Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Trust Management for Internet of Vehicles

The Internet of Vehicles (IoV) greatly improves the traffic environment and life efficiency using messages shared between vehicles. However, due to its complex network structure and high mobility, the messages shared between vehicles are not always reliable. To this, we propose a trust management system of IoV based on blockchain, which formalizes a complete vehicle reputation value calculation scheme to deal with the problem of calculating the credibility of messages. The proposed scheme can detect vehicles that send malicious messages and reduce their reputation values for punishing according to the rating mechanism. In addition, we design a blockchain-based data storage system that can prevent attackers from tampering with the reputation values stored in roadside units (RSUs). In view of the lack of calculation basis when roadside units verify the block, we also store the rating list it. Finally, we use the consensus mechanism that combines PoW and PoS to ensure that vehicles with a large change in reputation can be updated to the blockchain first. The simulation results show that the proposed scheme has an obvious limitation on malicious vehicles, and improves the accuracy of the vehicles&#x2019; judgment of events based on the received messages.
The State of the Art of Spatial Interfaces for 3D Visualization

Abstract We survey the state of the art of spatial interfaces for 3D visualization. Interaction techniques are crucial to data visualization processes and the visualization research community has been calling for more research on interaction for years. Yet, research papers focusing on interaction techniques, in particular for 3D visualization purposes, are not always published in visualization venues, sometimes making it challenging to synthesize the latest interaction and visualization results. We therefore introduce a taxonomy of interaction technique for 3D visualization. The taxonomy is organized along two axes: the primary source of input on the one hand and the visualization task they support on the other hand. Surveying the state of the art allows us to highlight specific challenges and missed opportunities for research in 3D visualization. In particular, we call for additional research in: (1) controlling 3D visualization widgets to help scientists better understand their data, (2) 3D interaction techniques for dissemination, which are under‐explored yet show great promise for helping museum and science centers in their mission to share recent knowledge, and (3) developing new measures that move beyond traditional time and errors metrics for evaluating visualizations that include spatial interaction.
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

Conducting text retrieval in a dense representation space has many intriguing advantages. Yet the end-to-end learned dense retrieval (DR) often underperforms word-based sparse retrieval. In this paper, we first theoretically show the learning bottleneck of dense retrieval is due to the domination of uninformative negatives sampled locally in batch, which yield diminishing gradient norms, large stochastic gradient variances, and slow learning convergence. We then propose Approximate nearest neighbor Negative Contrastive Learning (ANCE), a learning mechanism that selects hard training negatives globally from the entire corpus, using an asynchronously updated ANN index. Our experiments demonstrate the effectiveness of ANCE on web search, question answering, and in a commercial search environment, showing ANCE dot-product retrieval nearly matches the accuracy of BERT-based cascade IR pipeline, while being 100x more efficient. We also empirically validate our theory that negative sampling with ANCE better approximates the oracle gradient-norm based importance sampling, thus improves the convergence of stochastic training.
Conditional Sig-Wasserstein GANs for Time Series Generation

Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high dimensional probability measures. However, these methods struggle to capture the temporal dependence of joint probability distributions induced by time-series data. Furthermore, long time-series data streams hugely increase the dimension of the target space, which may render generative modeling infeasible. To overcome these challenges, we integrate GANs with mathematically principled and efficient path feature extraction called the signature of a path. The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterizes the law of the time-series model. In particular, we a develop new metric, (conditional) Sig-$W_1$, that captures the (conditional) joint law of time series models, and use it as a discriminator. The signature feature space enables the explicit representation of the proposed discriminators which alleviates the need for expensive training. Furthermore, we develop a novel generator, called the conditional AR-FNN, which is designed to capture the temporal dependence of time series and can be efficiently trained. We validate our method on both synthetic and empirical datasets and observe that our method consistently and significantly outperforms state-of-the-art benchmarks with respect to measures of similarity and predictive ability.
Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.
Aggregate Cash Systems: A Cryptographic Investigation of Mimblewimble

