Reviewerly/exHarmony huggingface dataset:
Information-Combining Differential Fault Attacks on DEFAULT

Differential fault analysis (DFA) is a very powerful attack vector for implementations of symmetric cryptography. Most countermeasures are applied at the implementation level. At ASIACRYPT 2021, Baksi et al. proposed a design strategy that aims to provide inherent cipher level resistance against DFA by using S-boxes with linear structures. They argue that in their instantiation, the block cipher DEFAULT, a DFA adversary can learn at most 64 of the 128 key bits, so the remaining brute-force complexity of $$2^{64}$$ is impractical. In this paper, we show that a DFA adversary can combine information across rounds to recover the full key, invalidating their security claim. In particular, we observe that such ciphers exhibit large classes of equivalent keys that can be represented efficiently in normalized form using linear equations. We exploit this in combination with the specifics of DEFAULT’s strong key schedule to recover the key using less than 100 faulty computation and negligible time complexity. Moreover, we show that even an idealized version of DEFAULT with independent round keys is vulnerable to our information-combining attacks based on normalized keys.
Sycon: a new milestone in designing ASCON-like permutations

ASCON is one of the elegant designs of authenticated encryption with associated data (AEAD) that was selected as the first choice for lightweight applications in the CAESAR competition, which also has been submitted to NIST lightweight cryptography standardization. ASCON has been in the literature for a while; however, there has been no successful AEAD which is secure and at the same time lighter than ASCON. In this article, we have overcome the challenge of constructing a permutation that is lighter than the ASCON permutation while ensuring a similar performance, and based on which we achieve a more lightweight AEAD which we call Sycon. Extensive security analysis of Sycon confirms that it provides the same level of security as that of ASCON. Our hardware implementation result shows that the Sycon permutation has 5.35% reduced area, compared to the ASCON permutation. This leads to a remarkable area reduction for Sycon AEAD which is about 14.95% as compared to ASCON AEAD. We regard Sycon as a new milestone as it is the lightest among all the AEADs belonging to the ASCON family.
An integrated human computer interaction scheme for object detection using deep learning

Human-computer interaction (HCI) and computer vision (CV) provide interesting communication features between machines and humans in different real-time applications. Visualization helps to improve the accuracy of detecting target communication objects for better interaction. This paper introduces an integrated detection-based interaction scheme (IDIS) for improving the accuracy and reliability of HCI systems. The input is fetched from the ranged object, and the interaction session is initiated after the classification and detection of the object. The process of robust, long-term interaction with the detected object is achieved through pre-classification. In this detection process, deep learning is used to identify the object and perform its intended interaction requirements. The recurrent process is used to identify the variations in interaction patterns and time. The allocation of interaction sessions is streamlined to improve the interaction span and detection accuracy. The proposed scheme's performance is verified using dataset sources for the following metrics: accuracy, delay, error, and interaction span.
Multi‐scale feature learning and temporal probing strategy for one‐stage temporal action localization

The aim of temporal action localization (TAL) is to determine the start and end frames of an action in a video. In recent years, TAL has attracted considerable attention because of its increasing applications in video understanding and retrieval. However, precisely estimating the duration of an action in the temporal dimension is still a challenging problem. In this paper, we propose an effective one-stage TAL method based on a self-defined motion data structure, called a dense joint motion matrix (DJMM), and a novel temporal detection strategy. Our method provides three main contributions. First, compared with mainstream motion images, DJMMs can preserve more pre-processed motion features and provides more precise detail representations. Furthermore, DJMMs perfectly solve the temporal information loss problem caused by motion trajectory overlaps within a certain time period. Second, a spatial pyramid pooling (SPP) layer, which is widely used in the object detection and tracking fields, is innovatively incorporated into the proposed method for multi-scale feature learning. Moreover, the SPP layer enables the backbone convolutional neural network (CNN) to receive DJMMs of any size in the temporal dimension. Third, a large-scale-first temporal detection strategy inspired by a well-developed Chinese text segmentation algorithm is proposed to address long-duration videos. Our method is evaluated on two benchmark data sets and one self-collected data set: Florence-3D, UTKinect-Action3D and HanYue-3D. The experimental results show that our method achieves competitive action recognition accuracy and high TAL precision, and its time efficiency and few-shot learning capabilities enable it to be utilized for real-time surveillance.
The Inverse G‐Wishart distribution and variational message passing

