Reviewerly/exHarmony huggingface dataset:
Shuffled shepherd political optimization‐based deep learning method for credit card fraud detection

Summary Digital transactions based on credit cards are gradually increasing concept due to expediency. The amount of fraudulent transactions has intensely enlarged in modern days, because of the fast development of e‐services, namely e‐finance, mobile payments, and e‐commerce as well as the promotion of credit cards. Criminal fraud behaviors and user's payment behaviors are frequently varying, thus performance improvement of the fraud identification method and its stability are more challenging processes. The Shuffled Shepherd Political Optimization‐based Deep Residual network (SSPO‐based DRN) scheme is established for credit card fraud identification in this research. The SSPO is developed by merging the Political Optimization (PO) and Shuffled Shepherd Optimization Algorithm (SSOA). The quantile normalization model is an effective preprocessing technique, which normalizes the data for effective detection. Moreover, fisher score and class information gain effectively select the required features. Data augmentation is employed for increasing the data size, thereby the detection performance is improved. The Deep Residual Network (DRN) is employed for credit card fraud recognition, which is trained by devised SSPO algorithm. The SSPO‐based DRN approach achieved enhanced performance with testing sensitivity of 0.9279, specificity of 0.9023, and accuracy of 0.9120.
Mining and reasoning of data uncertainty-induced imprecision in deep image classification

Existing deep image classification techniques strive to suppress data uncertainty for various reasons such as blur, occlusion, noise, and label error and advance to higher accuracy. However, they ignore data uncertainty-induced imprecision and thus do not work as intended. In this paper, we propose a deep open-source framework to mine and reason the imprecision of such training and test sets, which can present better performance and reduce the risk of misclassification. First, we design a label reassignment mechanism. It allows the network to reassign training labels and allow imperfect training samples with multiple labels. As a result, they are removed from the original classes and considered new imprecise samples to represent partial ignorance. Second, we propose a new imbalanced data enhancement architecture to learn a generalized representation of each (precise and imprecise) class. It helps the network fuse the auxiliary information from both precise and imprecise classes, which is beneficial to extract more distinctive class features from single-labeled samples and characterize uncertainty-induced imprecision in the test set by imprecise test samples. Afterward, methodological analyses and empirical evaluations are conducted. The proposed framework is demonstrated to present better performance on different typical networks (Resnet50, MobileNetV2, DenseNet121, EfficientNetB0, ShuffleNetV2, SENet, SqueezeNet, Xception) based on five publicly available datasets (Imagewoof-5, Flowers, Monkey, Butterfly, and Cifar-10). In addition, several targeted deep techniques for uncertain images or imprecise results are also employed as comparisons to prove the superiority of the proposed framework.
Adaptive genetic algorithm-based particle herding scheme for mitigating particle impoverishment

Particle filters (PFs) estimate the true parameter state from samples of states, called particles, that are drawn to construct an approximated posterior probability density function (PDF). Each drawn particle has its own probability of being the true state called importance weight. Both particle degeneracy and impoverishment are important problems found in generic PFs that cause particles to become trapped at local maxima. Genetic algorithms (GAs) were applied to herd the drawn particles to discover new high-likelihood state values in order to reshape the approximated posterior PDF and to enhance the accuracy of state estimation, while the particle diversity is maintained. We calculated the threshold weight for classifying particles as low-weight particles and high-weight particles. Each parent pair consists of a high-weight particle and a low-weight particle. High-weight particles are always kept unchanged. After each new offspring particle is found using GA operations, we must check its weight to ensure that it is actually superior to its low-weight parent. Otherwise, this offspring must not be allowed to replace its low-weight parent and must be destroyed. To ensure that the number of high-weight particles is adequate, some offspring particles evolve to be the new high-weight parent particles to produce high-weight offspring with further promoted diversity. Results showed that our approach significantly improves posterior PDF approximation and reduces errors in state estimation.
Distributed decentralized EKF for very large-scale networks with application to satellite mega-constellations navigation

The design of a decentralized and distributed filtering solution for large-scale networks of interconnected systems is addressed considering (i) generic nonlinear dynamics and (ii) generic coupled nonlinear outputs in a generic, possibly time-varying, topology. The local filters, which follow the structure of the extended Kalman filter, are implemented in each system, which estimates its own state exclusively. To be suitable for the heavily restricted implementation to very large-scale systems, a novel algorithm is proposed, which: (i) does not rely on instantaneous data transmission; (ii) allows local communication exclusively; and (iii) requires computational, memory, and data transmission resources for each system that do not scale with the dimension of the network. The scalability of the proposed algorithm allows for its application to the cooperative localization problem of very large-scale systems. In particular, it is applied herein to the on-board position estimation problem of LEO mega-constellations using GNSS featuring numerical simulations for the Starlink constellation.
AEGA: enhanced feature selection based on ANOVA and extended genetic algorithm for online customer review analysis

