Reviewerly/exHarmony huggingface dataset:
Dynamic analysis and application in medical digital image watermarking of a new multi-scroll neural network with quartic nonlinear memristor

Memristor is widely used in various neural bionic models because of its excellent characteristics in biological neural activity simulation. In this paper, a piecewise nonlinear function is used to transform the quartic memristor, which is introduced into the ternary Hopfield neural network (HNN) with self-feedback, and a piecewise quartic memristive chaotic neural network model with multi-scroll is constructed. Through simulation analysis, the number of scroll layers changes with memristor parameters and has significant coexistence of multi-scroll attractors and high initial value sensitivity has been found. Using its excellent unpredictability, a digital watermarking algorithm based on wavelet transform is improved and used in the protection of personal medical data. The results show that it not only improves the confidentiality and convenience, but also ensures its robustness and has good encryption effect.
INFERNO: Inference-Aware Neural Optimisation

Complex computer simulations are commonly required for accurate data modelling in many scientific disciplines, making statistical inference challenging due to the intractability of the likelihood evaluation for the observed data. Furthermore, sometimes one is interested on inference drawn over a subset of the generative model parameters while taking into account model uncertainty or misspecification on the remaining nuisance parameters. In this work, we show how non-linear summary statistics can be constructed by minimising inference-motivated losses via stochastic gradient descent such they provided the smallest uncertainty for the parameters of interest. As a use case, the problem of confidence interval estimation for the mixture coefficient in a multi-dimensional two-component mixture model (i.e. signal vs background) is considered, where the proposed technique clearly outperforms summary statistics based on probabilistic classification, which are a commonly used alternative but do not account for the presence of nuisance parameters.
Residual Compensation Networks for Heterogeneous Face Recognition

Heterogeneous Face Recognition (HFR) is a challenging task due to large modality discrepancy as well as insufficient training images in certain modalities. In this paper, we propose a new two-branch network architecture, termed as Residual Compensation Networks (RCN), to learn separated features for different modalities in HFR. The RCN incorporates a residual compensation (RC) module and a modality discrepancy loss (MD loss) into traditional convolutional neural networks. The RC module reduces modal discrepancy by adding compensation to one of the modalities so that its representation can be close to the other modality. The MD loss alleviates modal discrepancy by minimizing the cosine distance between different modalities. In addition, we explore different architectures and positions for the RC module, and evaluate different transfer learning strategies for HFR. Extensive experiments on IIIT-D Viewed Sketch, Forensic Sketch, CASIA NIR-VIS 2.0 and CUHK NIR-VIS show that our RCN outperforms other state-of-the-art methods significantly.
Local Deep Descriptor for Remote Sensing Image Feature Matching

Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network.
CFM-BD: A Distributed Rule Induction Algorithm for Building Compact Fuzzy Models in Big Data Classification Problems

Interpretability has always been a major concern for fuzzy rule-based classifiers. The usage of human-readable models allows them to explain the reasoning behind their predictions and decisions. However, when it comes to Big Data classification problems, fuzzy rule-based classifiers have not been able to maintain the good trade-off between accuracy and interpretability that has characterized these techniques in non-Big Data environments. The most accurate methods build too complex models composed of a large number of rules and fuzzy sets, while those approaches focusing on interpretability do not provide state-of-the-art discrimination capabilities. In this paper, we propose a new distributed learning algorithm named CFM-BD to construct accurate and compact fuzzy rule-based classification systems for Big Data. This method has been specifically designed from scratch for Big Data problems and does not adapt or extend any existing algorithm. The proposed learning process consists of three stages: 1) pre-processing based on the probability integral transform theorem; 2) rule induction inspired by CHI-BD and Apriori algorithms; 3) rule selection by means of a global evolutionary optimization. We conducted a complete empirical study to test the performance of our approach in terms of accuracy, complexity, and runtime. The results obtained were compared and contrasted with four state-of-the-art fuzzy classifiers for Big Data (FBDT, FMDT, Chi-Spark-RS, and CHI-BD). According to this study, CFM-BD is able to provide competitive discrimination capabilities using significantly simpler models composed of a few rules of less than 3 antecedents, employing 5 linguistic labels for all variables.
An Adaptive Density Peaks Clustering Method With Fisher Linear Discriminant

