Reviewerly/exHarmony huggingface dataset:
Digital and Decentralized Management of Patient Data in Healthcare Using Blockchain Implementations

Blockchain solutions offer efficient approaches for trustworthy data management, especially in the medical field when storing and processing sensitive patient data. Many institutional and industrial facilities have already recognized the importance of the technology for the health sector and have also formulated basic ideas, concepts and main use cases, but concrete implementations and executions are comparatively rare. This mini review examines current research on specific blockchain implementations in healthcare that go beyond the state of concept studies or theoretical implementation ideas and describes the most promising systems based on systematic literature research. The review shows that secure storage and easy access to complete patient data is becoming increasingly important. Blockchain technology can be used as a secure, transparent and digital way to meet these needs. Hybrid solutions consisting of conventional data storage and blockchain-based access management are increasingly being developed and implemented. The automation of blockchain processes through smart contracts is also recommended. The review further reveals ambiguities in the use of permissioned and permissionless blockchain frameworks, machine learning (ML) integration as well as the question of which data should be stored in the blockchain and how this should be viewed legally. Therefore, there is still a need for further research, especially on these aspects, in order to further establish the use of blockchains in healthcare.
Performance Evaluation of GINI Index and Information Gain Criteria on Geographical Data: An Empirical Study Based on JAVA and Python

In this paper, generation and performance comparison is established between information gain and GINI index on raw geographical dataset. Concrete results are drawn, and performance is measured by evolving decisions on the two generated datasets from the same closure. Afterward, MDL pruning is also applied on the both generated decision trees by reducing the number of rules without affecting the overall performance of the tree. In this study, primarily we have exploited ID3 learning algorithm in both Java and Python language for the prediction purposes. After analyzing the results, implementation in Java gives better performance results by attaining the networthy accuracy of 81.52%. Furthermore, we then employed new-fangled approach on same set of data which compares the overall performance using information gain and GINI index. The empirical results demonstrate that information gain has achieved outstanding results with an accuracy of 81.20% with only 12 PMML rule sets.
Enhanced Trust Based Access Control for Multi-Cloud Environment

Security is an essential part of the cloud environment. For ensuring the security of the data being communicated to and from the cloud server, a significant parameter called trust was introduced. Trust-based security played a... | Find, read and cite all the research you need on Tech Science Press
Tor Hidden Services: A Systematic Literature Review

Anonymous communications networks were created to protect the privacy of communications, preventing censorship and traffic analysis. The most famous anonymous communication network is Tor. This anonymous communication network provides some interesting features. Among them, we can mention that Tor can hide a user’s IP address when accessing to a service such as the Web, and it also supports Tor hidden services (THS) (now named onion services) as a mechanism to conceal the server’s IP address, used mainly to provide anonymity to websites. THS is an important research field in Tor. However, there is a lack of reviews that sum up the main findings and research challenges. In this article, we present a systematic literature review that aims to offer a comprehensive overview of the research made on THS by presenting the state-of-the-art and the different research challenges to be addressed. This review has been developed from a selection of 57 articles and presents main findings and advances regarding Tor hidden services, limitations found, and future issues to be investigated.
ADOPS: Aspect Discovery OPinion Summarisation Methodology based on deep learning and subgroup discovery for generating explainable opinion summaries

Opinion summarisation is concerned with generating structured summaries of multiple opinions in order to provide insightful knowledge to end users. We present the Aspect Discovery for OPinion Summarisation (ADOPS) methodology, which is aimed at generating explainable and structured opinion summaries. ADOPS is built upon aspect-based sentiment analysis methods based on deep learning and Subgroup Discovery techniques. The resultant opinion summaries are presented as interesting rules, which summarise in explainable terms for humans the state of the opinion about the aspects of a specific entity. We annotate and release a new dataset of opinions about a single entity on the restaurant review domain for assessing the ADOPS methodology, and we call it ORCo. The results show that ADOPS is able to generate interesting rules with high values of support and confidence, which provide explainable and insightful knowledge about the state of the opinion of a certain entity.
Algebraic Attacks on Rasta and Dasta Using Low-Degree Equations

