Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Enabled Effective Fault Prediction Techniques in Cloud Computing Environment for Improving Resource Optimization

The bulk of the fault tolerance techniques that are in use today laid their primary emphasis, in the event that a virtual machine fails, on the production of clones to replace it, rather than on the early prediction of the failure itself in advance. Several of the currently used techniques give migration priority over recovery in the event that a virtual machine (VM) fails. This is due to resource constraints and concerns with server availability. Examples of algorithms with a single objective include fault tolerance, migration prediction, and simply expecting failure. Another example is fault tolerance. In this research, we are aiming to determine the most effective strategy to transition from a system that is not operating well to one that does. It is essential to be able to predict the failure of a virtual machine in a timely manner due of issues such as squandered resources, energy, and cost. Since the beginning of cloud computing, there has been an issue with the dependability of virtual computers, often known as VMs. As an integral component of a fault tolerance system, preemptive measures are an absolute need in order to guarantee the continuation of service. As a consequence of this, it is vital to work toward enhancing and emphasizing the proactive failure prediction of virtual machines. The key motivations for this are decreased periods of downtime and enhanced scalability. A technique was utilized to transfer the resources that were predicted to fail from one virtual machine (VM) to another VM in a safe manner. Using the compression strategy reduced the amount of time needed to complete the migration, and resource utilization increased. This article provides artificial intelligence that enables effective fault prediction techniques in cloud computing to improve resource optimization.
Semantic segmentation using GSAUNet

Gabor filters well support computer vision tasks due to its steerable properties, that are useful for handling image transformation such as image rotation and image scaling. In this paper, the Gabor-based UNet (GSAUNet) model is proposed where the Gabor filters replace the few default filters in the UNet. Besides, it has adopted a squeeze-and-excitation module to increase channel interdependencies and attention mechanism to focus on an important part of the network. The performance of GSAUNet is assessed on the CamVid and Cityscape datasets that attain improvement for the semantic segmentation tasks.
Diversity, Fake News and Hate Speech: The German Response to Algorithmic Regulation

Germany is taking a pioneering role both in Europe and across the world when it comes to digital platform regulation. This chapter examines two new German laws, the Interstate Media Treaty and the Network Enforcement Act. I explore the development of the laws and analyse their different regulatory objectives. While the former aims to safeguard diversity of opinion, the latter tries to protect individuals from hate crime, including certain forms of hate speech, criminally punishable fake news and other unlawful content. The article follows an interdisciplinary approach, combining communication studies research with legal analysis.
Integration of Local and Global Features Explanation with Global Rules Extraction and Generation Tools

Widely used in a growing number of domains, Deep Learning predictors are achieving remarkable results. However, the lack of transparency (i.e., opacity) of their inner mechanisms has raised trust and employability concerns. Nevertheless, several approaches fostering models of interpretability and explainability have been developed in the last decade. This paper combines approaches for local feature explanation (i.e., Contextual Importance and Utility – CIU) and global feature explanation (i.e., Explainable Layers) with a rule extraction system, namely ECLAIRE. The proposed pipeline has been tested in four scenarios employing a breast cancer diagnosis dataset. The results show improvements such as the production of more human-interpretable rules and adherence of the produced rules with the original model.
Evaluation of Importance Estimators in Deep Learning Classifiers for Computed Tomography

Deep learning has shown superb performance in detecting objects and classifying images, ensuring a great promise for analyzing medical imaging. Translating the success of deep learning to medical imaging, in which doctors need to understand the underlying process, requires the capability to interpret and explain the prediction of neural networks. Interpretability of deep neural networks often relies on estimating the importance of input features (e.g., pixels) with respect to the outcome (e.g., class probability). However, a number of importance estimators (also known as saliency maps) have been developed and it is unclear which ones are more relevant for medical imaging applications. In the present work, we investigated the performance of several importance estimators in explaining the classification of computed tomography (CT) images by a convolutional deep network, using three distinct evaluation metrics. First, the model-centric fidelity measures a decrease in the model accuracy when certain inputs are perturbed. Second, concordance between importance scores and the expert-defined segmentation masks is measured on a pixel level by a receiver operating characteristic (ROC) curves. Third, we measure a region-wise overlap between a XRAI-based map and the segmentation mask by Dice Similarity Coefficients (DSC). Overall, two versions of SmoothGrad topped the fidelity and ROC rankings, whereas both Integrated Gradients and SmoothGrad excelled in DSC evaluation. Interestingly, there was a critical discrepancy between model-centric (fidelity) and human-centric (ROC and DSC) evaluation. Expert expectation and intuition embedded in segmentation maps does not necessarily align with how the model arrived at its prediction. Understanding this difference in interpretability would help harnessing the power of deep learning in medicine.
Stereo occlusion enhancement by the use of semantic information and monocular depth estimation

