Reviewerly/exHarmony huggingface dataset:
Lithological discrimination and mineralogical mapping using Landsat-8 OLI and ASTER remote sensing data: Igoudrane region, jbel saghro, Anti Atlas, Morocco

This study aims to delineate the lithological formations, in addition to a mineralogical mapping comparing between the ASTER and Landsat-8 OLI sensors in the Igoudrane region. The research was accomplished using BR along with spectral profiles of minerals, PCA, MNF, ICA, XRD, and reflectance spectrometry. The BR measured with ASTER revealed the presence of amphibole, illite, smectite, muscovite, phengite, basic degree index SiO2, calcite, chlorite, epidote, dolomite, hydroxides, and ferrous silicates. Furthermore, Landsat-8 OLI BR highlighted areas with oxides, hydroxides, and laterite. The spectral profiles of minerals showed their absorption in the VNIR and SWIR regions. The muscovite and illite (phyllic alteration) are distinguished by Al-OH absorption at 2.20 μm. The argillitic alteration is additionally defined by muscovite, illite, and Kaolinite, where Kaolinite has a high absorption at 0.9 μm. Chlorite and carbonates characterized the propylitic alteration zone, with an absorption ranging between 2.3 μm and 2.35 μm due to CO3 and Mg-OH compositions. The oxidation (hematite and jarosite) was distinguished by absorption near 0.95 μm and 2.3 μm, respectively, whereas the goethite spectral profile exhibits absorption near 1.4 μm and 2.2 μm. The absorption of smectite is 1.4 μm, near 2.2 μm. The amphibole had absorption near 1.4 μm and 2.3 μm, whereas the pyroxene had absorption near 1.4 μm and 2.3 μm. The first three components of PCA, MNF, and ICA showed the highest eigenvalues and gave an appreciable discrimination of the lithologies, especially with ASTER. Moreover, the XRD quantified the mineralogy of the rocks, which were compared to the BR of ASTER. The reflectance spectrometry also provided alteration minerals, such as muscovite, phengite, illite, Fe-Mg chlorite, Fe-chlorite, iron oxides, hydroxides, hornblende, and calcite. Overall, the methodology adopted has shown great performance and strong potential for mapping alteration areas and lithological discrimination in similar arid regions.
Digital assets rights management through smart legal contracts and smart contracts

Intellectual property rights (IPR) management needs to evolve in a digital world where not only companies but also many independent content creators contribute to our culture with their art, music, and videos. In this respect, blockchain has recently emerged as a promising infrastructure providing a trustworthy and immutable environment that, thanks to smart contracts, may enable more agile management of digital rights and streamline royalty payments. However, no widespread consensus has been reached on the ability of this technology to adequately manage and transfer IPR. This paper presents an innovative approach to digital rights management developed within the scope of an international research endeavour co-financed by the European Commission named MediaVerse. The approach proposes the combined usage of smart legal contracts and blockchain smart contracts to take care of the legally-binding contractual aspects of IPR and, at the same time, the need for notarization, rights transfer, and royalty payments. The work conducted represents a contribution to advancing the current literature on IPR management that may lead to an improved and fairer monetization process for content creators as a means of individual empowerment.
Optimal navigation for AGVs: A soft actor–critic-based reinforcement learning approach with composite auxiliary rewards

In this paper, we address the problem of real-time navigation and obstacle avoidance for automated guided vehicles (AGVs) in dynamic environments, which is a primary research area in collaborative control systems for AGVs. To overcome the computational inefficiency of recalculating optimal paths every time, we propose an improved Soft Actor–Critic (SAC)-based reinforcement learning methodology. This methodology utilizes a novel composite auxiliary reward structure and sum-tree prioritized experience replay (SAC-SP) to achieve real-time optimal feedback control. First, we formulate the navigation task as a Markov Decision Process that considers both static and dynamic obstacles. To accelerate the active learning of AGVs, we propose a novel strategy that uses composite auxiliary rewards. Next, we train the AGVs using the proposed SAC-SP methodology to handle real-time navigation with the composite auxiliary reward structure. The well-trained policy network can generate effective on-board optimal feedback actions given obstacle positions, targets, and AGV states. Simulation experiments demonstrate that our proposed method can steer AGVs to the destination with high robustness to original conditions and various obstacle restrictions, generating optimal feedback actions in the shortest amount of time.
Automatic item generation: foundations and machine learning-based approaches for assessments

