Reviewerly/exHarmony huggingface dataset:
Intelligent Proof-of-Trustworthiness-Based Secure Safety Message Dissemination Scheme for Vehicular Ad Hoc Networks Using Blockchain and Deep Learning Techniques

Vehicular ad hoc networks have emerged as the main building block for the future cooperative intelligent transportation system (cITS) to improve road safety and traffic efficiency and to provide passenger comfort. However, vehicular networks are decentralized, characterized by high mobility and dynamicity, and vehicles move in a hostile environment; such characteristics make VANET applications suffer many security and communication issues. Recently, blockchain has been suggested to solve several VANET issues including the dissemination of trustworthy life-threatening information. However, existing dissemination schemes are inefficient for safety messages and are vulnerable to malicious nodes and rely on the majority of honest assumptions. In the VANET context, adversaries may collude to broadcast false information causing serious safety threats. This study proposes an intelligent proof-of-trustworthiness-based secure safety message dissemination scheme (PoTMDS) to efficiently share only trustworthy messages. The consistency and plausibility of the message were evaluated based on a predictive model developed using a convolutional neural network and signal properties such as the received signal strength and angle of arrival. A blockchain-based data dissemination scheme was developed to share critical messages. Each vehicle calculates the proof of trustworthiness of the disseminated messages by comparing the received message with the output of the prediction model. The results showed that the proposed scheme reduced the consensus delay by 58% and improved the detection accuracy by 7.8%. Therefore, the proposed scheme can have an important role in improving the applications of future cITS.
Yet Another Effective Dendritic Neuron Model Based on the Activity of Excitation and Inhibition

Neuronal models have remained an important area of research in computer science. The dendritic neuron model (DNM) is a novel neuronal model in recent years. Previous studies have focused on training DNM using more appropriate algorithms. This paper proposes an improvement to DNM based on the activity of excitation and proposes three new models. Each of the three improved models are designed to mimic the excitation and inhibition activity of neurons. The improved model proposed in this paper is shown to be effective in the experimental part. All three models and original DNM have their own strengths, so it can be considered that the new model proposed in this paper well enriches the diversity of neuronal models and contributes to future research on networks models.
LCAM: Low-Complexity Attention Module for Lightweight Face Recognition Networks

Inspired by the human visual system to concentrate on the important region of a scene, attention modules recalibrate the weights of either the channel features alone or along with spatial features to prioritize informative regions while suppressing unimportant information. However, the floating-point operations (FLOPs) and parameter counts are considerably high when one is incorporating these modules, especially for those with both channel and spatial attentions in a baseline model. Despite the success of attention modules in general ImageNet classification tasks, emphasis should be given to incorporating these modules in face recognition tasks. Hence, a novel attention mechanism with three parallel branches known as the Low-Complexity Attention Module (LCAM) is proposed. Note that there is only one convolution operation for each branch. Therefore, the LCAM is lightweight, yet it is still able to achieve a better performance. Experiments from face verification tasks indicate that LCAM achieves similar or even better results compared with those of previous modules that incorporate both channel and spatial attentions. Moreover, compared to the baseline model with no attention modules, LCAM achieves performance values of 0.84% on ConvFaceNeXt, 1.15% on MobileFaceNet, and 0.86% on ProxylessFaceNAS with respect to the average accuracy of seven image-based face recognition datasets.
Reliability of a Linguistic Segmentation Procedure Specified by Systemic Functional Linguistics to Examine Extemporaneous Speech

Extemporaneous speech tasks provide an ecologically valid sample to examine speech acoustics, but differing methodologies exist in the literature for segmentation. Therefore, the purpose of this study was to examine the utility and reliability of a segmentation approach for extemporaneous speech specified by systemic functional linguistics (SFL) and its potential research and clinical applications.Ten speakers without communication disorders served as participants in this study, and they responded to self-selected extemporaneous speaking prompts. Two expert analysts and one clinician analyst utilized a segmentation procedure specified by SFL to segment the extemporaneous speech samples into clauses and clause complexes. Intra- and interrater reliability were calculated for each analyst and pair of analysts. Acoustic measures of duration, speech rate, and intercomplex pause durations were calculated for each clause complex.Analyses for both intra- and interrater reliability revealed high percent agreement that was significantly greater than chance for expert and clinician analysts and between each pair of analysts (p < .001). Acoustic analyses revealed expected variation in number and duration of spoken syllables of clause complexes between and within speakers.The segmentation approach for extemporaneous speech specified by SFL is a reliable method for trained analysts that is informed by lexico-grammar and allows for acoustic measurement of speech production. It is also a reliable method for clinician analysts for speakers without communication disorders, and future work will investigate its utility for speakers with motor speech disorders.https://doi.org/10.23641/asha.22357138.
Real-Time Measurement of Minimum Cross-Sectional Area and Radius of Curvature of Miniature Plate Specimens Using a High-Speed Laser Profiler for Calculating True Stress-True Strain Curves