Sentiment analysis involves extricating and interpreting people's views, feelings, beliefs, etc., about diverse actualities such as services, goods, and topics. People intend to investigate the users' opinions on the online platform to achieve better performance. Regardless, the high-dimensional feature set in an online review study affects the interpretation of classification. Several studies have implemented different feature selection techniques; however, getting a high accuracy with a very minimal number of features is yet to be accomplished. This paper develops an effective hybrid approach based on an enhanced genetic algorithm (GA) and analysis of variance (ANOVA) to achieve this purpose. To beat the local minima convergence problem, this paper uses a unique two-phase crossover and impressive selection approach, gaining high exploration and fast convergence of the model. The use of ANOVA drastically reduces the feature size to minimize the computational burden of the model. Experiments are performed to estimate the algorithm performance using different conventional classifiers and algorithms like GA, Particle Swarm Optimization (PSO), Recursive Feature Elimination (RFE), Random Forest, ExtraTree, AdaBoost, GradientBoost, and XGBoost. The proposed novel approach gives impressive results using the Amazon Review dataset with an accuracy of 78.60 %, F1 score of 79.38 %, and an average precision of 0.87, and the Restaurant Customer Review dataset with an accuracy of 77.70 %, F1 score of 78.24 %, and average precision of 0.89 as compared to other existing algorithms. The result shows that the proposed model outperforms other algorithms with nearly 45 and 42% fewer features for the Amazon Review and Restaurant Customer Review datasets.
Type-theoretic approaches to ordinals

In a constructive setting, no concrete formulation of ordinal numbers can simultaneously have all the properties one might be interested in; for example, being able to calculate limits of sequences is constructively incompatible with deciding extensional equality. Using homotopy type theory as the foundational setting, we develop an abstract framework for ordinal theory and establish a collection of desirable properties and constructions. We then study and compare three concrete implementations of ordinals in homotopy type theory: first, a notation system based on Cantor normal forms (binary trees); second, a refined version of Brouwer trees (infinitely-branching trees); and third, extensional well-founded orders. Each of our three formulations has the central properties expected of ordinals, such as being equipped with an extensional and well-founded ordering as well as allowing basic arithmetic operations, but they differ with respect to what they make possible in addition. For example, for finite collections of ordinals, Cantor normal forms have decidable properties, but suprema of infinite collections cannot be computed. In contrast, extensional well-founded orders work well with infinite collections, but almost all properties are undecidable. Brouwer trees take the sweet spot in the middle by combining a restricted form of decidability with the ability to work with infinite increasing sequences. Our three approaches are connected by canonical order-preserving functions from the "more decidable" to the "less decidable" notions. We have formalised the results on Cantor normal forms and Brouwer trees in cubical Agda, while extensional well-founded orders have been studied and formalised thoroughly by Escardo and his collaborators. Finally, we compare the computational efficiency of our implementations with the results reported by Berger.
Adaptive Cyberdefense with Deception: A Human–AI Cognitive Approach

Achieving a level of dynamic and adaptive autonomy in cyber defense is highly desirable to advance the capabilities of cyber defense to a whole new level of effectiveness. In such a futuristic cyber defense framework, human defenders, autonomy (Artificial Intelligence, AI), and cognitive models collaborate in a team to deploy the most effective defense strategies utilizing cyber deception. AI defenders have significantly larger capabilities than humans analysts, they can rapidly obtain activity throughout the network and predict potential vulnerabilities; cognitive models “clone” human memory (end users, attackers, and defenders) by tracing human actions and predict the next human decision. These capabilities are then transformed into explicit cyber defense strategies that the human defender can decide to deploy in the network. Such an ideal cyber defense framework is a vision of the future, and our current research program has achieved significant theoretical and empirical progress towards developing Human–AI teaming using cognitive models integrated into an Adaptive Cyber defense framework. In this chapter, we summarize our advances to the current generic and static techniques of cyber defense toward adaptive cyber defense using deception. We provide a high-level overview of the advances we have achieved by using a cognitive approach for modeling the attacker’s and end-user’s decisions; exploring various deception techniques; and performing empirical demonstrations of these deception techniques in tasks of increasing complexity and realism. We conclude with a discussion of the visionary framework of Human–AI Cognitive teaming for cyber defense, where we highlight the most important challenges ahead to achieve this vision.
Binary optimal linear codes with various hull dimensions and entanglement-assisted QECCs