Mimblewimble is an electronic cash system proposed by an anonymous author in 2016. It combines several privacy-enhancing techniques initially envisioned for Bitcoin, such as Confidential Transactions (Maxwell, 2015), non-interactive merging of transactions (Saxena, Misra, Dhar, 2014), and cut-through of transaction inputs and outputs (Maxwell, 2013). As a remarkable consequence, coins can be deleted once they have been spent while maintaining public verifiability of the ledger, which is not possible in Bitcoin. This results in tremendous space savings for the ledger and efficiency gains for new users, who must verify their view of the system. In this paper, we provide a provable-security analysis for Mimblewimble. We give a precise syntax and formal security definitions for an abstraction of Mimblewimble that we call an aggregate cash system. We then formally prove the security of Mimblewimble in this definitional framework. Our results imply in particular that two natural instantiations (with Pedersen commitments and Schnorr or BLS signatures) are provably secure against inflation and coin theft under standard assumptions.
Developing a green city assessment system using cognitive maps and the Choquet Integral

Equitable human well-being and environmental concerns in urban areas have, over the years, become increasingly challenging issues. This trend is related to both the complexity inherent in the multiple factors to be considered when evaluating eco-friendly cities (i.e., green cities) and the way this type of city’s sustainability depends on many evaluation criteria, which hampers all decision-making processes. Using a multiple criteria decision analysis (MCDA) approach, this study sought to develop a multiple-criteria model that facilitates the evaluation of green cities’ sustainability, based on cognitive mapping techniques and the Choquet integral (CI). Taking a constructivist and process-oriented stance, the research included identifying evaluation criteria and their respective interactions using a panel of experts with specialized knowledge in the subject under analysis. The resulting framework and its application were validated both by the panel members and a parliamentary representative of the Portuguese ecology party “Os Verdes” (The Greens), who confirmed that the evaluation system created distinguishes between cities according to how strongly they adhere to “green” principles. The advantages and limitations of the proposed framework are also discussed.
Online human activity recognition employing hierarchical hidden Markov models

In the last few years there has been a growing interest in Human activity recognition (HAR) topic. Sensor-based HAR approaches, in particular, has been gaining more popularity owing to their privacy preserving nature. Furthermore, due to the widespread accessibility of the internet, a broad range of streaming-based applications such as online HAR, has emerged over the past decades. However, proposing sufficiently robust online activity recognition approach in smart environment setting is still considered as a remarkable challenge. This paper presents a novel online application of Hierarchical Hidden Markov Model in order to detect the current activity on the live streaming of sensor events. Our method consists of two phases. In the first phase, data stream is segmented based on the beginning and ending of the activity patterns. Also, on-going activity is reported with every receiving observation. This phase is implemented using Hierarchical Hidden Markov models. The second phase is devoted to the correction of the provided label for the segmented data stream based on statistical features. The proposed model can also discover the activities that happen during another activity - so-called interrupted activities. After detecting the activity pane, the predicted label will be corrected utilizing statistical features such as time of day at which the activity happened and the duration of the activity. We validated our proposed method by testing it against two different smart home datasets and demonstrated its effectiveness, which is competing with the state-of-the-art methods.
Multilevel Techniques for Compression and Reduction of Scientific Data---The Multivariate Case

We develop a technique for multigrid adaptive reduction of data (MGARD). Special attention is given to the case of tensor product grids, where our approach permits the use of nonuniformly spaced grids in each direction, which can prove problematic for many types of data reduction methods. An important feature of our approach is the provision of guaranteed, computable bounds on the loss incurred by the reduction of the data. Many users are leery of lossy algorithms and will only consider using them provided that numerical bounds on the pointwise difference between the original and the reduced datasets are given. Accordingly, we develop techniques for bounding the loss measured in the $L^{\infty}(\Omega)$ norm, and we show that these bounds are realistic in the sense that they do not significantly overestimate the actual loss. The resulting loss indicators are used to guide the adaptive reduction of the data so that the reduced dataset meets a user-prescribed tolerance or memory constraint. Illustrative numerical examples, including the reduction of data arising from the simulation of a nonlinear reaction-diffusion problem, a turbulent channel flow, and a climate simulation, are provided.
Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images