Summary Message passing on a factor graph is a powerful paradigm for the coding of approximate inference algorithms for arbitrarily large graphical models. The notion of a factor graph fragment allows for compartmentalisation of algebra and computer code. We show that the Inverse G‐Wishart family of distributions enables fundamental variational message passing factor graph fragments to be expressed elegantly and succinctly. Such fragments arise in models for which approximate inference concerning covariance matrix or variance parameters is made, and are ubiquitous in contemporary statistics and machine learning.
Training Robust Deep Neural Networks on Noisy Labels Using Adaptive Sample Selection With Disagreement

Learning with noisy labels is one of the most practical but challenging tasks in deep learning. One promising way to treat noisy labels is to use the small-loss trick based on the memorization effect, that is, clean and noisy samples are identified by observing the network’s loss during training. Co-teaching+ is a state-of-the-art method that simultaneously trains two networks with small-loss selection using the “update by disagreement” strategy; however, it suffers from the problem that the selected samples tend to become noisy as the number of iterations increases. This phenomenon means that clean small-loss samples will be biased toward agreement data, which is the set of samples for which the two networks have the same prediction. This paper proposes an adaptive sample selection method to train deep neural networks robustly and prevent noise contamination in the disagreement strategy. Specifically, the proposed method calculates the threshold of the small-loss criterion by considering the loss distribution of the whole batch at each iteration. Then, the network is backpropagated by extracting samples below this threshold from the disagreement data. Combining the disagreement and agreement data of the two networks can suppress the degradation of the true-label rate of training data in a mini batch. Experiments were conducted using five commonly used benchmarks, MNIST, CIFAR-10, CIFAR-100, NEWS, and T-ImageNet to verify the robustness of the proposed method to noisy labels. The results show the proposed method improves generalization performance in an image classification task with simulated noise rates of up to 50%.
A Stepwise Matching Method for Multi-modal Image based on Cascaded Network

Template matching of multi-modal image has been a challenge to image matching, and it is difficult to balance the speed and the accuracy, especially for images with large sizes. Based on this, we propose a stepwise image matching method to achieve a precise location from the coarse-to-fine image matching by utilizing cascaded networks. In the proposed method, a coarse-grained matching network is firstly constructed to locate a rough matching position based on cross-correlating features of optical and SAR images. Specially, to enhance the credible matching position, a suppression network is designed to evaluate for the obtained cross-correlation feature and added into the coarse-grained network as a feedback. Secondly, a fine-grained matching network is constructed based on the obtained rough matching result to gain a more precise matching. In this part, ternary groups are utilized to construct the training samples. Interestingly, we apply the region with a few pixels offset as the negative class, which effectively distinguishes similar neighbourhoods of the rough matching position. Moreover, a modified Siamese network is used to extract features of SAR and optical images, respectively. Finally, experimental results illustrate that the proposed method obtains more precise matching compared with the state-of-the-art methods.
Hierarchical Modeling for Task Recognition and Action Segmentation in Weakly-Labeled Instructional Videos

This paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> focuses on task recognition and action segmentation in weakly-labeled instructional videos, where only the ordered sequence of video-level actions is available during training. We propose a two-stream framework, which exploits semantic and temporal hierarchies to recognize top-level tasks in instructional videos. Further, we present a novel top-down weakly-supervised action segmentation approach, where the predicted task is used to constrain the inference of fine-grained action sequences. Experimental results on the popular Breakfast and Cooking 2 datasets show that our two-stream hierarchical task modeling significantly outperforms existing methods in top-level task recognition for all datasets and metrics. Additionally, using our task recognition framework in the proposed top-down action segmentation approach consistently improves the state of the art, while also reducing segmentation inference time by 80-90 percent.
A Case Study in Computer-Assisted Meta-reasoning

We discuss human and mechanized reasoning with regards to the use of proof assistants, in particular Isabelle/HOL. We use the development of novel NAND- and NOR-based micro provers as a case study. Current, widely available automated reasoning technology is suitable for assisting humans with certain types of reasoning, like finding proofs for well-defined theorems. Other types of reasoning, like the discovery of new theorems, are notoriously difficult for mechanized reasoning. Our case study indicates that proof assistants are well suited as development tools for assuredly correct programs in languages like Haskell.
Exploiting Script Similarities to Compensate for the Large Amount of Data in Training Tesseract LSTM: Towards Kurdish OCR

