Reviewerly/exHarmony huggingface dataset:
Automatic Measurement of Pennation Angle from Ultrasound Images using Resnets

In this study, an automatic pennation angle measuring approach based on deep learning is proposed. Firstly, the Local Radon Transform (LRT) is used to detect the superficial and deep aponeuroses on the ultrasound image. Secondly, a reference line are introduced between the deep and superficial aponeuroses to assist the detection of the orientation of muscle fibers. The Deep Residual Networks (Resnets) are used to judge the relative orientation of the reference line and muscle fibers. Then, reference line is revised until the line is parallel to the orientation of the muscle fibers. Finally, the pennation angle is obtained according to the direction of the detected aponeuroses and the muscle fibers. The angle detected by our proposed method differs by about 1° from the angle manually labeled. With a CPU, the average inference time for a single image of the muscle fibers with the proposed method is around 1.6 s, compared to 0.47 s for one of the image of a sequential image sequence. Experimental results show that the proposed method can achieve accurate and robust measurements of pennation angle.
Generation of synchronized configuration space trajectories with workspace path constraints for an ensemble of robots

We present an approach to generate path-constrained synchronous motion for the coupled ensemble of robots. In this article, we refer to serial-link manipulators and mobile bases as robots. We assume that the relative motion constraints among the objects in the environment are given. We represent the motion constraints as path constraints and pose the problem of path-constrained synchronous trajectory generation as a non-linear optimization problem. Our approach generates configuration space trajectories for the robots to manipulate the objects such that the given motion constraints among the objects are satisfied. We present a method that formulates the problem as a discrete parameter optimization problem and solves it using successive constraint refinement techniques. The method adaptively selects the parametric representation of the configuration variables for a given scenario. It also generates an approximate solution as the starting point for the successive constraint refinement stages to reduce the computation time. We discuss in detail why successive constraint refinement strategies are useful for solving this class of problems. We demonstrate the effectiveness of the proposed method on challenging test cases in simulation and physical environments with high-degree-of-freedom robotic systems.
Teachers’ knowledge and intervention strategies to handle hate-postings

Hate-postings are an example of cyberhate that can happen among students in a school. However, no study to date has ever investigated this phenomenon from the perspectives of teachers and almost nothing is known about it in the school setting. Teachers (N= 130, 83% female) reported their knowledge, attitudes, and likely responses to a hypothetical hate-posting incident in which a student was devaluated, insulted and verbally harassed online because of his country of origin and religious affiliation, in addition to giving information about several school level variables. Based on answers to the 22 items of the newly developed handling hate-posting questionnaire (HHPQ), six teacher responses were identified with exploratory factor analysis. Teachers would most often alert other colleagues, followed by using victim-oriented rehabilitating strategies , working with the perpetrators’ parents, applying authority-based sanctions, seeking help from external professionals, and they would rather not ignore the incident. Different individual and school level variables predicted these responses. Teachers who had higher levels of moral disengagement and who reported that there were no school-wide guidelines were more likely to ignore the incident. Implications for the prevention of cyberhate are discussed.
Modulation Recognition Method of Complex Modulation Signal Based on Convolution Neural Network

With the emergence and further integration of computer technology, software radio technology and network information technology, signal analysis and processing are inseparable from the critical moment of modulation recognition. For the mixed data of the modulation signal and the secondary modulation signal, this paper simulates the modulation recognition by combining convolutional neural networks with different architectures and signal cyclic spectrum images under the Pytorch framework through image enhancement. This paper also expands the identification of the modulation mode of the secondary modulation signal and provides the improvement direction of the modulation signal data set.
Robustness Analysis of Face Obscuration

Face obscuration is needed by law enforcement and mass media outlets to guarantee privacy. Sharing sensitive content where obscuration or redaction techniques have failed to completely remove all identifiable traces can lead to many legal and social issues. Hence, we need to be able to systematically measure the face obscuration performance of a given technique. In this paper we propose to measure the effectiveness of eight obscuration techniques. We do so by attacking the redacted faces in three scenarios: obscured face identification, verification, and reconstruction. Threat modeling is also considered to provide a vulnerability analysis for each studied obscuration technique. Based on our evaluation, we show that the k-same based methods are the most effective.
Quantum Adversarial Machine Learning: Status, Challenges and Perspectives