Abstract True stress-true strain curves are fundamental for designing and analyzing structures such as fusion reactors. These curves are typically obtained by conducting tensile tests on round bar specimens. However, due to material dimension limitations, plate specimens are sometimes used instead of round bar specimens. Obtaining true stress-true strain curves experimentally from plate specimens can be challenging. To address this challenge, this study aims to obtain true stress-true strain curves of miniature plate specimens using both analytical and experimental methods. The analytical method involved inverse finite element method (FEM), while the experimental method utilized real-time measurement of the minimum cross-sectional area and radius of curvature of a miniature plate specimen with a high-speed laser profiler. Comparing the true stresses obtained from the analytical and experimental methods, we found that the difference was typically within 5%. These findings suggest that inverse FEM and laser profilometry are effective methods for determining the true stress-true strain of miniature plate specimens.
Position-Aware Relational Transformer for Knowledge Graph Embedding

Although Transformer has achieved success in language and vision tasks, its capacity for knowledge graph (KG) embedding has not been fully exploited. Using the self-attention (SA) mechanism in Transformer to model the subject-relation-object triples in KGs suffers from training inconsistency as SA is invariant to the order of input tokens. As a result, it is unable to distinguish a (real) relation triple from its shuffled (fake) variants (e.g., object-relation-subject) and, thus, fails to capture the correct semantics. To cope with this issue, we propose a novel Transformer architecture, namely, for KG embedding. It incorporates relational compositions in entity representations to explicitly inject semantics and capture the role of an entity based on its position (subject or object) in a relation triple. The relational composition for a subject (or object) entity of a relation triple refers to an operator on the relation and the object (or subject). We borrow ideas from the typical translational and semantic-matching embedding techniques to design relational compositions. We carefully design a residual block to integrate relational compositions into SA and efficiently propagate the composed relational semantics layer by layer. We formally prove that the SA with relational compositions is able to distinguish the entity roles in different positions and correctly capture relational semantics. Extensive experiments and analyses on six benchmark datasets show that achieves state-of-the-art performance on both link prediction and entity alignment.
Bias-Compensated Integral Regression for Human Pose Estimation

In human and hand pose estimation, heatmaps are a crucial intermediate representation for a body or hand keypoint. Two popular methods to decode the heatmap into a final joint coordinate are via an argmax, as done in heatmap detection, or via softmax and expectation, as done in integral regression. Integral regression is learnable end-to-end, but has lower accuracy than detection. This paper uncovers an induced bias from integral regression that results from combining the softmax and the expectation operation. This bias often forces the network to learn degenerately localized heatmaps, obscuring the keypoint's true underlying distribution and leads to lower accuracies. Training-wise, by investigating the gradients of integral regression, we show that the implicit guidance of integral regression to update the heatmap makes it slower to converge than detection. To counter the above two limitations, we propose Bias Compensated Integral Regression (BCIR), an integral regression-based framework that compensates for the bias. BCIR also incorporates a Gaussian prior loss to speed up training and improve prediction accuracy. Experimental results on both the human body and hand benchmarks show that BCIR is faster to train and more accurate than the original integral regression, making it competitive with state-of-the-art detection methods.
A reference database of Windows artifacts for file‐wiping tool execution analysis

Anti-forensic technology can play an effective role in protecting information, but it can make forensic investigations difficult. Specifically, file-wiping permanently erases evidence, making it challenging for investigators to determine whether a file ever existed and prolonging the investigation process. To address this issue, forensic researchers have studied anti-forensic techniques that detect file-wiping activities. Many previous studies have focused on the effects of file-wiping tools on $MFT, $LogFile, and $DATA, rather than on Windows artifacts. Additionally, previous studies that have examined Windows artifacts have considered different artifacts, making it difficult to study them in a comprehensive manner. To address this, we focused on analyzing traces in 13 Windows artifacts of 10 file-wiping tools' operations in the Windows operating system comprehensively. For our experiments, we installed each file-wiping tool on separate virtual machines and checked the traces that the tools left behind in each artifact. We then organized the results in a database format. Our analysis revealed that most of the tools left traces on other artifacts, except for JumpList, Open&SavePidlMRU, and lnk. There were also some cases where traces remained on the other three artifacts. Based on our research, forensic investigators can quickly identify whether a file-wiping tool has been used, and it can assist in decision-making for evidence collection and forensic triage.
A robust Cubature Kalman filter for nonlinear systems subject to randomly occurring measurement anomalies without a priori statistic