Stereoscopic techniques have the disadvantage of occlusions in the mismatched pixels between images. Monocular depth estimation together with semantic features is pro posed to improve the depth map at non-coincident areas.
From scattered data to actionable knowledge: flexible cyber security reporting in the military domain

Abstract Numerous cyber situational awareness models have been proposed in recent years. Yet, one of the main challenges still remains mostly unsolved, which is what information sources contribute to the process for establishing cyber situational awareness and how is relevant information collected. While previous scientific works focused on situational awareness models and decision support based on common operating pictures, ingesting and maintaining a consistent data basis for the cyber domain has rarely been studied in detail. However, this is crucial when data distributed across different systems need to be collected, vetted, correlated, de-duplicated, enriched and finally stored as a basis for flexible cyber security reporting. In this paper, we design an approach and a data model that enable to ingest and store the essential information from disparate organizational units and act as a basis for the flexible creation of cyber security reports. We describe the application of this approach and model in a case study together with the Austrian Ministry of Defense (MoD), in which we surveyed existing data sources and transfer paths and rated the applicability of the CCOP data model and accompanying processes in course of a proof-of-concept implementation.
Minimizing features while maintaining performance in data classification problems

High dimensional classification problems have gained increasing attention in machine learning, and feature selection has become essential in executing machine learning algorithms. In general, most feature selection methods compare the scores of several feature subsets and select the one that gives the maximum score. There may be other selections of a lower number of features with a lower score, yet the difference is negligible. This article proposes and applies an extended version of such feature selection methods, which selects a smaller feature subset with similar performance to the original subset under a pre-defined threshold. It further validates the suggested extended version of the Principal Component Loading Feature Selection (PCLFS-ext) results by simulating data for several practical scenarios with different numbers of features and different imbalance rates on several classification methods. Our simulated results show that the proposed method outperforms the original PCLFS and existing Recursive Feature Elimination (RFE) by giving reasonable feature reduction on various data sets, which is important in some applications.
Detection of Breast Masses in Mammograms by Incremental Discriminant Based Support Vector Machine Classifier and Active User Involvement

Breast masses are among the most studied mammary pathologies on mammographic images. The complexity and diversity of mass forms require the use of appropriate descriptors and techniques. In this work, we propose a mass detection process based on our novel incremental Discriminant Based Support Vector Machine classifier coupled with the active involvement of domain experts. It is a three steps process. In the first step, mammographic images are pre-processed by eliminating noise and enhancing contrast. The second step is a feature engineering one. Multiple descriptors are extracted from the mammographic images and feature space transformation and reduction are performed by PCA (Principal Component Analysis). In the third step, the mass detection is performed based on the incremental classification guided by user involvement. The user is first provided with a simple user interface. Hence, this interface allows him to interact with the mammographic image and select some parts. Based on the selection, the pixels are incrementally classified using the IDSVM, which combines both local and near-global variational information of the training data into the input space. Experiments on mammograms from the INbreast database, by reference to the ground truth results, demonstrate the validity of our method.
C-DESERT Score for Arabic Text Summary Evaluation

Text summary evaluation represents an important step after building any summarization system. Despite the important number of metrics that have been developed, there are a few metrics that evaluate Arabic text summary. In this paper, we present a new automatic metric for Arabic text summary evaluation. This metric combines ROUGE scores with documents embedding-based scores to build a regression model that predicts the manual score of an Arabic summary. First, we have constructed document embedding models with different vector sizes then, we have used these models to present each candidate and model summary as a document embedding vector. After that, a similarity score between the two document embedding vectors was calculated. Finally, we have combined several similarity scores based on the document embedding representation and the ROUGE scores to predict a manual score. Furthermore, in the combination phase, we have tried multiple regression models to obtain the most optimal predictive model. The obtained result showed that the proposed method outperforms all baseline metrics in the task of text summary evaluation and the task of the summarization system evaluation.
<i>Ex</i> <sup>2</sup> : Monte Carlo Tree Search‐based test inputs prioritization for fuzzing deep neural networks