Breast classification and detection using ultrasound imaging is considered a significant step in computer-aided diagnosis systems.Over the previous decades, researchers have proved the opportunities to automate the initial tumor classification and detection.The shortage of popular datasets of ultrasound images of breast cancer prevents researchers from obtaining a good performance of the classification algorithms.Traditional augmentation approaches are firmly limited, especially in tasks where the images follow strict standards, as in the case of medical datasets.Therefore besides the traditional augmentation, we use a new methodology for data augmentation using Generative Adversarial Network (GAN).We achieved higher accuracies by integrating traditional with GAN-based augmentation.This paper uses two breast ultrasound image datasets obtained from two various ultrasound systems.The first dataset is our dataset which was collected from Baheya Hospital for Early Detection and Treatment of Women's Cancer, Cairo (Egypt), we name it (BUSI) referring to Breast Ultrasound Images (BUSI) dataset.It contains 780 images (133 normal, 437 benign and 210 malignant).While the Dataset (B) is obtained from related work and it has 163 images (110 benign and 53 malignant).To overcome the shortage of public datasets in this field, BUSI dataset will be publicly available for researchers.Moreover, in this paper, deep learning approaches are proposed to be used for breast ultrasound classification.We examine two different methods: a Convolutional Neural Network (CNN) approach and a Transfer Learning (TL) approach and we compare their performance with and without augmentation.The results confirm an overall enhancement using augmentation methods with deep learning classification methods (especially transfer learning) when evaluated on the two datasets.
PuVAE: A Variational Autoencoder to Purify Adversarial Examples

Deep neural networks are widely used and exhibit excellent performance in many areas.However, they are vulnerable to adversarial attacks that compromise networks at inference time by applying elaborately designed perturbations to input data.Although several defense methods have been proposed to address specific attacks, other types of attacks can circumvent these defense mechanisms.Therefore, we propose Purifying Variational AutoEncoder (PuVAE), a method to purify adversarial examples.The proposed method eliminates an adversarial perturbation by projecting an adversarial example on the manifold of each class and determining the closest projection as a purified sample.We experimentally illustrate the robustness of PuVAE against various attack methods without any prior knowledge about the attacks.In our experiments, the proposed method exhibits performances that are competitive with state-of-the-art defense methods, and the inference time is approximately 130 times faster than that of Defense-GAN which is a state-of-the art purifier method.
Weight analysis for various prohibitory sign detection and recognition using deep learning

Traffic sign recognition is meaningful for real-world applications such as self-sufficient driving, traffic surveillance, and driver safety. However, traffic sign recognition is a hard problem because different sizes, illuminations, and noises affect the sign detection and recognition. This work recognizes Taiwan's prohibitory signs using deep learning methods. First, we develop a traffic sign database since there is no such kind of database available in Taiwan. Next, we adopt three different You Only Look Once (Yolo) networks (Yolo A, Yolo B, and Yolo C) and three various Yolo V3 SPP networks (Yolo D, Yolo E, and Yolo F) for prohibitory sign recognition. Finally, we conduct the comparative experiment of Yolo V3 and Yolo V3 SPP with different weights provided by the darknet framework (the best weight, the final weight, and the last weight). Experimental results show that the mean average precision (mAP) observation of all models that the Yolo V3 SPP is better than other models. Yolo D took the optimum average accuracy at 99.0%, followed by Yolo E and Yolo F 98.9%. The accuracy of Yolo V3 SPP is growing within the detection time, but it needs more time to identify the sign.
Graph U-Nets

We consider the problem of representation learning for graph data. Given images are special cases of graphs with nodes lie on 2D lattices, graph embedding tasks have a natural correspondence with image pixel-wise prediction tasks such as segmentation. While encoder-decoder architectures like U-Nets have been successfully applied to image pixel-wise prediction tasks, similar methods are lacking for graph data. This is because pooling and up-sampling operations are not natural on graph data. To address these challenges, we propose novel graph pooling and unpooling operations. The gPool layer adaptively selects some nodes to form a smaller graph based on their scalar projection values. We further propose the gUnpool layer as the inverse operation of the gPool layer. Based on our proposed methods, we develop an encoder-decoder model, known as the graph U-Nets. Experimental results on node classification and graph classification tasks demonstrate that our methods achieve consistently better performance than previous models. Along this direction, we extend our methods by integrating attention mechanisms. Based on attention operators, we proposed attention-based pooling and unpooling layers, which can better capture graph topology information. The empirical results on graph classification tasks demonstrate the promising capability of our methods.
DeepMIH: Deep Invertible Network for Multiple Image Hiding