Clustering is one of the most important topics in data mining and machine learning.The density peaks clustering (DPC) algorithm is a well-known density-based clustering method that can efficiently and effectively deal with non-spherical clusters.However, the computational methods of the local density and the distance measure are simple and easily ignore the correlation and the similarity between samples, and the manual setting of parameters has a great influence on the clustering results; therefore, the clustering performance of DPC is poor on the high-dimensional datasets.To address these issues, this paper presents an adaptive DPC algorithm with Fisher linear discriminant for the clustering of complex datasets, called ADPC-FLD.First, the kernel density estimation function is introduced to calculate the local density of the sample points.Pearson correlation coefficient between samples as weight is employed to construct a weighted Euclidean distance function to measure the distance between samples.This considers both the spatial structure and the correlation of the samples.Then, a novel density estimation entropy is proposed, and based on the minimization of density estimation entropy, the density estimation parameters are adaptively selected according to the distribution characteristics of the data, which can efficiently eliminate the influence of manual setting.Third, an adaptive strategy of cluster center selection is designed to avoid the error caused by the noise data as the cluster centers and the uncertainty of manually selecting the cluster centers.Finally, Fisher linear discriminant algorithm is used to eliminate the irrelevant information and reduce the dimensionality of high-dimensional data, following on which an adaptive DPC method is implemented on six synthetic datasets, thirteen UCI datasets and seven gene expression datasets for comparing with other related algorithms.The experimental results on 26 datasets show that the proposed algorithm significantly outperforms several outstanding clustering approaches in terms of clustering accuracy and efficiency.
A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems

The integration of machine learning techniques and metaheuristic algorithms is an area of interest due to the great potential for applications. In particular, using these hybrid techniques to solve combinatorial optimization problems (COPs) to improve the quality of the solutions and convergence times is of great interest in operations research. In this article, the db-scan unsupervised learning technique is explored with the goal of using it in the binarization process of continuous swarm intelligence metaheuristic algorithms. The contribution of the db-scan operator to the binarization process is analyzed systematically through the design of random operators. Additionally, the behavior of this algorithm is studied and compared with other binarization methods based on clusters and transfer functions (TFs). To verify the results, the well-known set covering problem is addressed, and a real-world problem is solved. The results show that the integration of the db-scan technique produces consistently better results in terms of computation time and quality of the solutions when compared with TFs and random operators. Furthermore, when it is compared with other clustering techniques, we see that it achieves significantly improved convergence times.
An Improved Authentication Scheme for Remote Data Access and Sharing Over Cloud Storage in Cyber-Physical-Social-Systems

Cyber-physical-social systems (CPSSs) epitomize an evolving paradigm, including the social, physical, and cyber world.The vital goal of CPSSs is to offer personalized, high-quality, and proactive services for the end-users.An ingenious framework for reliable services is required for CPSSs to achieve this purpose.In this regard, the cloud storage environment of cloud computing (having a great connection with the physical, cyber, and social world) requires a reliable framework for secure communication between cloud and users.Cloud storage provides various services that need scalable, cost-effective, and secure facilities of data management.Public cloud storage bound its users to maintain strict security considerations that are offered by cloud service providers.On the other hand, an opportunity for users is offered by private cloud storage to construct a controlled and self-managed model of data security.This mobile model is responsible for managing the sharing and accessing of data privately.Despite that, it induces new challenges of data security.One censorious problem is to ensure the authenticated and secure model of data-storage for accessing the data under the controlled environment of data accessibility.To tackle this challenge, many protocols have been developed.The problem is that all these protocols are unable to fulfill the required security efficiency and are susceptible to various security attacks.Recently, Tiwari et al. presented an authentication scheme for data sharing and access with a biometric feature.They claimed that their scheme resists significant security attacks.However, in this article, we show that the claim of Tiwari et al. for developing a secure scheme is not valid, and their protocol is insecure against user and server impersonation attacks.Moreover, the protocol of Tiwari et al. do not provide user anonymity.Therefore, we present an enhanced, secure, and convenient scheme for data access.Besides, in order to add the flexible distribution of data that is controlled by data-owner, our protocol provides proxy re-encryption in which the cloud server utilizes the proxy re-encryption key.Then, the data-owner generates the credential token during decryption for controlling user's accessibility.The security analysis determines that our proposed protocol resists numerous security attacks.Furthermore, performance analysis determines that our protocol has practical computation, communication, and storage costs as compared to various related protocols.Consequently, our introduced protocol achieves not only the security goals but also has performance efficiency comparable to numerous relevant protocols of cloud storage.
P-ODN: Prototype-based Open Deep Network for Open Set Recognition