Rasta and Dasta are two fully homomorphic encryption friendly symmetric-key primitives proposed at CRYPTO 2018 and ToSC 2020, respectively. We point out that the designers of Rasta and Dasta neglected an important property of the \(\chi \) operation. Combined with the special structure of Rasta and Dasta, this property directly leads to significantly improved algebraic cryptanalysis. Especially, it enables us to theoretically break 2 out of 3 instances of full Agrasta, which is the aggressive version of Rasta with the block size only slightly larger than the security level in bits. We further reveal that Dasta is more vulnerable against our attacks than Rasta for its usage of a linear layer composed of an ever-changing bit permutation and a deterministic linear transform. Based on our cryptanalysis, the security margins of Dasta and Rasta parameterized with \((n,\kappa ,r)\in \{(327,80,4),(1877,128,4),(3545,256,5)\}\) are reduced to only 1 round, where n, \(\kappa \) and r denote the block size, the claimed security level and the number of rounds, respectively. These parameters are of particular interest as the corresponding ANDdepth is the lowest among those that can be implemented in reasonable time and target the same claimed security level.
Comparative Analysis of Neural Networks Techniques to Forecast Global Horizontal Irradiance

Due to the continuous increasing importance of renewable energy sources as an alternative to fossil fuels, to contrast air pollution and global warming, the prediction of Global Horizontal Irradiation (GHI), one of the main parameters determining solar energy production of photovoltaic systems, represents an attractive topic nowadays. Solar irradiance is determined by deterministic factors (i.e. the position of the sun) and stochastic factors (i.e. the presence of clouds). Since the stochastic element is difficult to model, this problem can benefit from machine learning techniques, like artificial neural networks. This work proposes a methodology to forecast GHI in short- (i.e. from 15 min to 60 min) and mid-term (i.e. from 60 to 120 min) time horizons. For this purpose, we designed, optimised and compared four neural network architectures for time-series forecasting, respectively based on: i) Non-Linear Autoregressive, ii) Feed-Forward, iii) Long Short-Term Memory and iv) Echo State Network. The original data-set, consisting of GHI values sampled every 15min, has been pre-processed by applying different filtering techniques. Our results analysis compares the performance of the proposed neural networks identifying the best in terms of error rate and forecast horizon. This analysis highlights that the clear-sky index results the preferred filtering technique by giving greatly improvements in data-set pre-processing, and Echo State Network gives best accuracy results.
An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

Breast cancer is the most common type of cancer, and it is the reason for cancer death toll in women in recent years. Early diagnosis is essential to handle breast cancer patients for treatment at the right time. Screening with mammography is the preferred examination for breast cancer, as it is available worldwide and inexpensive. Computer-Aided Detection (CAD) systems are used to analyze medical images to detect breast cancer, early. The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations. Processing of mammogram images has four main steps: pre-processing, segmentation of the region of interest, feature extraction and classification of the images into normal or abnormal classes. This paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect masses. The pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter, removal of artifacts using morphological operations, and contrast enhancement using a fuzzy enhancement technique. The proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure (EME) and local contrast metrics. The comparison shows an outstanding performance of the proposed technique from the visual and numerical perspectives. The segmentation process is performed using Otsu's multiple thresholding method. This method segments the image regions into five classes with variable intensities using four thresholds. Its effectiveness is measured based on visual quality of the segmentation output, as it gives details about the image and positions of masses. The performance of the proposed framework is measured using Dice coefficient, Hausdorff, and Peak Signal-to-Noise Ratio (PSNR) metrics. The segmented tumor region with the proposed segmentation method is 81% of the ground truth region provided by an expert. Hence, the proposed framework achieves promising results for aiding radiologists in screening of mammograms, accurately.
Contact Mode Guided Motion Planning for Quasidynamic Dexterous Manipulation in 3D

This paper presents Contact Mode Guided Manipulation Planning (CMGMP) for 3D quasistatic and quasi-dynamic rigid body motion planning in dexterous manipulation. The CMGMP algorithm generates hybrid motion plans including both continuous state transitions and discrete contact mode switches, without the need for pre-specified contact sequences or pre-designed motion primitives. The key idea is to use automatically enumerated contact modes of environment-object contacts to guide the tree expansions during the search. Contact modes automatically synthesize manipulation primitives, while the sampling-based planning framework sequences those primitives into a coherent plan. We test our algorithm on fourteen 3D manipulation tasks, and validate our models by executing some plans open-loop on a real robot-manipulator system <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The video is available at https://youtu.be/JuLlliG3vGc.
An Efficient Blockchain Consensus Algorithm Based on Post-Quantum Threshold Signature

