Reviewerly/exHarmony huggingface dataset:
Socio-Semantic Information Retrieval of Structured Arabic Texts

We propose in this paper a new socio-semantic information retrieval system denoted RISSA. The indexing units are extracted based on the hybrid possibilistic morphological disambiguation approach. Then we improve this approach by integrating socio-semantic features. We propose a new approach that takes advantage of the social and semantic diversity and the structuring of the Arabic Hadith texts. The social aspect of the Hadith texts was highlighted by presenting a new search mode by Sanad and/or Metn and/or verses of Quran and/or poems and/or section titles. Thus, the search query can be observed along several axes: (i) A social axis taking advantage of the existing social relationships in the Hadith chains of narrators (Sanad), (ii) A semantic axis taking into account the Hadith decomposition in several topics useful to identify the meaning of the texts (Metn). We firstly experiment the RISSA system using the Kunuz collection of Hadith. Then, we investigate and discuss the impact of socio-semantic features on the morphological disambiguation of the structured Hadith Arabic texts.
Histological breast cancer classification using CNN and MLP based ensembles

Breast cancer affects thousands of people worldwide each year, artificial intelligence used for digital pathological computer-aided diagnosis for breast cancer classification is a valuable domain. The advancement of machine learning and deep learning methods, as well as pathology slide digitization for primary diagnosis, is a major development toward meeting the demand for more accurate breast tumor diagnosis, classification, and prediction. This paper proposes and evaluates a new approach consisting of deep hybrid homogenous ensemble method based on seven deep learning models for feature extraction (DenseNet 201, Inception V3, Inception ResNet V2, MobileNet V2, ResNet 50, VGG16, and VGG 19), a multi-layer perceptron (MLP) for classification and two combination rules (hard and weighted voting) for histological classification using the BreakHis dataset four magnification factors: 40X, 100X, 200X and 400X. The outcomes proved the potential of the proposed new approach since it outperformed its singles achieving an accuracy value of 98,3% for the MFs 40X and 100X and 97,7% for the MFs 200X and 400X.
Speckle-suppressed optical implementation for JTC-based image cryptosystem

Speckle noise results in a serious quality degradation in reconstructed image of the optical implementation for image cryptosystem using a joint transform correlation (JTC) architecture. In this paper, a speckle-suppressed optical implementation for JTC-based image cryptosystem is proposed. The encryption process is performed optically, while the decryption process is performed digitally. In this paper, the distribution of objects in the input plane is optimized on basis of analysis for the inherent noise of cryptosystem, such as dark current noise of the CCD camera. Consequently, speckle noise in the reconstructed image is effectively decreased. Furthermore, less times of record are required, and there is no increase in hardware and complexity. Simulations and experimental results demonstrate the validity and effectiveness of the proposed speckle-suppressed scheme.
A Study on the Application of Blockchain in Carbon Trading Systems

A systematic literature review is presented to understand and analyze the state of the art in Blockchain-based solutions applied to the carbon trading context. The main contributions of the work are twofold: i) updated literature review, emphasizing the contributions of each selected work; ii) classification of the papers based on their main contributions and application domains as well as summarizing and discussing challenges and open research issues. Our results reveal the need for research focusing on understanding the use of advanced technology in complex scenarios of trade and management of carbon credits. This work is intended for researchers who aim to develop methods and techniques that make use of Blockchain technology in the realm of carbon trading.
An adaptive and extensible framework to enhance end to end trustworthiness of traceability data

In recent years, many traceability systems have been developed. These systems can capture traceability data from the first link to the last link of a supply chain, which makes the traceability of a product from the consumer to its origin possible. However, current traceability systems are less effective in providing traceability data that can be trusted. This research proposes a framework that leverages blockchain smart contracts and a trust model to improve traceability data's trustworthiness. The framework's use of multi-set trust metrics makes it adaptable to all supply chain links. Furthermore, the framework's extensibility feature is enhanced through the use of metric developers who continuously assess supply chain trust needs and avail relevant trust metric packages to address the needs. This makes the proposed traceability solution more relevant and effective even when trust requirements change. We demonstrate the applicability of the framework using the Botswana beef supply chain case study.
Convolutional, Extra-Trees and Multi layer Perceptron

