Reviewerly/exHarmony huggingface dataset:
Value Iteration Residual Network with Self-attention

The Value Iteration Network (VIN) is a neural network widely used in path-finding reinforcement learning problems. The planning module in VIN enables the network to understand the nature of a problem, thus giving the network an impressive generalization ability. However, reinforcement learning (RL) with VIN can not guarantee efficient training due to the network depth and max-pooling operation. A great network depth makes it harder for the network to learn from samples when using gradient descent algorithms. The max-pooling operation may increase the difficulty of learning negative rewards due to overestimation. This paper proposes a new neural network, Value Iteration Residual Network (VIRN) with Self-Attention, using a unique spatial self-attention module and aggressive iteration to solve the above-mentioned problems. A preliminary evaluation using Mr. Pac-Man demonstrated that VIRN effectively improved the training efficiency compared with VIN.
A Multi-level Wavelet Decomposition Network for Image Super Resolution

Machine learning with deep convolutional neural network has seen huge adaptation in computer vision applications over the last decade. In this paper, we propose a novel network architecture to perform single image super resolution based on deep convolutional neural network (DCNN) and discrete wavelet transform (DWT). In fact, the discrete wavelet transform is applied in multi-levels on the low resolution image to divide it into four sub-bands. Then, the deep convolutional neural networks is applied only on the approximation sub-band of second level in order to reduce computational requirement. Also, we discovered that dropping the DD sub-band does not impact the perceptual quality of the reconstructed image. So we propose to replace by a matrix of zero. By training various images such as Set5 and Set14 datasets, good results are obtained allowing to validate the effectiveness and efficiency of the proposed method based on evaluation of PSNR and SSIM. The reconstructed image achieves a high resolution value in less run time than existing methods.
An Improved Model for Semantic Segmentation of Brain Lesions Using CNN 3D

The implementation of accurate automatic algorithms for brain tumor segmentation could improve disease diagnosis, treatment monitoring, and make large-scale studies of the pathology possible. In this study, we optimize the architecture used for the challenging task of brain lesion segmentation, which is based on the 3D convolutional neural network. In order to integrate broader local and contextual information, we use a two-channel architecture that processes input images at multiple scales simultaneously. Furthermore, we show that optimizing at the momentum value level, helps us achieve better results in the DSC, accuracy and sensitivity criteria. Our method has also been tested by the BRATS 2015 training dataset, where it has performed very well despite the simplicity of the method.
An Improved Multi-image Steganography Model Based on Deep Convolutional Neural Networks

In this paper, we present a DeepCNN grounded autoencoder-based picture steganography model that enables the extraction of spatiotemporal information from images. This model’s innovation is the process of concealing four pictures within one other image while taking into account size equivalency. To conceal many hidden pictures under a single, high-resolution cover image, we attempt to encode and decode them. This model’s quantitative output was organized using the quantitative index error per pixel, MSE, SSIM, and PSNR, and it performs admirably in comparison to earlier methods.
A Data Warehouse for Spatial Soil Data Analysis and Mining: Application to the Maghreb Region

Soil health affects soil functions, food production, and climate change. Understanding our soil is important for our environment and food security. Therefore, we propose a data warehouse architecture for storing, processing, and visualizing soil data. In this study, we focused on the Maghreb region for the first time. This region is highly vulnerable to climate change and food insecurity, even though, as far as we know, it has not yet been considered. The proposed data warehouse architecture involves data mining and data science techniques to extract value from soil data and support decision-making at various levels. In our case, the warehoused data were analyzed using exploratory spatial data analysis tools to explore the spatial distribution, heterogeneity, and autocorrelation of soil properties. In our experiments, we presented the results of organic carbon (OC) analysis because of its importance to regulate climate change. The highest values of OC were situated in Morocco and Tunisia and we noticed a great distribution similarity in Algeria and Libya. One of the most important results of the autocorrelation analysis is the presence of positive spatial autocorrelation that leads to a perfect spatial clustering situation in the whole Maghreb region. This motivated us to a future work that aims to detect and interpret these clusters.
Emotion Detection Based on Facial Expression Using YOLOv5

Human emotions can be understood from facial expressions, a type of nonverbal communication. Facial emotion recognition is a technology that analyzes emotions from various sources such as images and videos. Emotion recognition is an important topic due to its wide range of applications. In this paper, the YOLOv5 (You Look Only Once) model was used to detect basic human emotions. Capturing facial expressions aids to identify the emotion based on which various suggestions can be proposed, such as songs and movie recommendations. There are numerous models for emotion detection in different architectural styles. In this article, we present a less explored YOLOv5 model. It is more accurate and gives a real time result than the previous detection algorithms. 40 images from the FER2013 dataset were used to train our YOLOv5 model and accuracy of 50% was achieved with the proposed model.
A Manual Approach for Multimedia File Carving