This mini review summarizes the current state of knowledge about automatic item generation in the context of educational assessment and discusses key points in the item generation pipeline. Assessment is critical in all learning systems and digitalized assessments have shown significant growth over the last decade. This leads to an urgent need to generate more items in a fast and efficient manner. Continuous improvements in computational power and advancements in methodological approaches, specifically in the field of natural language processing, provide new opportunities as well as new challenges in automatic generation of items for educational assessment. This mini review asserts the need for more work across a wide variety of areas for the scaled implementation of AIG.
Optimizing Hill Climbing Algorithm for S-Boxes Generation

Nonlinear substitutions or S-boxes are important cryptographic primitives of modern symmetric ciphers. They are designed to complicate the plaintext-ciphertext dependency. According to modern ideas, the S-box should be bijective, have high nonlinearity and algebraic immunity, low delta uniformity, and linear redundancy. These criteria directly affect the cryptographic strength of ciphers, providing resistance to statistical, linear, algebraic, differential, and other cryptanalysis techniques. Many researchers have used various heuristic search algorithms to generate random S-boxes with high nonlinearity; however, the complexity of this task is still high. For example, the best-known algorithm to generate a random 8-bit bijective S-box with nonlinearity 104 requires high computational effort—more than 65,000 intermediate estimates or search iterations. In this article, we explore a hill-climbing algorithm and optimize the heuristic search parameters. We show that the complexity of generating S-boxes can be significantly reduced. To search for a random bijective S-box with nonlinearity 104, only about 50,000 intermediate search iterations are required. In addition, we generate cryptographically strong S-Boxes for which additional criteria are provided. We present estimates of the complexity of the search and estimates of the probabilities of generating substitutions with various cryptographic indicators. The extracted results demonstrate a significant improvement in our approach compared to the state of the art in terms of providing linear non-redundancy, nonlinearity, algebraic immunity, and delta uniformity.
Machine learning aided dimensionality reduction toward a resource efficient projective quantum eigensolver: Formal development and pilot applications

In recent times, a variety of hybrid quantum-classical algorithms have been developed that aim to calculate the ground state energies of molecular systems on Noisy Intermediate-Scale Quantum (NISQ) devices. Albeit the utilization of shallow depth circuits in these algorithms, the optimization of ansatz parameters necessitates a substantial number of quantum measurements, leading to prolonged runtimes on the scantly available quantum hardware. Through our work, we lay the general foundation for an interdisciplinary approach that can be used to drastically reduce the dependency of these algorithms on quantum infrastructure. We showcase these pertinent concepts within the framework of the recently developed Projective Quantum Eigensolver (PQE) that involves iterative optimization of the nonlinearly coupled parameters through repeated residue measurements on quantum hardware. We demonstrate that one may perceive such a nonlinear optimization problem as a collective dynamic interplay of fast and slow relaxing modes. As such, the synergy among the parameters is exploited using an on-the-fly supervised machine learning protocol that dynamically casts the PQE optimization into a smaller subspace by reducing the effective degrees of freedom. We demonstrate analytically and numerically that our proposed methodology ensures a drastic reduction in the number of quantum measurements necessary for the parameter updates without compromising the characteristic accuracy. Furthermore, the machine learning model may be tuned to capture the noisy data of NISQ devices, and thus the predicted energy is shown to be resilient under a given noise model.
Phishing Attacks on Cryptocurrency Investors in the Arab States of the Gulf

With the rapid development of technology in all fields, including the financial field, people have flocked to invest in cryptocurrencies, sometimes without prior knowledge or experience. This has prompted hackers to prey on inexperienced investors through many types of fraud and attacks, especially phishing attacks. Cryptocurrency investment transactions take place without intermediaries such as banks and monetary institutions. Investing in cryptocurrencies is a form of peer-to-peer transaction and takes place without the involvement of physical wallets. This study addresses cases where people may become victims of phishing attacks due to the nature of cryptocurrency investments. The aim of this study was to understand the concepts of various phishing attacks on cryptocurrencies and to measure the awareness of cryptocurrency investors in the Arab Gulf countries regarding the security risks associated with cryptocurrency investments. This research was conducted by distributing a questionnaire among cryptocurrency investors and collecting and analyzing all the survey responses. The results reveal a lack of awareness about how to deal with the security risks associated with cryptocurrency investments. The research concludes that the majority of cryptocurrency investors are unaware of how to deal with phishing attacks. Finally, we address future research directions and recommend actions that can be taken to increase investors’ awareness of this issue.
DPFS: DPU-Powered File System Virtualization

