Reviewerly/exHarmony huggingface dataset:
Security framework for IoT and deep belief network-based healthcare system using blockchain technology

Nowadays, the computerisation and information systems need very fast, easier and secure data analysis models. So, blockchain technology and deep learning concepts play important roles for data security and analysis in field of healthcare applications. Blockchain technology has optimal reliability in various sectors like healthcare, security, information management, smart home, etc. In this paper, blockchain technology is used for the healthcare system to provide a secure and reliable data transmission. Furthermore, IoT-based wearable devices are used to collect the data from the patients. Moreover, deep belief network (DBN) is proposed to classify the obtained data in order to predict the type of disease or problems of the patients. The experimental results shows that the proposed framework and classification model abruptly outperforms when compared with other existing techniques. The efficiency of the proposed framework is analysed based on the performance metrics like throughput and latency. The obtained loss ratio for the proposed model seems very less of 0.0126 when compared with other existing techniques like MLP, SVM, ANN, and CNN. The accuracy obtained for the proposed DBN classifier is above 95% for the increase in the number of patients.
Application of variable-order fractional calculus in neural networks: where do we stand?

After decades of evolution and advancement, artificial intelligence has profoundly affected all fields of science and engineering, and has started to revolutionize all aspects of today's life. In this regard, neural networks, which are a stepping stone in the search for artificial intelligence, play an important role. Motivated by this, the current special issue aims to explore recent trends and developments in the modeling, analysis, synchronization, and practical application of chaotic variable-order fractional neural networks.
Educational Multi-Question Generation for Reading Comprehension

Automated question generation has made great advances with the help of large NLP generation models.However, typically only one question is generated for each intended answer.We propose a new task, Multi-Question Generation, aimed at generating multiple semantically similar but lexically diverse questions assessing the same concept.We develop an evaluation framework based on desirable qualities of the resulting questions.Results comparing multiple question generation approaches in the two-question generation condition show a trade-off between question answerability and lexical diversity between the two questions.We also report preliminary results from sampling multiple questions from our model, to explore generating more than two questions.Our task can be used to further explore the educational impact of showing multiple distinct question wordings to students.
MultiVerS: Improving scientific claim verification with weak supervision and full-document context

The scientific claim verification task requires an NLP system to label scientific documents which SUPPORT or REFUTE an input claim, and to select evidentiary sentences (or rationales) justifying each predicted label.In this work, we present MULTIVERS, which predicts a fact-checking label and identifies rationales in a multitask fashion based on a shared encoding of the claim and full document context.This approach accomplishes two key modeling goals.First, it ensures that all relevant contextual information is incorporated into each labeling decision.Second, it enables the model to learn from instances annotated with a document-level fact-checking label, but lacking sentence-level rationales.This allows MULTIVERS to perform weakly-supervised domain adaptation by training on scientific documents labeled using high-precision heuristics.Our approach outperforms two competitive baselines on three scientific claim verification datasets, with particularly strong performance in zero / few-shot domain adaptation experiments.Our code and
Clinical Flair: A Pre-Trained Language Model for Spanish Clinical Natural Language Processing

Word embeddings have been widely used in Natural Language Processing (NLP) tasks.Although these representations can capture the semantic information of words, they cannot learn the sequence-level semantics.This problem can be handled using contextual word embeddings derived from pre-trained language models, which have contributed to significant improvements in several NLP tasks.Further improvements are achieved when pretraining these models on domain-specific corpora.In this paper, we introduce Clinical Flair, a domain-specific language model trained on Spanish clinical narratives.To validate the quality of the contextual representations retrieved from our model, we tested them on four named entity recognition datasets belonging to the clinical and biomedical domains.Our experiments confirm that incorporating domain-specific embeddings into classical sequence labeling architectures improves model performance dramatically compared to general-domain embeddings, demonstrating the importance of having these resources available.
Indoor Scene Recognition via Object Detection and TF-IDF

Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision and natural language processing (YOLO and TF-IDF, respectively). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.
Looking Inside the Black-Box: Logic-based Explanations for Neural Networks

