Reviewerly/exHarmony huggingface dataset:
UCoin: An Efficient Privacy Preserving Scheme for Cryptocurrencies

In cryptocurrencies, privacy of users is preserved using pseudonymity . However, it has been shown that pseudonymity does not result in anonymity if a user's transactions are linkable. This makes cryptocurrencies vulnerable to deanonymization attacks. The current solutions proposed in the literature suffer from at least one of the following issues: (1) requiring a trusted third–party entity, (2) poor performance, and (3) incompatible with the standard structure of cryptocurrencies. In this article, we propose Unlinkable Coin (UCoin), a secure mix–based approach to address these issues. In UCoin, the link between the input (payer) and output (payee) addresses in a transaction is broken. This is done by mixing the transactions of multiple users into a single aggregated transaction in which the output addresses have been secretly shuffled. In our protocol design, we first develop HDC–net, a secure shuffling protocol that enables a group of users to anonymously publish their data. Then, we deploy the proposed HDC–net protocol in the UCoin architecture (as a mixing unit) to generate the aggregate transactions. We show that UCoin (1) does not rely on a trusted third–party, (2) can mix 50 transactions in 6.3 seconds that is 18% faster than the current solutions, and (3) is fully compatible with the architecture of cryptocurrencies.
Hierarchical domain adaptation with local feature patterns

Domain adaptation is proposed to generalize learning machines and address performance degradation of models that are trained from one specific source domain but applied to novel target domains. Existing domain adaptation methods focus on transferring holistic features whose discriminability is generally tailored to be source-specific and inferiorly generic to be transferable. As a result, standard domain adaptation on holistic features usually damages feature structures, especially local feature statistics, and deteriorates the learned discriminability. To alleviate this issue, we propose to transfer primitive local feature patterns, whose discriminability are shown to be inherently more sharable, and perform hierarchical feature adaptation. Concretely, we first learn a cluster of domain-shared local feature patterns and partition the feature space into cells. Local features are adaptively aggregated inside each cell to obtain cell features, which are further integrated into holistic features. To achieve fine-grained adaptations, we simultaneously perform alignment on local features, cell features and holistic features, within which process the local and cell features are aligned independently inside each cell to maintain the learned local structures and prevent negative transfer. Experimenting on typical one-to-one unsupervised domain adaptation for both image classification and action recognition tasks, partial domain adaptation, and domain-agnostic adaptation, we show that the proposed method achieves more reliable feature transfer by consistently outperforming state-of-the-art models and the learned domain-invariant features generalize well to novel domains.
Artificial intelligence-aided privacy preserving trustworthy computation and communication in 5G-based IoT networks

5G offers unprecedented bandwidth coupled with extremely low latency and an extraordinarily high density of connections as well as the increased efficiency and reliability. These are slated to support 5G-based IoT services such as basic safety, fuel-economy, entertainment, surveillance, etc., which are observed in many applications, like vehicular networks and smart healthcare systems. However, cyber-attacks to the 5G network architecture pose severe risks to communication and computational services. Thus, we need to develop mechanisms to enhance the current untrustworthy 5G networks as well as to preserve 5G privacy capabilities, which then makes 5G workable for the modern operations and activities of IoT networks. In this paper, we firstly present the recent trust management and privacy preservation mechanisms, which are usually used for the communications of IoT networks. Then, we develop the artificial intelligence (AI)-aided framework to address the joint privacy preserving trustworthy communication and computation in 5G-enabled IoT networks, which is neglected in the existing research works. Finally, we highlight the potential future directions and their corresponding trust and privacy challenges in 5G-enabled IoT networks.
Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data

Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping.
Voice Pathology Detection Using Support Vector Machine Based on Different Number of Voice Signals

