Reviewerly/exHarmony huggingface dataset:
Evaluations in the Moodle-Mediated Music Teaching-Learning Environment

This study presents the use of automated data analysis procedures in the teaching-learning process, mediated by telematics platforms. It is based on the application of the principles of virtual learning, the use of the Internet and the automation of data analysis of information collected in Moodle. The application of analysis procedures for the assessment of music competences is proposed based on the data collected in an exam administered at the end of the course. The sample of the study consists of 1327 students (n = 1327) in the first year of Compulsory Secondary Education in Spain and measures the level of acquisition of the key competences denominated "cultural and artistic". The results are subjected to the K-means classification technique. This technique is used to obtain homogeneously distributed conglomerates which allow for an objective evaluation of the levels of acquisition of the key musical competences.
Cyber Autopsies: The Integration of Digital Forensics into Medical Contexts

The integration of information technology into medical environments introduces a variety of opportunities and challenges for the healthcare community. Medical devices such as ventilators, patient monitors, and infusion pumps, which once operated as standalone devices, now integrate network communication technology. As a result, these modern medical devices produce, store, and transmit large amounts of patient and therapy information. From a digital forensics' perspective, this information could provide a forensic investigator with a treasure trove of potential digital evidence. Hence, the purpose of this paper is to introduce and discuss the potential place and value of digital forensics processes within the context of healthcare providers, through five scenarios. The aim of the paper is twofold. First, it raises incentives for integrating digital forensics into various scenarios involving healthcare providers. Second, it encourages future research to address the adoption of digital forensics tools and techniques to assist stakeholders in the medical domain.
Demonstration of Software Defined Network Services Utilizing Quantum Key Distribution Fully Integrated with Standard Telecommunication Network

We present a demonstration of software defined networking (SDN) services utilizing quantum key distribution (QKD) technology, fully integrated with standard telecommunication network connecting production facilities of Telefonica in Madrid. All communications “co-propagate” over the same fiber infrastructure.
A Secure Storage Strategy for Blockchain Based on MCMC Algorithm

The exponential growth of storage space in blockchain network has become a serious problem to hinder the distribution of blockchain and the expansion of blockchain nodes. In this paper. We propose a security strategy for distributed storage blockchains, which can delete part of blockchains so that nodes only store part of a blockchain. We design a kind of semi-full node between full node and light node according to the requirement of the strategy, besides describe the process of deleting block and synchronizing block, and the running logic of the semi-full node. Finally, we perform comprehensive experiments of the truncated MCMC random algorithm. The results show that in the case of multi-node, the truncated block will not affect the block chain network. Compared with the traditional block design, our storage strategies can reduce storage requirements under most of situation, thus enable blockchains to be deployed on mobile or smaller storage computers.
Segmentation of Tau Stained Alzheimers Brain Tissue Using Convolutional Neural Networks

Alzheimers disease is characterized by complex changes in brain tissue including the accumulation of tau-containing neurofibrillary tangles (NFTs) and dystrophic neurites (DNs) within neurons. The distribution and density of tau pathology throughout the brain is evaluated at autopsy as one component of Alzheimers disease diagnosis. Deep neural networks (DNN) have been shown to be effective in the quantification of tau pathology when trained on fully annotated images. In this paper, we examine the effectiveness of three DNNs for the segmentation of tau pathology when trained on noisily labeled data. We train FCN, SegNet and U-Net on the same set of training images. Our results show that using noisily labeled data, these networks are capable of segmenting tau pathology as well as nuclei in as few as 40 training epochs with varying degrees of success. SegNet, FCN and U-Net are able to achieve a DICE loss of 0.234, 0.297 and 0.272 respectively on the task of segmenting regions of tau. We also apply these networks to the task of segmenting whole slide images of tissue sections and discuss their practical applicability for processing gigapixel sized images.
An End-to-End System for Unmanned Aerial Vehicle High-Resolution Remote Sensing Image Haze Removal Algorithm Using Convolution Neural Network

An end-to-end image dehazing method based on convolution neural network is presented to solve the problem in which Unmanned Aerial Vehicle (UAV) high-resolution remote sensing images have reduced image sharpness due to haze.First, the original atmospheric scattering model is adapted to get an end-to-end dehazing model.Then, several unknown parameters are unified into one parameter, and the unknown parameter is estimated by using a multiscale convolution neural network.Finally, the parameter estimates are incorporated into the dehazing model to get a haze-free image.For the no reference image dataset, we first train the network using existing datasets, and then the network is trained using a self-built dataset.In this article, the haze removal effect for different types of unmanned remote sensing images is tested and compared with those of the main dehazing algorithms.The experiments show that the algorithm in this article has different degrees of improvement regarding its visual effect and objective indicators.
DATA: Differentiable ArchiTecture Approximation With Distribution Guided Sampling

