Reviewerly/exHarmony huggingface dataset:
Secure Decentralized Access Control Policy for Data Sharing in Smart Grid

Smart grid has improved the security, efficiency of the power system and balanced the supply and demand by intelligent management, which enhanced stability and reliability of power grid. The key point to achieve them is real-time data and consumption data sharing by using fine-grained policies. But it will bring the leakage of the privacy of the users and the loss of data control rights of the data owner. The reported solutions can not give the best trade-off among the privacy protection, control over the data shared and confidentiality. In addition, they can not solve the problems of large computation overhead and dynamic management such as users' revocation. This paper aims at these problems and proposes a decentralized attribute-based data sharing scheme. The proposed scheme ensures the secure sharing of data while removing the central authority and hiding user's identity information. It uses attribute-based signcryption (ABSC) to achieve data confidentiality and authentication. Under this model, attribute-based encryption gives the access policies for users and keeps the data confidentiality, and the attribute-based signature is used for authentication of the primary ciphertext-integrity. It is more efficient than "encrypt and then sign" or "sign and then encrypt". In addition, the proposed scheme enables user's revocation and public verifiability. Under the random oracle model, the security and the unforgeability against adaptive chosen message attack are demonstrated.
Ticking-clock performance enhanced by nonclassical temporal correlations

We investigate the role of nonclassical temporal correlations in enhancing the performance of ticking clocks in a discrete-time scenario. We show that the problem of optimal models for ticking clocks is related to the violation of Leggett-Garg-type temporal inequalities formulated in terms of, possibly invasive, sequential measurements, but on a system with a bounded memory capacity. Ticking clocks inspire the derivation of a family of temporal inequalities showing a gap between classical and quantum correlations, despite involving no input. We show that quantum ticking-clock models achieving accuracy beyond the classical bound are also those violating Leggett-Garg-type temporal inequalities for finite sequences and we investigate their continuous-time limit. Interestingly, we show that optimal classical clock models in the discrete-time scenario do not have a well-defined continuous-time limit, a feature that is absent in quantum models.
Quality Measurement for High Dynamic Range Omnidirectional Image Systems

From the perspective of optimizing the visual experience of the omnidirectional system, this article explores the terminal image quality of the high dynamic range omnidirectional image (HOI) system for the first time. The HOI system has higher contrast and wider field of view perception, but its complex imaging process poses a challenge to quality measurement. In this article, we propose a novel quality measurement method for HOI (QM-HOI) system based on distortion perception and brain information-processing mechanism. Because HOI system may encounter single distortion or mixed distortion, the proposed method consists of two modules, i.e., local progressive distortion model (LPDM) and global mixed distortion model (GMDM). LPDM is designed to mainly extract local features based on the different perception effects that tone mapping (TM) operators impart to coding distortion in different luminance regions. GMDM is designed to jointly consider the structure distortion caused by coding and the color distortion of TM. The extracted features are formed into feature vectors to predict the visual quality of the distorted HOIs. A new HOI subjective evaluation dataset with multidistortion is established, called NBU-HOID, specialized for the design and testing of the proposed method. Experimental results show that the objective measurement results of the proposed method is better than the state-of-the-art methods, and more in line with the human perception. We will also provide the NBU-HOID dataset free of charge shortly afterward.
A security-enhanced joint transform correlator optical encryption system with cropping operation

A security-enhanced joint transform correlator optical encryption system with cropping operation is proposed in this paper. Cropping operation is usually used as an attack method in the field of optical encryption. However, in this paper, it is used as a means of resisting attacks. After conducting multiple simulations, we found that when the ciphertext is cropped within 10%, and the remaining part is transmitted, the encryption system can effectively resist multiple attacks without decreasing the decryption quality. This process is low in complexity and easy to operate. Both simulations and experiments are conducted to prove that our proposed method is correct and can effectively enhance the security of the encryption system.
Phenomena Explanation from Text: Unsupervised Learning of Interpretable and Statistically Significant Knowledge

Learning knowledge from text is becoming increasingly important as the amount of unstructured content on the Web rapidly grows. Despite recent breakthroughs in natural language understanding, the explanation of phenomena from textual documents is still a difficult and poorly addressed problem. Additionally, current NLP solutions often require labeled data, are domain-dependent, and based on black box models. In this paper, we introduce POIROT, a new descriptive text mining methodology for phenomena explanation from documents corpora. POIROT is designed to provide accurate and interpretable results in unsupervised settings, quantifying them based on their statistical significance. We evaluated POIROT on a medical case study, with the aim of learning the “voice of patients” from short social posts. Taking Esophageal Achalasia as a reference, we automatically derived scientific correlations with 79% F1-measure score and built useful explanations of the patients’ viewpoint on topics such as symptoms, treatments, drugs, and foods. We make the source code and experiment details publicly available ( https://github.com/unibodatascience/POIROT ).
Multiple Conformer Descriptors for QSAR Modeling

