in the Number of Fault Injections for Blind Fault Attack on SPN Block Ciphers

In 2014, a new fault analysis called blind fault attack (BFA) was proposed, in which attackers can only obtain the number of different faulty outputs without knowing the public data. The original BFA requires 480,000 fault injections to recover a 128-bit AES key. This work attempts to reduce the number of fault injections under the same attack assumptions. We analyze BFA from an information theoretical perspective and introduce a new probability-based distinguisher. Three approaches are proposed for different attack scenarios. The best one realized a 66.8% reduction of the number of fault injections on AES

A robust multiple heartbeats classification with weight-based loss based on convolutional neural network and bidirectional long short-term memory

Background: Analysis of electrocardiogram (ECG) provides a straightforward and non-invasive approach for cardiologists to diagnose and classify the nature and severity of variant cardiac diseases including cardiac arrhythmia. However, the interpretation and analysis of ECG are highly working-load demanding, and the subjective may lead to false diagnoses and heartbeats classification. In recent years, many deep learning works showed an excellent role in accurate heartbeats classification. However, the imbalance of heartbeat classes is universal in most of the available ECG databases since abnormal heartbeats are always relatively rare in real life scenarios. In addition, many existing approaches achieved prominent results by removing noise and extracting features in data preprocessing, which relies heavily on powerful computers. It is a pressing need to develop efficient and automatic light weighted algorithms for accurate heartbeats classification that can be used in portable ECG sensors.Objective: This study aims at developing a robust and efficient deep learning method, which can be embedded into wearable or portable ECG monitors for classifying heartbeats.Methods: We proposed a novel and light weighted deep learning architecture with weight-based loss based on a convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) that can automatically identify five types of ECG heartbeats according to the AAMI EC57 standard. It was also true that the raw ECG signals were simply segmented without noise removal and other feature extraction processing. Moreover, to tackle the challenge of classification bias due to imbalanced ECG datasets for different types of arrhythmias, we introduced a weight-based loss function to reduce the influence of over-weighted categories in the ECG dataset. For avoiding the influence of the division of validation dataset, k-fold method was adopted to improve the reliability of the model.Results: The proposed algorithm is trained and tested on MIT-BIH Arrhythmia Database, and achieves an average of 99.33% accuracy, 93.67% sensitivity, 99.18% specificity, 89.85% positive prediction, and 91.65% F1 score

Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors

This study delves into the intricacies of emotional contagion and its impact on performance within dyadic interactions. Specifically, it focuses on the context of stereotype-based stress (SBS) during collaborative problem-solving tasks among female pairs. Through an exploration of emotional contagion, this study seeks to unveil its underlying mechanisms and effects. Leveraging EEG-based hyperscanning technology, we introduced an innovative approach known as the functional Graph Contrastive Learning (fGCL), which extracts subject-invariant representations of neural activity patterns from feedback trials. These representations are further subjected to analysis using the Dynamic Graph Classification (DGC) model, aimed at dissecting the process of emotional contagion along three independent temporal stages. The results underscore the substantial role of emotional contagion in shaping the trajectories of participants' performance during collaborative tasks in the presence of SBS conditions. Overall, our research contributes invaluable insights into the neural underpinnings of emotional contagion, thereby enriching our comprehension of the complexities underlying social interactions and emotional dynamics.Comment: 14 pages, 4 figures, 5 table

Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition

Cross-Database Micro-Expression Recognition (CDMER) aims to develop the Micro-Expression Recognition (MER) methods with strong domain adaptability, i.e., the ability to recognize the Micro-Expressions (MEs) of different subjects captured by different imaging devices in different scenes. The development of CDMER is faced with two key problems: 1) the severe feature distribution gap between the source and target databases; 2) the feature representation bottleneck of ME such local and subtle facial expressions. To solve these problems, this paper proposes a novel Transfer Group Sparse Regression method, namely TGSR, which aims to 1) optimize the measurement and better alleviate the difference between the source and target databases, and 2) highlight the valid facial regions to enhance extracted features, by the operation of selecting the group features from the raw face feature, where each region is associated with a group of raw face feature, i.e., the salient facial region selection. Compared with previous transfer group sparse methods, our proposed TGSR has the ability to select the salient facial regions, which is effective in alleviating the aforementioned problems for better performance and reducing the computational cost at the same time. We use two public ME databases, i.e., CASME II and SMIC, to evaluate our proposed TGSR method. Experimental results show that our proposed TGSR learns the discriminative and explicable regions, and outperforms most state-of-the-art subspace-learning-based domain-adaptive methods for CDMER

Transportation Mode Detection Based on Permutation Entropy and Extreme Learning Machine

With the increasing prevalence of GPS devices and mobile phones, transportation mode detection based on GPS data has been a hot topic in GPS trajectory data analysis. Transportation modes such as walking, driving, bus, and taxi denote an important characteristic of the mobile user. Longitude, latitude, speed, acceleration, and direction are usually used as features in transportation mode detection. In this paper, first, we explore the possibility of using Permutation Entropy (PE) of speed, a measure of complexity and uncertainty of GPS trajectory segment, as a feature for transportation mode detection. Second, we employ Extreme Learning Machine (ELM) to distinguish GPS trajectory segments of different transportation. Finally, to evaluate the performance of the proposed method, we make experiments on GeoLife dataset. Experiments results show that we can get more than 50% accuracy when only using PE as a feature to characterize trajectory sequence. PE can indeed be effectively used to detect transportation mode from GPS trajectory. The proposed method has much better accuracy and faster running time than the methods based on the other features and SVM classifier

Retrieval Practice Facilitates Judgments of Learning Through Multiple Mechanisms: Simultaneous and Independent Contribution of Retrieval Confidence and Retrieval Fluency

Prior studies have shown that predictions of subsequent performance (i.e., Judgments of Learning, JoLs) following tests are more accurate than those following re-study and have suggested that retrieval practice allows people to base their predictions on the current retrieval outcomes so that they assign a higher likelihood of remembering to answers with high confidence. We speculated that other mechanisms, such as retrieval fluency during tests, might also be important for JoLs and that they both offer diagnostic information helping learners to make more accurate JoLs. In the present study, we asked participants to study word-pairs and undergo either a test or re-study trial. Two testing formats (cued-recall and multiple-choice) were administrated for the test condition in two experiments. After the initial test or re-study of the word-pair, participants rated their confidence in the current retrieval accuracy (test) or confidence in acquisition (re-study), followed by a JoL rating where participants predicted their performance in the final test one day later. The results of both experiments showed that the correlation between JoL ratings and the final accuracy was higher for test trials compared with re-study trials. Moreover, using mediation analyses, we found that this high correspondence was only partially mediated by participants’ confidence in initial tests. Both retrieval reaction time and retrieval confidence simultaneously mediated the correspondence between JoLs and the final accuracy, suggesting that participants were able to correctly base their JoLs on multiple sources of information that are made available through retrieval practice