Reviewerly/exHarmony huggingface dataset:
Permissioned Blockchain and Deep Learning for Secure and Efficient Data Sharing in Industrial Healthcare Systems

The industrial healthcaresystem has enabled the possibility of realizing advanced real-time monitoring of patients and enriched the quality of medical services through data sharing among intelligent wearable devices and sensors. However, this connectivity brings the intrinsic vulnerabilities related to security and privacy due to the need of continuous communication and monitoring over public network (insecure channel). Motivated from the aforementioned discussions, we integrate permissioned blockchain and smart contract with deep learning (DL) techniques to design a novel secure and efficient data sharing framework named PBDL. Specifically, PBDL first has a blockchain scheme to register, verify (using zero-knowledge proof), and validate the communicating entities using the smart contract-based consensus mechanism. Second, the authenticated data are used to propose a novel DL scheme that combines stacked sparse variational autoencoder (SSVAE) with self-attention-based bidirectional long short term memory (SA-BiLSTM). In this scheme, SSVAE encodes or transforms the healthcare data into new format, and SA-BiLSTM identifies and improves the attack detection process. The security analysis and experimental results using IoT-Botnet and ToN-IoT datasets confirm the superiority of the PBDL framework over existing state-of-the-art techniques.
In-Depth Mouse: Integrating Desktop Mouse into Virtual Reality

Virtual Reality (VR) has potential for productive knowledge work, however, midair pointing with controllers or hand gestures does not offer the precision and comfort of traditional 2D mice. Directly integrating mice into VR is difficult as selecting targets in a 3D space is negatively impacted by binocular rivalry, perspective mismatch, and improperly calibrated control-display (CD) gain. To address these issues, we developed Depth-Adaptive Cursor , a 2D-mouse driven pointing technique for 3D selection with depth-adaptation that continuously interpolates the cursor depth by inferring what users intend to select based on the cursor position, the viewpoint, and the selectable objects. Depth-Adaptive Cursor uses a novel CD gain tool to compute a usable range of CD gains for general mouse-based pointing in VR. A user study demonstrated that Depth-Adaptive Cursor significantly improved performance compared with an existing mouse-based pointing technique without depth-adaption in terms of time (21.2%), error (48.3%), perceived workload, and user satisfaction.
Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. In this research study, the main aim is to detect such frauds, including the accessibility of public data, high-class imbalance data, the changes in fraud nature, and high rates of false alarm. The relevant literature presents many machines learning based approaches for credit card detection, such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and XG Boost. However, due to low accuracy, there is still a need to apply state of the art deep learning algorithms to reduce fraud losses. The main focus has been to apply the recent development of deep learning algorithms for this purpose. Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes. The detailed empirical analysis is carried out using the European card benchmark dataset for fraud detection. A machine learning algorithm was first applied to the dataset, which improved the accuracy of detection of the frauds to some extent. Later, three architectures based on a convolutional neural network are applied to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A comprehensive empirical analysis has been carried out by applying variations in the number of hidden layers, epochs and applying the latest models. The evaluation of research work shows the improved results achieved, such as accuracy, f1-score, precision and AUC Curves having optimized values of 99.9%,85.71%,93%, and 98%, respectively. The proposed model outperforms the state-of-the-art machine learning and deep learning algorithms for credit card detection problems. In addition, we have performed experiments by balancing the data and applying deep learning algorithms to minimize the false negative rate. The proposed approaches can be implemented effectively for the real-world detection of credit card fraud.
Speculative Taint Tracking (STT)

Speculative execution attacks present an enormous security threat, capable of reading arbitrary program data under malicious speculation, and later exfiltrating that data over microarchitectural covert channels. Since these attacks first rely on being able to read arbitrary data (potential secrets), a conservative approach to defeat all attacks is to delay the execution of instructions that read those secrets, until those instructions become non-speculative.
Interledger Approaches