The most widely used QSAR approaches are mainly based on 2D molecular representation which ignores stereoconfiguration and conformational flexibility of compounds. 3D QSAR uses a single conformer of each compound which is difficult to choose reasonably. 4D QSAR uses multiple conformers to overcome the issues of 2D and 3D methods. However, many of existing 4D QSAR models suffer from the necessity to pre-align conformers, while alignment-independent approaches often ignore stereoconfiguration of compounds. In this study we propose a QSAR modeling approach based on transforming chirality-aware 3D pharmacophore descriptors of individual conformers into a set of latent variables representing the whole conformer set of a molecule. This is achieved by clustering together all conformers of all training set compounds. The final representation of a compound is a bit string encoding cluster membership of its conformers. In our study we used Random Forest, but this representation can be used in combination with any machine learning method. We compared this approach with conventional 2D and 3D approaches using multiple data sets and investigated the sensitivity of the approach proposed to tuning parameters: number of conformers and clusters.
Quantum Neural Networks for Resource Allocation in Wireless Communications

This study exploits a quantum neural network (QNN) for resource allocation in wireless communications. A QNN is presented to reduce time complexity while still maintaining performance. Moreover, a reinforcement-learning- inspired QNN (RL-QNN) is presented to improve the perfor- mance. Quantum circuit design of the QNN is presented to ensure the practical implementation in noisy intermediate-scale quantum (NISQ) computers. For the QNN, the complexity and the number of required qubits are analyzed as well. As a particular use case, the QNN is utilized for user grouping in non-orthogonal multiple access. The results reveal that the QNN schemes have lower complexities and similar performance in terms of the achievable sum rate when compared with that of the classical neural network.
NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis

Multi-View Stereo (MVS) is a core task in 3D computer vision. With the surge of novel deep learning methods, learned MVS achieves more complete depth maps than classical approaches, but still relies on building a memory intensive dense cost volume. Novel View Synthesis (NVS) is a parallel line of research and has recently seen an increase in popularity with Neural Radiance Field (NeRF) models, which optimize a per scene radiance field. However, NeRF methods do not generalize to novel scenes and are slow to train and test. We propose to bridge the gap between these two methodologies with a novel network that can recover 3D scene geometry as a distance function, together with high-resolution color images. Our method uses only a sparse set of images as input and can generalize well to novel scenes. Additionally, we propose a coarse-to-fine sphere tracing approach in order to significantly increase speed. We show on various datasets that our method reaches comparable accuracy to per-scene optimized methods while being able to generalize and running significantly faster.
An Improved Extended Kalman Filter for Radar Tracking of Satellite Trajectories

Nonlinear state estimation problem is an important and complex topic, especially for real-time applications with a highly nonlinear environment. This scenario concerns most aerospace applications, including satellite trajectories, whose high standards demand methods with matching performances. A very well-known framework to deal with state estimation is the Kalman Filters algorithms, whose success in engineering applications is mostly due to the Extended Kalman Filter (EKF). Despite its popularity, the EKF presents several limitations, such as exhibiting poor convergence, erratic behaviors or even inadequate linearization when applied to highly nonlinear systems. To address those limitations, this paper suggests an improved Extended Kalman Filter (iEKF), where a new Jacobian matrix expansion point is recommended and a Frobenius norm of the cross-covariance matrix is suggested as a correction factor for the a priori estimates. The core idea is to maintain the EKF structure and simplicity but improve its accuracy. In this paper, two case studies are presented to endorse the proposed iEKF. In both case studies, the classic EKF and iEKF are implemented, and the obtained results are compared to show the performance improvement of the state estimation by the iEKF.
Looking for the Signs: Identifying Isolated Sign Instances in Continuous Video Footage

In this paper, we focus on the task of one-shot sign spotting, i.e. given an example of an isolated sign (query), we want to identify whether/where this sign appears in a continuous, co-articulated sign language video (target). To achieve this goal, we propose a transformer-based network, called Sign-Lookup. We employ 3D Convolutional Neural Networks (CNNs) to extract spatio-temporal representations from video clips. To solve the temporal scale discrepancies between the query and the target videos, we construct multiple queries from a single video clip using different frame-level strides. Self-attention is applied across these query clips to simulate a continuous scale space. We also utilize another self-attention module on the target video to learn the contextual within the sequence. Finally mutual-attention is used to match the temporal scales to localize the query within the target sequence. Extensive experiments demonstrate that the proposed approach can not only reliably identify isolated signs in continuous videos, regardless of the signers' appearance, but can also generalize to different sign languages. By taking advantage of the attention mechanism and the adaptive features, our model achieves state-of-the-art performance on the sign spotting task with accuracy as high as 96% on challenging benchmark datasets and significantly outperforming other approaches.
Novel use of logistic regression and likelihood ratios for the estimation of gender of the writer from a database of handwriting features