Quantum adversarial machine learning Is regarded as a promising approach for studying vulnerabilities of machine learning approaches in adversarial settings and developing defense solutions for adversarial inputs and manipulations in quantum systems. In this paper, we present a current status, proposed approaches and challenges in quantum adversarial machine learning by concentrating on the problems and proposed solutions. We also outline the anticipated problems and perspectives for quantum-assisted machine learning in Near-term quantum computers and limitation in datasets, applications and adversarial examples. With this article, we hope that the readers can have a more thorough understanding of quantum adversarial machine learning and the research trends in this area.
Answer Questions with Right Image Regions: A Visual Attention Regularization Approach

Visual attention in Visual Question Answering (VQA) targets at locating the right image regions regarding the answer prediction, offering a powerful technique to promote multi-modal understanding. However, recent studies have pointed out that the highlighted image regions from the visual attention are often irrelevant to the given question and answer, leading to model confusion for correct visual reasoning. To tackle this problem, existing methods mostly resort to aligning the visual attention weights with human attentions. Nevertheless, gathering such human data is laborious and expensive, making it burdensome to adapt well-developed models across datasets. To address this issue, in this article, we devise a novel visual attention regularization approach, namely, AttReg, for better visual grounding in VQA. Specifically, AttReg first identifies the image regions that are essential for question answering yet unexpectedly ignored (i.e., assigned with low attention weights) by the backbone model. And then a mask-guided learning scheme is leveraged to regularize the visual attention to focus more on these ignored key regions. The proposed method is very flexible and model-agnostic, which can be integrated into most visual attention-based VQA models and require no human attention supervision. Extensive experiments over three benchmark datasets, i.e., VQA-CP v2, VQA-CP v1, and VQA v2, have been conducted to evaluate the effectiveness of AttReg. As a by-product, when incorporating AttReg into the strong baseline LMH, our approach can achieve a new state-of-the-art accuracy of 60.00% with an absolute performance gain of 7.01% on the VQA-CP v2 benchmark dataset. In addition to the effectiveness validation, we recognize that the faithfulness of the visual attention in VQA has not been well explored in literature. In the light of this, we propose to empirically validate such property of visual attention and compare it with the prevalent gradient-based approaches.
Simultaneous Hand–Eye and Intrinsic Calibration of a Laser Profilometer Mounted on a Robot Arm

A method for simultaneous laser profilometer and hand–eye calibration in relation to an industrial robot as well as its implementation is presented. In contrast to other methods, the new calibration procedure requires the measurement of only one reference geometry to calculate all the transformation parameters. The reference geometry is measured with a laser profilometer from 15 different poses. The intrinsic parameters of the profilometer, as well as the extrinsic (hand–eye) parameters, are then numerically optimized to achieve the minimum deviation between the reference and the measured geometry. The method was characterized with experiments that revealed a standard deviation of the displacements between the reference geometry after the calibration of less than 0.105 mm in the case of using the robot-arm actuator and 0.046 mm in case of using a 5-axis CNC milling machine. The entire procedure, including measurement and calculation, can be completely automated and lasts less than 10 min. This opens up possibilities for regular on-site recalibration of the entire system.
A New Message Expansion Structure for Full Pipeline SHA-2

Once there are constant or infrequently changed bits (COIBs) in two adjacent input messages of SHA-2, the switching power of input messages data registers (IMD-REGs) used for COIBs will disappear. Meanwhile, when full pipeline SHA-2 is applied in a certain application scenario where the IMD-REGs used for COIBs can be removed, more area of full pipeline SHA-2 can be saved as the proportion of IMD-REGs in message word registers increases. This paper proposes a new message expansion structure for full pipeline SHA-2 to increase the proportion of IMD-REGs. By inserting two expanders in last part of expansion structure pipeline stages and rescheduling the expander, the consumption rate of input messages will be decreased and the proportion of IMD-REGs will be increased. Compared with normal message expansion structure, the ratio of IMD-REGs to total message word registers in the proposed structure is increased from 15.1% to 41.6% for full pipeline SHA256, and 11.2% to 32.4% for full pipeline SHA512. When COIBs exists in adjacent input messages, the power and area advantages of proposed new message expansion structure have been demonstrated by FPGA and ASIC implementations.
Learned snakes for 3D image segmentation