Abstract Most of the existing recognition algorithms are proposed for closed set scenarios, where all categories are known beforehand. However, in practice, recognition is essentially an open set problem. There are categories we know called “knowns”, and there are more we do not know called “unknowns”. Enumerating all categories beforehand is never possible, consequently, it is infeasible to prepare sufficient training samples for those unknowns. Applying closed set recognition methods will naturally lead to unseen-category errors. To address this problem, we propose the prototype-based Open Deep Network (P-ODN) for open set recognition tasks. Specifically, we introduce prototype learning into open set recognition. Prototypes and prototype radiuses are trained jointly to guide a CNN network to derive more discriminative features. Then P-ODN detects the unknowns by applying a multi-class triplet thresholding method based on the distance metric between features and prototypes. Manual labeling the unknowns which are detected in the previous process as new categories. Predictors for new categories are added to the classification layer to “open” the deep neural networks to incorporate new categories dynamically. The weights of new predictors are initialized exquisitely by applying a distances based algorithm to transfer the learned knowledge. Consequently, this initialization method speeds up the fine-tuning process and reduce the samples needed to train new predictors. Extensive experiments show that P-ODN can effectively detect unknowns and needs only few samples with human intervention to recognize a new category. In the real world scenarios, our method achieves state-of-the-art performance on the UCF11, UCF50, UCF101 and HMDB51 datasets.
Information Scrambling in Quantum Neural Networks

The quantum neural network is one of the promising applications for near-term noisy intermediate-scale quantum computers. A quantum neural network distills the information from the input wavefunction into the output qubits. In this Letter, we show that this process can also be viewed from the opposite direction: the quantum information in the output qubits is scrambled into the input. This observation motivates us to use the tripartite information, a quantity recently developed to characterize information scrambling, to diagnose the training dynamics of quantum neural networks. We empirically find strong correlation between the dynamical behavior of the tripartite information and the loss function in the training process, from which we identify that the training process has two stages for randomly initialized networks. In the early stage, the network performance improves rapidly and the tripartite information increases linearly with a universal slope, meaning that the neural network becomes less scrambled than the random unitary. In the latter stage, the network performance improves slowly while the tripartite information decreases. We present evidences that the network constructs local correlations in the early stage and learns large-scale structures in the latter stage. We believe this two-stage training dynamics is universal and is applicable to a wide range of problems. Our work builds bridges between two research subjects of quantum neural networks and information scrambling, which opens up a new perspective to understand quantum neural networks.
Investigating Transformers for Automatic Short Answer Grading

Recent advancements in the field of deep learning for natural language processing made it possible to use novel deep learning architectures, such as the Transformer, for increasingly complex natural language processing tasks. Combined with novel unsupervised pre-training tasks such as masked language modeling, sentence ordering or next sentence prediction, those natural language processing models became even more accurate. In this work, we experiment with fine-tuning different pre-trained Transformer based architectures. We train the newest and most powerful, according to the glue benchmark, transformers on the SemEval-2013 dataset. We also explore the impact of transfer learning a model fine-tuned on the MNLI dataset to the SemEval-2013 dataset on generalization and performance. We report up to 13% absolute improvement in macro-average-F1 over state-of-the-art results. We show that models trained with knowledge distillation are feasible for use in short answer grading. Furthermore, we compare multilingual models on a machine-translated version of the SemEval-2013 dataset.
High-Speed and Secure PRNG for Cryptographic Applications