(2021). Novel use of logistic regression and likelihood ratios for the estimation of gender of the writer from a database of handwriting features. Australian Journal of Forensic Sciences. Ahead of Print.
A Novel Raster Map Exchange Scheme Based on Visual Cryptography

Raster map is an image that has been discretized in space and brightness, and it is an important carrier of geospatial data. With the rapid development of Internet and big data technologies, preserving the privacy of raster map has become an urgent task. To solve these issues, we propose a novel extended visual cryptography scheme to securely store a raster map into other two meaningful halftone maps in the paper. The scheme avoids the random-looking shares of visual cryptography schemes which are vulnerable and hard to manage. We first apply the halftone and color decomposition methods to transform a color secret map into halftone images. After that, we encode the secret map block by block to avoid pixel expansion. At last, by optimizing the selection of encrypted blocks, we achieve a high-quality secret recovery from generated multiple equal-sized shares. The technique used is to employ a versatile and secure raster map exchange. Experimental results show that, compared with previous work, the proposed scheme significantly improves the performance of recovered raster maps.
Fusion of Skeleton and Inertial Data for Human Action Recognition Based on Skeleton Motion Maps and Dilated Convolution

Human activity recognition (HAR) has become a hot research topic, due to its wide application prospect. Fusion-based methods can be used to complement single sensing modality methods. This paper presents the simultaneous utilization of skeleton data and inertial signals—which are captured at the same time using a Kinect depth camera and ten wearable inertial sensors —within a fusion framework, in order to achieve more robust human action recognition, compared to situations where each sensing modality is used individually. Skeleton data captured by the Kinect depth camera are transformed into a weighted front-view skeleton motion map (WF-SMM), a weighted multi-view skeleton motion map (WM-SMM), and a 3D weighted skeleton motion map (3DW-SMM), which are then fed as inputs into a convolutional neural network. Meanwhile, the inertial data are transformed into 2D inertial images, then fed into a 2D dilated convolutional neural network. Two types of fusion are considered: decision-level fusion and feature-level fusion. Experiments were conducted using the publicly available Changzhou University Multimodal Human Action Data set (CZU-MHAD), in which simultaneous skeleton sequence and inertial signals were captured for a total of 22 actions. The results obtained indicate that both the decision- and feature-level fusion approaches generate higher recognition accuracies, compared to the approaches where each sensing modality is used individually. The highest accuracy (of 98.90%) was obtained with the decision-level fusion approach using 3DW-SMM. In addition, some experiments are conducted on the continuous action streams generated based on the CZU-MHAD with different score threshold. And the highest f1 score 82.05 % is obtained with the threshold of 0.4.
Frequency-based Automated Modulation Classification in the Presence of Adversaries

Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals. Recent work has demonstrated the ability of deep learning to achieve robust AMC performance using raw in-phase and quadrature (IQ) time samples. Yet, deep learning models are highly susceptible to adversarial interference, which cause intelligent prediction models to misclassify received samples with high confidence. Furthermore, adversarial interference is often transferable, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification network. In this work, we present a novel receiver architecture consisting of deep learning models capable of withstanding transferable adversarial interference. Specifically, we show that adversarial attacks crafted to fool models trained on time-domain features are not easily transferable to models trained using frequency-domain features. In this capacity, we demonstrate classification performance improvements greater than 30% on recurrent neural networks (RNNs) and greater than 50% on convolutional neural networks (CNNs). We further demonstrate our frequency feature-based classification models to achieve accuracies greater than 99% in the absence of attacks.
Resource allocation using Dynamic Pricing Auction Mechanism for supporting emergency demands in Cloud Computing

