Reviewerly/exHarmony huggingface dataset:
On multivariate randomized classification trees: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si243.svg" display="inline" id="d1e4677"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math>-based sparsity, VC dimension and decomposition methods

Decision trees are widely-used classification and regression models because of their interpretability and good accuracy. Classical methods such as CART are based on greedy approaches but a growing attention has recently been devoted to optimal decision trees. We investigate the nonlinear continuous optimization formulation proposed in Blanquero et al. (2020) for training sparse optimal randomized classification trees. Sparsity is important not only for feature selection but also to improve interpretability. We first consider alternative methods to sparsify such trees based on concave approximations of the l0 “norm”. Promising results are obtained on 24 datasets in comparison with the original l1 and l∞ regularizations. Then, we derive bounds on the VC dimension of multivariate randomized classification trees. Finally, since training is computationally challenging for large datasets, we propose a general node-based decomposition scheme and a practical version of it. Experiments on larger datasets show that the proposed decomposition method is able to significantly reduce the training times without compromising the testing accuracy.
Understanding and explaining convolutional neural networks based on inverse approach

Interpretability and explainability of machine learning systems have received ever-increasing attention, especially for deep neural networks. In the case of convolutional neural networks (CNNs), their properties are usually explained by generating local explanation maps (e.g., visualizing the contribution of individual pixels to a given prediction). In this paper, we propose a new framework that analyzes the inner workings of CNNs in terms of neural activations. To be precise, we consider a forward-pass as sequential activations of neurons and develop its inverse process, so that the inverse preserves the physical meaning of neuron activations. Our inverse process is formulated as a constrained optimization problem, and we solve the problem with the gradient projection algorithm. The proposed approach can provide equivalent visualization results to several conventional methods, and thus can be a reference tool for CNN visualization. Also, the attributions generated by our inverse method yield the state-of-the-art deletion scores and visualize the contribution of colors as well as shape features.
Do Minimal Complexity Least Squares Support Vector Machines Work?

The minimal complexity support vector machine is a fusion of the support vector machine (SVM) and the minimal complexity machine (MCM), and results in maximizing the minimum margin and minimizing the maximum margin. It works to improve the generalization ability of the L1 SVM (standard SVM) and LP (Linear Programming) SVM. In this paper, we discuss whether it also works for the LS (Least Squares) SVM. The minimal complexity LS SVM (MLS SVM) is trained by minimizing the sum of squared margin errors and minimizing the maximum margin. This results in solving a set of linear equations and a quadratic program, alternatingly. According to the computer experiments for two-class and multiclass problems, the MLS SVM does not outperform the LS SVM for the test data although it does for the cross-validation data.
Controlling 3D Objects in 2D Image Synthesis

Abstract In this work, we propose a method that enforces explicit control over various attributes during the image generation process in a generative adversarial net. We propose a semi-supervised learning procedure that allows us to use a quantized approximation of object orientation for learning continuous object rotations. As a result, among many other attributes, our proposed method allows us to control object orientation in scenes that are rendered according to our specifications.
Intelligent analysis of the quality of education through teaching practices on virtual campuses

Abstract ICTs have been increasingly involved in teaching–learning processes due to the potential offered by the tools as well as to the set of demands derived from the political and health situations of the social environment. In this sense, the introduction of virtual campuses as complex systems that centralize the entire technological component that complements traditional teaching processes has meant a change of paradigm with repercussions at the teaching and pedagogical level. In this context, the purpose of this study aims to analyze students’ perception of the use of virtual campus and how to enhance the quality of the educational process using intelligent systems. For this purpose, 318 students that use virtual campus have been surveyed. The results show that there are three variables that predominantly influence the quality of teaching–learning processes using virtual campuses: frequent contact with teachers through the platform, the digital competence of the student, and the adaptation of training content to the students’ prior knowledge. This information can be useful, as it allows them to establish guidelines to guide the practices of their teaching teams in technological environments, guaranteeing the suitability of the teaching–learning process and improving the evaluation processes and the assessment of their own educational work.
The circuit realization of a fifth‐order multi‐wing chaotic system and its application in image encryption