Deep neural network-based methods have recently enjoyed great popularity due to their effectiveness in solving difficult tasks. Requiring minimal human effort, they have turned into an almost ubiquitous solution in multiple domains. However, due to the size and complexity of typical neural network models' architectures, as well as the sub-symbolical nature of the representations generated by their neuronal activations, neural networks are essentially opaque, making it nearly impossible to explain to humans the reasoning behind their decisions. We address this issue by developing a procedure to induce human-understandable logic-based theories that attempt to represent the classification process of a given neural network model, based on the idea of establishing mappings from the values of the activations produced by the neurons of that model to human-defined concepts to be used in the induced logic-based theory. Exploring the setting of a synthetic image classification task, we provide empirical results to assess the quality of the developed theories for different neural network models, compare them to existing theories on that task, and give evidence that the theories developed through our method are faithful to the representations learned by the neural networks that they are built to describe.
Q-learning based Path Planning Method for UAVs using Priority Shifting

Path planning is a crucial part of autonomous navigation when regarding autonomous aerial vehicles, often demanding different priorities such as the length, safety or energy consumption. Dynamic programming and geometric methods have been applied to solve this problem, but in recent years, more work has been developed using artificial intelligence approaches, such as reinforcement learning. In this paper we propose an offline path planning method for static environments using Q-learning. An optimal policy is found weighting three important factors: path length, safety and energy consumption. Due to a well balanced exploring/exploiting ratio, the proposed method can lead the agent to the desired destination starting from anywhere in the map. Simulations are done in different scenarios to address the performance of the proposed method and it showcased that the algorithm is able to find feasible paths in each scenario while regarding different set of priorities.
Investigating the Applicability of Horizontal to Tilted Sky-Diffuse Solar Irradiation Transposition Models for Key Libyan Cities

The global solar irradiation incident on a tilted surface with different tilt angles is one of the most important requirements for simulating solar energy systems. However, such data is often not available. For this reason scientists, database platforms and commercial software resort to employ transposition models to convert horizontal solar irradiation into tilted solar irradiation incident on any inclined surface at any degree. The present study considered a wide range of the most commonly used horizontal to tilted transposition models around the world. In the absence of measured information, the horizontal solar radiation was obtained from Solargis for six locations in Libya, which were carefully selected to represent all Libyan territories. Three cities were selected in the north (Tripoli, Sirte and Benghazi), a city in the middle (Al-Jufra) and two cities in the south (Sabha and Kufra). The proposed approach was applied to these cities for selected 22 transposition models and for various tilt angles (10°-90°) in order to determine the best model for use in each area at any given tilt angle. According to the obtained results, it is recommended to use Bugler; 1988 model as the most accurate model for the northern and central regions of the country, and Lui&Jordan model; 1963 as the most applicable model for the southern region.
CCrFS: Combine Correlation Features Selection for Detecting Phishing Websites Using Machine Learning

Internet users are continually exposed to phishing as cybercrime in the 21st century. The objective of phishing is to obtain sensitive information by deceiving a target and using the information for financial gain. The information may include a login detail, password, date of birth, credit card number, bank account number, and family-related information. To acquire these details, users will be directed to fill out the information on false websites based on information from emails, adverts, text messages, or website pop-ups. Examining the website’s URL address is one method for avoiding this type of deception. Identifying the features of a phishing website URL takes specialized knowledge and investigation. Machine learning is one method that uses existing data to teach machines to distinguish between legal and phishing website URLs. In this work, we proposed a method that combines correlation and recursive feature elimination to determine which URL characteristics are useful for identifying phishing websites by gradually decreasing the number of features while maintaining accuracy value. In this paper, we use two datasets that contain 48 and 87 features. The first scenario combines power predictive score correlation and recursive feature elimination; the second scenario is the maximal information coefficient correlation and recursive feature elimination. The third scenario combines spearman correlation and recursive feature elimination. All three scenarios from the combined findings of the proposed methodologies achieve a high level of accuracy even with the smallest feature subset. For dataset 1, the accuracy value for the 10 features result is 97.06%, and for dataset 2 the accuracy value is 95.88% for 10 features.
Deepfakes Classification of Faces Using Convolutional Neural Networks