The hull of a linear code $C$ is the intersection of $C$ with its dual. To the best of our knowledge, there are very few constructions of binary linear codes with the hull dimension $\ge 2$ except for self-orthogonal codes. We propose a building-up construction to obtain a plenty of binary $[n+2, k+1]$ codes with hull dimension $\ell, \ell +1$, or $\ell +2$ from a given binary $[n,k]$ code with hull dimension $\ell$. In particular, with respect to hull dimensions 1 and 2, we construct all binary optimal $[n, k]$ codes of lengths up to 13. With respect to hull dimensions 3, 4, and 5, we construct all binary optimal $[n,k]$ codes of lengths up to 12 and the best possible minimum distances of $[13,k]$ codes for $3 \le k \le 10$. As an application, we apply our binary optimal codes with a given hull dimension to construct several entanglement-assisted quantum error-correcting codes(EAQECC) with the best known parameters.
Mortality Prediction of Various Cancer Patients via Relevant Feature Analysis and Machine Learning

Abstract Breast, lung, prostate, and stomach cancers are the most frequent cancer types globally. Early-stage detection and diagnosis of these cancers pose a challenge in the literature. When dealing with cancer patients, physicians must select among various treatment methods that have a risk factor. Since the risks of treatment may outweigh the benefits, treatment schedule is critical in clinical decision making. Manually deciding which medications and treatments are going to be successful takes a lot of expertise and can be hard. In this paper, we offer a computational solution to predict the mortality of various types of cancer patients. The solution is based on the analysis of diagnosis, medication, and treatment parameters that can be easily acquired from electronic healthcare systems. A classification-based approach introduced to predict the mortality outcome of cancer patients. Several classifiers evaluated on the Medical Information Mart in Intensive Care IV (MIMIC-IV) dataset. Diagnosis, medication, and treatment features extracted for breast, lung, prostate, and stomach cancer patients and relevant feature selection done with Logistic Regression. Best F 1 scores were 0.74 for breast, 0.73 for lung, 0.82 for prostate, and 0.79 for stomach cancer. Best AUROC scores were 0.94 for breast, 0.91 for lung, 0.96 for prostate, and 0.88 for stomach cancer. In addition, using relevant features, results were very similar to the baseline for each cancer type. Using less features and a robust machine-learning model, the proposed approach can be easily implemented in hospitals when there are limited data and resources available.
Robust Local Light Field Synthesis via Occlusion-aware Sampling and Deep Visual Feature Fusion

Abstract Novel view synthesis has attracted tremendous research attention recently for its applications in virtual reality and immersive telepresence. Rendering a locally immersive light field (LF) based on arbitrary large baseline RGB references is a challenging problem that lacks efficient solutions with existing novel view synthesis techniques. In this work, we aim at truthfully rendering local immersive novel views/LF images based on large baseline LF captures and a single RGB image in the target view. To fully explore the precious information from source LF captures, we propose a novel occlusion-aware source sampler (OSS) module which efficiently transfers the pixels of source views to the target view’s frustum in an occlusion-aware manner. An attention-based deep visual fusion module is proposed to fuse the revealed occluded background content with a preliminary LF into a final refined LF. The proposed source sampling and fusion mechanism not only helps to provide information for occluded regions from varying observation angles, but also proves to be able to effectively enhance the visual rendering quality. Experimental results show that our proposed method is able to render high-quality LF images/novel views with sparse RGB references and outperforms state-of-the-art LF rendering and novel view synthesis methods.
A DRL-based online VM scheduler for cost optimization in cloud brokers

The virtual machine (VM) scheduling problem in cloud brokers that support cloud bursting is fraught with uncertainty due to the on-demand nature of Infrastructure as a Service (IaaS) VMs. Until a VM request is received, the scheduler does not know in advance when it will arrive or what configurations it demands. Even when a VM request is received, the scheduler does not know when the VM’s lifecycle expires. Existing studies begin to use deep reinforcement learning (DRL) to solve such scheduling problems. However, they do not address how to guarantee the QoS of user requests. In this paper, we investigate a cost optimization problem for online VM scheduling in cloud brokers for cloud bursting to minimize the cost spent on public clouds while satisfying specified QoS restrictions. We propose DeepBS, a DRL-based online VM scheduler in a cloud broker which learns from experience to adaptively improve scheduling strategies in environments with non-smooth and uncertain user requests. We evaluate the performance of DeepBS under two request arrival patterns which are respectively based on Google and Alibaba cluster traces, and the experiments show that DeepBS has a significant advantage over other benchmark algorithms in terms of cost optimization.
Cheer for me: effect of non-player character audience feedback on older adult users of virtual reality exergames

