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As usage of military artificial intelligence (AI) expands, so will anti-AI countermeasures, known as adversarials. International humanitarian law offers many protections through its obligations in attack, but the nature of adversarials generates ambiguity regarding which party (system user or opponent) should incur attacker responsibilities. This article offers a cognitive framework for legally analyzing adversarials. It explores the technical, tactical and legal dimensions of adversarials, and proposes a model based on foreseeable harm to determine when legal responsibility should transfer to the countermeasure's author. The article provides illumination to the future combatant who ponders, before putting on their adversarial sunglasses: "Am I conducting an attack?" [ABSTRACT FROM AUTHOR]

This paper tackles the critical challenge of detecting and mitigating unintended political bias in offensive meme detection. Political memes are a powerful tool that can be used to influence public opinion and disrupt voters' mindsets. However, current visual-linguistic models for offensive meme detection exhibit unintended bias and struggle to accurately classify non-offensive and offensive memes. This can harm the fairness of the democratic process either by targeting minority groups or promoting harmful political ideologies. With Hindi being the fifth most spoken language globally and having a significant number of native speakers, it is essential to detect and remove Hindi-based offensive memes to foster a fair and equitable democratic process. To address these concerns, we propose three debiasing techniques to mitigate the overrepresentation of majority group perspectives while addressing the suppression of minority opinions in political discourse. To support our approach, we curate a comprehensive dataset called Pol_Off_Meme, designed especially for the Hindi language. Empirical analysis of this dataset demonstrates the efficacy of our proposed debiasing techniques in reducing political bias in internet memes, promoting a fair and equitable democratic environment. Our debiased model, named D R T I M Att Adv , exhibited superior performance compared to the CLIP-based baseline model. It achieved a significant improvement of +9.72% in the F1-score while reducing the False Positive Rate Difference (FPRD) by -16% and the False Negative Rate Difference (FNRD) by -14.01%. Our efforts strive to cultivate a more informed and inclusive political discourse, ensuring that all opinions, irrespective of their majority or minority status, receive adequate attention and representation. [ABSTRACT FROM AUTHOR]

The article examines the procedural opportunities available to the defense party for evidence collection in Ukraine's criminal process. It is emphasized that despite the declared equality of rights of the parties to collect and submit items, documents, and evidence to the court under Article 22 of the Criminal Procedure Code of Ukraine, during pre-trial investigation, the prosecution party has a number of advantages over the defense party in terms of available means of evidence. The procedural opportunities available to the defense party for evidence collection have been analyzed, comparing them with the actual evidentiary capabilities of the prosecution party. The author underscores that the current Ukrainian criminal procedural legislation contains precedents granting the defense party certain powers traditionally associated with the activities of the prosecution party. It is proposed to grant the defense party, the victim, and the representative of a legal entity, subject to criminal proceedings, the right to perform certain procedural actions during the pre-trial investigation stage, which currently can only be carried out by the prosecution party. According to the author, expanding the evidentiary powers of the listed subjects is possible provided that some principles are adhered to, including verifiability of procedural actions' results, creating no obstacles for the prosecution party, the usefulness of such expansion, and coercion is avoided. The mentioned participants in the criminal proceedings may be granted the opportunity to conduct inspections of computer data, interrogate witnesses and suspects, undergo voluntary inspections of a person (including medical ones), extract data from technical devices and technical means, and so forth. [ABSTRACT FROM AUTHOR]

The investigating judge was first mentioned in the French Code of Criminal Procedure in 1808, and then in other countries, including Russia, where a similar procedural person was called a court investigator and was abolished in the early years of Soviet power. Currently, there is a debate in national jurisprudence about the return of the court investigator, showing the ambiguous attitude to this problem. The purpose of the article is to analyze the current state of legal regulation of the institute of investigating judges in the French criminal procedure legislation in order to determine the effectiveness of the performance of its tasks; as well as the nature of criminal procedure relations between the participants of the French criminal proceedings in comparison to similar national ones. The findings make it possible to present a coherent, logical, time-tested construction of the institution of the investigating judge. These activities are characterized by much more detailed regulation, including in the interests of the defense, with a general focus on the protection of individual rights, the exclusion of accusatory bias and the existence of effective safeguards for this purpose. The article shows the real possibilities of the investigating judge to exercise his autonomy and independence from the prosecutor. At the same time, such regulation is sufficiently effective to achieve an objective approach. Other positive consequences are possible in the form of elements of attorney investigation and the deposit of certain types of evidence.