In this paper, we propose a novel approach for building and initializing deep neural networks based on extremely randomized trees (extra-trees) an ensemble learning method for both classification and regression and feature extraction techniques. We use convolutional neural networks (CNNs), a family of modern deep learning models, extensively used in the area of computer vision and image classification, to improve the accuracy and generalization performance of classifiers. First, a CNN model is built to automatically extract multi-level features from the data. Second, a random forest obtains the structures of the trees. Finally, the neural networks (MLP) are built. This hybrid method combines two standard adaptive methods: decision trees and artificial neural networks. In this article, we illustrate the structure of the hybrid method, the problems occurring during the building of the model, and the solutions for these problems. The experimental results indicate that the proposed approach achieves consistently high performance for a variety of regression and classification tasks. These results should motivate further studies seeking to develop accurate and efficient tree-based models.
Bottom-up approach to translate Tunisian dialect texts in Social Networks

Dialect translation is a motivating task for both industrial and academic fields. Indeed, migrating to a standard language facilitates communication between people throughout the world, and facilitates application of Natural Language Processing tasks such as automatic opinion analysis, semantic analysis, etc. We describe, in this work, an effort to build a Neural Machine Translation (NMT) model in order to translate the comments posted on social media intended for the Tunisian community. It is a question of dealing with the Tunisian dialect (TD) in Social Networks (SN). Due to the orthographic ambiguity presented by the TD, we experiment different configurations corpora and NMT models following a bottom-up approach. The best configuration resulted in building a translation model which achieved a BLEU score of 69.22% on a test corpus.
Property Graph Access Control Using View-Based and Query-Rewriting Approaches

Managing and storing big data is non-trivial for traditional relational databases (RDBMS). Therefore, the NoSQL (Not Only SQL) database management system emerged. It is ca-pable of handling the vast amount and the heterogeneity of data. In this research, we are interested in one of its trending types, the graph database, namely, the Directed Property Graph (DPG). This type of database is powerful in dealing with complex relationships ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{e}.\mathrm{g}$</tex> ., social networks). However, its sen-sitive and private data must be protected against unauthorized access. This research proposes a security model that aims at exploiting and combining the benefits of Access Control, View-Based, and Query-Rewriting approaches. This is a novel combination for securing DPG.
Towards an intelligent adaptive security framework for preventing and detecting credit card fraud

As fraud and cybercrime become more frequent and sophisticated, preventing users from being exposed to risk is a significant challenge for the scientific community. Current banking systems use One Time Password (OTP) sent to the mobile phone to prevent user impersonation attacks. However, it could be compromised by man-in-the-middle attacks. It also presents usability and unavailability problems if the SMS service is inaccessible, especially when traveling or in case of a damaged or stolen phone. Using Machine Learning (ML) techniques to detect and prevent fraudulent actions allows financial organizations to stay one step ahead of fraudsters, whose scale and sophistication continue to grow. Specifically, ML systems can be used to identify additional authentication factors to make the system more robust against attacks. To prevent identity theft attacks and minimize fraudulent transactions, we propose in this work a comprehensive design that combines adaptive multi-factor authentication and adaptive fraud detection for banking systems based on the use of ML technology.
Towards An Accurate Stacked Ensemble Learning Model For Thyroid Earlier Detection

Thyroid disease is one of the most common endocrine disorders worldwide. However, thyroid conditions can be challenging to diagnose because symptoms are very similar to those of other diseases. A proper diagnosis depends on clinical examination and many blood tests involving a large amount of complex data that is difficult to interpret. Early thyroid detection is crucial since it significantly reduces complications and minimizes death risk. The main objective of this study is to create an accurate framework for improving the diagnostic accuracy of thyroid diseases. For this purpose, we propose a three-stage approach based on dimensionality reduction using feature selection, data sampling to handle the data-imbalance problem, and stacked ensemble learning instead of a single machine learning algorithm to give the final prediction. This research shows that the proposed approach can diagnose thyroid disease more accurately than existing techniques, achieving 99.49% of precision and 99.46% in terms of F1-score.
Underwater image segmentation based on multi-scale residual attention

In this paper, we propose a multi-scale residual attention network (MSR-Net) segmentation algorithm, which uses the ResNet50 residual network as the backbone feature extraction network and introduces a multi-scale channel attention mechanism. The MSR-Net uses the ResNet50 residual network as the backbone feature extraction network and introduces a multi-scale channel attention mechanism, which enables the network model to retain more complete sample edge information, significantly improves the segmentation capability of the model and ensures its network performance, which can effectively meet the needs of underwater image segmentation-related tasks. The proposed network is tested on the DUT-USEG dataset, and the recall, accuracy and average cross-merge ratio are 74.17%, 83.21% and 65.96%, respectively. As shown by the experimental results, compared with the classical U-Net, PSPNet and DeepLabV3, the performance indexes of the method in this paper are significantly improved.
Blind Two Party ECDSA Signing Based Homomorphic Encryption over Message Passing