Summary In this paper, a new fifth‐order eight‐wing chaotic system is proposed on the basis of a five‐dimensional four‐wing chaotic system. The resulting new system is used as a basis for introducing a method that can control both the number of wings of the system and the direction of the change in the number of wings. Add a rounding function with a cosine function or a sine function to the system equation. The number of wings of the control system is controlled by changing the parameter values of the rounding function. This method can significantly increase the complexity of the system when applied in multiple directions of the system equations. And this method can achieve the purpose of controlling the wing number of the whole system by changing only one parameter. By observing the phase diagram and bifurcation diagram of the system, it is found that it has more complex chaotic properties and rich dynamical behavior and there are coexisting attractors. The chaotic properties of the system are confirmed by observing the Poincaré cross section and the 0–1 test results of the system. Afterward, the simulation circuit of the fifth‐order system is built in the simulation software Multisim. And the obtained phase diagram is consistent with the results obtained in MATLAB which confirmed the realizability of the system. An FPGA hardware implementation of this fifth‐order eight‐wing system has also been completed at the end of the paper to demonstrate the implementability of the system. And the results are highly consistent with those obtained in the MATLAB software. The fifth‐order chaotic system is combined with the dislocation algorithm and diffusion algorithm to apply to the image encryption system. The encryption system contains a variety of encryption algorithms for secondary encryption of images including Arnold dislocation and repeat‐free scrambling algorithm. The diffusion algorithm includes the addition of the modulus length diffusion and multiplicative operation diffusion based on the GF(257) domain. After a series of performance analysis, the encryption system is proved to have good secrecy effect and great application value in the image encryption neighborhood.
DISTERNING: Distance Estimation Using Machine Learning Approach for COVID-19 Contact Tracing and Beyond

Since the coronavirus disease 19 (COVID-19) outbreak, the epidemiological analysis has raised a strong requirement for more effective and accurate contact tracing solution. However, the existing contact tracing solutions either lacked the evaluation of tracing proximity or the features used for the tracing proximity evaluation were susceptible to certain negative environmental factors (e.g., body shielding). In this article, we propose a novel distance estimation algorithm based on machine learning for contact tracing: DISTERNING, where we leverage machine learning algorithms including Learning Vector Quantization, Regression, and Deep Feed-forward (DFF) Neural Network, data processing methods, and digital filters to process the Bluetooth signal information collected by the mobile phone for contact distance estimation. A contact tracing scheme based on edge computing is also proposed for algorithm deployment due to the requirements of the computational power. Compared with the existing contact tracing solutions, our algorithm considers the factors that have significant negative influence on the Bluetooth signal for distance estimation in reality. The evaluation results show that when the collected Bluetooth signal is influenced by real-world negative environmental factors, employing our proposed algorithm DISTERNING can keep the accuracy of the estimated distance reliable. The output distance can be combined with some medical models to conduct infection risk assessments.
Extending ontology pitfalls for better ontology evaluation

This article presents a framework that detects potential ontology building errors to improve the ontology quality. These potential errors are called ontology pitfalls in the literature. This work extends the existing ontology pitfall set in the literature and suggests new solutions for ontology repair. We have also developed a Java implementation for detection of the proposed pitfalls. These pitfalls were evaluated with well-known ontologies using this implementation.
Three-level atom–field in the context of time-dependent coupling and power-lower potentials

In the framework of time-varying coupling and power-law potentials, we investigate quantum entanglement and quantum Fisher information in a system that consists of a three-level atom interacting with a quantized field. The results illustrate that the quantum entanglement and quantum Fisher information's temporal evolution depend critically on the physical properties of the field and its coupling to the atom. It is noteworthy to notice that the quantifiers are sensitive to the exponent parameter of the potential with and without time-varying coupling. Considerable nonlocal correlation with the accuracy of parameter estimation can be achieved through the proper control of physical parameters.
Revisiting graph neural networks from hybrid regularized graph signal reconstruction

Graph neural networks (GNNs) have shown strong graph-structured data processing capabilities. However, most of them are generated based on the message-passing mechanism and lack of the systematic approach to guide their developments. Meanwhile, a unified point of view is hard to explain the design concepts of different GNN models. This paper presents a unified optimization framework from hybrid regularized graph signal reconstruction to establish the connection between the aggregation operations of different GNNs, showing that exploring the optimal solution is the process of GNN information aggregation. We use this new framework to mathematically explain several classic GNN models and summarizes their commonalities and differences from a macro perspective. The proposed framework not only provides convenience to understand GNNs, but also has a guiding significance for the proposal of new GNNs. Moreover, we design a model-driven fixed-point iteration method and a data-driven dictionary learning network according to the corresponding optimization objective and sparse representation. Then the new model, GNN based on model-driven and data-driven (GNN-MD), is established by using alternating iteration methods. We also theoretically analyze its convergence. Numerous node classification experiments on multiple datasets illustrate that the proposed GNN-MD has excellent performance and outperforms all baselines on high-feature-dimension datasets.
Identifying potential porphyry copper mineralization at the Zhu’nuo ore-cluster district in western Gangdese, southern Tibet: Insights from shortwave infrared (SWIR) spectrometry and geochemical anomalies

