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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.

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]

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

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]

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]

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.

Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency. To reduce supervision requirements, the recent deep image prior framework instead conjoins untrained MRI priors with the imaging operator during inference. Yet, canonical convolutional architectures are suboptimal in capturing long-range relationships, and priors based on randomly initialized networks may yield suboptimal performance. To address these limitations, here we introduce a novel unsupervised MRI reconstruction method based on zero-Shot Learned Adversarial TransformERs (SLATER). SLATER embodies a deep adversarial network with cross-attention transformers to map noise and latent variables onto coil-combined MR images. During pre-training, this unconditional network learns a high-quality MRI prior in an unsupervised generative modeling task. During inference, a zero-shot reconstruction is then performed by incorporating the imaging operator and optimizing the prior to maximize consistency to undersampled data. Comprehensive experiments on brain MRI datasets clearly demonstrate the superior performance of SLATER against state-of-the-art unsupervised methods. [ABSTRACT FROM AUTHOR]

Adversarial attacks have been demonstrated to fool the deep classification networks. There are two key characteristics of these attacks: firstly, these perturbations are mostly additive noises carefully crafted from the deep neural network itself. Secondly, the noises are added to the whole image, not considering them as the combination of multiple components from which they are made. Motivated by these observations, in this research, we first study the role of various image components and the impact of these components on the classification of the images. These manipulations do not require the knowledge of the networks and external noise to function effectively and hence have the potential to be one of the most practical options for real-world attacks. Based on the significance of the particular image components, we also propose a transferable adversarial attack against unseen deep networks. The proposed attack utilizes the projected gradient descent strategy to add the adversarial perturbation to the manipulated component image. The experiments are conducted on a wide range of networks and four databases including ImageNet and CIFAR-100. The experiments show that the proposed attack achieved better transferability and hence gives an upper hand to an attacker. On the ImageNet database, the success rate of the proposed attack is up to 88.5%, while the current state-of-the-art attack success rate on the database is 53.8%. We have further tested the resiliency of the attack against one of the most successful defenses namely adversarial training to measure its strength. The comparison with several challenging attacks shows that: (i) the proposed attack has a higher transferability rate against multiple unseen networks and (ii) it is hard to mitigate its impact. We claim that based on the understanding of the image components, the proposed research has been able to identify a newer adversarial attack unseen so far and unsolvable using the current defense mechanisms. [ABSTRACT FROM AUTHOR]

We consider optimal transport-based distributionally robust optimization (DRO) problems with locally strongly convex transport cost functions and affine decision rules. Under conventional convexity assumptions on the underlying loss function, we obtain structural results about the value function, the optimal policy, and the worst-case optimal transport adversarial model. These results expose a rich structure embedded in the DRO problem (e.g., strong convexity even if the non-DRO problem is not strongly convex, a suitable scaling of the Lagrangian for the DRO constraint, etc., which are crucial for the design of efficient algorithms). As a consequence of these results, one can develop efficient optimization procedures that have the same sample and iteration complexity as a natural non-DRO benchmark algorithm, such as stochastic gradient descent. [ABSTRACT FROM AUTHOR]

As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought. Traditional defending approaches are no longer sufficient to detect both known and unknown attacks to high accuracy. Machine learning intrusion detection systems have proven their success in identifying unknown attacks with high precision. Nevertheless, machine learning models are also vulnerable to attacks. Adversarial examples can be used to evaluate the robustness of a designed model before it is deployed. Further, using adversarial examples is critical to creating a robust model designed for an adversarial environment. Our work evaluates both traditional machine learning and deep learning models’ robustness using the Bot-IoT dataset. Our methodology included two main approaches. First, label poisoning, used to cause incorrect classification by the model. Second, the fast gradient sign method, used to evade detection measures. The experiments demonstrated that an attacker could manipulate or circumvent detection with significant probability.

The present study, utilizing a descriptive approach and quantitative analysis, analyzed questions in the political interviews in Iran and the United States in order to show what types of adversarial are used in the journalists’ questions and whether there are differences in the use of adversarial between Iranian and American journalists. To this end, the questions addressing the presidents in Iran (i.e. Presidents AhmadiNejad and Roohani) and presidents in the US (i.e. Presidents Obama and Trump), around 70 journalists (35 in each corpus) in political press conference, were randomly collected from 2012 to 2017. The data were then analyzed based Clayman et al.’s (2006) framework to examine how language is used to express adversarial questions. The findings showed that preface tilt was significantly used more in American corpus while other-referencing frames and global adversarial were significantly used more in Iranian corpus. Moreover, in the two corpora, negative questions were the least frequently used type of question and declarative questions was absent in American corpus.