Blockchain is a distributed and append-only data management system maintained by many nodes from Peer-to-Peer (P2P) networks, where one of the most important and successful applications is Bitcoin. In blockchain P2P networks, consensus algorithms are playing a key role and take a lot of time. For example, Bitcoin uses Proof-of-Work (PoW) consensus algorithm, which takes 60 minutes to acknowledge a new transaction. Compared with the traditional data management technology, blockchain systems are very inefficient. Thus, it is very crucial to improve the blockchain consensus. We present threshold digital signature techniques to exploit the blockchain consensus mechanism. First, we propose a threshold signature scheme based on NP-hard problem, i.e., solving quadratic equations in a finite field, which is considered to be secure against quantum computer attacks and traditional attacks. Second, we further propose an efficient blockchain consensus algorithm based on the new threshold signature scheme. Third, we build a new blockchain system based on the consensus algorithm. The analysis of the blockchain system shows that the efficiency and security is must better than current blockchain systems.
Unsupervised and Self-Adaptative Techniques for Cross-Domain Person Re-Identification

Person Re-Identification (ReID) across non-overlapping cameras is a challenging task, and most works in prior art rely on supervised feature learning from a labeled dataset to match the same person in different views. However, it demands the time-consuming task of labeling the acquired data, prohibiting its fast deployment in forensic scenarios. Unsupervised Domain Adaptation (UDA) emerges as a promising alternative, as it performs feature adaptation from a model trained on a source to a target domain without identity-label annotation. However, most UDA-based methods rely upon a complex loss function with several hyper-parameters, hindering the generalization to different scenarios. Moreover, as UDA depends on the translation between domains, it is crucial to select the most reliable data from the unseen domain, avoiding error propagation caused by noisy examples on the target data — an often overlooked problem. In this sense, we propose a novel UDA-based ReID method that optimizes a simple loss function with only one hyper-parameter and takes advantage of triplets of samples created by a new offline strategy based on the diversity of cameras within a cluster. This new strategy adapts and regularizes the model, avoiding overfitting the target domain. We also introduce a new self-ensembling approach, which aggregates weights from different iterations to create a final model, combining knowledge from distinct moments of the adaptation. For evaluation, we consider three well-known deep learning architectures and combine them for the final decision. The proposed method does not use person re-ranking nor any identity label on the target domain and outperforms state-of-the-art techniques, with a much simpler setup, on the Market to Duke, the challenging Market1501 to MSMT17, and Duke to MSMT17 adaptation scenarios.
An Automatically Verified Prototype of the Tokeneer ID Station Specification

The Tokeneer project was an initiative set forth by the National Security Agency (NSA, USA) to be used as a demonstration that developing highly secure systems can be made by applying rigorous methods in a cost-effective manner. Altran UK was selected by NSA to carry out the development of the Tokeneer ID Station. The company wrote a Z specification later implemented in the SPARK Ada programming language, which was verified using the SPARK Examiner toolset. In this paper, we show that the Z specification can be readily and naturally encoded in the $$\{log\}$$ { l o g } set constraint language, thereby generating a functional prototype. Furthermore, we show that $$\{log\}$$ { l o g } 's automated proving capabilities can discharge all the proof obligations concerning state invariants as well as important security properties. As a consequence, the prototype can be regarded as correct with respect to the verified properties. This provides empirical evidence that Z users can use $$\{log\}$$ { l o g } to generate correct prototypes from their Z specifications. In turn, these prototypes enable or simplify some verification activities discussed in the paper.
Gene Extraction of Leizhou Kiln Porcelain Patterns Based on Safety Internet of Things and Its Application in Modern Design

Porcelain art is a classic art treasure in the history of human civilization. After thousands of years of development, it carries the wisdom and sweat of the ancients and draws us a masterpiece that spans thousands of years. China is the hometown of porcelain. As the essence of Chinese art, porcelain has retained the traditional artistic characteristics of our country for thousands of years. Porcelain design can not only capture people's living experience and cultural understanding, but also express the thoughts or feelings of predecessors. This thesis aims to study the application of Leizhou kiln porcelain pattern gene extraction and modern design in the secure Internet of Things. The extraction of Leizhou kiln porcelain patterns in this paper is a very rigorous technical work. First, a security IoT monitoring and early warning system must be established. Secondly, a field survey of Leizhou Museum and Zhanjiang City Museum will be carried out to take photos of Leizhou kiln porcelain from various angles, combined with literature. Sort out the first-hand porcelain materials and classify the porcelain according to the age of production. Then use software such as photoshop, Adobe illustrator, CorelDRAW and other software to extract unit patterns from the patterns on the Leizhou kiln porcelain in turn, use symmetry, exaggeration and other basic skeleton deformation processing, refer to the popular color matching, to form a modern and unique pattern. The results of the empirical analysis show that we must use the Internet of Things technology to take safety measures before Leizhou kiln porcelain pattern gene extraction, which can make the safety reach 91%, and we must also apply the extracted patterns to modern designs. It does not mean that the porcelain patterns are directly used. What we want to achieve is an innovative way of inheritance, so that these porcelain patterns will be presented to everyone with a brand-new mental outlook.
ASAR 2021 Competition on Online Signal Restoration Using Arabic Handwriting Dhad Dataset