The Zhu’nuo ore-cluster district in the western Gangdese belt, southern Tibet, is characterized by a significant Cu geochemical anomaly, where the Zhu’nuo super-large porphyry Cu deposit (PCD) was discovered by stream sediment geochemical method. However, due to the extended coverage of volcanic rocks of the Linzizong group and the large area of ore-forming element anomalies, it is quite difficult to locate the concealed PCDs only by geochemical method. Therefore, the application of new technical methods is urgently needed to guide the exploration breakthroughs in the Zhu’nuo district. Recently, our studies have shown that the wavelength variation of diagnostic absorption peaks of alteration minerals measured by shortwave infrared (SWIR) spectrometry is closely related to the mineralization temperature in the Zhu’nuo PCD, which can be footprints to trace the hydrothermal and/or mineralization (H/M) centers of porphyry ore systems. Hence, we carried out SWIR studies in the Luobuzhen-Cimabanshuo areas with high Cu geochemical anomaly. In this study, sixteen alteration minerals were identified by SWIR, consisting mainly of white mica, chlorite, epidote, and kaolinite. Among them, the white mica and chlorite were widely distributed in the study area, and the Al-OH wavelength position of white mica (∼2200 nm; Pos2200) and Fe-OH wavelength position of chlorite (∼2250 nm; Pos2250) varied between 2191–2219 nm and 2239–2260 nm, respectively. Moreover, there is an increasing trend of Pos2200 from porphyry center to periphery, whereas Pos2250 shows the opposite tendency. When combined with geochemical anomalies of stream sediments and geological characteristics and SWIR features in the Zhu’nuo PCD in study area, three potential H/M centers, namely Cimabanshuo, South Luobuzhen and Ya’nuri, were determined, which are dominated by short wave anomalies (Pos2200, 2191–2206 nm; Pos2350, 2243–2250 nm). Significantly, we showed that the Dongshibu-Hongshan-Luobuzhen may belong to the same porphyry system by SWIR. And SWIR implies that the true center is located in South Luobuzhen (H/M II), which has great prospecting potential in its depth, rather than the previously recognized Dongshibu Cu prospect delineated by stream sediment data. We found that the Dongshibu Cu prospect does not show any alteration information associated with porphyry mineralization as reflected by SWIR, consistent with the barren Cu results from the late drilling validation. This decoupling may be caused by the long-term migration and weathering of stream sediment samples, during which the obtained geochemical anomaly do not represent their in-situ information. Therefore, caution must be taken when using stream sediment data to delineate prospecting targets. Instead, SWIR is a potentially powerful vectoring tool to aid exploration for porphyry systems, especially in the western regions where the level of prospecting work is extremely low.
Weaponizing Actions in Multi-Agent Reinforcement Learning: Theoretical and Empirical Study on Security and Robustness

Cooperative Multi-Agent Reinforcement Learning (c-MARL) enables a team of agents to determine the global optimal policy that maximizes the sum of their accumulated rewards. This paper investigates the robustness of c-MARL to a novel adversarial threat, where we target and weaponize one agent, termed the compromised agent, to create natural observations that are adversarial for its team. The goal is to lure the compromised agent to follow an adversarial policy that pushes activations of its cooperative agents’ policy networks off distribution. This paper shows mathematically the exploitation steps of such an adversarial policy in the centralized-learning and decentralized-execution paradigm of c-MARL. We also empirically demonstrate the susceptibility of the state-of-the-art c-MARL algorithms, namely MADDPG and QMIX, to the compromised agent threat by deploying four attack strategies in three environments in white and black box settings. By targeting a single agent, our attacks yield highly negative impact on the overall team reward in all environments, reducing it by at least 33% and at most 89.6%. Finally, we provide recommendations on improving the robustness of c-MARL.
Identifying Necessary and Sufficient Conditions for the Enforcement Problem of Argumentation Frameworks

In the dynamics research of argumentation frameworks (AFs), the enforcement problem deals with changing an AF for the purpose of ensuring that a certain set of desired arguments becomes (part of) an extension. In this paper we focus on expansions of an AF where solely the addition of new arguments and attacks is allowed and the original framework remains unchanged. Existing results about the enforcement problem under strong and normal expansion are all sufficient conditions. We argue that necessary and sufficient conditions are essential concerning the solvability of the enforcement problem. Specifically, two necessary and sufficient conditions are identified for the non-strict enforcement in respectively the odd-length cycle free and the even-length cycle free AFs under strong expansion. This result can be used to determine that when new arguments satisfying the condition are unavailable, enforcing the desired set simply becomes unsolvable.
A New Semantics for Action Language m$$\mathcal {A}^*$$