The presence of an audience and its feedback could affect people's performance and experience during an event, especially related to sports such as tennis or boxing. Similarly, in videogames, players' gameplay could be affected if there is an audience and its feedback in response to players' performance in the environment. The inclusion of an audience with non-player characters (NPC) is common in videogames in general. However, there is a limited exploration of the use of an NPC audience in virtual reality (VR) exergames, especially focusing on elderly players. To fill this gap, this work examines the effect of an NPC audience and its associated feedback (with/without) on elderly users of VR exergames. In a user study, we used 120 NPC in a virtual audience. Results showed that the presence of the NPC audience with responsive feedback led to higher performance (with a higher success rate of performing gesture actions, more successful combinations of actions (or combos for short) performed, and more opponent's combos prevented) and better gameplay experience (with higher levels of competence, autonomy, relatedness, immersion, and intuitive controls) of elderly players. Our results can help frame the design and engineering of VR exergames that are targeted at elderly users to help them have an enhanced gameplay experience and improve their health.
A new multimedia cryptosystem using chaos, quaternion theory and modular arithmetic

Based on the combination of quaternion numbers, residual matrices, and chaotic attractors, a new cryptosystem is proposed for multimedia processing files such as images and audio. The key employed in this encryption schema consists of an image with a wide and sensitive range, obtained from the Julia Quaternion set rendered using a computational tool. Due to the use of quaternion matrices mixing the information between RGB layers and audio samples was possible, whereas using XOR operation and residual matrices modulus 257, added high sensitivity to small perturbations during encryption, key preparation and decryption processes, to such an extent that a minimal change in the image or in the audio leads to a totally different encryption result. The use of dynamic programming also reduced the processing time for matrix operations on the ℤ257 ring. To corroborate security of the algorithm, different tests were performed, including the National Institute of Standards and Technology test obtaining different indicators that were compared with other scientific references of similar works, finding behavioral patterns in accordance with those referenced works.
Using distributed ledger technology for digital forensic investigation purposes on tendering projects

Abstract The South African Local Government (SALG) uses the tendering system to procure goods and services. Some of these tendering projects are aimed at promoting socio-economic and industrial policies. Hence, the tendering system used by SALG should be fair, transparent, competitive, cost-effective, equitable, and free from corruption. However, the mismanagement of the tendering system might lead to interruption of operations, late service delivery, rising costs, and most importantly, fraud and corruption. The use of paperwork to share project information might lead to the mismanagement of the tendering project because it might contribute towards illicit altering of project information during the process. The purpose of this study is to develop a Blockchain prototype that might be used to securely share project information with all the parties interested in the tendering project. It is recommended that the adoption of the proposed solution will enable various organisations to have access to real-time data, allowing them to have access to the entire project history regardless of their geographical location. Access to real-time data would promote real-time auditing and digital forensic investigations because both auditors and investigators will have access to credible digital evidence or project information of their interest in real-time.
Introduction to Blockchain Technology

Blockchain is a novel decentralized technology that is used to share, replicate, and synchronize data across different geographical locations. It guarantees a trusted transaction in any untrustworthy environment. There is no central administrator or central authority to control all the data-related aspects of blockchain technology. A blockchain network depends on the consensus algorithm that must be agreed upon by all the entities for any new transaction. There are numerous advantages of blockchain, such as security, trust, open source, traceability, transparency, and many more, which make it very popular to apply in different sectors. This chapter first covers all the technologies behind blockchain. Then, some fundamental aspects of blockchain, such as types of blockchain, features, advantages, and disadvantages have been discussed. Many applications of blockchain technology in several sectors are presented in this chapter in order to demonstrate its functional value. Finally, some challenges in the adoption of blockchain technology have been discussed, which can be beneficial for researchers doing their research in blockchain.
LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks

The use of learning curves for decision making in supervised machine learning is standard practice, yet understanding of their behavior is rather limited. To facilitate a deepening of our knowledge, we introduce the Learning Curve Database (LCDB), which contains empirical learning curves of 20 classification algorithms on 246 datasets. One of the LCDB's unique strength is that it contains all (probabilistic) predictions, which allows for building learning curves of arbitrary metrics. Moreover, it unifies the properties of similar high quality databases in that it (i) defines clean splits between training, validation, and test data, (ii) provides training times, and (iii) provides an API for convenient access (pip install lcdb). We demonstrate the utility of LCDB by analyzing some learning curve phenomena, such as convexity, monotonicity, peaking, and curve shapes. Improving our understanding of these matters is essential for efficient use of learning curves for model selection, speeding up model training, and to determine the value of more training data.