Fuzzing is considered to be an essential approach to guarantee the reliability of deep neural networks (DNNs) based systems. The DNN fuzzing leverages various inputs prioritization methods to guide the testing process. The current research mainly focus on constructing testing metrics that symbolize the logical representation of the DNN to guide the generation of test cases, which neglects the potential performance brought by implementing heuristic algorithm. Moreover, the straightforward implementation of queue structure can not represent the metamorphic relationships between generated inputs in DNN fuzzing. Therefore, developing the appropriate heuristic algorithm-based inputs prioritization method is critical to improve the performance of DNN fuzzers. In this paper, we propose a Monte Carlo Tree Search (MCTS) based inputs prioritization method called E x 2 $E{x}^{2}$ (Exploration and Exploitation) that formulates DNN testing exploration as the sequential decision process. The technique introduces an innovative tree-structure design that schedules inputs from the statistical perspective. Different from traditional DNN testing, the batch pool is maintained in the form of nodes in MCTS. The links between nodes precisely represent the metamorphic relationship between input batches, which indicates the potential value for in-depth search. Furthermore, a novel simulation mechanism is implemented to adapt MCTS in DNN testing, which attain better coverage feedback. The effectiveness of our method is comprehensively investigated on six popular deep learning models from LeNet and VGG families. The comparison experiments are conducted between DeepHunter, TensorFuzz, and DeepSmartFuzzer to demonstrate efficacy on various testing metrics. The experimental results show that the E x 2 $E{x}^{2}$ significantly enhance the coverage gain of DNN fuzzing up to 30% against the best performance in comparison groups.
On the Applicability of Fuzzy Lines in Circular Hough Transform in Lesion Segmentation on Liver CT Images

Most of the lesions that grow in the liver and need to be found on a CT take are roundish, though especially in the case of malignant lesions the irregularity of the shape is also rather common. Classical Hough transform, however, which is one of the most used methods for finding circles, usually fails to find the contours of these objects, because of the larger or smaller irregularity of the shapes. Introducing a fuzzification in the edge filtered version of the image, which is usually the basis of a Hough transform, makes the Hough transform more flexible for using in liver CT image analysis.
PreF: Predicting job failure on supercomputers with job path and user behavior

Abstract Large numbers of jobs are executed on supercomputers almost every day. Unfortunately, many jobs would fail for various reasons, resulting in the waste of resources and the prolonged waiting time for queuing jobs. Job failure prediction can guide adjustment measures in advance, which is vital to the system's overall execution efficiency and reliability. Aiming at the problem that the existing job failure prediction methods are single, the collection of job features is complex and challenging to apply. This article strives to study whether these failed jobs can be predicted with known and synthetic features. We perform a comprehensive analysis of large amounts of historical data and various features and find that two novel features (running path and retry count) can predict job failure well. The running path indicates the application type a job belongs to, and the retry count reflects the user's behavior when the job fails. We propose a job failure prediction framework called PreF on supercomputers using machine learning based on the novel features. The experimental results show that PreF can correctly identify over 89% of jobs, outperforming the latest related methods on the comprehensive evaluation indicator (S_score) by around 4%.
Analysis of International Public Emotional Responses Toward the COVID-19 Vaccine

The pharmaceutical companies developing the COVID-19 vaccine and national governments continuously disseminate information regarding the vaccine’s effectiveness and vaccination status to reassure the public about vaccination. However, because interest in vaccines and vaccination status vary between the populations of different countries, it is likely that emotional responses toward vaccines also vary between countries. The current study analyzed and compared the emotions expressed by people in Japan, the United States, Great Britain, Canada, Australia, and India regarding the vaccine by analyzing tweets, on the basis of the Plutchik's wheel of emotions. In addition, a textual analysis was conducted using dependency analysis and burst detection to obtain information regarding positive changes in people’s feelings toward vaccines. Our approach was useful for clarifying the emotional responses of people in various countries regarding the vaccine, and informing approaches for positively changing people’s feelings about vaccines.
A Textual Information Extraction Application based on XML Data Models and a Multidimensional Natural Language Processing Pipeline Approach

