Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Identity Management System on Blockchain Using Zk-SNARK

Privacy plays a crucial role in the internet era, where many applications allow people to communicate and use their services through the internet. Privacy-preserving Identity Management (PPIdM) system is a scheme that helps manage users’ identities and protects users’ privacy by enabling users to authenticate themselves without disclosing their real identities. The PPIdM system also allows users to reveal some minor identity attributes while others remain secret selectively. However, anonymity also encourages malicious users to break the system’s policy and commit crimes since their real identities are anonymous. Existing PPIdM systems use the identity provider (IP) as a medium to verify users’ identity attributes, record all users’ real identities, and ensure that malicious users’ identities are traceable. Therefore, users’ identities are hidden from all entities but the IP. However, the user’s privacy is vulnerable because there is nothing to guarantee that the IP is always honest and not curious about their users’ activities and private information. This paper proposes a PPIdM system on the blockchain that helps users manage their identity attributes and keeps their real identities secret from all entities, including the IP. Still, the system’s consensus can trace malicious users’ real identities if they violate the system’s policy. The PPIdM’s security requirements are analyzed and proved informally using the game-based proof scheme. The main idea of this study is to combine zk-SNARK, a type of zero-knowledge proof (ZKP), Shamir’s secret sharing (SSS), and several other cryptographic techniques.
A D2D-Aided Federated Learning Scheme With Incentive Mechanism in 6G Networks

Pervasive new era applications are expected to involve massive amount of data to implement intelligent distributed frameworks based on machine learning, supported by sixth generation (6G) networks technology to offer fast and reliable communications. Federated Learning (FL) is rapidly emerging as promising privacy-preserving solution to train machine learning models in a distributed fashion. However, users are often not too inclined to take part in the learning process without receiving compensation. Hence, to overcome this drawback, the functional integration of a proper devices incentive mechanism with an efficient approach for the devices selection in a same FL framework becomes essential. In this regard, this paper proposes a FL framework involving a one-side matching theory-based incentive mechanism to select and encourage users to take part of the process with the aim at minimizing the FL process convergence time and maximizing the users profit. Furthermore, this paper faces with the possibility to overcome bad communication link conditions by resorting to device-to-device communications among users in order to lower the energy wasted and improve the convergence time of the FL process. In particular, an echo-state-network, running in local at each user site, has been considered to forecast channel conditions in a reliable manner. Performance evaluation has highlighted the improvements in convergence time and energy consumption of the proposed FL framework in comparison with conventional approaches, hence, highlighting its suitability for applications in the upcoming 6G networks.
Optimization of smoothing factor for fast non-local means algorithm in high pitch based low-dose computed tomography images with tin-filter

In this study, we applied the fast non-local means (FNLM) algorithm to high-pitch low-dose computed tomography (CT) images using a tin-filter. This study was conducted via a simulation study using a Shepp-Logan phantom and an experimental approach using American association of physicists in medicine (AAPM) phantom images. To optimize the smoothing factor of the FNLM algorithm, the values were adjusted from 0.01 to 0.05 at intervals of 0.001 and applied to the CT image. The smoothing factor of the optimal FNLM algorithm, derived through various quantitative evaluations, was 0.03. According to the results, when the optimized FNLM algorithm was applied to the acquired CT image with a low-dose protocol based on a high pitch condition, the estimated coefficient of variation, contrast-to-noise ratio, and signal-to-noise ratio were improved by 8.7, 6.7, and 8.0 times, respectively, compared to the general CT scanning condition (none). In addition, compared with the noise filtering methods mainly used in the past, both simulation and real experimental results proved that the application efficiency of the FNLM algorithm was high in the obtained low-dose-based CT images. In summary, the applicability of the FNLM algorithm in CT images obtained under low-dose conditions with high pitch using a tin-filter was demonstrated, and the derived research results are expected to be actively utilized in the diagnostic medical field to help reduce exposure doses and improve image quality.
A component framework for the runtime enforcement of safety properties