Applications based on Long-Short-Term Memory (LSTM) require large amounts of data for their training. Tesseract LSTM is a popular Optical Character Recognition (OCR) engine that has been trained and used in various languages. However, its training becomes obstructed when the target language is not resourceful. This research suggests a remedy for the problem of scant data in training Tesseract LSTM for a new language by exploiting a training dataset for a language with a similar script. The target of the experiment is Kurdish. It is a multi-dialect language and is considered less-resourced. We choose Sorani, one of the Kurdish dialects, that is mostly written in Persian-Arabic script. We train Tesseract using an Arabic dataset, and then we use a considerably small amount of texts in Persian-Arabic to train the engine to recognize Sorani texts. Our dataset is based on a series of court case documents in the Kurdistan Region of Iraq. We also fine-tune the engine using 10 Unikurd fonts. We use Lstmeval and Ocreval to evaluate the outputs. The result indicates the achievement of 95.45% accuracy. We also test the engine using texts outside the context of court cases. The accuracy of the system remains close to what was found earlier indicating that the script similarity could be used to overcome the lack of large-scale data.
Spatial likelihood voting with self-knowledge distillation for weakly supervised object detection

Weakly supervised object detection (WSOD), which is an effective way to train an object detection model using only image-level annotations, has attracted considerable attention from researchers. However, most of the existing methods, which are based on multiple instance learning (MIL), tend to localize instances to the discriminative parts of salient objects instead of the entire content of all objects. In this paper, we propose a WSOD framework called the Spatial Likelihood Voting with Self-knowledge Distillation Network (SLV-SD Net). In this framework, we introduce a spatial likelihood voting (SLV) module to converge region proposal localization without bounding box annotations. Specifically, in every iteration during training, all the region proposals in a given image act as voters voting for the likelihood of each category in the spatial dimensions. After dilating the alignment on the area with large likelihood values, the voting results are regularized as bounding boxes, which are then used for the final classification and localization. Based on SLV, we further propose a self-knowledge distillation (SD) module to refine the feature representations of the given image. The likelihood maps generated by the SLV module are used to supervise the feature learning of the backbone network, encouraging the network to attend to wider and more diverse areas of the image. Extensive experiments on the PASCAL VOC 2007/2012 and MS-COCO datasets demonstrate the excellent performance of SLV-SD Net. In addition, SLV-SD Net produces new state-of-the-art results on these benchmarks.
Liver tumor segmentation using a new asymmetrical dilated convolutional semantic segmentation network in <scp>CT</scp> images

Abstract Liver cancer has become one of the commonly occurring cancers in men and women. The segmented computed tomography (CT) image of the tumor is the major source for a doctor to diagnose liver diseases. This article proposes a fully automatic system based on an asymmetric dilated convolutional encoder–decoder neural (ADCEDN) network for the problem of accurately extracting the liver and tumor from abdominal CT slices. Specifically, two ADCEDN networks are used in a cascaded form to perform both liver and liver abnormality segmentation. We enrich the encoder module by incorporating the multiscale contextual information through dilated convolutions that enhance the capacity and efficiency of the network. Hybrid dilated convolution across depths perform depth‐wise separable convolution by providing a less steep increase in dilation rate and thus denser sampling that can reduce the computation cost and several metrics while maintaining better performance. Also, the encoder–decoder network is made asymmetric, thereby making the network less complex and more generalizable. The suggested method produced an average dice similarity coefficient score of 96.54% and 76.16% for liver segmentation and liver abnormality segmentation on the 3D Image Reconstruction for Comparison of Algorithm Database data set and 96.42% and 75.7% on the Liver Tumor Segmentation data set. The test outcomes show that the proposed ADCEDN attains state‐of‐the‐art performance in both segmentation of liver and tumors.
Enable data privacy, dynamics, and batch in public auditing scheme for cloud storage system

With the popularity of cloud computing, cloud storage technology has also been widely used. Among them, data integrity verification is a hot research topic. At present, the realization of public auditing has become the development trend of integrity verification. Most existing public auditing schemes rarely consider some indispensable functions at the same time. Thus, in this paper, we propose a comprehensive public auditing scheme (PDBPA) that can simultaneously realize data block privacy protection, data dynamics, and multi- user batch auditing. Our PDBPA scheme is implemented in bilinear pairing. By adding random masking in the audit phase, with the help of the characteristics of homomorphic verifiable tags (HVTs), it can not only ensure that the TPA performs the audit work correctly, but also prevent it from exploring the user’s sensitive data. In addition, by utilizing the modified index hash table (MIHT), data dynamics can be effectively achieved. Furthermore, we provide a specific process for the TPA to perform batch audits for multiple users. Moreover, we formally prove the security of the scheme; while achieving the audit correctness, it can resist three types of attacks.
Higher-Order Topological Phase of Interacting Photon Pairs

Topological phases open a door to such intriguing phenomena as unidirectional propagation and disorder-resilient localization at a stable frequency. Recently discovered higher-order topological phases further extend the concept of topological protection enabling versatile control over localization in multiple dimensions. Motivated by the recent advances in quantum technologies such as large coherently operating qubit ensembles, we predict and investigate the higher-order topological phase of photon pairs emerging due to effective photon-photon interaction and described by the extended version of Bose-Hubbard model. Being feasible for state-of-the-art experimental capabilities, the designed model provides an interesting example of interaction-induced topological transitions in the few-particle two-dimensional system.
Fine-Grained Language Identification in Scene Text Images