Stroke reconstruction from offline handwriting is an important research field. This article presents Online Signal Restoration (OSR) using Arabic Handwriting Dhad Dataset competition organized at ASAR 2021. The goal of this competition is to collect different systems and compare recent advances in online handwriting recovery. This competition has attracted 4 teams from Regim lab. The participating systems were evaluated on known data and tested on unknown dataset. The evaluation metrics are based on Root Mean Squared Error (RMSE), Euclidean Distance (ED) and visual comparison of the recovered velocity. This paper details the proposed competition by describing the used dataset, the participating systems and their effectiveness based on the evaluation metrics.
ASTER-Based Remote Sensing Image Analysis for Prospection Criteria of Podiform Chromite at the Khoy Ophiolite (NW Iran)

A single chromite deposit occurrence is found in the serpentinized harzburgite unit of the Khoy ophiolite complex in northwest Iran, which is surrounded by dunite envelopes. This area has mountainous features and extremely rugged topography with difficult access, so prospecting for chromite deposits by conventional geological mapping is challenging. Therefore, using remote sensing techniques is very useful and effective, in terms of saving costs and time, to determine the chromite-bearing zones. This study evaluated the discrimination of chromite-bearing mineralized zones within the Khoy ophiolite complex by analyzing the capabilities of ASTER satellite data. Spectral transformation methods such as optimum index factor (OIF), band ratio (BR), spectral angle mapper (SAM), and principal component analysis (PCA) were applied on the ASTER bands for lithological mapping. Many chromitite lenses are scattered in this ophiolite, but only a few have been explored. ASTER bands contain improved spectral characteristics and higher spatial resolution for detecting serpentinized dunite in ophiolitic complexes. In this study, after the correction of ASTER data, many conventional techniques were used. A specialized optimum index factor RGB (8, 6, 3) was developed using ASTER bands to differentiate lithological units. The color composition of band ratios such as RGB ((4 + 2)/3, (7 + 5)/6, (9 + 7)/8), (4/1, 4/7, 4/5), and (4/3 × 2/3, 3/4, 4/7) produced the best results. The integration of information extracted from the image processing algorithms used in this study mapped most of the lithological units of the Khoy ophiolitic complex and new prospecting targets for chromite exploration were determined. Furthermore, the results were verified by comprehensive fieldwork and previous studies in the study area. The results of this study indicate that the integration of information extracted from the image processing algorithms could be a broadly applicable tool for chromite prospecting and lithological mapping in mountainous and inaccessible regions such as Iranian ophiolitic zones.
Coarse-to-fine pseudo supervision guided meta-task optimization for few-shot object classification

Few-Shot Learning (FSL) is a challenging and practical learning pattern, aiming to solve a target task which has only a few labeled examples. Currently, the field of FSL has made great progress, but largely in the supervised setting, where a large auxiliary labeled dataset is required for offline training. However, the unsupervised FSL (UFSL) problem where the auxiliary dataset is fully unlabeled has been seldom investigated despite of its significant value. This paper focuses on the more general and challenging UFSL problem and presents a novel method named Coarse-to-Fine Pseudo Supervision-guided Meta-Learning (C2FPS-ML) for unsupervised few-shot object classification. It first obtains prior knowledge from an unlabeled auxiliary dataset during unsupervised meta-training, and then use the prior knowledge to assist the downstream few-shot classification task. Coarse-to-Fine Pseudo Supervisions in C2FPS-ML aim to optimize meta-task sampling process in unsupervised meta-training stage which is one of the dominant factors for improving the performance of meta-learning based FSL algorithms. Human can learn new concepts progressively or hierarchically following the coarse-to-fine manners. By simulating this human's behaviour, we develop two versions of C2FPS-ML for two different scenarios: one is natural object dataset and another one is other kinds of dataset (e.g., handwritten character dataset). For natural object dataset scenario, we propose to exploit the potential hierarchical semantics of the unlabeled auxiliary dataset to build a tree-like structure of visual concepts. For another scenario, progressive pseudo supervision is obtained by forming clusters in different similarity aspects and is represented by a pyramid-like structure. The obtained structure is applied as the supervision to construct meta-tasks in meta-training stage, and prior knowledge from the unlabeled auxiliary dataset is learned from the coarse-grained level to the fine-grained level. The proposed method sets the new state of the art on the gold-standard miniImageNet and achieves remarkable results on Omniglot while simultaneously increases efficiency.