Safety assurance of a complex system cannot be completely ensured at design/development time since most uncertainties and unknowns are revealed when the system is deployed in a real environment. Safety assurance at runtime can be addressed by using models formalizing those safety assertions the system has to guarantee during operation, and specifying enforcement strategies aimed at preserving or eventually restoring safety. This paper presents an approach to runtime safety enforcement of software systems based on the MAPE-K control loop architecture for system monitoring and control, and on the State Machine as runtime model to specify safety assertions and enforcement strategies for steering the correct system behavior. The enforcer software is designed to act as a proxy system which wraps around the software system to realize safety enforcement, both as black-box enforcement on unsafe I/O events and as gray-box enforcement on unsafe internal system changes. The proposed approach is supported by a component framework called RSE (Runtime Safety Enforcement) that is here illustrated by means of two real case studies in the health-care domain.
Deep learning aided cyclostationary feature analysis for blind modulation recognition in massive MIMO systems

Blind modulation recognition (BMR) has been proposed as a promising approach for massive multiple-input multiple-output (M-MIMO) systems to support massive user equipment (UE). In this work, we devise a new uplink BMR algorithm based on a deep learning (DL) aided cyclostationary feature (CF) analysis approach. Firstly, we propose the so-called minimum description length (MDL) method aided complex fast independent component analysis (CFICA) algorithm, to separate the multi-user (MU) signals subjected to arbitrary, different modulation schemes with an unknown number of transmitters. Then, we tailor a convolutional deep belief network (CDBN) to analyze the relation between CF and modulation type (MT) for generating the BMR results. As the proposed BMR process is invoked blindly, it is particularly suitable for non-cooperative MU communication scenarios. Simulation results demonstrate the effectiveness of the proposed technique.
LGM-GNN: A Local and Global Aware Memory-Based Graph Neural Network for Fraud Detection

Graphs have been widely adopted to accomplish fraud detection tasks because of their inherently favorable structure to capture the intricate features in many complicated scenarios, especially in some modern e-commerce situations that have various relation attributes like transactions. These works tend to utilize the direct aggregate information about neighbor nodes of the target node or the aggregation of the neighbor information after the conditional filter and mostly use local information but ignore global information. However, in some cases, local abnormal points are detected in the global view, so global information is very important for fraud detection. In this article, we propose a local and global aware memory-based graph neural network for fraud detection (LGM-GNN). It first obtains the preliminary node embedding through relation-aware embedding and then interactively aggregates the local and global memory network to fuse and utilize the local and global information. Finally, the node embeddings of different levels are aggregated through the hierarchical information aggregator. Extensive experiments show our proposed LGM-GNN outperforms other SOAT methods on two real-world fraud detection datasets.
The impact of cost function globality and locality in hybrid quantum neural networks on NISQ devices

Abstract Quantum neural networks (QNNs) are often challenged with the problem of flat cost function landscapes during training, known as barren plateaus (BP). A solution to potentially overcome the problem of the BP has recently been proposed by Cerezo et al In this solution, it is shown that, for an arbitrary deep quantum layer(s) in QNNs, a global cost function (all qubits measured in an n -qubit system) will always experience BP, whereas a local cost function (single qubit measured in an n -qubit system) can help to alleviate the problem of BP to a certain depth ( <?CDATA $\mathcal{O}(\mathrm {log}(n)$?> )). In this paper, we empirically analyze the locality and globality of the cost function in hybrid quantum neural networks. We consider two application scenarios namely, binary and multi-class classification, and show that for multiclass classification, the local cost function setting does not follow the claims of Cerezo et al ; that is, the local cost function does not result in an extended quantum layer’s depth. We also show that for multiclass classification, the overall performance in terms of accuracy for the global cost function setting is significantly higher than the local cost function setting. On the other hand, for binary classification, our results show that the local cost function setting follows the claims of Cerezo et al , and results in an extended depth of quantum layers. However, the global cost function setting still performs slightly better than the local cost function.
DeepTransHAR: a novel clustering-based transfer learning approach for recognizing the cross-domain human activities using GRUs (Gated Recurrent Units) Networks

Deep learning models have gained widespread acceptance in recent decades for their ability to recognize human activities. In terms of automated feature extraction capability and memory of the time-series streaming sensor data, these models beat traditional machine-learning techniques. Retraining the model as changes are made in the Human Activity Recognition (HAR) community is frequently a costly and time-consuming exercise. The research challenge arises when the model is trained on the source domain and predicted target domain due to the occurrence of 3-S (subjects, sensors, and sampling rates) conflicts. To address these research challenges, we proposed the Cross-Domain Activities Analysis (CDAA) and clustering-based DeepTransHAR model for recognizing human activities using smartphone sensor data. The CDAA technique has contributed to building the activity clusters by including the source and its target activities. The distance between the source and target domain have computed using the Wasserstein distance equation. Further, the proposed DeepTransHAR model carried out the Gated Recurrent Units (GRUs) layers. The lightweight DeepTransHAR model automatically extracts the efficient features from source sensory activity data for recognizing the target activities. We experimented with two openly accessible benchmark datasets, WISDM and KU-HAR, to assess the performance of our model. While comparing with the base RNN, LSTM, and Bi-LSTM model, the DeepTransHAR model achieved the highest average accuracy of 86.89%, F1 score of 89.94%, precision of 83.20%, recall of 90.85%, and elapsed average total training time of 18.30 s, and 55.33% saved training time.
Standing on the Shoulders of AI Giants