In voice pathology detection system, machine learning algorithms play an important role in the classification process. Most of the developed systems in voice pathology detection used a fixed number of voice signals. However, the performance of such systems may be affected by the increasing or decreasing number of voice signals. In other words, the system may not achieve the desirable detection accuracy if the number of voice signals used in the system changes. This paper presents a voice pathology detection system based on a different number of voice signals. In this work, the voice signals for the vowel /a/ of healthy and pathological classes are taken from the Saarbrücken voice database (SVD). The features of voice signals are then extracted using Mel-Frequency Cepstral Coefficient (MFCC). For the classification part, the Support Vector Machine (SVM) is used to classify the voice signals into healthy or pathological. Furthermore, the proposed system is evaluated in terms of accuracy, specificity, and sensitivity. The experiments results show that the highest achieved accuracy, specificity, and sensitivity for the proposed SVM are 84.37%, 90.90%, and 80.95%, respectively.
The Architecture, Classification, and Unsolved Research Issues of Big Data extraction as well as decomposing the Internet of Vehicles (IoV)

with the faster advancement of the Internet of Vehicles and the increasing growth of units of information demonstrate a critical role of big data in Internet of vehicles. The big data has gained a lot of traction in academics and industry. It helps in making factual decision making and a lot in expanding business and industry. Additionally, the data gathered from linked automobiles was observed, also crowd sourcing, urban development may be boosted oversight. The objective provides some sort of comprehensive survey of all relevant self scrutiny publications published in early year. We conducted a thorough review of the study articles in order to identify possible. So the survey demonstrates how big data may enact a critical part in delivering accurate as well as meaningful forecasts, as well as a detailed review of numerous approaches, devices, and styles for utilizing information in Internet of Vehicles. Adjacent to an overview of the most recent research on the use of big data in Internet of vehicles, additionally, the stated has been proposed. Also the paper extract and decompose a lot of information on traffic of vehicle interneting. It includes suggestions and directions for open working in internet of vehicles.
Cervical Cancer Classification from Pap Smear Images Using Modified Fuzzy C Means, PCA, and KNN

Cervical cancer is having the second-highest mortality rate next to breast cancer among women in developing countries. Early detection of the abnormality is the only way to prevent morbidity. As the decision about the abnormality of the cell is made manually by the traditional Pap smear test – the clinical test conducted for the detection of cervical cancer is more prone to false-negative and false-positive cases. This paper presents a novel approach for the automatic detection of cervical cancer using modified fuzzy C-means, extracting the geometrical and texture features, Principal Component Analysis (PCA), and classification. Modified fuzzy C-means show promising results in segmenting the input image into meaningful regions even when there is uncertainty. PCA is being performed to reduce the dimensionality of the data set by maintaining only the uncorrelated features thereby reducing the processing time of the algorithm. The classification of the pap smear images into normal and abnormal cells is being done by K Nearest Neighbour (KNN) classification with k-fold cross-validation and the result obtained in the proposed method is being compared with Fine Gaussian SVM, Ensemble Bagged trees, and Linear Discriminant. The efficiency of the proposed method is measured by calculating minimum accuracy, maximum accuracy, average accuracy, sensitivity, specificity, F1-score, and precision. The experimental results of the proposed method show impressive results with minimum accuracy 94.15%, maximum accuracy 96.28%, average accuracy 94.86%, sensitivity 97.96%, specificity 83.65%, F1-score 96.87%, and precision 96.31% for threefold cross-validation.
Multi-Instance Ensemble Learning With Discriminative Bags

Multi-instance learning (MIL) is more general and challenging than traditional supervised learning in that labels are given at the bag level. The popular feature mapping approaches convert each bag into an instance in the new feature space. However, most of them hardly maintain the distinguishability of bags, and the MIL model does not support self-reinforcement. In this article, we propose the multi-instance ensemble learning with discriminative bags (ELDB) algorithm with two new techniques. The bag selection technique obtains a discriminative bag set (dBagSet) according to two parts. First, considering the space and label distribution of the data, the bag selection process is optimized through discriminative analysis to obtain the basic dBagSet. Second, with the state and action transfer strategy, a dBagSet with better distinguishability is obtained through self-reinforcement. The ensemble technique trains a series of classifiers with these dBagSets and obtains the final weighted model. The experimental results show that ELDB is superior to the state-of-the-art MIL mapping solutions.
MEXchange: A Privacy-Preserving Blockchain-Based Framework for Health Information Exchange Using Ring Signature and Stealth Address