The recovery and examination of digital data has become a significant part of numerous criminal investigations today. Given the ever-expanding number of individual digital gadgets, for example PDAs, tablets and cell phones, we as a whole accumulate, store, and generate an enormous amount of data. A portion of this data might be valuable proof for examination and might be utilized in courts. During the past two decades, significant research has been made towards characterizing devices for the analysis of evidence originating from different sources. This work attempts to provide an automated solution for the extraction and carving of data from digitally generated dumps or image files of the evidence. The process of file carving proposed in this work is suitable for readers of diverse backgrounds.
Triple Negative Breast Cancer and Non-Triple Negative Breast Cancer Recurrence Prediction Using Boosting Models

The medical physicist has a crucial role in the choice of the treatment plan which should be accurate to avoid the treatment failure, especially cancer recurrence. In this work, we tacked into account all hormone receptor status cases including invasive Triple-Negative Breast cancer (TNBC) and non-Triple-Negative Breast Cancer (non-TNBC) to predict recurrence using machine learning and ensemble learning models including AdaBoost, XGBoost, Weighted logistic regression, weighted support vector classifier (weighted SVC), balanced Bagging, and logistic regression (LR). For this study, we have used data from The Cancer Imaging Archive (TCIA) including 922 biopsy-confirmed invasive breast cancer patients, from January 2000 to March 2014. We have applied six machine learning and ensemble learning classifiers to predict recurrence in TNBC and non-TNBC patients using demographic, clinical, pathology, genomic, treatment, and other data. Our approach consists of two paths: we first used all the data to build the prediction model. Then, we performed Feature Selection and SMOTE to select the most relevant features and to deal with the imbalanced data, before applying the machine learning techniques. We have used precision, recall, F1-score, and ROC curve to evaluate the performance of each method. As a result, the boosting method XGBoost using Feature Selection and SMOTE showed the highest recurrence predictive power, thanks to its ability to handle the imbalanced and noisy data, which could be very useful for the medical physicist in the decision-making to avoid treatment failure.
Machine Learning for Medical Image Analysis: A Survey

Machine learning techniques have made significant progress in recent years in the field of healthcare by assisting clinicians in treatment interventions, identification, detection along with the classification of a variety of diseases, including Brain Tumors, Breast Cancer, diagnosis of diabetic retinopathy, as well as, more recently in dealing with the COVID-19 pandemic and its associated medical challenges. To that end, medical image processing employs certain strategies and techniques for manipulating images, which include image acquisition, storage, presentation, and transmission. In this paper, we have overviewed several machine learning and deep learning approaches that are commonly utilized for various image processing tasks, such as image segmentation, image detection, image classification, and image compression on different medical imaging modalities, among which are computed tomography (CT), magnetic resonance imaging (MRI), and x-ray radiography. Focusing on supervised learning and deep learning techniques when applied to medical imaging analysis to enhance the performance in different medical applications using a number of algorithms such as CNNs that lead to efficient outcomes in automatic feature extraction and acceleration of the training process in the detection and classification of different diseases. The methods collected in this survey paper and the results are discussed, and the main conclusions are highlighted.
HealthBlock: A Secured Healthcare System Using Blockchain

Blockchain technology enables a distributed and decentralized environment with no more central authority. To increase the accuracy of electronic healthcare records (EHRs) and establish a secured patient-centric approach, Blockchain can be a smart solution in this case. Blockchain is a distributed ledger technology that allows for the secured transfer of medical records while a transaction is created in the network. As Blockchain is decentralized and transparent if one user tampers with medical records of transactions, all other nodes/participants would cross-reference each other and easily pinpoint the node/participants with the wrong information. In this paper, we proposed a smart contact-based Blockchain technology and implemented it in the Hyperledger framework to make the healthcare system more confidential and secure. Moreover, Smart contracts give us more security while transacting data and reduce the entire transaction cost, and also make faster transaction speed.
Toward Embedding Hyperparameters Optimization: Analyzing Their Impacts on Deep Leaning-Based Text Classification