Snakes or active contour models are classical methods for boundary detection and segmentation, which deform an initial contour (for 2D image) or a surface (for 3D image) towards the boundary of the desired object. Such snakes models are ideal choices for handling medical image segmentation problems since they are very efficient and require fewer memories by solely tracking the explicit curves or surfaces. However, traditional snakes models solved by the level set method suffer from numerical instabilities and are usually difficult to deal with topological changes. In this paper, we propose a learned snakes model for 3D medical image segmentation, where both the initial and final surfaces are estimated using deep neural networks in end-to-end regimes. The merit of our learned snakes model is that we can realize 3D segmentation by finding a 2D surface based on 2D convolutional neural networks rather than using 3D networks or cutting the volume into 2D slices. Experiments on the Medical Segmentation Decathlon spleen dataset against both 2D- and 3D-based networks demonstrate our model achieving the state-of-the-art accuracy and efficiency, which not only enjoys a 1% higher DSC but also saves more than 90% computational time compared to the well-established elastic boundary projection model Ni et al. [1].
CSSG: A cost‐sensitive stacked generalization approach for software defect prediction

Summary The prediction of software artifacts on defect‐prone (DP) or non‐defect‐prone (NDP) classes during the testing phase helps minimize software business costs, which is a classification task in software defect prediction (SDP) field. Machine learning methods are helpful for the task, although they face the challenge of data imbalance distribution. The challenge leads to serious misclassification of artifacts, which will disrupt the predictor's performance. The previously developed stacking ensemble methods do not consider the cost issue to handle the class imbalance problem (CIP) over the training dataset in the SDP field. To bridge this research gap, in the cost‐sensitive stacked generalization (CSSG) approach, we try to combine the staking ensemble learning method with cost‐sensitive learning (CSL) since the CSL purpose is to reduce misclassification costs. In the cost‐sensitive stacked generalization (CSSG) approach, logistic regression (LR) and extremely randomized trees classifiers in cases of CSL and cost‐insensitive are used as a final classifier of stacking scheme. To evaluate the performance of CSSG, we use six performance measures. Several experiments are carried out to compare the CSSG with some cost‐sensitive ensemble methods on 15 benchmark datasets with different imbalance levels. The results indicate that the CSSG can be an effective solution to the CIP than other compared methods.
Adversarial attacks and defenses in physiological computing: a systematic review

Physiological computing uses human physiological data as system inputs in real time.It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based biometrics.Physiological computing increases the communication bandwidth from the user to the computer, but is also subject to various types of adversarial attacks, in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output, leading to possible user confusion, frustration, injury, or even death.However, the vulnerability of physiological computing systems has not been paid enough attention to, and there does not exist a comprehensive review on adversarial attacks to them.This paper fills this gap, by providing a systematic review on the main research areas of physiological computing, different types of adversarial attacks and their applications to physiological computing, and the corresponding defense strategies.We hope this review will attract more research interests on the vulnerability of physiological computing systems, and more importantly, defense strategies to make them more secure.
Optimizing Attention for Sequence Modeling via Reinforcement Learning

Attention has been shown highly effective for modeling sequences, capturing the more informative parts in learning a deep representation. However, recent studies show that the attention values do not always coincide with intuition in tasks, such as machine translation and sentiment classification. In this study, we consider using deep reinforcement learning to automatically optimize attention distribution during the minimization of end task training losses. With more sufficient environment states, iterative actions are taken to adjust attention weights so that more informative words receive more attention automatically. Results on different tasks and different attention networks demonstrate that our model is of great effectiveness in improving the end task performances, yielding more reasonable attention distribution. The more in-depth analysis further reveals that our retrofitting method can help to bring explainability for baseline attention.
Exploiting the Back-Gate Biasing Technique as a Countermeasure Against Power Analysis Attacks

Fully depleted silicon-on-insulator (FD-SOI) technology is renowned for its back-gate bias voltage controllability. It allows devices fabricated with FD-SOI technology to be optimized for low power consumption or high performance with proper back-gate biases, depending on the required application. This article proposes using the back-gate biasing technique in novel countermeasures against power analysis attacks. Theoretical explanations are discussed, and realistic differential power analysis (DPA) attacks, targeting AES-128 encryption on a 65-nm STOB 32-bit RISC-V microcontroller, are conducted to justify the proposed idea. The experimental results show that when compared with applying no bias, applying our first proposal, which involves using forward back-gate bias, not only improves the test device performance but also enhances its resistance to DPA attacks. Moreover, vulnerability to DPA attacks is kept unchanged when a reverse back-gate bias is applied to achieve low power consumption. The DPA resistance is even more vital when combining the back-gate bias technique with a lower supply voltage. The number of power traces required to retrieve the secret key successfully increases by 14.5 times in the best case. Even better DPA resistance can be obtained when the back-gate bias of the targeted microcontroller is dynamically randomized, as suggested by our second proposal of a random dynamic back-gate bias (RDBB). When RDBB is applied, the number of power traces required to retrieve the secret key successfully significantly increases by 33.4 times.
Enabling Digital Forensics Readiness for Internet of Vehicles