Health information exchange (HIE) refers to the integrated management and secure sharing of health information among healthcare entities.HIE improves healthcare quality and streamline healthcare administrative work.These advantages have propelled health-care stakeholders to implement HIE.However, challenged by issues such as security, privacy, and costs, HIE is not widespread.Recent studies have suggested blockchain-based HIE for solving security and privacy issues.Unfortunately, existing blockchain-based HIE studies do not consider the privacy issues caused by analyzing senders and receivers of transactions in the blockchain.In this work, we suggest MEXchange, a novel blockchain-based privacypreserving HIE that prevents the privacy issue by obscuring the sender and concealing receiver addresses.We propose smart contracts and workflow that use ring signature and stealth address for blockchainbased HIE.Software components and implementation of MEXchange on the Ethereum private network are discussed.We evaluate MEXchange quantitatively by measuring the transaction latency and throughput of exchanging.Also, we evaluate MEXchange qualitatively using the requirements of the Office of National Coordinator for Health Information Technology (ONC).Moreover, we proceed threat modeling based on STRIDE.Finally, we compare MEXchange with Ancile, FHIRChain, Integrating the Healthcare Enterprise Cross-Enterprise Document Sharing (IHE XDS), and MedRec.The MEXchange lowers barriers to the application of blockchain-based HIE systems by mitigating privacy and security issues among healthcare stakeholders.
MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions

The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques. In comparison, limited effort has been made at assessing the fitness of these datasets for the video-language grounding task. Recent works have begun to discover significant limitations in these datasets, suggesting that state-of-the-art techniques commonly overfit to hidden dataset biases. In this work, we present MAD (Movie Audio Descriptions), a novel benchmark that departs from the paradigm of augmenting existing video datasets with text annotations and focuses on crawling and aligning available audio descriptions of mainstream movies. MAD contains over 384, 000 natural language sentences grounded in over 1, 200 hours of videos and exhibits a significant reduction in the currently diagnosed biases for video-language grounding datasets. MAD's collection strategy enables a novel and more challenging version of video-language grounding, where short temporal moments (typically seconds long) must be accurately grounded in diverse long-form videos that can last up to three hours. We have released MAD's data and baselines code at https://github.com/Soldelli/MAD.
Investigation of Magneto-/Radio-Metric Behavior in Order to Identify an Estimator Model Using K-Means Clustering and Artificial Neural Network (ANN) (Iron Ore Deposit, Yazd, IRAN)

The study area is located near Toot village in the Yazd province of Iran, which is considered in terms of its iron mineralization potential. In this area, due to radioactivity, radiometric surveys were performed in a part of the area where magnetometric studies have also been performed. According to geological studies, the presence of magnetic anomalies can have a complex relationship with the intensity of radioactivity of radioactive elements. Using the K-means clustering method, the centers of the clusters were calculated with and without considering the coordinates of radiometric points. Finally, the behavior of the two variables of magnetic field strength and radioactivity of radioactive elements relative to each other was studied, and a mathematical relationship was presented to analyze the behavior of these two variables relative to each other. On the other hand, the increasing and then decreasing behavior of the intensity of the Earth’s magnetic field relative to the intensity of radioactivity of radioactive elements shows that it is possible to generalize the results of magnetometric surveys to radiometry without radiometric re-sampling in this region and neighboring areas. For this purpose, using the general regression neural network and backpropagation neural network (BPNN) methods, radiometric data were estimated with very good accuracy. The general regression neural network (GRNN) method, with more precision in estimation, was used as a model for estimating the radiation intensity of radioactive elements in other neighboring areas.
Continuous dissipative phase transitions with or without symmetry breaking

Abstract The paradigm of second-order phase transitions (PTs) induced by spontaneous symmetry breaking (SSB) in thermal and quantum systems is a pillar of modern physics that has been fruitfully applied to out-of-equilibrium open quantum systems. Dissipative phase transitions (DPTs) of second order are often connected with SSB, in close analogy with well-known thermal second-order PTs in closed quantum and classical systems. That is, a second-order DPT should disappear by preventing the occurrence of SSB. Here, we prove this statement to be wrong, showing that, surprisingly, SSB is not a necessary condition for the occurrence of second-order DPTs in out-of-equilibrium open quantum systems . We analytically prove this result using the Liouvillian theory of DPTs, and demonstrate this anomalous transition in a paradigmatic laser model, where we can arbitrarily remove SSB while retaining criticality, and on a Z 2 -symmetric model of a two-photon Kerr resonator. This new type of PT cannot be interpreted as a ‘semiclassical’ bifurcation, because, after the DPT, the system steady state remains unique.
A trustable architecture over blockchain to facilitate maritime administration for MASS systems