The article gives a brief historical description of the role of the court in the collection of evidence. The arguments in favor of strengthening the role of the court for the recovery of evidence are presented. In particular, the Author substantiates the conclusion that it is necessary to impose on the court the obligation to independently demand evidence in civil proceedings. The Author comes to the conclusion that strengthening the role of the court in the process of proof in civil proceedings are dictated by the requirements due to the many flaws in civil procedural law, the unequal social status of participants in the process, making decisions not in accordance with actual circumstances of the case.

Abstract Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very efficient. In an up-to-date comparison of state-of-the-art classification algorithms in tabular data, tree boosting outperforms deep learning (Zhang et al. in Expert Syst Appl 82:128–150, 2017). For this reason, we have developed a novel approach of adversarial gradient tree boosting. The objective of the algorithm is to predict the output Y with gradient tree boosting while minimizing the ability of an adversarial neural network to predict the sensitive attribute S. The approach incorporates at each iteration the gradient of the neural network directly in the gradient tree boosting. We empirically assess our approach on four popular data sets and compare against state-of-the-art algorithms. The results show that our algorithm achieves a higher accuracy while obtaining the same level of fairness, as measured using a set of different common fairness definitions.

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.

In the long-term deployment of mobile robots, changing appearance brings challenges for localization. When a robot travels to the same place or restarts from an existing map, global localization is needed, where place recognition provides coarse position information. For visual sensors, changing appearances such as the transition from day to night and seasonal variation can reduce the performance of a visual place recognition system. To address this problem, we propose to learn domain-unrelated features across extreme changing appearance, where a domain denotes a specific appearance condition, such as a season or a kind of weather. We use an adversarial network with two discriminators to disentangle domain-related features and domain-unrelated features from images, and the domain-unrelated features are used as descriptors in place recognition. Provided images from different domains, our network is trained in a self-supervised manner which does not require correspondences between these domains. Besides, our feature extractors are shared among all domains, making it possible to contain more appearance without increasing model complexity. Qualitative and quantitative results on two toy cases are presented to show that our network can disentangle domain-related and domain-unrelated features from given data. Experiments on three public datasets and one proposed dataset for visual place recognition are conducted to illustrate the performance of our method compared with several typical algorithms. Besides, an ablation study is designed to validate the effectiveness of the introduced discriminators in our network. Additionally, we use a four-domain dataset to verify that the network can extend to multiple domains with one model while achieving similar performance. [ABSTRACT FROM AUTHOR]

El principio de igualdad de armas es un mandato esencial que consiste en que “cada parte debe tener una oportunidad razonable para presentar su caso en condiciones que no la pongan en desventaja con respecto a su oponente”1. Esta figura, a pesar de su connotada y evidente importancia, no ha sido objeto de estudio profundo y serio en la doctrina y la jurisprudencia colombianas. Lo anterior ha permitido un uso irresponsable y equivocado de este concepto, y es de ahí que nace la necesidad de este artículo, el cual tiene por objeto, sin pretender ser exhaustivo, dar una mayor claridad sobre este mandato de op­timización, empezando por revisar sus antecedentes; pasando por el análisis de cada uno de sus elementos esenciales; la referencia a algunas categorías desarrolladas en la jurisdicción internacional que son de suma importancia para alcanzar un entendimiento íntegro de dicho principio; el señalamiento de las diferentes posiciones sobre su fundamento, definición y alcance; y una diferenciación con otros derechos y principios con los que suele confundirse. De esta manera, se sentarán las bases para que, posteriormente, sea posible abrir un debate organizado acerca de esta figura que haga viable un ejercicio optimizado y responsable de ella.