Multiple image hiding aims to hide multiple secret images into a single cover image, and then recover all secret images perfectly. Such high-capacity hiding may easily lead to contour shadows or color distortion, which makes multiple image hiding a very challenging task. In this paper, we propose a novel multiple image hiding framework based on invertible neural network, namely DeepMIH. Specifically, we develop an invertible hiding neural network (IHNN) to innovatively model the image concealing and revealing as its forward and backward processes, making them fully coupled and reversible. The IHNN is highly flexible, which can be cascaded as many times as required to achieve the hiding of multiple images. To enhance the invisibility, we design an importance map (IM) module to guide the current image hiding based on the previous image hiding results. In addition, we find that the image hidden in the high-frequency sub-bands tends to achieve better hiding performance, and thus propose a low-frequency wavelet loss to constrain that no secret information is hidden in the low-frequency sub-bands. Experimental results show that our DeepMIH significantly outperforms other state-of-the-art methods, in terms of hiding invisibility, security and recovery accuracy on a variety of datasets.
Incremental multi-view spectral clustering

Multi-view learning has attracted increasing attention in recent years, and the existing multi-view learning methods learn a consensus result by collecting all views. These methods have two obvious limitations. First, it is not scalable; with limited computational resources it would be difficult, if not impossible, to collect and process a large collection of views together. Second, in many applications views of data are available over time; it is infeasible to apply the existing multi-view learning methods to such streaming views. To address the two limitations, in this paper we propose a novel incremental multi-view spectral clustering (IMSC) method. In IMSC, instead of ensembling the collection of all views simultaneously, we integrate them one by one in an incremental way. We first learn an initial model from a small number of views; next when a new view is available, we need only use it to update the model and apply the updated model to learn a consensus result. This method is scalable and applicable to streaming views. To further reduce the time and space complexity, we apply low rank approximation by means of the well-known random Fourier features to construct the base kernels and do low rank SVD decompositions accordingly. The theoretical analysis and experimental results on benchmark data sets show that our incremental multi-view spectral clustering method is significantly faster in efficiency than the existing state-of-the-art non-incremental ones and is comparable or even better in clustering quality.
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

Noisy labels are ubiquitous in real-world datasets, which poses a challenge for robustly training deep neural networks (DNNs) as DNNs usually have the high capacity to memorize the noisy labels. In this paper, we find that the test accuracy can be quantitatively characterized in terms of the noise ratio in datasets. In particular, the test accuracy is a quadratic function of the noise ratio in the case of symmetric noise, which explains the experimental findings previously published. Based on our analysis, we apply cross-validation to randomly split noisy datasets, which identifies most samples that have correct labels. Then we adopt the Co-teaching strategy which takes full advantage of the identified samples to train DNNs robustly against noisy labels. Compared with extensive state-of-the-art methods, our strategy consistently improves the generalization performance of DNNs under both synthetic and real-world training noise.
An Image Copy-Move Forgery Detection Method Based on SURF and PCET

With the maturity of image editing software, image content has been forged frequently, posing potential threats to many critical fields. To detect forgery images effectively, this paper proposes an image copy-move forgery detection (CMFD) method based on speeded-up robust feature (SURF) and polar complex exponential transform (PCET). Firstly, image is divided into non-overlapping irregular image blocks by superpixel segmentation. Then, these image blocks are separated into two categories: smooth regions and texture regions. Secondly, after finding the keypoints by SURF, the PCET coefficients are extracted and utilized for searching similar features by feature matching algorithm. Thirdly, a strategy is used to eliminate false matched points and find the regions with dense matched points. It combines the random sample consensus (RANSAC) algorithm and a filtering scheme. Finally, mathematical morphology and an iterative strategy are adopted to refine the tampered regions. Compared with other CMFD methods, the proposed method can detect the forgery which occurs in high-brightness smooth regions or forgery images involving similar but genuine regions. Experimental results also indicate the proposed method can resist different distortions by various attacks, including rotation, scaling, blurring, joint photographic expert group (JPEG) compression, and noise addition.