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By establishing the initial legal status of the person held criminally liable as a "state of innocence", the presumption of innocence exerts a substantial influence on the entire criminal process: it determines the direction and rules of the criminal-procedural evidence during the entire process; it conditions and guarantees the inherence of the rights and freedoms of the accused, making it impossible to restrict them in the absence of sufficient grounds and necessities to do so. In the study, on the basis of the analysis of the provisions on the presumption of innocence in criminal proceedings, the opinion that the purpose of criminal procedural evidence is to obtain authentic knowledge of the guilt of the person in committing the crime, i.e. to rebut the presumption of innocence, is justified. This objective can be achieved only if all the circumstances of the case are fully and objectively investigated. The theoretical and practical importance of the study on the impact of the presumption of innocence on the distribution of the burden of proof in criminal proceedings is determined by the interpretation of the current problems of the criminal process, examined from the position of the presumption of innocence as a principle of the criminal process, focused on strengthening the procedural guarantees of the rights and freedoms of the individual. [ABSTRACT FROM AUTHOR]

This article discusses the stability of a two-species ecosystem composed of an ammensal (x) and an adversarial (y) species that are continuously harvested. A mathematical model is defined by a system of two nonlinear ordinary differential equations of first order. The considered system's boundedness is investigated. The local stability of the system is described using a variational matrix, while the global stability is examined using Lyapunov's function. The prerequisite for the system to exist in bionomic equilibrium has been identified. The ideal harvesting technique is determined using the maximal principle proposed by Pontryagin. In MATLAB simulations, the stability of the deterministic system is demonstrated for the specified set of parameters.

The database of faces containing sensitive information is at risk of being targeted by unauthorized automatic recognition systems, which is a significant concern for privacy. Although there are existing methods that aim to conceal identifiable information by adding adversarial perturbations to faces, they suffer from noticeable distortions that significantly compromise visual perception, and therefore, offer limited protection to privacy. Furthermore, the increasing prevalence of appearance anxiety on social media has led to users preferring to beautify their faces before uploading images. In this paper, we design a novel face database protection scheme via beautification with chaotic systems. Specifically, we construct the adversarial face with better visual perception via beautification for each face in the database. In the training, the face matcher and the beautification discriminator are federated against the generator, prompting it to generate beauty-like perturbations on the face to confuse the face matcher. Namely, the pixel changes produced by face beautification mask the adversarial perturbations. Moreover, we use chaotic systems to disrupt the order of adversarial faces in the database, further mitigating the risk of privacy leakage. Our scheme has been extensively evaluated through experiments, which show that it effectively defends against unauthorized attacks while also yielding good visual results. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images that mimic the content of datasets they have been trained to replicate. One recurrent theme in medical imaging, is whether GANs can also be as effective at generating workable medical data, as they are for generating realistic RGB images. In this paper, we perform a multi-GAN and multi-application study, to gauge the benefits of GANs in medical imaging. We tested various GAN architectures, from basic DCGAN to more sophisticated style-based GANs, on three medical imaging modalities and organs, namely: cardiac cine-MRI, liver CT, and RGB retina images. GANs were trained on well-known and widely utilized datasets, from which their FID scores were computed, to measure the visual acuity of their generated images. We further tested their usefulness by measuring the segmentation accuracy of a U-Net trained on these generated images and the original data. The results reveal that GANs are far from being equal, as some are ill-suited for medical imaging applications, while others performed much better. The top-performing GANs are capable of generating realistic-looking medical images by FID standards, that can fool trained experts in a visual Turing test and comply to some metrics. However, segmentation results suggest that no GAN is capable of reproducing the full richness of medical datasets. [ABSTRACT FROM AUTHOR]