Identifying the language of the text in scene images is crucial for various applications. Studies that focus on identifying the script, which is a set of letters used for writing in a given language, in scene text images already exist. However, these works do not distinguish between different languages written in the same script and are thus unable to meet the needs of many applications. To address this challenge, we study a novel task: fine-grained language identification in scene text images, which aims to distinguish languages that share the same script. The datasets that include samples in seven languages, which are Dutch, English, French, Italian, German, Spanish, and Portuguese, are constructed. Furthermore, well-designed end-to-end trainable neural networks are proposed for fine-grained language identification, where semantic information concerning the text is mined and utilized to assist the language identification. We train the networks on the synthetic dataset and evaluate them with the collected real dataset. The experimental results demonstrate that the proposed frameworks are effective.
Emotion Wheel Attention-Based Emotion Distribution Learning

Emotion distribution learning is an effective multi-emotion analysis model proposed in recent years.Its core idea is to record the expression degree of examples on each emotion through emotion distribution, which is suitable for handling emotion analysis tasks with emotional ambiguity.To solve the problem that the prior knowledge of emotion psychology is seldom considered in the existing emotion distribution learning methods, we propose an Emotion Wheel Attention based Emotion Distribution Learning (EWA-EDL) model.EWA-EDL generates a prior emotion distribution describing the relevance of emotional psychology for each basic emotion, and then directly integrates the prior knowledge based on the emotion wheel into the deep neural network through the attention mechanism.The deep network of EWA-EDL is trained using an end-to-end approach to learn both emotion distribution prediction and emotion classification tasks.The EWA-EDL architecture includes five main parts: input layer, convolutional layer, pooling layer, attention layer and multi-task loss layer.Extensive comparative experiments on eight commonly used textual emotion datasets show that EWA-EDL outperforms the comparison emotion distribution learning methods on both emotion distribution prediction and emotion classification task.
Stochastic Learning Rate Optimization in the Stochastic Approximation and Online Learning Settings

In this work, multiplicative stochasticity is applied to the learning rate of stochastic optimization algorithms, giving rise to stochastic learning-rate schemes. In-expectation theoretical convergence results of Stochastic Gradient Descent equipped with this novel stochastic learning rate scheme under the stochastic setting, as well as convergence results under the online optimization settings are provided. Empirical results consider the case of an adaptively uniformly distributed multiplicative stochasticity equipped with a stochastic learning rate.
Anticipatory Path Planning for Continuum Arms in Dynamic Environments

Continuum arms are more adaptable to their environments and inherently human-friendly compared to their rigid counterparts. Path planning of continuum arms is an active research area with many challenges. The hyper-redundancy of continuum arms, which renders them highly versatile, is their curse in path planning. This problem becomes even more challenging in dynamic environments in the presence of mobile obstacles. In this paper, we propose an anticipatory path planning approach for continuum arms in dynamic environments. Our approach is based on obstacle prediction coupled with temporal graphs to model the dynamic environment. We evaluate the proposed approach’s performance and compare it to prevailing path planning approaches for continuum arms in dynamic environments.
Automatic First Arrival Time Identification Using Fuzzy <i>C</i>-Means and AIC

Accurate first arrival picking plays a crucial role in microseismic data processing. However, it is challenging to guarantee satisfactory accuracy with conventional approaches when the signal-to-noise ratio (SNR) of data is low. This article proposes an automatic first arrival time picking method based on fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> means clustering (FCM) and Akaike information criterion (AIC). The proposed method consists of three steps: clustering, rough picking, and adjusting. First, we employ FCM to divide each data point into the signal cluster and the noise cluster according to a fuzzy partition. Second, unlike conventional FCM-based picking approaches, we utilize Otsu’s method to determine a data-dependent threshold, instead of an artificially predefined one, to obtain a coarse result of the microseismic event interval from clustering partition. Finally, note that the microseismic event data points are concentrated in amplitude and also correlated in time. Therefore, we employ the AIC of the clustering partition to seek time-varying information to adjust the coarse result. Besides, we investigated several commonly used characteristic factors to introduce a supervised guideline for feature selection in first arrival picking with FCM. At last, we carried out simulations and real field data tests to verify the reliability of the proposed method. The experimental results demonstrate that the proposed method outperforms the short-and long-time average ratio (SLTA) method, the AIC method, and the conventional FCM-based picking method.