Neural architecture search (NAS) is inherently subject to the gap of architectures during searching and validating. To bridge this gap effectively, we develop Differentiable ArchiTecture Approximation (DATA) with Ensemble Gumbel-Softmax (EGS) estimator and Architecture Distribution Constraint (ADC) to automatically approximate architectures during searching and validating in a differentiable manner. Technically, the EGS estimator consists of a group of Gumbel-Softmax estimators, which is capable of converting probability vectors to binary codes and passing gradients reversely, reducing the estimation bias in a differentiable way. To narrow the distribution gap between sampled architectures and supernet, further, the ADC is introduced to reduce the variance of sampling during searching. Benefiting from such modeling, architecture probabilities and network weights in the NAS model can be jointly optimized with the standard back-propagation, yielding an end-to-end learning mechanism for searching deep neural architectures in an extended search space. Conclusively, in the validating process, a high-performance architecture that approaches to the learned one during searching is readily built. Extensive experiments on various tasks including image classification, few-shot learning, unsupervised clustering, semantic segmentation and language modeling strongly demonstrate that DATA is capable of discovering high-performance architectures while guaranteeing the required efficiency. Code is available at https://github.com/XinbangZhang/DATA-NAS.
A User Experience Questionnaire for VR Locomotion: Formulation and Preliminary Evaluation

When evaluating virtual reality (VR) locomotion techniques, the user experience metrics that are used are usually either focused on specific experiential dimensions or based on non-standardised, subjective reporting. The field would benefit from a standard questionnaire for evaluating the general user experience of VR locomotion techniques. This paper presents a synthesised user experience questionnaire for VR locomotion, which is called the VR Locomotion Experience Questionnaire (VRLEQ). It comprises the Game Experience Questionnaire (GEQ) and the System Usability Scale (SUS) survey. The results of the VRLEQ's application in a comparative, empirical study ( $$n = 26$$ ) of three prevalent VR locomotion techniques are described. The questionnaire's content validity is assessed at a preliminary level based on the correspondence between the questionnaire items and the qualitative results from the study's semi-structured interviews. VRLEQ's experiential dimensions' scoring corresponded well with the semi-structured interview remarks and effectively captured the experiential qualities of each VR locomotion technique. The VRLEQ results facilitated and quantified comparisons between the techniques and enabled an understanding of how the techniques performed in relation to each other.
GazeRoomLock: Using Gaze and Head-Pose to Improve the Usability and Observation Resistance of 3D Passwords in Virtual Reality

Authentication has become an important component of Immersive Virtual Reality (IVR) applications, such as virtual shopping stores, social networks, and games. Recent work showed that compared to traditional graphical and alphanumeric passwords, a more promising form of passwords for IVR is 3D passwords. This work evaluates four multimodal techniques for entering 3D passwords in IVR that consist of multiple virtual objects selected in succession. Namely, we compare eye gaze and head pose for pointing, and dwell time and tactile input for selection. A comparison of a) usability in terms of entry time, error rate, and memorability, and b) resistance to real world and offline observations, reveals that: multimodal authentication in IVR by pointing at targets using gaze, and selecting them using a handheld controller significantly improves usability and security compared to the other methods and to prior work. We discuss how the choice of pointing and selection methods impacts the usability and security of 3D passwords in IVR.
Efficient and sparse neural networks by pruning weights in a multiobjective learning approach

Overparameterization and overfitting are common concerns when designing and training deep neural networks, that are often counteracted by pruning and regularization strategies. However, these strategies remain secondary to most learning approaches and suffer from time and computational intensive procedures. We suggest a multiobjective perspective on the training of neural networks by treating its prediction accuracy and the network complexity as two individual objective functions in a biobjective optimization problem. As a showcase example, we use the cross entropy as a measure of the prediction accuracy while adopting an l1-penalty function to assess the total cost (or complexity) of the network parameters. The latter is combined with an intra-training pruning approach that reinforces complexity reduction and requires only marginal extra computational cost. From the perspective of multiobjective optimization, this is a truly large-scale optimization problem. We compare two different optimization paradigms: On the one hand, we adopt a scalarization-based approach that transforms the biobjective problem into a series of weighted-sum scalarizations. On the other hand we implement stochastic multi-gradient descent algorithms that generate a single Pareto optimal solution without requiring or using preference information. In the first case, favorable knee solutions are identified by repeated training runs with adaptively selected scalarization parameters. Numerical results on exemplary convolutional neural networks confirm that large reductions in the complexity of neural networks with negligible loss of accuracy are possible.
Seismic signal enhancement based on the low‐rank methods