Due to the fundamentally different approach underlying quantum cryptography (QC), it has not only become competitive, but also has significant advantages over traditional cryptography methods.Such significant advantage as theoretical and informational stability is achieved through the use of unique quantum particles and the inviolability of quantum physics postulates, in addition it does not depend on the intruder computational capabilities.However, even with such impressive reliability results, QC methods have some disadvantages.For instance, such promising trend as quantum secure direct communicationeliminates the problem of key distribution, since it allows to transmit information by open channel without encrypting it.However, in these protocols, each bit is confidential and should not be compromised, therefore, the requirements for protocol stability are increasing and additional security methods are needed.For a whole class of methods to ensure qutrit QC protocols stability, reliable trit generation method is required.In this paper authors have developed and studied trit generation method and software tool TriGen v.2.0 PRNG.Developed PRNG is important for various practical cryptographic applications (for example, trit QC systems, IoT and Blockchain technologies).Future research can be related with developing fully functional version of testing technique and software tool.
[Invited Paper] Fast and Accurate Whole-Body Pose Estimation in the Wild and Its Applications

Recently, multi-person pose estimation techniques have drawn significant attention in both academia and industry due to their great potential utility. Here, we present a fast and accurate in-the-wild whole-body pose estimation system. Our system detects not only body keypoints, but also foot and hand keypoints, with high accuracy in real time. Furthermore, we present two typical applications of our system, in the areas of fitness and speed climbing, and describe the optimization of these applications, which has yielded both computing acceleration and improved detection accuracy.
A multidisciplinary ensemble algorithm for clustering heterogeneous datasets

Clustering is a commonly used method for exploring and analysing data where the primary objective is to categorise observations into similar clusters. In recent decades, several algorithms and methods have been developed for analysing clustered data. We notice that most of these techniques deterministically define a cluster based on the value of the attributes, distance, and density of homogenous and single-featured datasets. However, these definitions are not successful in adding clear semantic meaning to the clusters produced. Evolutionary operators and statistical and multi-disciplinary techniques may help in generating meaningful clusters. Based on this premise, we propose a new evolutionary clustering algorithm (ECAStar) based on social class ranking and meta-heuristic algorithms for stochastically analysing heterogeneous and multiple-featured datasets. The ECAStar is integrated with recombinational evolutionary operators, Levy flight optimisation, and some statistical techniques, such as quartiles and percentiles, as well as the Euclidean distance of the K-means algorithm. Experiments are conducted to evaluate the ECAStar against five conventional approaches: K-means (KM), K-meansPlusPlus (KMPlusPlus), expectation maximisation (EM), learning vector quantisation (LVQ), and the genetic algorithm for clusteringPlusPlus (GENCLUSTPlusPlus).
Cross Reality (XR): Challenges and Opportunities Across the Spectrum

Abstract Cross Reality (XR) resources hold promise for enhancing instruction and learning experiences in and out of the classroom. Appropriate XR applications can provide the foundation for new types of learning environments and experiences while bringing users together to create unique communities of inquiry and practice. Here we explore the opportunities and benefits of harnessing the affordances of XR while exploring the challenges associated with implementation. Recommendations and implications for future research are also addressed.
Case-based Reasoning for Natural Language Queries over Knowledge Bases

It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -a paradigm known as case-based reasoning (CBR).We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases.CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it.On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance.For example, on the COMPLEXWEBQUESTIONS dataset, CBR-KBQA outperforms the current state of the art by 11% on accuracy.Furthermore, we show that CBR-KBQA is capable of using new cases without any further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing unseen KB entities as well as relations.* * Now director of AI at EY CESA,