In the recent years, petabytes of data is being generated and uploaded online every second. To successfully detect fake contents, a deepfake detection technique is used to determine whether the uploaded content is real or fake. In this paper, a convolutional neural network-based model is proposed to detect the fake face images. The generative adversarial networks and data augmentation are used to generate the face dataset for real and fake face classification. Transfer learning techniques from pretrained deep models such as VGG16 and ResNet50 are employed in the proposed model. The proposed model is evaluated on three benchmark datasets, namely 140k Real and Fake Faces, Real and Fake Face Detection, and Fake Faces. The proposed model attained accuracies over the three datasets are 95.85%, 53.25%, and 88.63%, respectively. Moreover, to improve the obtained results of the proposed model, we combine it with other pretrained models of VGG16 and ResNet50 to construct deep ensembles. The overall performance is greatly improved with the ensemble model achieving accuracies on the three datasets as 98.79%, 75.79%, and 95.52%, respectively. Furthermore, the obtained results also show that the proposed models have superior performance than existing models.
Invariant Risk Minimization

We introduce Invariant Risk Minimization (IRM), a learning paradigm to estimate invariant correlations across multiple training distributions. To achieve this goal, IRM learns a data representation such that the optimal classifier, on top of that data representation, matches for all training distributions. Through theory and experiments, we show how the invariances learned by IRM relate to the causal structures governing the data and enable out-of-distribution generalization.
Efficient feature descriptor selection for improved Arabic handwritten words recognition

&lt;span lang="EN-US"&gt;Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the &lt;/span&gt;&lt;em&gt;&lt;span lang="EN-US"&gt;Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis&lt;/span&gt;&lt;/em&gt;&lt;span lang="EN-US"&gt; (IFN/ENIT). Then, we trained the k-nearest neighbor’s algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance evaluation results of the three k-NN generated models, the majority-voting algorithm is used to combine the prediction results. A high recognition rate of up to 99.88% is achieved, far exceeding the state-of-the-art results using the IFN/ENIT dataset. The obtained results highlight the reliability of the proposed system for the recognition of handwritten Arabic words.&lt;/span&gt;
Vehicle Tracking on Satellite Video Based on Historical Model

Vehicle tracking on satellite videos poses a challenge for the existing object tracking algorithms due to the few features, object occlusion and similar objects appearance.To improve the performance of the object tracking algorithm, a historical modelbased tracker intended for satellite videos is proposed in this study.It updates the tracker by using the historical model of each frame in the video, which contains plenty of object information and background information, so as to improve tracking ability on fewfeature objects.Furthermore, a historical model evaluation scheme is designed to obtain reliable historical models, which ensures that the tracker is sensitive to the object in the current frame, thus avoiding the impact caused by changes in object appearance and background.Besides, to solve the drift issue of the tracker caused by object occlusion and the appearance of similar objects, an anti-drift tracker correction scheme is proposed as well.According to the comparative experiments conducted on satellite videos dataset SatSOT, our tracker produces an excellent performance.Moreover, sensitivity analysis, varying criteria comparative experiments and ablation experiments are conducted to demonstrate that the proposed schemes are effective in improving the Precision and Success Rate of the tracker.
A new hybrid method based on Aquila optimizer and tangent search algorithm for global optimization

Since no single algorithm can provide the optimal solutions for all problems, new metaheuristic methods are always being proposed or developed by combining current algorithms or creating adaptable versions. Metaheuristic methods should have a balanced exploitation and exploration stages. One of these two talents may be sufficient in some metaheuristic methods, while the other may be insufficient. By integrating the strengths of the two algorithms and hybridizing them, a more efficient algorithm can be formed. In this paper, the Aquila optimizer-tangent search algorithm (AO-TSA) is proposed as a new hybrid approach that uses the intensification stage of the tangent search algorithm (TSA) instead of the limited exploration stage to improve the Aquila optimizer's exploitation capabilities (AO). In addition, the local minimum escape stage of TSA is applied in AO-TSA to avoid the local minimum stagnation problem. The performance of AO-TSA is compared with other current metaheuristic algorithms using a total of twenty-one benchmark functions consisting of six unimodal, six multimodal, six fixed-dimension multimodal, and three modern CEC 2019 benchmark functions according to different metrics. Furthermore, two real engineering design problems are also used for performance comparison. Sensitivity analysis and statistical test analysis are also performed. Experimental results show that hybrid AO-TSA gives promising results and seems an effective method for global solution search and optimization problems.
eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics

Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions.Due to the high volume of data shared in the recent years, ML algorithms are more accurate and reliable since training and testing phases are more precise.An important concept to analyze when defining ML algorithms concerns adversarial machine learning attacks.These attacks aim to create manipulated datasets to mislead ML algorithm decisions.In this work, we propose new approaches able to detect and mitigate malicious adversarial machine learning attacks against a ML system.In particular, we investigate the Carlini-Wagner (CW), the fast gradient sign method (FGSM) and the Jacobian based saliency map (JSMA) attacks.The aim of this work is to exploit detection algorithms as countermeasures to these attacks.Initially, we performed some tests by using canonical ML algorithms with a hyperparameters optimization to improve metrics.Then, we adopt original reliable AI algorithms, either based on eXplainable AI (Logic Learning Machine) or Support Vector Data Description (SVDD).The obtained results show how the classical algorithms may fail to identify an adversarial attack, while the reliable AI methodologies are more prone to correctly detect a possible adversarial machine learning attack.The evaluation of the proposed methodology was carried out in terms of good balance between FPR and FNR on real world application datasets: Domain Name System (DNS) tunneling, Vehicle Platooning and Remaining Useful Life (RUL).In addition, a statistical analysis was performed to improve the robustness of the trained models, including evaluating their performance in terms of runtime and memory consumption.
Boosting k-means clustering with symbiotic organisms search for automatic clustering problems

Kmeans clustering algorithm is an iterative unsupervised learning algorithm that tries to partition the given dataset into k pre-defined distinct non-overlapping clusters where each data point belongs to only one group. However, its performance is affected by its sensitivity to the initial cluster centroids with the possibility of convergence into local optimum and specification of cluster number as the input parameter. Recently, the hybridization of metaheuristics algorithms with the K-Means algorithm has been explored to address these problems and effectively improve the algorithm's performance. Nonetheless, most metaheuristics algorithms require rigorous parameter tunning to achieve an optimum result. This paper proposes a hybrid clustering method that combines the well-known symbiotic organisms search algorithm with K-Means using the SOS as a global search metaheuristic for generating the optimum initial cluster centroids for the K-Means. The SOS algorithm is more of a parameter-free metaheuristic with excellent search quality that only requires initialising a single control parameter. The performance of the proposed algorithm is investigated by comparing it with the classical SOS, classical K-means and other existing hybrids clustering algorithms on eleven (11) UCI Machine Learning Repository datasets and one artificial dataset. The results from the extensive computational experimentation show improved performance of the hybrid SOSK-Means for solving automatic clustering compared to the standard K-Means, symbiotic organisms search clustering methods and other hybrid clustering approaches.
Semi-supervised semantic segmentation with cross teacher training

Convolutional neural networks can achieve remarkable performance in semantic segmentation tasks. However, such neural network approaches heavily rely on costly pixel-level annotation. Semi-supervised learning is a promising resolution to tackle this issue, but its performance still far falls behind the fully supervised counterpart. This work proposes a cross-teacher training framework with three modules that significantly improves traditional semi-supervised learning approaches. The core is a cross-teacher module, which could simultaneously reduce the coupling among peer networks and the error accumulation between teacher and student networks. In addition, we propose two complementary contrastive learning modules. The high-level module can transfer high-quality knowledge from labeled data to unlabeled ones and promote separation between classes in feature space. The low-level module can encourage low-quality features learning from the high-quality features among peer networks. In experiments, the cross-teacher module significantly improves the performance of traditional student–teacher approaches, and our framework outperforms state-of-the-art methods on benchmark datasets.