ABSTRACT Based on the fact that the Hankel matrix constructed by noise‐free seismic data is low‐rank, low‐rank approximation (or rank‐reduction) methods have been widely used for removing noise from seismic data. Due to the linear‐event assumption of the traditional low‐rank approximation method, it is difficult to define a rank that optimally separates the data subspace into signal and noise subspaces. For preserving the most useful signal energy, a relatively large rank threshold is often chosen, which inevitably leaves residual noise. To reduce the energy of residual noise, we propose an optimally damped rank‐reduction method. The optimal damping is applied via two steps. In the first step, a set of optimal damping weights is derived. In the second step, we derive an optimal singular value damping operator. We review several traditional low‐rank methods and compare their performance with the new one. We also compare these low‐rank methods with two sparsity‐promoting transform methods. Examples demonstrate that the proposed optimally damped rank‐reduction method could get significantly cleaner denoised images compared with the state‐of‐the‐art methods.
BIDI: A classification algorithm with instance difficulty invariance

In artificial intelligence, an expert/intelligent systems can emulate the decision-making ability of human experts. A good classification algorithm can provide significant assistance to expert/intelligent systems in solving a variety of practical problems. In classification, the “hard” instances may be outliers or noisy instances that are difficult to learn, which may confuse the classifier and induce the overfitting problem in the case of placing much emphasis on them. In fact, the difficulty of instances is crucial for improving the generalization and credibility of classification. Unfortunately, nearly all the existing classifiers ignore this important information. In this paper, the classification difficulty of each instance is introduced from a statistical perspective, which is an inherent characteristic of the instance itself. Then, a new classification algorithm named “boosting with instance difficulty invariance (BIDI)” is proposed by incorporating the classification difficulty of instances. The BIDI conforms to the human cognition that easy instances are misclassified with a lower probability than difficult ones, and performs better with respect to generalization. The key insight of BIDI can provide relevant guidance for researchers to improve the generalization and credibility of classifiers in the expert systems of decision support systems. Experimental results demonstrate the effectiveness of BIDI in real-world data sets, indicating that it has great potential for solving many classification tasks of expert systems such as disease diagnosis and credit card fraud detection. Although the classification difficulty has strong statistical significance, its implementation remains computationally expensive. A fast method demonstrating rationality and feasibility is also proposed to approximate instances’ classification difficulty.
Improved Population-Based Incremental Learning of Bayesian Networks with partly known structure and parallel computing

Bayesian Network is a frequently-used model for fault detection and diagnosis in industrial processes. In this article, some modifications are made to Population-Based Incremental Learning and the improved algorithm is applied to structure learning of Bayesian networks. A pre-training step with K2 algorithm is added to the Population-Based Incremental Learning process to obtain an initial probability vector. Then, an elitist strategy is introduced into this method, providing a better way to update the probability vector. Individuals generated in every iteration, and elites in history are utilized to update the vector. The nature of this method makes it possible to learn the bayesian network whose structure is partly known, for sometimes we can specify some parts of the structure with prior process knowledge. A benchmark network Alarm and an industrial process are provided for performance evaluation and comparisons. Furthermore, we parallelize the algorithm to make it more efficient to learn Bayesian Networks. The speed of Improved Population-Based Incremental Learning has been improved significantly after parallelization.
Application of Transfer Learning in Infrared Pedestrian Detection

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in our life. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. However, the lack of large labeled dataset obstructs the usage of convolutional neural networks (CNN) for detecting in thermal infrared images. Most existing dataset focus on visible images, while thermal infrared images are helpful for detection even in a dark environment. To address this problem, we propose the use of transfer learning to improve the accuracy of infrared pedestrian detection. We pretrain a convolutional neural network on a large dataset (which contains 1.8 million images with 654 categories), then use the convolutional neural network as a fixed feature extractor for the task of infrared pedestrian detection. The average precision of detection using ImageNet pretrained model alone is 83.34%. By adding ours pretrained model, the average precision has improved to 84.78%. We believe that the method of transfer learning can be extended to other infrared detection applications and achieve other breakthroughs.
Receiver operating characteristic curves and confidence bands for support vector machines