In the last few years, an enormous amount of unstructured text documents has been added to the World Wide Web because of the availability of electronics gadgets and increases the usability of the Internet. Using text classification, this large amount of texts are appropriately organized, searched, and manipulated by the high resource language (e.g., English). Nevertheless, till now, it is a so-called issue for low-resource languages (like Bengali). There is no usable research and has conducted on Bengali text classification owing to the lack of standard corpora, shortage of hyperparameters tuning method of text embeddings and insufficiency of embedding model evaluations system (e.g., intrinsic and extrinsic). Text classification performance depends on embedding features, and the best embedding hyperparameter settings can produce the best embedding feature. The embedding model default hyperparameters values are developed for high resource language, and these hyperparameters settings are not well performed for low-resource languages. The low-resource hyperparameters tuning is a crucial task for the text classification domain. This study investigates the influence of embedding hyperparameters on Bengali text classification. The empirical analysis concludes that an automatic embedding hyperparameter tuning (AEHT) with convolutional neural networks (CNNs) attained the maximum text classification accuracy of 95.16 and 86.41% for BARD and IndicNLP datasets.
Trade Management System Using R3 Corda Blockchain

Blockchain is a cutting-edge technology that offers corporate networks the ability to save transactions, carry out operations, and communicate information by building trust in an inclusive environment. Blockchain technology is a breakthrough in encryption and security that is used in different types of industries, such as healthcare, supply chain and logistics, retail and e-commerce, and finance. Blockchain is a peer-to-peer decentralized network of node and it helps in providing security by using encryption and hashing algorithm. The hash of each node is carried to the next node forming a chain of connection. Any changes in the data will affect the hash value, thus breaking the chain. Even though security is achieved in the blockchain technology, privacy in blockchain is a significant research topic that needs to addressed. Since every block is visible to all the nodes it creates a threat to privacy in blockchain. In this paper, secure supply chain management is implemented that preserves privacy between all the stake holders involved in the process. The proposed solution does not require a trusted third party and it is implemented with R3 corda, a private blockchain with high level of trust. A case study proves the behaviour of the flow and the states created by recording the transactions.
Optimal Uncoordinated Unique IDs

In the Uncoordinated Unique Identifiers Problem (UUIDP) there are n independent instances of an algorithm A that generates IDs from a universe (1, ..., m) , and there is an adversary that requests IDs from these instances. The goal is to design A such that it minimizes the probability that the same ID is ever generated twice across all instances, that is, minimizes the collision probability. Crucially, no communication between the instances of A is possible. Solutions to the UUIDP are often used as mechanisms for surrogate key generation in distributed databases and key-value stores. In spite of its practical relevance, we know of no prior theoretical work on the UUIDP.
Generative Datalog with Stable Negation

Extending programming languages with stochastic behaviour such as probabilistic choices or random sampling has a long tradition in computer science. A recent development in this direction is a declarative probabilistic programming language, proposed by Barany et al. in 2017, which operates on standard relational databases. In particular, Barany et al. proposed generative Datalog, a probabilistic extension of Datalog that allows sampling from discrete probability distributions. Intuitively, the output of a generative Datalog program P on an input database D is a probability space over the minimal models of D and P, the so-called possible outcomes. This is a natural generalization of the (deterministic) semantics of Datalog, where the output of a program on a database is their unique minimal model. A natural question to ask is how generative Datalog can be enriched with the useful feature of negation, which in turn leads to a strictly more expressive declarative probabilistic programming language. In particular, the challenging question is how the probabilistic semantics of generative Datalog with negation can be robustly defined. Our goal is to provide an answer to this question by interpreting negation according to the stable model semantics.
CoBiCo: A model using multi-stage ConvNet with attention-based Bi-LSTM for efficient sentiment classification

The rapid growth of social media and specialized websites that provide critical product reviews has resulted in a massive collection of information for customers worldwide. These data could contain a wealth of information, such as product reviews, market forecasting, and the polarity of sentiments. In these challenges, machine learning and deep learning algorithms give the necessary capabilities for sentiment analysis. In today’s competitive markets, it’s critical to grasp reviewer opinions and sentiments by extracting and analyzing their characteristics. The research aims to develop an optimised model for evaluating sentiments and categorising them into proper categories. This research proposes a unique, novel hybridised model that integrates the advantages of deep learning methods Dual LSTM (Long Short Term Memory) and CNN (Convolution Neural Network) with word embedding technique. The performance of different word embedding techniques is compared to select the best embedding for the implementation in the proposed model. Furthermore, a multi-convolution approach with attention-oriented BiLSTM is applied. To test the validity of the performance of the proposed model, standard metrics were applied. The outcome indicates that the suggested model achieves a significantly improved accuracy of 96.56%, superior to other models.
A dual-stream hybrid model for blind image quality assessment

