Reviewerly/exHarmony huggingface dataset:
Networking and cryptography library with a non-repudiation flavor for blockchain

Abstract Blockchain is currently one of the most widely discussed inventions in the information and communication technology industry. It is a pillar of the fourth industrial revolution and it is a cryptographically demanding technology that is regarded as one of the most influential topics in academia. Many blockchain platforms currently utilize third-party cryptographic libraries that offer many cryptographic primitives in order to ensure users' protection against cyber-attacks. The Networking and Cryptography library (NaCl) is an open-source library for cryptographic primitives. NaCl is known to be one of the best libraries that provide usability property. Although NaCl is easy to use and ensures: confidentiality, integrity, and authenticity, it fails to provide the non-repudiation service. In this paper, an improvement to the blockchain platforms is proposed by enhancing the NaCl library to achieve the non-repudiation property that enhances the security level of the implemented blockchain platform. In NaCl, to provide the aforementioned security services, messages are signed and then encrypted. Therefore, the non-repudiation service is not provided. In this paper, the proposed solution is based on adding a signature block to enable a non-repudiation property. First, logical analysis is conducted using the BAN logic on the NaCl library to prove that it does not provide the non-repudiation property. Subsequently, a modification to the library is proposed, and the correctness of the proposed solution is proven using BAN logic. The analysis suggests that the proposed solution fixes the aforementioned problem.
Research and Development of Integrated Design System for Drilling Engineering

According to the current research status of drilling design in China, according to the characteristics of drilling engineering design in network environment, the architecture of integrated design system of drilling engineering in network environment is put forward. Using computer database technology and network technology, a drilling engineering integrated design system is constructed on a unified hardware and software platform, which integrates design basis, data support, drilling engineering design, cementing design, drilling fluid design and design result management. The system is implemented based on VS2008 and Oracle9i. The function and main characteristics of the system are introduced in detail. The application of this system makes it a reality for multiple drilling engineers or designers distributed in different areas to carry out collaborative design according to certain specifications and processes on the basis of full information sharing with geological design and drilling engineering process.
Deepfake detection of occluded images using a patch-based approach

DeepFake involves the use of deep learning and artificial intelligence techniques to produce or change video and image contents typically generated by GANs. Moreover, it can be misused and leads to fictitious news, ethical and financial crimes, and also affects the performance of facial recognition systems. Thus, detection of real or fake images is significant specially to authenticate originality of people’s images or videos. One of the most important challenges in this topic is obstruction that decreases the system precision. In this study, we present a deep learning approach using the entire face and face patches to distinguish real/fake images in the presence of limitations of blurring, compression, scaling and especially obstruction with a three-path decision: first entire-face reasoning, second a decision based on the concatenation of feature vectors of face patches, and third a majority vote decision based on these features. To test our approach, new data sets including real and fake images are created. For producing fake images, StyleGAN and StyleGAN2 are trained by FFHQ images and also StarGAN and PGGAN are trained by CelebA images. The CelebA and FFHQ data sets are used as real images. The proposed approach reaches higher results in early epochs than other methods and increases the SoTA results by 0.4% $$-$$ 7.9% in the different built data sets. In addition, we have shown in experimental results that weighing the patches may improve accuracy.
ROMSS: a rational optional multi-secret sharing scheme based on reputation mechanism

Abstract The traditional threshold secret sharing scheme only allows the participants’ sub-secret shares to be used once in the reconstruction process. Several multi-secret sharing schemes have been proposed that are related to cloud computing, aiming to improve reconstruction efficiency. Rational secret sharing is a technique that combines secret sharing with game theory. In traditional rational multi-secret sharing, participants must reconstruct all secrets, resulting in unnecessary overhead. Rational participants will act dishonestly to maximize their own interests, leading to a prisoner’s dilemma and incomplete secret reconstruction. Additionally, when sharing multiple secrets, the Dealer must distribute the sub-secret shares of all secrets to the participants, increasing overhead. In this paper, we propose a rational optional multi-secret sharing scheme based on a reputation mechanism that selectively reconstructs secrets according to participants’ needs in the context of cloud computing. Our scheme introduces a reputation mechanism to evaluate participants’ reputation values to avoid their dishonest behaviors. Furthermore, we adopt a broadcast encryption matrix so that participants only need to receive a single sub-secret share to participate in multi-secret reconstruction. Our security analysis shows that the proposed scheme can effectively constrain the self-interested behavior of rational participants and reduce the overhead in the process, thus multi-secret sharing scheme can provide more efficient and secure solutions for secret sharing in key management and distributive storage for the cloud scenarios.
Structural controls mineralization in strike-slip fault terminals, case study: Kaybarkuh region in East of Iran