Due to extensive deployment in cryptocurrencies, two party ECDSA signature have obtained much attention. We could divide the signature generation procedure over message into two stages, mainly, to maintain anonymity via reducing the decryption step and securing the message passing transmission channel. Preserving the two stages simultaneously is arduous. However, the existing literature fails to achieve the two stages simultaneously: either the proof of anonymity is not explained well, or message passing leaks some information. This work proposes a blind two party ECDSA signing based homomorphic encryption over message passing. In this work, the first stage is maintained by blind signature, and the second is achieved by a homomorphic encryption with respect to an analytical operation. Our proposed work maintains the aforementioned two stages simultaneously.
Evaluating Interpretability of Multilayer Perceptron and Support Vector Machines for Breast Cancer Classification

The best-performing machine learning (ML) models suffer from a lack of interpretability. This study conducted an empirical evaluation of two interpretability techniques, global surrogate and local interpretable model-agnostic explanations (LIME). Experiments on two black boxes, a multilayer perceptron, and support vector machines were carried out on two breast cancer tabular datasets. The results show that local interpretability can work along the global one to provide insights into the model. Quantitative evaluations show that the global surrogate slightly outperforms LIME. Interpretability techniques have the potential to fix the interpretability trade-off for opaque models.
Shapes2Toon: Generating Cartoon Characters from Simple Geometric Shapes

Cartoons are an important part of our entertain-ment culture. Though drawing a cartoon is not for everyone, creating it using an arrangement of basic geometric primitives that approximates that character is a fairly frequent technique in art. The key motivation behind this technique is that human bodies-as well as cartoon figures-can be split down into various basic geometric primitives. Numerous tutorials are available that demonstrate how to draw figures using an appropriate arrangement of fundamental shapes, thus assisting us in creating cartoon characters. This technique is very beneficial for children in terms of teaching them how to draw cartoons. In this paper, we develop a tool-shape2toon-that aims to automate this approach by utilizing a generative adversarial network which combines geometric primitives (i.e. circles) and generate a cartoon figure (i.e. Mickey Mouse) depending on the given approximation. For this purpose, we created a dataset of geometrically represented cartoon characters. We apply an image-to-image translation technique on our dataset and report the results in this paper. The experimental results show that our system can generate cartoon characters from input layout of geometric shapes. In addition, we demonstrate a web-based tool as a practical implication of our work.
A Fast Hybrid Deep Neural Network Model for pushing behavior detection in human crowds

Deep learning technology is regarded as one of the latest advances in data science and analytics due to its learning abilities from the data [1]. As a result, deep learning is widely applied in the human crowd analysis domain [2]. Although it has achieved remarkable success in this area, a fast and robust model for pushing behavior detection in the human crowd is unavailable. This paper proposes a model that allows crowd-monitoring systems to detect pushing behavior early, helping organizers make timely decisions before dangerous situations appear. This particularly becomes more challenging when applied to real-time video streams of crowded events, which the proposed model accomplishes with reasonable time latency. To achieve this, the model employs a hybrid deep neural network.
Computational Linguistics Finds its Voice

Advances in artificial intelligence permit computers to converse with humans in seemingly realistic ways.
Retracted: Effect of Protein Nutrition Level on Protein Metabolism during Volleyball Exercise Based on Edge Computing in the Medical System

[This retracts the article DOI: 10.1155/2022/1614748.].
Consensus-Based Labeled Multi-Bernoulli Filter With Adaptive Event-Triggered Communication

The existing event-triggered distributed multi-target filters utilize a constant triggering threshold. Although the communication burden is reduced, it does not adapt well to dynamic environments. In this brief, a novel consensus-based labeled multi-Bernoulli (LMB) filter with event-triggered strategy is introduced with the triggering threshold being decided by a bounded threshold function. The theoretical analysis proves that the information discrepancy of the proposed algorithm is bounded in Kullback-Leibler (KL) divergence sense. The performance of the proposed algorithms is demonstrated in a distributed multi-target tracking scenario via numerical simulations.
The importance of expert knowledge in big data and machine learning

Abstract According to popular belief, big data and machine learning provide a wholly novel approach to science that has the potential to revolutionise scientific progress and will ultimately lead to the ‘end of theory’. Proponents of this view argue that advanced algorithms are able to mine vast amounts of data relating to a given problem without any prior knowledge and that we do not need to concern ourselves with causality, as correlation is sufficient for handling complex issues. Consequently, the human contribution to scientific progress is deemed to be non-essential and replaceable. We, however, following the position most commonly represented in the philosophy of science, argue that the need for human expertise remains. Based on an analysis of big data and machine learning methods in two case studies—skin cancer detection and protein folding—we show that expert knowledge is essential and inherent in the application of these methods. Drawing on this analysis, we establish a classification of the different kinds of expert knowledge that are involved in the application of big data and machine learning in scientific contexts. We address the ramifications of a human-driven expert knowledge approach to big data and machine learning for scientific practice and the discussion about the role of theory. Finally, we show that the ways in which big data and machine learning both influence and are influenced by scientific methodology involve continuous conceptual shifts rather than a rigid paradigm change.
A Hybrid Mechanism for Auto Text Categorization in Web Documents