Classical strategy as a concept encompasses two different logics (instrumentality and adversariality) as well as two different modes (decision-making and performance). In modern strategy, these modes have been on diverging paths, with varying interpretations privileging one logic above the other. Game theory focuses on decision-making, but encompasses both adversariality and instrumentality. Operational art focuses on performance, but in an adversarial context. The ends, ways, means model emphasises performance in an instrumental context. Each is imbalanced and inadequate when faced with the challenge of comprehending and controlling war. Strategic studies must make a return to balanced interpretations of strategy. [ABSTRACT FROM AUTHOR]

This article examines the concept of defense disclosures within the theory of managerialism in criminal proceedings in Ghana. Through a doctrinal and comparative legal analysis with the English jurisdiction, it finds that in substance, the requirement of defense disclosure seeks to move the criminal process from its core protectionist ideology that insulates the accused from matters of proof toward a managerial process informed by objectives of truth-finding, trial efficiency and case management. Ironically, this new direction in the criminal trial process is in practice denounced as being at odds with the procedural due process values that shield the accused from matters of proof and pretrial disclosures. The problem is that unlike in England where the move towards defense disclosures is informed by a clear policy change, the managerial policy introduced by the Judiciary in Ghana is not grounded in any articulated theory or policy direction. While pursuing a path of ensuring effective criminal adjudication through mutual disclosures by the parties, it is important to find a proper balance between the denounced but yet adopted procedural concept of defense disclosures and the highly valued protectionist rights of the accused.

Achieving a fair trial and ensuring justice in the trial is initially subject to identifying the principles governing the trial and adapting it to the principles and rules of the national and transnational legal system and adapting it to the circumstances prevailing in each judicial authority. As a quasi-judicial authority with a conciliatory approach and compromise, on the one hand, the deliberations of the council in accordance with the laws and regulations are not subject to the procedures and principles of the trial, and on the other hand, the deliberations of the council in terms of principles and rules are subject to the rules of civil and criminal procedure. Due to the absence of the judge in the council meeting, this issue causes inconsistencies and conflicts in the issuance of the verdict. Sub-vote is the result of the review process and in practice the council judge does not play a key role in it. Therefore, in order to comply with the rules and principles of procedure, it is necessary to separate the issues raised in the council in terms of compromise and compensation from the beginning. Compromise issues without observing the court proceedings by the members of the council and dispute issues with the presence of the council judge in the hearing in accordance with the principles of the court and the governing procedures to be considered in order to achieve a fair trial in practice in this judicial authority.Keywords: Judicial Authority, Dispute Resolution Council, Fair Trial

Social media platforms have grown in importance as sources of information and as a complement to traditional media. If informative and relevant tweets on social media platforms can be effectively detected and analyzed during crisis events, it will help humanitarian organizations with situational awareness and planning relief activities. In this work, we propose an adversarial multimodal auto-encoder model for detecting and analyzing crisis-related tweets, which analyzes the complex multimodal content of tweets and integrates adversarial strategies to generate a joint representation containing information from multiple sources. Through extensive experiments on real datasets, we demonstrate the superior performance of the proposed model. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) requires a precise diagnosis in order to be managed and rehabilitated. Non-invasive neuroimaging methods are disease markers that can be used to help diagnose ASD. The majority of available techniques in the literature use functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. Traditional supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text, images, and videos, but their performance and robustness are restricted by the size of the accompanying training data. Deep learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering algorithms. It takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural networks. Generative modelling, also known as Generative Adversarial Networks (GANs), is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original dataset. GANs are an exciting and rapidly changing field that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains, most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the past. In this paper, we present a novel conditional generative adversarial network, or cGAN for short, which is a form of GAN that uses a generator model to conditionally generate images. In terms of prediction and accuracy, they outperform the standard GAN. The proposed model is 74% more accurate than the traditional methods and takes only around 10 min for training even with a huge dataset. [ABSTRACT FROM AUTHOR]