While blockchains and more generally distributed ledger technologies (DLTs) are passing over their hype curve peak, their shortcomings are becoming more apparent. One relatively recent approach to address their performance, scalability, privacy, and other problems are to use multiple different DLTs instead of relying on just one. While there are no really established standards for combining several DLTs, a few repeating patterns can be observed. In this paper, we present a survey of interledger approaches, discussing and comparing their underlying mechanisms. A shared motivation for all of the discussed interledger solutions is to move away from the “one chain rules them all” model to one that allows the interconnection of multiple ledgers, with different features and advantages, while also supporting innovation. The interledger approaches discussed in this survey include (1) atomic cross-chain transactions, (2) transactions across a network of payment channels, (3) the W3C Interledger Protocol (ILP), (4) bridging, (5) sidechains, and (6) ledger-of-ledgers. The approaches are compared according to whether they support the transfer or the exchange of value, their interconnection trust mechanism, complexity, scalability, and transaction cost.
Multimodal continuous ant colony optimization for multisensor remote sensing image registration with local search

Due to the large differences between different imaging sensors, multisensor remote sensing image registration is a challenging work. Multisensor remote sensing image registration can be formulated as a multimodal problem, and general optimization methods may get trapped into a local optimum when solving complex multimodal problems. In this paper, we introduce a multimodal continuous ant colony optimization algorithm for multisensor remote sensing image registration, and an efficient optimization method is designed as local search operation. Multimodal continuous ant colony optimization algorithm can preserve high diversity and has the global search ability for multimodal problems. Meanwhile, efficient local search operation can improve the efficiency and provide the accurate result. The experimental results have demonstrated the effectiveness and robustness of the proposed method.
New Incremental Learning Algorithm With Support Vector Machines

Incremental learning is one of the most effective methods of learning accumulated data and large-scale data. The newly increased samples of the previously known works on incremental learning are usually independent and identically distributed. To study how dependent sampling methods influence the learning ability of incremental support vector machines (ISVM) algorithm, in this paper we introduce an ISVM based on Markov resampling (MR-ISVM), and give the experimental research on the learning ability of the MR-ISVM algorithm. The experimental results indicate that the MR-ISVM algorithm has not only smaller misclassification rates and sparser of the obtained classifiers, but also less total time of sampling and training compared to ISVM based on randomly independent sampling. We also compare it with other ISVM algorithms.
Dynamic linear response quantum algorithm

The dynamic linear response of a quantum system is critical for understanding both the structure and dynamics of strongly interacting quantum systems, including neutron scattering from materials, photon and electron scattering from atomic systems, and electron and neutrino scattering by nuclei. We present a general algorithm for universal quantum computers to calculate the dynamic linear response function with controlled errors and to obtain information about specific final states that can be directly compared to experimental observations.
Unsupervised Deep Learning of Compact Binary Descriptors

Binary descriptors have been widely used for efficient image matching and retrieval. However, most existing binary descriptors are designed with hand-craft sampling patterns or learned with label annotation provided by datasets. In this paper, we propose a new unsupervised deep learning approach, called DeepBit, to learn compact binary descriptor for efficient visual object matching. We enforce three criteria on binary descriptors which are learned at the top layer of the deep neural network: 1) minimal quantization loss, 2) evenly distributed codes and 3) transformation invariant bit. Then, we estimate the parameters of the network through the optimization of the proposed objectives with a back-propagation technique. Extensive experimental results on various visual recognition tasks demonstrate the effectiveness of the proposed approach. We further demonstrate our proposed approach can be realized on the simplified deep neural network, and enables efficient image matching and retrieval speed with very competitive accuracies.
Firefly Algorithm based Feature Selection for Arabic Text Classification

Due to the large number of documents available in the internet, emails and digital libraries, document classification is becoming a crucial task extremely required. It is commonly achieved after performing feature selection that consists of selecting appropriate features to enhance the classification accuracy. Most of feature selection based text classification methods rely on building a term-frequency inverse-document frequency feature vector which is not usually efficient. In addition, numerous document classification studies are focused on English language. This paper deals with Arabic Text Classification which is not intensively studied due to the complexity of Arabic language. A new firefly algorithm based feature selection method is proposed. This algorithm has been successfully applied in different combinatorial problems. However, it has not been involved in feature selection concept to deal with Arabic Text Classification. To validate this technique, Support Vector Machine classifier is used as well as three evaluation measures including precision, recall and F-measure. Furthermore, experiments on OSAC real dataset along with a comparison with the state-of-the-art methods are performed. The proposed method achieves a precision value equals to 0.994. The results confirm the efficiency of the proposed feature selection method in improving Arabic Text Classification accuracy.
Privacy-Preserving Auction for Big Data Trading Using Homomorphic Encryption