Web personalization has become such a popular paradigm nowadays, that almost all e-commerce websites are including it in their websites. The main objective of web personalization is driven by grouping similar web pages. The text categorization principle becomes a challenge when daily users visit numerous pages. This paper develops a hybrid framework which categorizes the text extracted from a web document, by applying Neighbourhood Preserving Embedding algorithm and then Particle Swarm Optimization algorithm on the extracted text groups, resulting into a group of web documents which contain similar texts. The proposed mechanism relatively has a high performance which improves with time, and as the size of web documents increase, the particle swarm algorithm also evolves in its nature.
Effect of cavity leakage on time-dependent spectrum of two atoms Tavis-Cummings model

We study the atomic emission spectrum and cavity field spectrum of dipole–dipole coupled two atoms interacting with a single-mode radiation field in a non-ideal cavity. We solved the quantum master equation by a method that combined analytical analysis and numerical solution. The explicit expression of spectral peak position and numerical results of spectral structure are given for the atoms in entangled state and the field in vacuum initially. The results shown that the decay rate of cavity field has obvious influence on the intensity of spectra but a little effect on the spectral structure. The cavity dissipation makes also the measurement time an important factor in measuring spectral structure.
Application of Improved SFM Adaptive Threshold Algorithm in Automatic 3D Reconstruction of Remote Sensing Images

Motion recovery structure method has become a research hotspot of 3D reconstruction because of its advantages of low equipment cost, easy to carry, wide range of applicable scenes and so on. This paper proposes to use RANSAC adaptive threshold method to improve the SFM model estimation process, and proposes to combine the feature-based matching (FBM) method and the area based matching (ABM) method with NCC (normalized cross correlation) and LSM (least squares image matching) to perform FBM at the top of the image pyramid, Then, the location of matching points is adjusted layer by layer by using the region matching method with improved NCC and LSM, and finally a high-precision matching location is obtained. The modified matching points are used for basic matrix f estimation, so that the threshold can be adaptive to various scenes and data sets, and improve the accuracy of model estimation and final three dimensional reconstructions.
Safety enforcement via programmable strategies in Maude

This work aims to provide a general mechanism for safety enforcement in rewriting logic computations. Our technique relies on an assertion-guided model transformation that leverages the newly defined Maude strategy language for ensuring rich safety policies in non-deterministic programs. The transformed system is guaranteed to comply with user-defined invariants that are expressed in a strategy-based, pattern-matching logic, thus preventing the concurrent system to reach any unsafe states. The performance and scalability of the technique is empirically evaluated and benchmarked on a set of realistic programs.
Co-designed metaoptoelectronic deep learning

A metaoptical system is co-designed with electronic hardware to implement deep learning image recognition. The optical convolution block includes a reflective metasurface to perform one layer of a deep neural network. The optical and digital components are jointly optimized to perform an image classification task attaining 65% accuracy, which is close to the 66% accuracy of a fully-digital network where the optical block is replaced by a digital convolution layer.
Differential analysis of the NBC algorithm based on mixed integer linear programming model

Abstract This paper mainly studies mixed integer linear programming (MILP)‐based cryptanalysis on round‐reduced NBC algorithm with generalized Feistel structure. First, each component of the algorithm is equivalently described by linear inequality equations, and the propagation rules of differential characteristics and linear approximation are described, and an automatic search algorithm model of differential mode and linear mask mode based on MILP is established, and the minimum number of active S‐boxes and the corresponding differential mode and linear mask mode paths are obtained. Second, according to the NBC algorithm's 16‐bit block pull‐wire position permutation characteristics, the optimal full diffusion rounds of NBC‐128 algorithm and NBC‐256 algorithm are obtained by using approximate matrix multiplication theory to be six and eight respectively. Finally, based on the optimal number of full diffusion rounds of the algorithm, an automatic search algorithm model of impossible differential cryptanalysis is established, and the set of input differential patterns is traversed to obtain 11‐round impossible differential distinguishers of NBC‐128 algorithm and 14‐round impossible differential distinguishers of NBC‐256 algorithm respectively.
Automated Creation of an Intent Model for Conversational Agents

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