In this work, we investigate the problem of state estimation for a class of nonlinear systems subjected to randomly occurring measurement anomalies (ROMAs) without a priori statistic. To address the problem, first, a novel measurement model is constructed, in which the anomalous measurements and anomaly probability are modeled as Gaussian mixture distribution (GMD) and Beta distribution, respectively. Different from the existing researches assuming that the statistical information of anomalous measurements is known in advance, the model does not require a priori statistical knowledge of anomalous measurements. Moreover, by adaptive learning of the anomaly probability, the measurement model is identical with the classical cubature Kalman filter (CKF) in the absence of measurement anomalies. Then, the variational Bayesian inference (VBI) is employed to approximately calculate the joint posterior distribution of the system state and unknown parameters, and a robust filter is derived. Finally, the effectiveness of our filter is demonstrated by the numerical simulation.
Enhancing correlated big data privacy using differential privacy and machine learning

Abstract Data are often correlated in real-world datasets. Existing data privacy algorithms did not consider data correlation an inherent property of datasets. This data correlation caused privacy leakages that most researchers left unnoticed. Such privacy leakages are often caused by homogeneity, background knowledge, and linkage attacks, and the probability of such attacks increases with the magnitude of correlation among data. This problem further got magnified by the large size of real-world datasets, and we refer to these large datasets as ’Big Data.’ Several researchers proposed algorithms using machine learning models, correlation analysis, and data privacy algorithms to prevent privacy leakages due to correlation in large-sized data. The current proposed work first analyses the correlation among data. We studied the Mutual Information Correlation analysis technique and the distance correlation analysis technique for data correlation analysis. We found out distance correlation analysis technique to be more accurate for high-dimensional data. It then divides the data into blocks using the correlation computed earlier and applies the differential privacy algorithm to ensure the data privacy expectations. The results are derived based upon multiple parameters such as data utility, mean average error, variation with data size, and privacy budget values. The results showed that the proposed methodology provides better data utility when compared to the works of other researchers. Also, the data privacy commitments offered by the proposed method are comparable to the other results. Thus, the proposed methodology gives a better data utility while maintaining the required data privacy commitments.
An extensive pixel-level augmentation framework for unsupervised cross-modality domain adaptation

Convolutional neural networks (CNNs) have achieved great success in automating the segmentation of medical images. Nevertheless, when a trained CNN is tested on a new domain there is a performance degradation due to the distribution shift. In this work, we present an unsupervised Extensive Pixel-level Augmentation framework (EPA) for cross-modality domain adaptation. EPA implements a two-phase image- and feature-level adaptation method. In the first phase, the source domain images are mapped to target domain in pixel space using the CycleGAN, StAC-DA, and CUT translation models. This creates an augmented translated dataset 3 times bigger than the original. In phase 2, a deeply supervised U-Net network is trained to segment the target images using a semi-supervised adversarial learning approach. In particular, a set of discriminator networks are trained to distinguish between the target and source domain segmentations, while the U-Net aims to fool them. EPA is tested on the task of brain structure segmentation from the Crossmoda 2022 Grand Challenge, being ranked within the top 12 submissions of the testing phase. Moreover, we demonstrate that augmenting the size of the mapped dataset through distinct translation methods is crucial for increasing the segmentation accuracy of the model.
Highly accurate deep registration networks for large deformation estimation in compression ultrasound

AI guidance on compression ultrasound is a problem for 2D segmentation networks, which produce inconsistent labels. This is aided by registration but classical untrained approaches cannot handle the large deformations and the noisy background movement and convolutional models like VoxelMorph do not reach the robust accuracy required. Meanwhile, large deformations are typically estimated with multi-warp networks that comprise "correlation layers", but they are resource-intensive and not easily applicable on end-devices in clinical context. We propose to replace the "correlation layer" with a differentiable convex optimisation block and perform end-to-end training of the convolutional feature backbone for improved performance.
Dev2vec: Representing domain expertise of developers in an embedding space