In this study, we have investigated the status of faults in terms of precession and subsequence, and their relationship with vein mineralization in Kaybarkuh intrusive body in East of Iran. At least, three generations of faults are evidenced in Dasht-e-Bayaz (DB) fault terminal. During formation of faults, the stress orientation in the region has changed at least once probably due to DB fault evolution. Mineralization, especially gold and copper, is formed along the third-generation faults and sometimes on the fault surfaces. It can be predicted that mineralization also happened in the tensioned area of Kal-Shur covered by salt playa and Quaternary sediments, which requires subsurface and geophysics studies.
Distance Matters: A Distance-Aware Medical Image Segmentation Algorithm

The transformer-based U-Net network structure has gained popularity in the field of medical image segmentation. However, most networks overlook the impact of the distance between each patch on the encoding process. This paper proposes a novel GC-TransUnet for medical image segmentation. The key innovation is that it takes into account the relationships between patch blocks based on their distances, optimizing the encoding process in traditional transformer networks. This optimization results in improved encoding efficiency and reduced computational costs. Moreover, the proposed GC-TransUnet is combined with U-Net to accomplish the segmentation task. In the encoder part, the traditional vision transformer is replaced by the global context vision transformer (GC-VIT), eliminating the need for the CNN network while retaining skip connections for subsequent decoders. Experimental results demonstrate that the proposed algorithm achieves superior segmentation results compared to other algorithms when applied to medical images.
Research on Public Air Route Network Planning of Urban Low-Altitude Logistics Unmanned Aerial Vehicles

As urban populations continue to grow and road traffic congestion worsens, traditional ground logistics has become less efficient. This has led to longer logistics times and increased costs. Therefore, unmanned aerial vehicle (UAV) logistics has become increasingly popular. However, free-planned routes cannot meet the safety and efficiency requirements of urban airspace mobility. To address this issue, a public air route network for low-altitude logistics UAVs needs to be established in urban areas. This paper proposes a public route network planning method based on the obstacle-based Voronoi diagram and A* algorithm, as follows: Firstly, construct a city airspace grid model in which the characteristics of the airspace are mapped onto the grid map. Introduce an obstacle clustering algorithm based on DBSCAN to generate representative obstacle points as the Voronoi seed nodes. Utilize the Voronoi diagram to establish the initial route network. Then, conduct an improved path planning by employing the A* algorithm for obstacle avoidance in route edges that pass through obstacles. To ensure the safe operation of drones, set constraints on the route safety interval. This process will generate a low-altitude public air route network for urban areas. After considering the flight costs of logistics UAVs at different altitudes, the height for the route network layout is determined. Finally, the route network evaluation indicators are established. The simulation results demonstrate that compared with the city road network planning method and the central radial network planning method, the total route length is shortened by 7.1% and 9%, respectively, the airspace coverage is increased by 9.8% and 35%, respectively, the average network degree is reduced by 52.6% and 212%, respectively, and the average flight time is reduced by 19.4s and 3.7s, respectively. In addition, by solving the network model using the Dijkstra algorithm, when the energy cost and risk cost weights are 0.6 and 0.4, respectively, and the safety interval is taken as 15 m, the total path cost value of the planned trajectory is minimized.
Salient object detection algorithm based on diversity features and global guidance information

Aiming at the problems of traditional salient object detection methods such as fuzzy boundary and insufficient information integrity, a salient object detection network composed of feature diversity enhancement module, global information guidance module and feature fusion module is proposed. Firstly, asymmetric convolution, cavity convolution and common convolution are spliced to form a feature diversity enhancement module to extract different types of spatial features corresponding to each feature layer. Secondly, the global information guidance module transmits the information captured by the coordinate attention mechanism to each feature layer through the global guidance stream, so as to learn the semantic relationship between different feature layers and alleviate the dilution effect; Finally, the feature fusion module receives the high-level features output from the previous layer, the low-level features of the corresponding layer and the global context information generated by the global information guidance module, and the cascade feature diversity enhancement module gradually integrates the multi-level features to refine the saliency feature map. Comparative experiments on five public data sets show that this method can achieve the highest values of 0.959 and 0.030 in F-measure and MAE. Compared with other seven advanced algorithms, it has better detection performance.
Research and Application of the Smart Valve in the PCOC