Many problems that appear in biomedical decision-making, such as diagnosing disease and predicting response to treatment, can be expressed as binary classification problems. The support vector machine (SVM) is a popular classification technique that is robust to model misspecification and effectively handles high-dimensional data. The relative costs of false positives and false negatives can vary across application domains. The receiving operating characteristic (ROC) curve provides a visual representation of the trade-off between these two types of errors. Because the SVM does not produce a predicted probability, an ROC curve cannot be constructed in the traditional way of thresholding a predicted probability. However, a sequence of weighted SVMs can be used to construct an ROC curve. Although ROC curves constructed using weighted SVMs have great potential for allowing ROC curves analyses that cannot be done by thresholding predicted probabilities, their theoretical properties have heretofore been underdeveloped. We propose a method for constructing confidence bands for the SVM ROC curve and provide the theoretical justification for the SVM ROC curve by showing that the risk function of the estimated decision rule is uniformly consistent across the weight parameter. We demonstrate the proposed confidence band method using simulation studies. We present a predictive model for treatment response in breast cancer as an illustrative example.
Digital Evidence Object Model for Situation Awareness and Decision Making in Digital Forensics Investigation

The aim of a forensic investigation is to provide situation awareness in terms of identification and preservation of digital evidence, extraction of information, and analysis of extracted information to facilitate time-critical decision making. Digital forensic investigation is a process of collecting, examining, and analyzing digital data from various places such as digital devices, networks, and big data in the cloud. Here we propose a novel digital evidence object (DEO) model for the reduction of forensics data in digital forensic investigation and describe its application. The proposed DEO model is based on the synergy of category theory and integration of 5Ws (Who, What, When, Where, and Why) of digital investigation analysis techniques for digital evidence acquisition. We present a real-life case study to demonstrate its suitability for assisting computer forensics experts in the digital evidence investigation. Our results demonstrate that the application of the DEO model can noticeably decrease the number of false positive evidence objects submitted to a forensics expert, thus reducing his/her workload and improving decision making performance in a time-critical setting.
GANRED

In recent years, deep neural networks (DNN) have become an important type of intellectual property due to their high performance on various classification tasks. As a result, DNN stealing attacks have emerged. Many attack surfaces have been exploited, among which cache timing side-channel attacks are hugely problematic because they do not need physical probing or direct interaction with the victim to estimate the DNN model. However, existing cache-side-channel-based DNN reverse engineering attacks rely on analyzing the binary code of the DNN library that must be shared between the attacker and the victim in the main memory. In reality, the DNN library code is often inaccessible because 1) the code is proprietary, or 2) memory sharing has been disabled by the operating system. In our work, we propose GANRED, an attack approach based on the generative adversarial nets (GAN) framework which utilizes cache timing side-channel information to accurately recover the structure of DNNs without memory sharing or code access. The benefit of GANRED is four-fold. 1) There is no need for DNN library code analysis. 2) No shared main memory segment between the victim and the attacker is needed. 3) Our attack locates the exact structure of the victim model, unlike existing attacks which only narrow down the structure search space. 4) Our attack efficiently scales to deeper DNNs, exhibiting only linear growth in the number of layers in the victim DNN.
L2,1-Extreme Learning Machine: An Efficient Robust Classifier for Tumor Classification

With the development of cancer research, various gene expression datasets containing cancer information show an explosive growth trend. In addition, due to the continuous maturity of single-cell RNA sequencing (scRNA-seq) technology, the protein information and pedigree information of a single cell are also continuously mined. It is a technical problem of how to classify these high-dimensional data correctly. In recent years, Extreme Learning Machine (ELM) has been widely used in the field of supervised learning and unsupervised learning. However, the traditional ELM does not consider the robustness of the method. To improve the robustness of ELM, in this paper, a novel ELM method based on L2,1-norm named L2,1-Extreme Learning Machine (L2,1 -ELM) has been proposed. The method introduces L2,1-norm on loss function to improve the robustness, and minimizes the influence of noise and outliers. Firstly, we evaluate the new method on five UCI datasets. The experiment results prove that our method can achieve competitive results. Next, the novel method is applied to the problem of classification of cancer samples and single-cell RNA sequencing datasets. The experimental results on The Cancer Genome Atlas (TCGA) datasets and scRNA-seq datasets prove that ELM and its variants has great potential in the classification of cancer samples.