Cyber-Physical Systems (smart grid, smart transportation, smart cities, etc.), driven by advances in Internet of Things (IoT) technologies, will provide the infrastructure and integration of smart applications to accelerate the generation and collection of big data to an unprecedented scale. As a fundamental commodity in our current information age, big data is a crucial key to competitiveness in modern commerce. In this paper, we address the issue of privacy preservation for data auction in CPS by leveraging the concept of homomorphic cryptography and secure network protocol design. Specifically, we propose a generic Privacy-Preserving Auction Scheme (PPAS), in which the two independent entities of Auctioneer and Intermediate Platform comprise an untrusted third-party trading platform. Via the implementation of homomorphic encryption and one-time pad, a winner in the auction process can be determined and all bidding information is disguised. Yet, to further improve the security of the privacy-preserving auction, we additionally propose an Enhanced Privacy-Preserving Auction Scheme (EPPAS) that leverages an additional signature verification mechanism. The feasibilities of both schemes are validated through detailed theoretical analyses and extensive performance evaluations, including assessment of the resilience to attacks. In addition, we discuss some open issues and extensions relevant to our scheme.
Relative CNN-RNN: Learning Relative Atmospheric Visibility From Images

We propose a deep learning approach for directly estimating relative atmospheric visibility from outdoor photos without relying on weather images or data that require expensive sensing or custom capture. Our data-driven approach capitalizes on a large collection of Internet images to learn rich scene and visibility varieties. The relative CNN-RNN coarse-to-fine model, where CNN stands for convolutional neural network and RNN stands for recurrent neural network, exploits the joint power of relative support vector machine, which has a good ranking representation, and the data-driven deep learning features derived from our novel CNN-RNN model. The CNN-RNN model makes use of shortcut connections to bridge a CNN module and an RNN coarse-to-fine module. The CNN captures the global view while the RNN simulates human's attention shift, namely, from the whole image (global) to the farthest discerned region (local). The learned relative model can be adapted to predict absolute visibility in limited scenarios. Extensive experiments and comparisons are performed to verify our method. We have built an annotated dataset consisting of about 40000 images with 0.2 million human annotations. The large-scale, annotated visibility data set will be made available to accompany this paper.
Ideal Lattice-Based Anonymous Authentication Protocol for Mobile Devices

The rapid advances of wireless communication technologies along with the popularity of mobile devices are enabling users to access various web services anywhere and anytime. Due to the openness of wireless communications, security becomes a vital issue. To provide secure communication, many anonymous authentication protocols in mobile client-server environments based on classical mathematical hard assumptions (i.e., discrete logarithm problem or integer factorization problem) have been presented in last two decades. However, both of the two assumptions can be solved by postquantum computers in polynomial time, which means these protocols are never secure in the postquantum era. To mitigate such types of attacks, we propose an ideal lattice-based anonymous authentication protocol for mobile client-server environments. A detailed security proof shows that our proposed protocol is provably secure under a random oracle model based on the difficulty of the ring learning with errors problem. Furthermore, the informal security analysis and experimental implementation show that our proposed protocol is practical for real-world mobile client-server environments.
Task scheduling in heterogeneous cloud environment using mean grey wolf optimization algorithm

The primary objective of task scheduling involves scheduling the task on resources and minimizing the objective of the schedule. In this study, we proposed mean grey wolf optimization algorithm to enhance the system performance there by depleting the scheduling issues. The main objective of this method is minimizing the makespan and energy consumption. The objective of the proposed algorithms has been evaluated using CloudSim toolkit for standard workload. The outcome of the simulation result shows that the proposed Mean GWO algorithm renders comparatively ample result than the other existing algorithms.
Edge detection methods based on RBF interpolation

Edge detection is a widely used tool in signal/image processing with the aim of identifying abrupt changes or discontinuities in a signal/digital image. For the detection of jump discontinuities in 1D problems, we present an iterative method based on interpolation with Variably Scaled Kernels (VSKs). This is shown to outperform an existing iterative edge detection method based on multiquadric (MQ) radial basis function interpolation. To extend our purely one-dimensional edge detector to any dimension, we then introduce an innovative non iterative technique that detects jumps/edges by identifying the local maxima of the normalized absolute values of the RBF interpolant coefficients. The RBF interpolant is built-upon the compactly supported C2 Wendland function and exploits its advantageous properties to provide a robust and low-cost method. Numerical examples in 1D and 2D are included to illustrate its effectiveness and efficiency. In the context of edge detection for digital images, comparisons with Canny method are also presented.
Probabilistic Completeness of RRT for Geometric and Kinodynamic Planning With Forward Propagation