Changqing Gas Field (PCOC) are typical low permeability reservoir. There are nearly 80% dewatering gas recovery time of period during the full life cycle of gas wells in the PCOC. The gas wells couldn’t regularly manufacturing if dewatering treatment not been taken. Edge computing and embedded program technique were applied to the Smart valve, in order to solve the problem on huge amounts of operation on control valves and dewatering gas recovery of gas well. There are embedded programs integrated in Smart valves, by which data of gas wells could analysed and processed in well-situ, rather than transmitting to SCADA and waiting for feedback. Smart valves are intelligent mind of gas well, which have realizing self-management of gas well. Smart valve with embedded program have already equipped on more than thousands of wells in PCOC, which had simplified data process and management manner. Smart valve have advancing economic benefit greatly, by which have directly reducing human workload on gas wells for 90%, and indirectly reducing construction cost of gas well-site for 30%. Smart valve have substituting for the manual operation to a great extent, both on gas well control valves and dewatering gas recovery. It developed a new pattern of gas well management, which had set an leading example for other low permeability reservoir or tight gas reservoir.
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It is known that the smaller the values of Gowers U2 and U3 norms of a Boolean function, the greater its strength against linear and quadratic approximations. This article gives recursive relation between the Gowers U2 and U3 norms of Boolean functions and their restriction to hyperplanes. These results will help analyze the second and third-order Gowers norms of Boolean functions in a higher number of variables.
A Fuzzy Logic-Based Approach to Handle Uncertainty in Artificial Intelligence

Most of the information in a typical expert system's knowledge base is imprecise, incomplete, or not completely accurate. Thus, managing uncertainty is an innately essential issue in the design of systems based on artificial intelligence. This chapter discusses one method of uncertainty management that is based on the usage of fuzzy logic, which is the logic behind approximation or, equivalently, fuzzy reasoning. A key characteristic of fuzzy logic for managing uncertainty in artificial intelligence is that it gives a systematic framework for dealing with fuzzy quantifiers such as most, many, few, not very many, almost all, seldom, approximately 0.5, and so on. In this sense, fuzzy logic incorporates both predicate logic and probability theory, allowing for the management of many forms of uncertainty within a single conceptual framework. This chapter discussed various characteristics of fuzzy logic-based systems, the concept of membership functions, the architecture of fuzzy logic-based systems, different defuzzification methods, various case studies, and the advantages and limitations of fuzzy logic-based systems.
Probability and Bayesian Theory to Handle Uncertainty in Artificial Intelligence

The goal of this chapter is to introduce the reader to the notion of probabilistic reasoning and Bayesian theory to deal with uncertainty in artificial intelligence. Many challenges in artificial intelligence (reasoning, planning, learning, perception, and robotics) need the agent to work with incomplete or imprecise knowledge. Artificial intelligence researchers have created a variety of sophisticated tools for tackling these challenges using principles from probability and Bayesian theory. Bayesian networks are a powerful tool for solving a wide range of issues, including reasoning (through the Bayesian inference algorithm), learning (via the expectation–maximization method), planning (via decision networks), and perception (via dynamic Bayesian networks). Probabilistic techniques may also be used to filter, forecast, smooth, and provide explanations for data streams, supporting perception systems in analyzing time-dependent processes. I hope this chapter has helped you better comprehend the notion of dealing with uncertainty in artificial intelligence systems through the use of probability and Bayesian theory.
Introduction to Handling Uncertainty in Artificial Intelligence

Uncertainty is a prevalent phenomenon in everyday interactions and many aspects of human life, particularly when dealing with information from several sources. These data may be untrustworthy because of their sources or gathering techniques, such as random sampling or other statistical methods rather than categorical means. Uncertainty may be caused by insufficient facts or information, as well as unclear or inconsistent information. The problem of uncertainty cannot be eliminated in most activities that demand cognitive behavior. Uncertainty is quantified in artificial intelligence by utilizing relative frequencies or by integrating several statistical models based on data and information acquired from various sources. Some of these metrics are objective, while others may be provided by domain experts. All of these measurements are often utilized to draw inferences and conclusions. That is, the expert system should be able to defend its uncertainty estimates and reasoning techniques. This chapter discusses the common challenges in handling uncertainty in artificial intelligence, some numeric and symbolic approaches to handling uncertainty in artificial intelligence, and comparisons between different above said approaches.
The Dempster–Shafer Theory to Handle Uncertainty in Artificial Intelligence