Modern data and information systems usually contain considerable amounts of data and documents and thus provide a large amount of information. The automatic extraction of domain-specific information is all the more important in order to improve work with such systems. If information is available as free text information, machine processing can prove to be a difficult technical hurdle. State-of-the-art approaches use modern Natural Language Processing (NLP) methods to solve such tasks. In this paper, we want to introduce a data-driven approach, applying an XML data model to an application-specific scenario, using different NLP methods, which are combined into a multidimensional pipeline. It is important to understand how certain NLP methods can be used and what their limitations are. Individual modern NLP methods are often not sufficient and resilient enough to solve complex information extraction tasks. Therefore, it has to be examined how such problems can be alleviated or circumvented by a combination of different NLP methods. As a distinction to categorical grammar models, all cases considered here should be available as free text. The approach presented in this paper is still a work in progress, yet first evaluation results will be given.
Blockchain-enabled energy sector management

Optimal energy management in networked microgrids uses the blockchain concept to ensure security in energy transactions. Each energy transaction in networked microgrids is assumed to be a block with a hash address. A new transaction is considered to be another block of chaincode with a new hash address which is chained to the previous block’s hash address. For each block, using the cryptographic salt method, a random hash is used to produce a unique hash address with SHA-256 algorithm. The crowdsourcing concept is being applied in the energy system to create new operational models to obtain a real-time energy management system. This chapter proposes a methodology for the evaluation of the trust value of different nodes in the system. The Bayesian framework using beta probability distribution is considered for trust value evaluation for adequate energy transactions among various nodes. The hash keys and timestamps have been generated using python programming.
Decentralized platform for energy exchange: a case study

In blockchain-based decentralized energy trading, prosumers and consumers share a common platform for storing energy exchange information without the intervention of a third-party. When there is surplus energy production, for example, solar power, individuals may act as prosumers till demand exceeds supply and also be active buyers to charge their vehicles. In a peer-to-peer network that promotes an incentive-based energy trading platform, prosumers can invest their surplus renewable energy in a decentralized power market through smart grid. In addition, it's quite intricate to integrate a P2P network with blockchain technology to uphold privacy. The proposed energy trading platform is proficient in trading energy without fraud, resilient, and actively promotes secure energy transactions by eliminating intermediate costs. P2P energy trading encourages consumers and prosumers to do business directly with each other. In the former centralized energy system, the operators were affected by network congestion and loss of data.
Uncertainty-based decision support system for gaming applications

A dynamic world has different uncertainties. These uncertainties always impact adversely while making decisions. Existing systems sometimes fail as they are trained without considering uncertainty inclusion due to the dynamic nature of the problem. This is quite observed in gaming, which is most dynamic and contributes adversely while deciding for the next move. Strategic games have fewer uncertainties rather than ground sports. Many types of factors add uncertainty to the system. There is a need of handling the required uncertainty which will help in making the decision. Also while finding similarities between games or matches, player and playing style results don’t depict exact similarities between them. There is a need to measure uncertainty-based similarities as it helps in deciding the situation of the game or player. Here Uncertainty based decision support system is proposed which takes uncertainty as input rather than only considering patterns of input. Patterns always help if the system is more static while considering a dynamic system where we need to consider patterns and uncertainties in the scenarios. Results are shown on limited types of moves in game data and how uncertainty-based similarity and next move selection are improved.
Numerical Simulation and Active Protection of Lightning Discharge Based on Quantum Heuristic Evolutionary Algorithm

Thunder is a discharge phenomenon that often occurs in nature. Due to its physical influences such as strong current, high temperature, strong shock waves, and strong electromagnetic radiation, it has a huge destructive effect instantly, which may bring serious threats to people’s lives and property safety. This study aimed to study the lightning discharge numerical simulation and active protection based on the quantum heuristic evolutionary algorithm and proposed to apply the lightning discharge numerical simulation to the prevention of lightning disasters. This article gives a detailed description of the quantum algorithm, the generation, and harm of lightning discharge. The genetic algorithm is used to optimize the lightning data simulation algorithm, and the optimization process is introduced in detail. In addition, this article conducts related experiments on lightning discharge numerical simulation and active protection. The experimental results show that targeted active protection and effective numerical simulation are important measures to prevent lightning disasters. Active lightning protection measures can reduce lightning by 30%. Losses are caused by disasters.