The action language m $$\mathcal {A}^*$$ employs the notion of update models in defining transitions between states. Given an action occurrence and a state, the update model of the action occurrence is automatically constructed from the given state and the observability of agents. A main criticism of this approach is that it cannot deal with situations when agents’ have incorrect beliefs about the observability of other agents. The present paper addresses this shortcoming by defining a new semantics for m $$\mathcal {A}^*$$ . The new semantics addresses the aforementioned problem of m $$\mathcal {A}^*$$ while maintaining the simplicity of its semantics; the new definitions continue to employ simple update models, with at most three events for all types of actions, which can be constructed given the action specification and independently from the state in which the action occurs.
Using Ethereum Smart Contracts for Payment Transactions

Blockchain as a technology is being more and more common, both in software development industry and in many spheres of business, such as law, finance and many others. The use of blockchain technology provides many advantages in terms of decentralization, information security and concealment. Ethereum, which provides the possibility of using smart contracts, stands out as one of the most frequently used blockchain platforms. The use of smart contracts provides a more efficient alternative to traditional way of the key aspects of business, such as making payments, traditional payment being the one executed in e-payment system, or specifically EMV payment systems. The use of smart contracts in the execution on the Ethereum platform is researched in this paper. The paper showcases overview of blockchain tecnology and an example of a smart contract written in the Solidity programming language, over which the examination was performed and the main aspects were explained. Main vulnerabilities of Solidity programming language are given. Finally, in the conclusion, comparison, or rather the advantages of payment systems based on blockchain over the traditional electronic payment systems are also considered.
Integrated Structure and Mineralization Study Using Aero-Magnetic, Aero-Spectrometric and Remote Sensing Data at Esh El-Mallaha Area, Eastern Desert, Egypt

The main scope of this research is to detect geologic structure trends affecting the study area, determine uranium anomalous areas and define alteration zones.Airborne magnetic data were used to detect the geologic structure trends affecting the study area through applying edge detectors such as total horizontal derivative, analytic signal and tilt derivative.The radio-spectrometry data and Landsat image data were used in determining the uranium anomalous areas and alteration zones.The integration between geology, magnetic and Landsat image was applied through constructing lineaments density map for the three data sets resulting in the leading of NW-SE trend all over the area.This integration makes clear that the basement (Red Sea hills), Esh El-mallaha range and G. Zeit are limiting two large basins (West Mallaha and Zeit).In addition, the main areas of uranium enrichment (Duwi formation at Esh El-Mallaha range) are found to be well related to alteration zones.
Pyramidal position attention model for histopathological image segmentation

The level of performance achieved in the classification of histopathological images has not yet been reached in the segmentation area. This is because the global context information sufficient for classification is not sufficient for segmentation. Especially, high tissue diversity in histopathological images and the fact that tissues in the same class have quite different colors, patterns and geometries make the segmentation problem difficult. In this study, a novel hybrid architecture (PAMSegNet) is presented that provides high segmentation accuracy in histopathological images. This architecture, which has a pyramid data processing strategy, has been provided with the Position Attention Module (PAM) and Boundary aware Module (BM) to extract global and local attributes more accurately. In addition, with the deep supervised technique used, both contents (global and local) were evaluated together in the segmentation decision. Segmentation architectures (Deeplabv3 +, SegNet, U-Net) with a strong backbone in the literature are used for performance comparison. The proposed architecture has been found to provide high segmentation accuracy (71.6% mIoU and 86.4% PA).
Toward underwater image enhancement: new dataset and white balance priors-based fusion network

Raw underwater images are degraded due to light attenuation and particle scattering. It is challenging to obtain authentic reference images. Hence, existing underwater datasets may be short of paired and trainable images. Many data-driven approaches are therefore difficult to implement. To address the above issues, we mainly make two contributions. First, a paired artificial underwater dataset (i.e., PAU-Wild) is constructed, including more than 3500 pairs of images of seven types. PAU-Wild is the first attempt to simulate real-world underwater images with external filters. Second, to enhance underwater images, a convolutional neural network using multistage fusion is proposed. Our model introduces two white balance (WB) priors into the fusion strategy. In addition, a joint loss function is designed to preserve content features and edge features. The experimental results demonstrate that our method is effective and even outperforms several state-of-the-art methods.