Maritime Autonomous Surface Ship (MASS) is widely deemed as the future of global shipping.The cyber vulnerability has however been identified as an emerging problem and a potential barrier influencing MASS development.This paper, through the investigation of the fundamental trust problem with regards to the cyber security of MASS systems, aims to develop a blockchain-based scheme for the trust management of MASS.The innovative idea of using blockchain within the MASS context is that the mobile entities in the MASS operational environment constitute a decentralized opportunity network, which makes blockchain an appealing tool to provide a solution to evaluating and maximizing the trust over the dynamics of the entities.This paper elaborates the mechanism by which the MASS entities participate in keeping the main chain.Firstly, the paper illustrates how the Belief of Trust (BoT) among the entities is encoded and assembled into the chain, to allow MASS entities to have an initial judgement towards another entity before they get acquainted.Secondly, at the consensus layer of blockchain technique, it addresses how the witness, who has a temporary right of producing a block and append it to the chain, can be elected among the nodes and how to incent the nodes to maintain the blockchain from a proof-of-stake perspective.Finally, this paper describes how the MASS entities can use the certificate dependence information to evaluate the trust transition in the MASS operating environment.Typical scenarios are delineated to show the procedure of certificate inquiry, handover of controls between maritime supervision centers and shore-side remote control centers in case of the occurrence of unexpected events.The findings provide any entity in an MASS network with an effective solution to evaluating the degree of trust he can have for any targeted node/participant.They can therefore help choose better (more trustable) nodes to maintain the MASS network's knowledge of evidence to judge the trust on an unknown member.
Computer-aided diagnosis of breast cancer in ultrasonography images by deep learning

Ultrasonography of the breast mass is an important imaging technology for diagnosing breast cancer. In China, ultrasound equipment is widely used in medical institutions. Patients obtain a report with highlighted ultrasonography images through an initial clinical screening. However, analyzing these images manually is highly subjective for the variation in the clinical competence of doctors, resulting in poor consistency and low sensitivity. In this study, an automated breast cancer diagnosis system is developed to increase diagnostic accuracy. The system is deployed on mobile phones, takes a photo of the ultrasound report as input and performs diagnosis on each image. The developed system consists of three subsystems. The first subsystem is to reduce noise in the taken photos, reconstructing high-quality images. We develop the first subsystem based on the frameworks of stacked denoising autoencoders and generative adversarial networks. The second subsystem is to classify images into malignant and non-malignant; to extract high-level features from the images, deep convolutional neural networks are employed. The third subsystem is to detect anomalies in model performances, reducing false negative rates. Generative adversarial networks are utilized to distinguish false negative samples from true negative samples. 18,225 breast ultrasonography images and 2416 ultrasound reports are collected to train and evaluate the system. Experimental results show that the performance of our system is comparable to that of human experts. It is believed that this is the first system for breast cancer diagnosis deployed on mobile phones. The developed system is integrated with a cloud computing platform and accessible online to aid in the initial screening and diagnosis of breast cancer, thereby promoting earlier treatment, reducing the morbidity and mortality.
Global solar radiation models: A critical review from the point of view of homogeneity and case study

Access to reliable solar radiation data is of paramount importance for the sustainable development of mankind. However, solar radiation measurements are available in few meteorological stations, so models are used to estimate global solar radiation from other climate variables. This paper presents a compilation of 165 global solar radiation models that is representative of the progress over the last 100 years. The models were classified according to the type of variables used and a large majority of dimensionally non-homogeneous equations were identified. Furthermore, it was observed that comparisons between various models studied in different regions over recent decades were occasionally contradictory, so that no single existing model seems to have definitively outperformed the others. In this context, homogeneity analysis provides a procedure to improve the quality of models by reducing the presence of variables implicit in numerical coefficients, which deserves to be valued as a methodological tool. In addition, a previously introduced temperature-based homogeneous model was updated using recent experimental data and revising the functional form of the local geographic factor dependent on elevation and distance to the sea. Using site-calibrated coefficients, the modified model provides accurate results at 22 meteorological stations in northern Spain, located over a distance of about 1000 km. Using a general equation, acceptable predictions are obtained for the 21 stations with the highest climatic similarity.