This article reviews the key artificial intelligence (AI) breakthroughs made by two AI innovation giants, OpenAI and DeepMind.
Constructing Immunized Stego-image for Secure Steganography via Artificial Immune System

Adaptive image steganography is the process of embedding secret messages into undetectable regions of a cover image through the design of a distortion function by a steganographer. Since the state-of-the-art steganalyzers are mainly based on image residual analysis, it is reasonable to modify stego image for withstanding steganalysis by reducing or eliminating the image residual distance between cover and stego image. However, simply modifying stego images may lead to message extraction failure and the introduction of additional detectable artifacts. In this paper, we propose a novel secure steganography strategy by constructing immunized stego-image via an artificial immune system, called ISteg, which ensures the accurate extraction of hidden data while enhancing the security against steganalyzers. Inspired by the biological immune system, we use an artificial immune system (AIS) to build ISteg. Specifically, ISteg generates the immunized stego-image by automatically modifying the stego to maximize the affinity of the antibody. The affinity is developed to evaluate antibody quality according to the Euclidean distance between the residual co-occurrence matrix features of the cover image and the modified stego image. In this manner, the so-called immunized stego-image is generated. Extensive experimental results demonstrate that the proposed ISteg strategy can effectively improve the security performance of existing steganography.
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement

Poor illumination greatly affects the quality of obtained images.In this paper, a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement.DEANet combines the frequency and content information of images and is divided into three subnetworks: decomposition, enhancement, and adjustment networks, which perform image decomposition; denoising, contrast enhancement, and detail preservation; and image adjustment and generation, respectively.The model is trained on the public LOL dataset, and the experimental results show that it outperforms the existing state-of-the-art methods regarding visual effects and image quality.
Power monitoring in a feedforward photonic network using two output detectors

Abstract Programmable feedforward photonic meshes of Mach–Zehnder interferometers are computational optical circuits that have many classical and quantum computing applications including machine learning, sensing, and telecommunications. Such devices can form the basis of energy-efficient photonic neural networks, which solve complex tasks using photonics-accelerated matrix multiplication on a chip, and which may require calibration and training mechanisms. Such training can benefit from internal optical power monitoring and physical gradient measurement for optimizing controllable phase shifts to maximize some task merit function. Here, we design and experimentally verify a new architecture capable of power monitoring any waveguide segment in a feedforward photonic circuit. Our scheme is experimentally realized by modulating phase shifters in a 6 × 6 triangular mesh silicon photonic chip, which can non-invasively (i.e., without any internal “power taps”) resolve optical powers in a 3 × 3 triangular mesh based on response measurements in only two output detectors. We measure roughly 3% average error over 1000 trials in the presence of systematic manufacturing and environmental drift errors and verify scalability of our procedure to more modes via simulation.
An Emotion-Based Rating System for Books Using Sentiment Analysis and Machine Learning in the Cloud

Sentiment analysis (SA), and emotion detection and recognition from text (EDRT) are recent areas of study that are closely related to each other. Sentiment analysis strives to identify and detect neutral, positive, or negative feelings from text. On the other hand, emotion analysis seeks to identify and distinguish types of feelings such as happiness, surprise, grief, disgust, fear, and anger through the expression of texts. We suggest a four-level strategy in this paper for recommending the best book to users. The levels include semantic network grouping of comparable sentences, sentiment analysis, reviewer clustering, and recommendation system. The semantic network groups comparable sentences at the first level utilizing pre-processed data from reviewer and book datasets using the parts of speech (POS) tagger. In order to extract keywords from the pre-processed data, feature extraction uses the bag of words (BOW) and term frequency-inverse document frequency (TF-IDF) approaches. SA is performed at the second level in two phases: training and testing, employing deep learning methodologies such as convolutional neural networks (CNN)-long short-term memory (LSTM). The results of this level are sent into the third level (clustering), which uses the clustering method to group the reviewers by age, location, and gender. In the last level, the model assessment is carried out with accuracy, precision, recall, sensitivity, specificity, G-mean, and F1-measure. The book suggestion system is designed to provide the highest level of accuracy within a minimum number of epochs when compared to the state-of-the methods, SVM, CNN, ANN, LSTM, and Bi-directional (BI)-LSTM.
Improving Data Sparsity in Recommender Systems Using Matrix Regeneration with Item Features