Learning-based translation between MRI contrasts involves supervised deep models trained using high-quality source- and target-contrast images derived from fully-sampled acquisitions, which might be difficult to collect under limitations on scan costs or time. To facilitate curation of training sets, here we introduce the first semi-supervised model for MRI contrast translation (ssGAN) that can be trained directly using undersampled k-space data. To enable semi-supervised learning on undersampled data, ssGAN introduces novel multi-coil losses in image, k-space, and adversarial domains. The multi-coil losses are selectively enforced on acquired k-space samples unlike traditional losses in single-coil synthesis models. Comprehensive experiments on retrospectively undersampled multi-contrast brain MRI datasets are provided. Our results demonstrate that ssGAN yields on par performance to a supervised model, while outperforming single-coil models trained on coil-combined magnitude images. It also outperforms cascaded reconstruction-synthesis models where a supervised synthesis model is trained following self-supervised reconstruction of undersampled data. Thus, ssGAN holds great promise to improve the feasibility of learning-based multi-contrast MRI synthesis. [ABSTRACT FROM AUTHOR]

This article discusses the stability of a two-species ecosystem composed of an ammensal (x) and an adversarial (y) species that are continuously harvested. A mathematical model is defined by a system of two nonlinear ordinary differential equations of first order. The considered system's boundedness is investigated. The local stability of the system is described using a variational matrix, while the global stability is examined using Lyapunov's function. The prerequisite for the system to exist in bionomic equilibrium has been identified. The ideal harvesting technique is determined using the maximal principle proposed by Pontryagin. In MATLAB simulations, the stability of the deterministic system is demonstrated for the specified set of parameters. [ABSTRACT FROM AUTHOR]

Craniomaxillofacial defects caused by congenital or acquired reasons seriously affect patients' physical and mental health. How to accurately and objectively repair the morphology of craniomaxillofacial tissues and organs through surgery is a difficult problem, and the preoperative virtual design is crucial. Traditional preoperative virtual design methods include mirror technology, statistical shape model, and deformable template. However, these methods are complex, time‐consuming, and only applicable to some types of defects. Therefore, a general, intelligent, and personalized craniomaxillofacial defect virtual reconstruction system is desired. To solve this problem, a novel deep learning method, RecGAN, is proposed in this paper. RecGAN can learn the bone morphology of normal people, repair the defect intelligently based on the patient's remaining bone, and fully adapt to the special conditions of different patients. Currently, there are no open‐source maxillofacial data sets available. Thus, a new maxillofacial computed tomography image data set with 500 simulated cases and 100 clinical cases is constructed to train and validate the method. The experimental results show that RecGAN can effectively restore the normal bone and tissue morphology of the patient's craniomaxillofacial defect area, solve the problem that there is no objective repair method for craniomaxillofacial defect, and achieve the best effect in the same type of research. The proposed intelligent craniomaxillofacial defect virtual reconstruction system based on RecGAN is expected to be applied in future clinical practice. [ABSTRACT FROM AUTHOR]

True-color imagery of satellites provides various atmospheric and surface information for intuitive understanding and visualization. This article presents a conditional generative adversarial network method for generating daytime and nighttime hypothetical visible (VIS) bands of the advanced meteorological imager (AMI) sensor onboard the geostationary Korea multipurpose satellite. The AMI datasets in the form of albedo and brightness temperature (${{\boldsymbol{T}}}_{\boldsymbol{B}}$) were normalized and denormalized between 0 and 1 data-to-data (D2D) translation. The D2D model was trained and tested using data pair of the albedos at AMI VIS bands and AMI infrared (IR) bands or ${{\boldsymbol{T}}}_{\boldsymbol{B}}$ differences between two AMI IR bands. The constructed D2D model showed that the statistical results of bias, root-mean-square-error, and correlation coefficient between the observed and D2D-generated AMI VIS bands during daytime were −0.006 and 0.047 in albedo, and 0.941 for the blue band; −0.007 and 0.05 in albedo and 0.939 for the green band; −0.01 and 0.061 in albedo, and 0.917 for the red band, respectively. The proposed D2D method is being officially used by the Korea meteorological administration. Except for simulating desert areas as clouds at night, the D2D model demonstrated excellent performance, generating hypothetical AMI VIS bands at day and night. Consequently, this article could significantly contribute to the monitoring and understanding of meteorological phenomena over one-third of the Earth. Additionally, the method can be extended to other geostationary weather satellites, including Himawari-8, Fengyun-4A, meteosat third generation, and geostationary operational environmental satellites.

Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. However, CNNs are designed to perform local processing with compact filters, and this inductive bias compromises learning of contextual features. Here, we propose a novel generative adversarial approach for medical image synthesis, ResViT, that leverages the contextual sensitivity of vision transformers along with the precision of convolution operators and realism of adversarial learning. ResViT’s generator employs a central bottleneck comprising novel aggregated residual transformer (ART) blocks that synergistically combine residual convolutional and transformer modules. Residual connections in ART blocks promote diversity in captured representations, while a channel compression module distills task-relevant information. A weight sharing strategy is introduced among ART blocks to mitigate computational burden. A unified implementation is introduced to avoid the need to rebuild separate synthesis models for varying source-target modality configurations. Comprehensive demonstrations are performed for synthesizing missing sequences in multi-contrast MRI, and CT images from MRI. Our results indicate superiority of ResViT against competing CNN- and transformer-based methods in terms of qualitative observations and quantitative metrics. [ABSTRACT FROM AUTHOR]

Identification of a heterogeneous conductivity field and reconstruction of a contaminant release history are key aspects of subsurface remediation. These two goals are achieved by combining model predictions with sparse and noisy hydraulic head and concentration measurements. Solution of this inverse problem is notoriously difficult due to, in part, high dimensionality of the parameter space and high computational cost of repeated forward solves. We use a convolutional adversarial autoencoder (CAAE) to parameterize a heterogeneous non‐Gaussian conductivity field via a low‐dimensional latent representation. A three‐dimensional dense convolutional encoder‐decoder (DenseED) network serves as a forward surrogate of the flow and transport model. The CAAE‐DenseED surrogate is fed into the ensemble smoother with multiple data assimilation (ESMDA) algorithm to sample from the Bayesian posterior distribution of the unknown parameters, forming a CAAE‐DenseED‐ESMDA inversion framework. The resulting CAAE‐DenseED‐ESMDA inversion strategy is used to identify a three‐dimensional contaminant source and conductivity field. A comparison of the inversion results from CAAE‐ESMDA with physical flow and transport simulator and from CAAE‐DenseED‐ESMDA shows that the latter yields accurate reconstruction results at the fraction of the computational cost of the former. Plain Language Summary: Identification of a heterogeneous conductivity field and reconstruction of a contaminant release history are key aspects of subsurface remediation. These two goals are achieved by combining model predictions with sparse and noisy hydraulic head and concentration measurements. Solution of this inverse problem is notoriously difficult due to, in part, high dimensionality of the parameter space and high computational cost of repeated forward solves. We develop a deep‐learning strategy to identify a three‐dimensional contaminant source and conductivity field from sparse observations. Key Points: Our deep‐learning strategy reconstructs three‐dimensional conductivity field and contaminant release historyConductivity parameterization with convolutional adversarial autoencoder reduces the inverse problem's dimensionalityConvolutional encoder‐decoder acts as a surrogate of forward models; ensemble smoother approximates parameters' posterior distribution [ABSTRACT FROM AUTHOR]

The level of realization of the right to a fair trial is one of the crucial indicators of democracy in any state. In order to ensure this right, all the minimum standards deriving from it must be clearly understood by law enforcement agencies and their practice must meet these standards. As equality of arms, the right to a fair trial, is not directly enshrined in the text of Article 6 of the European Convention on Human Rights (hereafter - the Convention) and is of implicit character; the issues like its essence, content and the way it should manifest in practice are open for discussion. For this reason, the focus on those issues is highly relevant. The aim of this article is, with reference to the case law of the European Court of Human Rights (hereafter - ECHR) and the modern doctrine based on this right, to explain the role of this principle and the essence of its mutual relations with the other elements of the right to a fair trial. Selected case law of ECHR bears great interest compared with other decisions and is discussed in the form of empirical materials of the study. From the doctrinal materials, interpretation of Article 6 of the Convention and theoretical sources related to the European standards in the criminal procedure are also analyzed. The article exercises methods of dialectical comprehension; they are determinism, induction, deduction, case studies and methods of law interpretation. As a result of the study, a unique doctrinal commentary has been obtained in the context of adversariality and impartial and independent court principles of the concept of equality of arms, as well as, interaction of the minimum rights of persons subject to criminal prosecution, guaranteed by the Convention.