As we move towards hyper-converged cloud solutions, the efficiency and overheads of distributed file systems at the cloud tenant side (i.e., client) become of paramount importance. Often, the clientside driver of a cloud file system is complex and CPU intensive, deeply coupled with the backend implementation, and requires optimizing multiple intrusive knobs. In this work, we propose to decouple the file system client from its backend implementation by virtualizing it with an off-the-shelf DPU using the Linux virtio-fs software stack. The decoupling allows us to offload the file system client execution to a DPU, which is managed and optimized by the cloud provider, while freeing the host CPU cycles. DPFS, our proposed framework, is 4.4× more host CPU efficient per I/O, delivers comparable performance to a tenant with zero-configuration and without modification of their host software stack, while allowing workload and hardware specific backend optimizations. The DPFS framework and its artifacts are publically available at https://github.com/IBM/DPFS.
Validity Index for Clustered Data in Non-negative Space

We propose a novel nonparametric cluster validity index which can be used to evaluate the unknown number of existing clusters prevailing a data set, to assess the quality of classification for a clustered set of data members, or to compare the clustering output obtained from different algorithms. Our efficient measure depends only on the observation-wise distances of the non-negative clustered data from their origin given in an arbitrary dimensional space. Its fast implementation makes it appealing for big data analysis, whereas the high-dimensional applicability widens its usefulness. Easy interpretation, simple algorithm, speedy computation and great performance, shown in terms of data study, establish our advised validity index as a strong cluster accuracy measure among the acknowledged ones from the literature. AMS subject classification: 62H30
Improving YOLOv7-Tiny for Infrared and Visible Light Image Object Detection on Drones

To address the phenomenon of many small and hard-to-detect objects in drone images, this study proposes an improved algorithm based on the YOLOv7-tiny model. The proposed algorithm assigns anchor boxes according to the aspect ratio of ground truth boxes to provide prior information on object shape for the network and uses a hard sample mining loss function (HSM Loss) to guide the network to enhance learning from hard samples. This study finds that the aspect ratio difference of vehicle objects under drone perspective is more obvious than the scale difference, so the anchor boxes assigned by aspect ratio can provide more effective prior information for the network than those assigned by size. This study evaluates the algorithm on a drone image dataset (DroneVehicle) and compares it with other state-of-the-art algorithms. The experimental results show that the proposed algorithm achieves superior average precision values on both infrared and visible light images, while maintaining a light weight.
Communication compression techniques in distributed deep learning: A survey

Nowadays, the training data and neural network models are getting increasingly large. The training time of deep learning will become unbearably long on a single machine. To reduce the computation and storage burdens, distributed deep learning has been put forward to collaboratively train a large neural network model with multiple computing nodes in parallel. The unbalanced development of computation and communication capabilities has led to training time being dominated by communication time, making the communication overhead a major challenge toward efficient distributed deep learning. Communication compression is an effective method to alleviate communication overhead, and it has evolved from simple random sparsification or quantization to versatile strategies or data structures. In this survey, existing communication compression techniques are reviewed and classified to provide a bird's eye view. The main properties of each class of compression methods are analyzed, and their applications or theoretical convergence are described if necessary. This survey is potentially helpful for researchers and engineers to understand the up-to-date achievements on the communication compression techniques that accelerate the training of large deep learning models.
A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasets

The missing data mechanism is a relevant problem in Machine Learning (ML) and biomedical informatics communities. Real-world Electronic Health Record (EHR) datasets comprise several missing values, thus revealing a high level of spatiotemporal sparsity in the predictors' matrix. Several approaches in the state-of-the-art tried to deal with this problem by proposing different data imputation strategies that (i) are often unrelated to the ML model, (ii) are not conceived for EHR data where laboratory exams are not prescribed uniformly over time and percentage of missing values is high (iii) exploit only univariate and linear information on the observed features. Our paper proposes a data imputation strategy based on a clinical conditional Generative Adversarial Network (ccGAN) capable of imputing missing values by exploiting non-linear and multivariate information across patients. Unlike other GAN data imputation-based approaches, our method deals explicitly with the high level of missingness of routine EHR data by conditioning the imputing strategy to the observable values and those fully-annotated. We demonstrated the statistical significance of the ccGAN to other state-of-the-art approaches in terms of imputation (around 19.79% of gain to the best competitor) and predictive performance (up to 1.60% of gain to the best competitor) on a real multi-diabetic centers dataset. We also demonstrated its robustness across different missingness rates (up to 1.61% of gain to the best competitor in the highest missingness rates condition) on an additional benchmark EHR dataset.
Hierarchical dual graph convolutional network for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a kind of fine-grained task that predicts sentiment polarities for the given aspects in a sentence. Recently, graph-based methods such as graph convolutional networks (GCNs), graph attention networks (GATs) and their extensions have been used to model the syntactic structures of sentences and have achieved promising results. However, these graph-based methods generally homogenize dependencies and result in the loss of syntactic information. In addition, models that use only syntax-based graph methods are unable to identify the sentiments in informal reviews. In this study, we propose a dependency feature-aware hierarchical dual-graph convolutional network (HD-GCN) model, which consists of two specifically designed GCNs for extracting syntactic and semantic information. Specifically, for semantic information extraction, we leverage a self-attention matrix to represent the correlations between nodes. For syntactic information, we design a dependency feature-aware GCN that adaptively learns the mappings of various dependencies in the adjacency matrix. Moreover, we stack the network into multiple layers, and each layer learns different relation matrices to better obtain different levels of linguistic features. Extensive experiments are conducted on the SemEval 2014 and Twitter datasets, and the obtained experimental results are superior to those of the state-of-the-art models on the ABSA task, proving the effectiveness of our model.
Improving trust and confidence in medical skin lesion diagnosis through explainable deep learning

Abstract A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems. Research in eXplainable Artificial Intelligence (XAI) is trying to solve this issue. However, often XAI approaches are only tested on generalist classifier and do not represent realistic problems such as those of medical diagnosis. In this paper, we aim at improving the trust and confidence of users towards automatic AI decision systems in the field of medical skin lesion diagnosis by customizing an existing XAI approach for explaining an AI model able to recognize different types of skin lesions. The explanation is generated through the use of synthetic exemplar and counter-exemplar images of skin lesions and our contribution offers the practitioner a way to highlight the crucial traits responsible for the classification decision. A validation survey with domain experts, beginners, and unskilled people shows that the use of explanations improves trust and confidence in the automatic decision system. Also, an analysis of the latent space adopted by the explainer unveils that some of the most frequent skin lesion classes are distinctly separated. This phenomenon may stem from the intrinsic characteristics of each class and may help resolve common misclassifications made by human experts.
ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images

Acute Lymphocytic Leukemia is a type of cancer that occurs when abnormal white blood cells are produced in the bone marrow which do not function properly, crowding out healthy cells and weakening the immunity of the body and thus its ability to resist infections. It spreads quickly in children's bodies, and if not treated promptly it may lead to death. The manual detection of this disease is a tedious and slow task. Machine learning and deep learning techniques are faster than manual detection and more accurate. In this paper, a deep feature selection-based approach ResRandSVM is proposed for the detection of Acute Lymphocytic Leukemia in blood smear images. The proposed approach uses seven deep-learning models: ResNet152, VGG16, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0 and ResNet50 for deep feature extraction from blood smear images. After that, three feature selection methods are used to extract valuable and important features: analysis of variance (ANOVA), principal component analysis (PCA), and Random Forest. Then the selected feature map is fed to four different classifiers, Adaboost, Support Vector Machine, Artificial Neural Network and Naïve Bayes models, to classify the images into leukemia and normal images. The model performs best with a combination of ResNet50 as a feature extractor, Random Forest as feature selection and Support Vector Machine as a classifier with an accuracy of 0.900, precision of 0.902, recall of 0.957 and F1-score of 0.929.
Pre-service teachers’ vocabularies of the language of science in the context of learning about electrons and photons

Teaching and learning the language of science is an important part of science education. Learning the vocabulary of science plays a key role in learning the language of science. The meaning of abstract scientific terms builds on their connections with other terms and how they are used. In this research, we study pre-service physics teachers’ physics-related vocabularies and investigate how rich a vocabulary they use and what similarities and differences there are in their vocabularies regarding electrons and photons. We investigate the connectedness of physics terms by categorizing them according to their role in explaining quantum physics and carry out a lexical network analysis for N=60 written reports. The analysis shows that vocabularies do not share much similarity and the reports reflect narrow images of photons and electrons. We conclude that science teacher education needs to pay attention to explicit teaching of the language of science for pre-service teachers.