A generalized technique for representing uncertainty is the Dempster–Shafer theory (introduced in Glenn Shafer’s 1976 book A Mathematical Theory of Evidence). It evaluates sets of propositions (rather than single propositions) and assigns to each set an interval within which the set's degree of conviction must fall. This is especially beneficial when each piece of data implicates many potential conclusions, and the support for each conclusion is estimated based on the overlapping contributions of various pieces of evidence. In addition, unlike classical probability theory, Dempster–Shafer's theory allows some of the belief to be “unassigned” to any of the candidate conclusions (to represent the relative state of ignorance in the face of insufficient knowledge). These characteristics of the theory make it ideal for knowledge representation in some areas, such as the domain of uncertainty. This chapter discusses the basic terms used, the main components of the Dempster–Shafer theory, the Dempster–Shafer rule of combination, various case studies, and the advantages and limitations of the Dempster–Shafer theory.
Decision-Making Under Uncertainty in Artificial Intelligence

Many critical tasks, such as wildfire control, aviation collision avoidance, and disaster response, require decision-making under uncertainty. It is critical to account for the numerous causes of uncertainty when making or advising decisions when creating automated decision support systems. Accounting for these sources of uncertainty and properly balancing the system's numerous aims may be difficult. This chapter introduces the types of decisions, and steps in decision-making under uncertainty, discusses the criterion for deciding under uncertainty, overviews the concept of utility and expected utility theory, discusses decision network and various case studies related to decision-making under uncertain scenarios, overviews the advantages and limitations of decision-making under uncertain scenarios in artificial intelligence.
Feature Selection Using a Hybrid Approach Depends on Filter and Wrapper Methods for Accurate Breast Cancer Diagnosis

Breast cancer is the biggest cause of mortality in women, outscoring all other malignancies. Diagnosing breast cancer is hard because the disease is complicated, treatment methods change, and there are many different kinds of patients. Information technology and artificial intelligence contribute to improve diagnostic procedures, which are critical for care and treatment as well as reducing and controlling cancer recurrence. The primary part of this research is to develop a new feature selection strategy based on a hybrid approach that combines two methods for selecting features: the filter and the wrapper. In two stages, this method reduces the number of features from 30 to 15 to increase and improve classification accuracy. The suggested method was tested using the Wisconsin Breast Cancer Dataset dataset (WDBC). To enhance the classification of breast cancer tumors, a soft voting classifier was used in this study. The proposed methodology outperforms previous research, achieving 1 for the F1 score, 1 for AUC, 1 for recall, 1 for precision, and 100% for accuracy. Furthermore, 10-fold cross-validation has a 98.2% accuracy rate.
Data Mining for Junior Data Scientists

It is crucial for junior data scientists to learn computer programming as data science software packages may not always cater to the requirements of data analysis. Python provides a vast library of algorithms for data analysis, including NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn. NumPy and Pandas aid in organizing datasets as part of the pre-processing stage, while Matplotlib and Seaborn offer a range of data visualization commands. These visualization tools are instrumental in data exploration processes, such as creating histograms and scatter plots, and displaying data mining results like cluster analysis outcomes. Scikit-learn is a popular library in the data science industry that offers various data mining commands for regression, decision constructs, and cluster analysis, covering both supervised and unsupervised learning. Therefore, junior data scientists must learn Python programming for data science applications, especially when using software packages that require editing the model using Python commands.
A probabilistic semantics for modal QAD in standard Arabic

Gradable Epistemic Modals (GEMs) such as certain or likely have been analyzed to have the semantics of gradable adjectives, which constrain their epistemic modality. This squib analyzes the modal use of the sentential particle qad in Standard Arabic which has the following puzzling behavior: particle qad gives rise to the certainty/likelihood modality meaning only when it is composed with the tense expression of its propositional complement. Modal qad then manifests itself as an extreme, non-gradable modal with no lexically encoded but grammatically derived scale meaning. A probabilistic semantics is proposed to derive the truth conditions of the epistemic modal meaning of qad based on a monotonic, probabilistic time-world branching model. The analysis assigns maximum degree of likelihood to qad in the past tense which triggers certainty. It also assigns a minimum degree of likelihoods to qad in the present tense which triggers possibility.