Cloud resources provide various categories of (VM) virtual machine requests which is assigned with clients for an exact timespan. Currently, the method of VM scheduling within the Cloud environment is decided by a fixed-price scheduling algorithm, during which the user pays a fixed amount per unit time so as to obtain the resources. However, such a scheduling algorithm is not effective for Cloud even though the Cloud resources are dynamically allocated and released. To address this issue, the adaptive scheduling algorithm called as Dynamic pricing based Combinatorial Auction allocation mechanism is proposed. It will be used to increase the resource utilization as well as user satisfaction through dynamic pricing with the combinatorial auction. Our proposed market-based scheduling algorithm uses the principle of auction mechanism for the purpose of extends the satisfaction of Cloud suppliers and clients. This technique reconstructs the current preferences of resource allotment so as to allot resources in advance for emergent virtual machine demands. Then Collective-target augmentation numerical prototype is demonstrated, which forms the minimal execution equivalent range connecting physical machines with virtual machines, and the objective of resource allotment is to obtain minimal quantity of physical machines. The simulation experimental outcomes express that the proposed scheduling methodology and Collective-target augmentation numerical prototypes are capable to adequately increase the quality of service (QoS), improves profit of suppliers and resource utilization. • In our paper, we proposed Dynamic pricing with combinatorial action mechanism to get efficient performance. • Our DPCAM provides higher user satisfaction. • It increases resource utilization rate. • Using our scheme all the providers may get a chance to win an auction. • So that quality of service can be achieved.
Deniable Fully Homomorphic Encryption from Learning with Errors

We define and construct Deniable Fully Homomorphic Encryption based on the Learning With Errors (LWE) polynomial hardness assumption. Deniable FHE enables storing encrypted data in the cloud to be processed securely without decryption, maintaining deniability of the encrypted data, as well the prevention of vote-buying in electronic voting schemes where encrypted votes can be tallied without decryption. Our constructions achieve compactness independently of the level of deniability- both the size of the public key and the size of the ciphertexts are bounded by a fixed polynomial, independent of the detection probability achieved by the scheme. This is in contrast to all previous constructions of deniable encryption schemes (even without requiring homomorphisms) which are based on polynomial hardness assumptions, originating with the seminal work of Canetti, Dwork, Naor and Ostrovsky (CRYPTO 1997) in which the ciphertext size grows with the inverse of the detection probability. Canetti et al. argued that this dependence "seems inherent", but our constructions illustrate this is not the case. We note that the Sahai-Waters (STOC 2014) construction of deniable encryption from indistinguishability obfuscation achieves compactness and can be easily modified to achieve deniable FHE as well, but it requires multiple, stronger sub-exponential hardness assumptions, which are furthermore not post-quantum secure. In contrast, our constructions rely only on the LWE polynomial hardness assumption, as currently required for FHE even without deniability. The running time of our encryption algorithm depends on the inverse of the detection probability, thus the scheme falls short of achieving simultaneously compactness, negligible deniability probability and polynomial encryption time. Yet, we believe that achieving compactness is a fundamental step on the way to achieving all properties simultaneously as has been the historical journey for other primitives such as functional encryption. Our constructions support large message spaces, whereas previous constructions were bit by bit, and can be run in online-offline model of encryption, where the bulk of computation is independent of the message and may be performed in an offline pre-processing phase. This results in an efficient online phase whose running time is independent of the detection probability. At the heart of our constructions is a new way to use bootstrapping to obliviously generate FHE ciphertexts so that it supports faking under coercion.
WLAN interference signal recognition using an improved quadruple generative adversarial network

Cross-technology interference sources poses a great challenge for improving throughputs of wireless local area networks (WLAN) and wireless interference signal recognition (WISR) can provide a precondition for mitigating this problem. The quadruple generative adversarial network (QGAN) has shown its prevailing performance for specific emitter identification (SEI). In this paper, an enhanced collaborative learning mechanism is proposed to improve QGAN's performance for WISR. ACGAN is involved in the Improved-QGAN architecture to substitute original GAN, and loss functions are further optimized for generative, representation and classification sub-networks. Besides, a lightweight model based on knowledge distillation (KD) is presented to reduce memory consumption and computational complexity at inference phase. Numerical results indicate that the proposed Improved-QGAN outperforms the other baseline algorithms both on the experimental dataset and benchmark dataset.
On Selfholding Attack Impact on Imperfect PoW Blockchain Networks

Proof-of-Work (PoW) blockchain systems like Bitcoin and Ethereum are vulnerable to selfholding attack. The prior modeling-based works about this attack only considered Bitcoin and assumed that there were at most two honest pools in a perfect network (no natural fork in such networks). However, a blockchain network is imperfect due to block propagation delay, which can lead to forking. Moreover, there may be more than two pools under attack. This paper aims for a quantitative analysis of an imperfect PoW blockchain network system under selfholding attack. We develop a novel stochastic model and derive formulas to evaluate the effect of selfholding attack on miner revenue, system security and system performance. Our work can be used to analyze the scenario where there are any number of pools suffering selfholding attack in both Ethereum and Bitcoin. The model in this paper can capture the behaviors of a more realistic and more general scenario, compared with the existing models. Moreover, our model and formulas can also be applied to evaluate a blockchain system, which uses a similar reward mechanism and is vulnerable to selfholding attack. Our work can help design a more secure blockchain incentive mechanism and an in-pool reward mechanism.