This paper presents a client/server privacy-preserving network in the context of multicentric medical image analysis. Our approach is based on adversarial learning which encodes images to obfuscate the patient identity while preserving enough information for a target task. Our novel architecture is composed of three components: 1) an encoder network which removes identity-specific features from input medical images, 2) a discriminator network that attempts to identify the subject from the encoded images, 3) a medical image analysis network which analyzes the content of the encoded images (segmentation in our case). By simultaneously fooling the discriminator and optimizing the medical analysis network, the encoder learns to remove privacy-specific features while keeping those essentials for the target task. Our approach is illustrated on the problem of segmenting brain MRI from the large-scale Parkinson Progression Marker Initiative (PPMI) dataset. Using longitudinal data from PPMI, we show that the discriminator learns to heavily distort input images while allowing for highly accurate segmentation results. Our results also demonstrate that an encoder trained on the PPMI dataset can be used for segmenting other datasets, without the need for retraining. The code is made available at: https://github.com/bachkimn/Privacy-Net-An-Adversarial-Approach-forIdentity-Obfuscated-Segmentation-of-MedicalImages [ABSTRACT FROM AUTHOR]

Explaining cases of stopping and dropping the litigation and their reasons in the Jordanian Code of Procedure, the problematic of this study is to explain the stopping of the Jordanian legislator from cases of stopping and dropping the litigation and the adequacy of the legal texts regulating it in Jordanian legislation. The two researchers followed the descriptive and analytical approach in analyzing legal texts, particularly the texts of the Jordanian Code of Civil Procedure, the jurisprudence, and the opinions of the jurists. Likewise, the comparative approach, whenever necessary, in order to enrich the study, and the researchers reached a number of findings and recommendations, and the most important result was: that the litigation may be intercepted by situations that lead to affecting the course of its outcome, such as endowment and omission. The researchers recommended the necessity of expanding the study of the judicial litigation and what influences it in the cases of stopping and abrogation, due to its importance in determining the legal positions of the parties. [ABSTRACT FROM AUTHOR]

We derive an algorithm that achieves the optimal (within constants) pseudo-regret in both adversarial and stochastic multi-armed bandits without prior knowledge of the regime and time horizon.1 The algorithm is based on online mirror descent (OMD) with Tsallis entropy regularization with power = 1=2 and reduced-variance loss estimators. More generally, we define an adversarial regime with a self-bounding constraint, which includes stochastic regime, stochastically constrained adversarial regime (Wei and Luo, 2018), and stochastic regime with adversarial corruptions (Lykouris et al., 2018) as special cases, and show that the algorithm achieves logarithmic regret guarantee in this regime and all of its special cases simultaneously with the optimal regret guarantee in the adversarial regime. The algorithm also achieves adversarial and stochastic optimality in the utility-based dueling bandit setting. We provide empirical evaluation of the algorithm demonstrating that it significantly outperforms Ucb1 and Exp3 in stochastic environments. We also provide examples of adversarial environments, where Ucb1 and Thompson Sampling exhibit almost linear regret, whereas our algorithm suers only logarithmic regret. To the best of our knowledge, this is the first example demonstrating vulnerability of Thompson Sampling in adversarial environments. Last but not least, we present a general stochastic analysis and a general adversarial analysis of OMD algorithms with Tsallis entropy regularization for 2 [0; 1] and explain the reason why = 1=2 works best. [ABSTRACT FROM AUTHOR]

Owing to the advantages of low storage cost and high query efficiency, cross-modal hashing has received increasing attention recently. As failing to bridge the inherent modality gap between modalities, most existing cross-modal hashing methods have limited capability to explore the semantic consistency information between different modality data, leading to unsatisfactory search performance. To address this problem, we propose a novel deep hashing method named Multi-Task Consistency-Preserving Adversarial Hashing (CPAH) to fully explore the semantic consistency and correlation between different modalities for efficient cross-modal retrieval. First, we design a consistency refined module (CR) to divide the representations of different modality into two irrelevant parts, i.e., modality-common and modality-private representations. Then, a multi-task adversarial learning module (MA) is presented, which can make the modality-common representation of different modalities close to each other on feature distribution and semantic consistency. Finally, the compact and powerful hash codes can be generated from modality-common representation. Comprehensive evaluations conducted on three representative cross-modal benchmark datasets illustrate our method is superior to the state-of-the-art cross-modal hashing methods. [ABSTRACT FROM AUTHOR]

Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very efficient. In an up-to-date comparison of state-of-the-art classification algorithms in tabular data, tree boosting outperforms deep learning (Zhang et al. in Expert Syst Appl 82:128–150, 2017). For this reason, we have developed a novel approach of adversarial gradient tree boosting. The objective of the algorithm is to predict the output Y with gradient tree boosting while minimizing the ability of an adversarial neural network to predict the sensitive attribute S. The approach incorporates at each iteration the gradient of the neural network directly in the gradient tree boosting. We empirically assess our approach on four popular data sets and compare against state-of-the-art algorithms. The results show that our algorithm achieves a higher accuracy while obtaining the same level of fairness, as measured using a set of different common fairness definitions. [ABSTRACT FROM AUTHOR]

Compared to facial expression recognition, expression synthesis requires a very high-dimensional mapping. This problem exacerbates with increasing image sizes and limits existing expression synthesis approaches to relatively small images. We observe that facial expressions often constitute sparsely distributed and locally correlated changes from one expression to another. By exploiting this observation, the number of parameters in an expression synthesis model can be significantly reduced. Therefore, we propose a constrained version of ridge regression that exploits the local and sparse structure of facial expressions. We consider this model as masked regression for learning local receptive fields. In contrast to the existing approaches, our proposed model can be efficiently trained on larger image sizes. Experiments using three publicly available datasets demonstrate that our model is significantly better than ℓ 0 , ℓ 1 and ℓ 2 -regression, SVD based approaches, and kernelized regression in terms of mean-squared-error, visual quality as well as computational and spatial complexities. The reduction in the number of parameters allows our method to generalize better even after training on smaller datasets. The proposed algorithm is also compared with state-of-the-art GANs including Pix2Pix, CycleGAN, StarGAN and GANimation. These GANs produce photo-realistic results as long as the testing and the training distributions are similar. In contrast, our results demonstrate significant generalization of the proposed algorithm over out-of-dataset human photographs, pencil sketches and even animal faces. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is one of the hottest topics in our society, especially when it comes to solving data-analysis problems. Industry are conducting their digital shifts, and AI is becoming a cornerstone technology for making decisions out of the huge amount of (sensors-based) data available in the production floor. However, such technology may be disappointing when deployed in real conditions. Despite good theoretical performances and high accuracy when trained and tested in isolation, a Machine-Learning (M-L) model may provide degraded performances in real conditions. One reason may be fragility in treating properly unexpected or perturbed data. The objective of the paper is therefore to study the robustness of seven M-L and Deep-Learning (D-L) algorithms, when classifying univariate time-series under perturbations. A systematic approach is proposed for artificially injecting perturbations in the data and for evaluating the robustness of the models. This approach focuses on two perturbations that are likely to happen during data collection. Our experimental study, conducted on twenty sensors’ datasets from the public University of California Riverside (UCR) repository, shows a great disparity of the models’ robustness under data quality degradation. Those results are used to analyse whether the impact of such robustness can be predictable—thanks to decision trees—which would prevent us from testing all perturbations scenarios. Our study shows that building such a predictor is not straightforward and suggests that such a systematic approach needs to be used for evaluating AI models’ robustness.

El presente documento tiene como propósito efectuar una aproximación, desde el punto de vista jurisprudencial y legal, del desarrollo que ha tenido la prueba oficiosa en el sistema penal adversarial de tendencia acusatoria, implementado en Colombia con la Ley 906 de 2004, a fin de establecer la tensión existente entre los principios de imparcialidad, igualdad de armas y legalidad, frente al principio de justicia material y en el contexto del rol del juez en el proceso. En efecto, esta es una de las discusiones que enfrentan quienes administran justicia y quienes acuden a ella para resolver los conflictos sociales, en la que se evidencia que la construcción de un sistema acusatorio es un proceso inacabado y en constante evolución. Para ello se hace necesario abordar no solo un concepto meramente legalista, sino que se requiere recabar en la filosofía misma del sistema penal acusatorio y las realidades en que este se aplica, para cumplir los cometidos sociales para los cuales fue creado.

Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control systems and machinery, these adversarial errors could grow to be a serious problem. Despite numerous studies over the past few years, the field of adversarial ML is still considered alchemy, with no practical unbroken defenses demonstrated to date, leaving PHM practitioners with few meaningful ways of addressing the problem. We introduce turbidity detection as a practical superset of the adversarial input detection problem, coping with adversarial campaigns rather than statistically invisible one-offs. This perspective is coupled with ROCtheoretic design guidance that prescribes an inexpensive domain adaptation layer at the output of a deep learning model during an attack campaign. The result aims to approximate the Bayes optimal mitigation that ameliorates the detection model’s degraded health. A proactively reactive type of prognostics is achieved via Monte Carlo simulation of various adversarial campaign scenarios, by sampling from the model’s own turbidity distribution to quickly deploy the correct mitigation during a real-world campaign.