Accurate assessment of the domain expertise of developers is essential for assigning the proper candidate to contribute to a project, or to attend a job role. Since the potential candidate can come from a large pool, the automated assessment of this domain expertise is a desirable goal. While previous methods have had some success within a single software project, the assessment of a developer’s domain expertise from contributions across multiple projects is more challenging. In this paper, we employ doc2vec to represent the domain expertise of developers across multiple projects as embedding vectors, and assess expertise level from authored code fragments. For this purpose, we derived embedding vectors from different sources that contain evidence of developers’ expertise, such as the description of repositories they contributed, their issue resolving history, and API calls in their commits. We name it dev2vec and demonstrate its effectiveness in representing and assessing the technical specialization of developers. Our results indicate that encoding the expertise of developers in an embedding vector outperforms state-of-the-art methods and improves the F1-score up to 21%. Moreover, our findings suggest that the “issue resolving history” of developers is the most informative source of information to represent the domain expertise of developers in embedding spaces. Our proposed approach sheds light on the effectiveness of representing the technical expertise of developers in embedding vectors, and it can act as initial filtering for recruiters and project managers.
Distance map supervised landmark localization for MR-TRUS registration

Image-guided interventional procedures often require registering multi-modal images to visualize and analyze complementary information. For example, prostate cancer biopsy benefits from fusing transrectal ultrasound (TRUS) imaging with magnetic resonance (MR) imaging to optimize targeted biopsy. However, cross-modal image registration is a challenging task. This is especially true when the appearance of two image modalities are vastly different. Researchers have seeked other methods bridge the modality difference. Haskins et al. 1 designed a deep similarity metric to describe the difference between MR and US images. Some works directly use a DNN to regress the spatial relationship between images .2 Aside from image similarity, Balakrishnan et al.3 also explored using segmentation DICE as a loss function to assess the registration quality. Hu et al.4 used soft probabilistic DICE of multiple landmark segmentations to provide smoother guidance for MR-TRUS registration. Such method, however, requires extensive labeling of not only organs, but small cysts and lesions as well. This level of requirement largely limits the feasibility of such method. Since registration quality is most reliably evaluated with target registration error (TRE), it is sensible to directly make use of the anatomical landmark targets from images. Moreover, while image modalities and thus textures differ, anatomical landmarks are the only information shared across the moving and the fixed images. Song et al. 5 used contrastive loss-guided pre-training methods to maximize the similarity between similar anatomical structures. Sun et al.6 proposed to perform pre-alignment for MR-TRUS registration with manually labeled landmarks on both images. However, such a procedure is still far from automatic due to the requirement of manual input at inference time. Heinrich et al.7 made use of a landmark detection method specifically designed for lung computed tomography (CT) registration, which is not generalizable to other tasks. In this work, we propose to explicitly use the landmarks of prostate to guide the MR-TRUS image registration. We first train a deep neural network to automatically localize a set of meaningful landmarks, and then directly generate the affine registration matrix from the location of these landmarks. For landmark localization, instead of directly training a network to predict the landmark coordinates, we propose to regress a full-resolution distance map of the landmark, which is demonstrated effective in avoiding statistical bias to unsatisfactory performance and thus improving performance. We then use the predicted landmarks to generate the affine transformation matrix, which outperforms the clinicians’ manual rigid registration by a significant margin in terms of TRE.
GAN based ROI conditioned synthesis of medical image for data augmentation

Synthetic data is considered to be a promising solution for data privacy and scarcity. Some studies have shown that synthetic data generated from a simple GAN-based model enables privacy-preserving data sharing and data augmentation also in the medical imaging field. However, there are some limitations in applying this approach to real world situations: 1) Since generative models needs large amount of data to be trained, it is hard to be applied for small data situation. 2) Even after successfully training generative models, it is hard to guarantee which class the synthesized data corresponds to, especially for non-conditional generative models, so it needs to be re-labeled. Here, we propose GAN based ROI conditioned synthesis of medical Image for data augmentation. We used StyleGAN2 to learn the distribution of CXR and Bayesian image reconstruction for ROI-conditioned synthesis from the distribution. In the 4-class classification of CXRs showing normal, pneumonia, pleural effusion, and pneumothorax, using synthetic data for data sharing showed comparable performance to centralized learning, slightly better in terms of AUROC. Also, using synthetic data for augmentation, the accuracy and AUROC showed up to 6.5% and 8.9% increases, respectively.