The rapidly exploring random tree (RRT) algorithm has been one of the most prevalent and popular motion-planning techniques for two decades now. Surprisingly, in spite of its centrality, there has been an active debate under which conditions RRT is probabilistically complete. We provide two new proofs of probabilistic completeness (PC) of RRT with a reduced set of assumptions. The first one for the purely geometric setting, where we only require that the solution path has a certain clearance from the obstacles. For the kinodynamic case with forward propagation of random controls and duration, we only consider in addition mild Lipschitz-continuity conditions.These proofs fill a gap in the study of RRT itself. They also lay sound foundations for a variety of more recent and alternative sampling based methods, whose PC property relies on that of RRT.
Revising the measurement process in the variational quantum eigensolver: is it possible to reduce the number of separately measured operators?

Current implementations of the Variational Quantum Eigensolver (VQE) technique for solving the electronic structure problem involve splitting the system qubit Hamiltonian into parts whose elements commute within their single qubit subspaces. The number of such parts rapidly grows with the size of the molecule. This increases the computational cost and can increase uncertainty in the measurement of the energy expectation value because elements from different parts need to be measured independently. To address this problem we introduce a more efficient partitioning of the qubit Hamiltonian using fewer parts that need to be measured separately. The new partitioning scheme is based on two ideas: (1) grouping terms into parts whose eigenstates have a single-qubit product structure, and (2) devising multi-qubit unitary transformations for the Hamiltonian or its parts to produce less entangled operators. The first condition allows the new parts to be measured in the number of involved qubit consequential one-particle measurements. Advantages of the new partitioning scheme resulting in severalfold reduction of separately measured terms are illustrated with regard to the H2 and LiH problems.
Two-Dimensional Quaternion PCA and Sparse PCA

Benefited from quaternion representation that is able to encode the cross-channel correlation of color images, quaternion principle component analysis (QPCA) was proposed to extract features from color images while reducing the feature dimension. A quaternion covariance matrix (QCM) of input samples was constructed, and its eigenvectors were derived to find the solution of QPCA. However, eigen-decomposition leads to the fixed solution for the same input. This solution is susceptible to outliers and cannot be further optimized. To solve this problem, this paper proposes a novel quaternion ridge regression (QRR) model for two-dimensional QPCA (2D-QPCA). We mathematically prove that this QRR model is equivalent to the QCM model of 2D-QPCA. The QRR model is a general framework and is flexible to combine 2D-QPCA with other technologies or constraints to adapt different requirements of real-world applications. Including sparsity constraints, we then propose a quaternion sparse regression model for 2D-QSPCA to improve its robustness for classification. An alternating minimization algorithm is developed to iteratively learn the solution of 2D-QSPCA in the equivalent complex domain. In addition, 2D-QPCA and 2D-QSPCA can preserve the spatial structure of color images and have a low computation cost. Experiments on several challenging databases demonstrate that 2D-QPCA and 2D-QSPCA are effective in color face recognition, and 2D-QSPCA outperforms the state of the arts.
When Ostrom Meets Blockchain: Exploring the Potentials of Blockchain for Commons Governance

Blockchain technologies have generated enthusiasm, yet their potential to enable new forms of governance remains largely unexplored. Two confronting standpoints dominate the emergent debate around blockchain-based governance: discourses characterized by the presence of techno-determinist and market-driven values, which tend to ignore the complexity of social organization; and critical accounts of such discourses which, while contributing to identifying limitations, consider the role of traditional centralized institutions as inherently necessary to enable democratic forms of governance. In this article, we draw on Ostrom’s principles for self-governance of communities to explore the transformative potential of blockchain beyond such standpoints. We approach blockchain through the identification and conceptualization of six affordances that this technology may provide to communities: tokenization, self-enforcement and formalization of rules, autonomous automatization, decentralization of power over the infrastructure, increasing transparency, and codification of trust. For each affordance, we carry out a detailed analysis situating each in the context of Ostrom’s principles, considering both the potentials of algorithmic governance and the importance of incorporating communities’ social practices into blockchain-based tools to foster forms of self-governance. The relationships found between these affordances and Ostrom’s principles allow us to provide a perspective focused on blockchain-based commons governance.