Vehicles nowadays are equipped with a vast amount of sensors that collect data for the vehicle and its environment. This, combined with the acceleration of the automotive industry towards interconnected and autonomous cars, suggests that security and specifically the ability to detect compromised nodes, collect and preserve evidence of an attack or malicious activities emerge as a priority in successfully deploying the Internet of Vehicle ecosystem. Until today Digital Forensics attempts are concerned with in vehicle forensics. In this paper we present the challenges of integrating digital forensics in an IoV ecosystem and we introduce the Attack Attribution and Forensics Readiness Tool of the nIoVe system, an integrated holistic cybersecurity solution for IoV.
Credit Card Fraud Detection Using Deep Learning

This paper discusses a method in the fraud detection interface area. The approach proposed is to use imbalanced highly skewed transactional data and a convolutional network for the detection of frauds. The dataset used here is the machine learning kaggle dataset for credit card fraud detection that contains highly skewed data. The evaluated features are 1 for fraud and 0 for non-fraud class. The analysis of fraud detection was an important tool in banking sectors. Nowadays, the artificial neural network has become the least successful method for credit card fraud detection. The system currently used to detect fraud is plagued by misclassifications and highly false positives. In such situations here this research paper uses the in cooperation of convolutional neural network layers in an attempt to build a model for detecting credit card fraud that gives us a high level of accuracy. The goal is to predict fraud under 300 epochs, the current approach can be classified accurately at 99.62 %.
Spatial-Temporal Action Localization With Hierarchical Self-Attention

This paper proposes a novel architecture for spatial-temporal action localization in videos. The new architecture first employs a two-stream 3D convolutional neural network (3D-CNN) to provide initial action detection. Next, a new Hierarchical Self-Attention Network (HiSAN), the core of this architecture, is developed to learn the spatial-temporal relationships of key actors. Spatial Gaussian priors (SGP) are also imbued to the bidirectional self-attention to enhance HiSAN in modelling the relationships of neighboring actors. Such a combination of 3D-CNN and SGP augmented HiSAN allows us to effectively extract both of the spatial context information and the long-term temporal dependency to improve action localization accuracy. Afterwards, a new fusion strategy is employed, which first re-scores the bounding boxes to settle the inconsistent detection scores caused by background clutter or occlusion, and then aggregates the motion and appearance information from the two-stream network with the motion saliency to alleviate the impact of camera movement. Finally, a tube association network based on the self-similarity of the actors’ appearance and spatial information across frames is addressed to efficaciously construct the action tubes. Simulations on four widespread datasets reveal the efficacy of the new approach.
Artificial Intelligence and Fraud Detection

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Unsupervised Image Classification for Deep Representation Learning

Deep clustering against self-supervised learning (SSL) is a very important and promising direction for unsupervised visual representation learning since it requires little domain knowledge to design pretext tasks. However, the key component, embedding clustering, limits its extension to the extremely large-scale dataset due to its prerequisite to save the global latent embedding of the entire dataset. In this work, we aim to make this framework more simple and elegant without performance decline. We propose an unsupervised image classification framework without using embedding clustering, which is very similar to standard supervised training manner. For detailed interpretation, we further analyze its relation with deep clustering and contrastive learning. Extensive experiments on ImageNet dataset have been conducted to prove the effectiveness of our method. Furthermore, the experiments on transfer learning benchmarks have verified its generalization to other downstream tasks, including multi-label image classification, object detection, semantic segmentation and few-shot image classification.
Parallel Binary Image Cryptosystem Via Spiking Neural Networks Variants

Due to the inefficiency of multiple binary images encryption, a parallel binary image encryption framework based on the typical variants of spiking neural networks, spiking neural P (SNP) systems is proposed in this paper. More specifically, the two basic units in the proposed image cryptosystem, the permutation unit and the diffusion unit, are designed through SNP systems with multiple channels and polarizations (SNP-MCP systems), and SNP systems with astrocyte-like control (SNP-ALC systems), respectively. Different from the serial computing of the traditional image permutation/diffusion unit, SNP-MCP-based permutation/SNP-ALC-based diffusion unit can realize parallel computing through the parallel use of rules inside the neurons. Theoretical analysis results confirm the high efficiency of the binary image proposed cryptosystem. Security analysis experiments demonstrate the security of the proposed cryptosystem.