With the development of the Web, users spend more time accessing information that they seek. As a result, recommendation systems have emerged to provide users with preferred contents by filtering abundant information, along with providing means of exposing search results to users more effectively. These recommendation systems operate based on the user reactions to items or on the various user or item features. It is known that recommendation results based on sparse datasets are less reliable because recommender systems operate according to user responses. Thus, we propose a method to improve the dataset sparsity and increase the accuracy of the prediction results by using item features with user responses. A method based on the content-based filtering concept is proposed to extract category rates from the user–item matrix according to the user preferences and to organize these into vectors. Thereafter, we present a method to filter the user–item matrix using the extracted vectors and to regenerate the input matrix for collaborative filtering (CF). We compare the prediction results of our approach and conventional CF using the mean absolute error and root mean square error. Moreover, we calculate the sparsity of the regenerated matrix and the existing input matrix, and demonstrate that the regenerated matrix is more dense than the existing one. By computing the Jaccard similarity between the item sets in the regenerated and existing matrices, we verify the matrix distinctions. The results of the proposed methods confirm that if the regenerated matrix is used as the CF input, a denser matrix with higher predictive accuracy can be constructed than when using conventional methods. The validity of the proposed method was verified by analyzing the effect of the input matrix composed of high average ratings on the CF prediction performance. The low sparsity and high prediction accuracy of the proposed method are verified by comparisons with the results by conventional methods. Improvements of approximately 16% based on K-nearest neighbor and 15% based on singular value decomposition, and a three times improvement in the sparsity based on regenerated and original matrices are obtained. We propose a matrix reconstruction method that can improve the performance of recommendations.
Data driven recurrent generative adversarial network for generalized zero shot image classification

Traditional Generative Adversarial Network (GAN) based Generalized Zero Shot Learning (GZSL) methods usually suffer from a problem that these methods ignore the differences between classes when using the standard normal distribution to fit the true distribution of each category, and the incompleteness of a single adversarial training makes the model unable to capture all the characteristics of the samples. To address this problem, a data-driven recurrent adversarial generative network is proposed in this paper. We first synthesize visual prototypes for unseen classes using the transformation from semantic attributes to visual prototypes learned on seen classes. Then, some noise is generated from these prototypes to synthesize the unseen samples according to the corresponding semantic attributes. During the sample generation process, a recurrent generative adversarial network is designed to facilitate the generated visual features to be more representative. Extensive experiments on five popular datasets as well as detailed ablation studies demonstrate the effectiveness and superiority of the proposed method.
Sustainability: Fundamentals-Based Approach to Paying It Forward

Sustainability has become a critical problem confronting the community. Ecological issues such as climate change and CO2 emissions, economical frictions such as energy supplies, and socio-political issues such as wars threaten growth and equity. How can technology help?
Noise-aware clustering based on maximum correntropy criterion and adaptive graph regularization

Graph-based clustering is a basic subject in the field of machine learning, but most of them still have the following deficiencies. First, similarity graph construction and data division into corresponding classes are always divided into two independent steps. Second, noise contained in real data may cause the learned similarity graph to be inaccurate. Third, the traditional metrics based on Euclidean distance is difficult to tackle non-Gaussian noise. In order to eliminate these limitations, a noise-aware clustering based on correntropy and adaptive graph regularization method (NCCAGR) is proposed. 1) In order to change the problem from two-steps to single-step, we formulate a joint clustering learning framework that simultaneously learns a robust similarity graph and performs data clustering; 2) To overcome the influence of noise, we construct a Laplacian matrix and perform adaptive graph regularization based on clean data; 3) By introducing the correntropy to solve the problem of non-Gaussian noise and heavy tail in the original data. Furthermore, a half-quadratic optimization method is used to transform the problem into a quadratic form to facilitate subsequent solutions. Finally, experiments show that the proposed method not only has high performance, but also outperforms both classical methods and state-of-the-art methods in robustness.