International criminal tribunals have procedural rules of both an adversarial and inquisitorial nature. Such tribunals portray that it is possible to amalgamate what are often thought of as dichotomous models. This essay seeks to show that these are, in fact, not dichotomous models but are increasingly converging in both national and international judicial systems. The reasons for the adversarial/inquisitorial distinction being a redundant conversation are threefold: firstly, there is no longer a “pure” adversarial or inquisitorial system, with national judicial systems increasingly incorporating elements of both; secondly, the norms of human rights necessitate the convergence of the two models; and finally, the unique context and goals of international justice mean that the perceived dichotomy of the adversarial/inquisitorial system is no longer relevant. As a result, international criminal tribunals portray that it is possible to incorporate elements of both systems into a single judicial system.

Observing the modernization of judicial systems from the point of view of the administration leads to obtaining results that are difficult to find in the legal literature. We propose to study the Santa Fe penal system, in which an adversarial process has been implemented, which is characterized by the development of hearings, where the parties perform their role orally and, mainly, the judge makes his decision orally. In order to specify each of the oral hearings, the legal framework created an administrative organization, separate from the jurisdictional one and endowed with specific functions, which help and provide support to the judges in their work. Analyzing the number of resources that are available and the degree of use made of them to hold a certain volume of oral hearings, will allow us to know about the use of the production factors involved in the criminal process, which has as result is the total number of hearings held. In our study on criminal justice in Santa Fe, specifically in the jurisdictions of the Santa Fe and Rosario headquarters, for the period 2014 to 2018, we have found quantitative evidence of a decrease in average productivity with greater resource allocation, despite growing production total audiences. In other words, the increase in the number of personnel or the construction of a greater number of courtrooms, although they positively affect total production, shows a decrease in their average performance. [ABSTRACT FROM AUTHOR]

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The aim of this article is to emphasize the main features of the Plea Agreement procedure in the European traditional systems, common law and civil law, as well as the features of this concept as it has been implemented into the proceedings of some European countries and, accordingly, to analyse the reasons for which, the expertise of these already implemented procedures might be a pathway to solve many shortcomings of the national jurisdictions. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) architecture based models have high expressive power and learning capacity. However, they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation. Realizing this, many researchers have started to design methods to exploit the drawbacks of deep learning based algorithms questioning their robustness and exposing their singularities. In this paper, we attempt to unravel three aspects related to the robustness of DNNs for face recognition: (i) assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks, (ii) detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and (iii) making corrections to the processing pipeline to alleviate the problem. Our experimental evaluation using multiple open-source DNN-based face recognition networks, and three publicly available face databases demonstrates that the performance of deep learning based face recognition algorithms can suffer greatly in the presence of such distortions. We also evaluate the proposed approaches on four existing quasi-imperceptible distortions: DeepFool, Universal adversarial perturbations, l 2 , and Elastic-Net (EAD). The proposed method is able to detect both types of attacks with very high accuracy by suitably designing a classifier using the response of the hidden layers in the network. Finally, we present effective countermeasures to mitigate the impact of adversarial attacks and improve the overall robustness of DNN-based face recognition. [ABSTRACT FROM AUTHOR]

A key limitation of deep convolutional neural network (DCNN)-based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.g., MRI) are able to train a segmentation network for another imaging modality (e.g., CT). In this paper, we propose an end-to-end synthetic segmentation network (SynSeg-Net) to train a segmentation network for a target imaging modality without having manual labels. SynSeg-Net is trained by using: 1) unpaired intensity images from source and target modalities and 2) manual labels only from source modality. SynSeg-Net is enabled by the recent advances of cycle generative adversarial networks and DCNN. We evaluate the performance of the SynSeg-Net on two experiments: 1) MRI to CT splenomegaly synthetic segmentation for abdominal images and 2) CT to MRI total intracranial volume synthetic segmentation for brain images. The proposed end-to-end approach achieved superior performance to two-stage methods. Moreover, the SynSeg-Net achieved comparable performance to the traditional segmentation network using target modality labels in certain scenarios. The source code of SynSeg-Net is publicly available. 1 https://github.com/MASILab/SynSeg-Net [ABSTRACT FROM AUTHOR]