Blind image quality assessment (BIQA) is a fundamental task in computer vision. Humans can evaluate image quality from local and global aspects without information on reference images. Inspired by this, we propose a BIQA method named DS-IQA by mimicking human visual system (HVS). A dual-stream hybrid module is established to get dual-stream quality-aware features. A CNN branch is used to mimic the active inference process of HVS to extract local quality-aware features. An enhanced Transformer-branch is used to extract global quality-aware features by modeling nonlocal relations of image patches. Finally, a quality evaluator based on Transformer layers is developed to map the dual-stream features and output the final quality score. The proposed approach is evaluated on five databases. The PLCC of DS-IQA reaches 0.975, 0.938, and 0.963 respectively on synthetic databases (LIVE, TID2013, CSIQ), and individual distortion experimental on TID2013 shows that DS-IQA outperforms in 8 of the 24 distortion categories on TID2013. On authentic databases (LIVEC, KonIQ-10k), the PLCC of DS-IQA reaches 0.887, 0.918, experiments show the superiority of the proposed method over other state-of-the-art BIQA metrics.
Simultaneous combined XRF-XRD analysis of geological sample: New methodological approach for on-site analysis on New-Caledonian Ni-rich harzburgite

There is a growing interest in on-site, real-time analytical solutions for mining and environmental projects to characterize large areas and/or volumes of raw materials that are sometimes highly heterogeneous in terms of elemental distribution and mineralogy. Several fast and cost-effective methods are used for rapid on-site screening and real-time chemical and mineralogical characterization, such as portable X-ray fluorescence (pXRF) and X-ray diffraction (pXRD). However, these methods are not always applicable due to limitations in the detection and quantification of light elements (Mg, Al, Si) for pXRF or complex or minor minerals for pXRD, whose results need to be supported by laboratory analysis. This study presents a new methodological approach for in situ rapid chemical and mineralogical characterization of samples, based on the use of a transportable instrument (called ID2B) that allows, in a single acquisition step, a combined XRD-XRF analysis to identify and quantify the chemical elements and their associated minerals. The HI0 harzburgite sample from New Caledonia used to evaluate the data was analyzed in the laboratory (SEM-EDS, EPMA, XRF and XRD) and with the ID2B instrument to highlight the potential of our new methodology. In order to demonstrate the interest of using the ID2B combined XRF-XRD analysis approach directly in the field, where sample preparation is not always easy to implement, this comparison was made on the same sample (HI0), prepared in two different ways, either as a powderized (optimal preparation) or as-sawn (unprepared) sample. After automated processing of the combined XRF-XRD datasets acquired with the ID2B instrument, the chemical elements and mineralogical phases identified on both the powder and as-sawn samples are identical to the laboratory analyses. The chemical proportions calculated from the combined XRF-XRD data sets are also close to the laboratory XRF analysis with relative errors <5 % for Al, Mg and Si and even closer for Ca, Cr, Mn, Ni and Fe. The variability in the calculated chemical proportions is attributed to the sample heterogeneity highlighted by the mineral proportions that vary slightly between the laboratory XRD and XRD ID2B analyses of the powder, and more pronounced for the as-sawn XRD ID2B analysis. These observations show that the combined XRF-XRD approach performed on powder and as-sawn samples provides accurate chemical and mineralogical results to those obtained in the laboratory. The deployment of this new methodological approach directly on the field can provide valuable chemical and mineralogical analyses.
Digitalization: On the Way to a New Copyright Architecture?

The digitalization not only impacts the financial markets or the supply chains but also the intellectual property rights framework. Copyright-protected works are suitable to be digitized in the form of a token and then be licensed or transferred on the distributed ledger technology. Such an architecture does have efficiency benefits, but some weaknesses (limited scope of involved persons, enforcement) can also not be overlooked.
Intrusion Detection in Intelligent Connected Vehicles Based on Weighted Self-Information

The Internet of Vehicles (IoV) empowers intelligent and tailored services for intelligent connected vehicles (ICVs). However, with the increasing number of onboard external communication interfaces, ICVs face the challenges of malicious network intrusions. The closure of traditional vehicles had led to in-vehicle communication protocols, including the most commonly applied controller area network (CAN), and a lack of security and privacy protection mechanisms. Therefore, to protect the connected vehicles and IoV systems from being attacked, an intrusion-detection method is proposed based on the features extracted from the arbitration identifier (ID) field of CAN messages. Specifically, a sliding window is used to continuously extract a frame of streaming CAN messages first. Afterward, the weighted self-information of the CAN message ID is defined, and both the weighted self-information and the normalized value of an ID are extracted as features. Based on the extracted features, a lightweight one-class classifier, the local outlier factor (LOF), is used to identify the outliers and detect malicious network intrusion attacks. Simulation experiments were conducted based on the public CAN dataset provided by the HCR Lab. The proposed method, using four different one-class classifiers, was analyzed, and it is also benchmarked with three information entropy-based intrusion-detection methods in the literature. The experimental results indicate that, compared to the benchmarks, the proposed method dramatically improves the detection accuracy for injection attacks, namely denial-of-service (DoS) and spoofing, especially when the number of injected messages is low.