bluuebunny/crossref_metadata_2025_split huggingface dataset:
On the Evaluation of Climate Change Impact Models for Adaptation Decisions
Authors: Wagener Thorsten 
Published: January 2022

AbstractDetailed understanding of the potential local or regional implications of climate change is required to guide decision- and policy-makers when developing adaptation strategies and designing infrastructure solutions suitable for potential future conditions. Impact models that translate potential future climate conditions into variables of interest (such as drought or flood risk) are needed to create the required causal connection between climate and impact for scenario-based analyses. Recent studies suggest that the main strategy for the validation of such models (and hence the justification for their use) still heavily relies on the comparison with historical observations. In this short paper, the author suggests that such a comparison alone is insufficient and that global sensitivity analysis provides additional possibilities for model evaluation to ensure greater transparency and better robustness of model-based analyses. Global sensitivity analysis can be used to demonstrate that the parameters defining intervention options (such as land use choices) adequately control the model output (even under potential future conditions); it can be used to understand the robustness of model outputs to input uncertainties over different projection horizons, the relevance of model assumptions, and how modelled environmental processes change with climatic boundary conditions. Such additional model evaluation would strengthen the stakeholder confidence in model projections and therefore into the adaptation strategies derived with the help of these model outputs.
Modelling Risk Reduction Measures to Minimise Future Impacts of Storms at Coastal Areas
Authors: Ferreira Óscar 
Published: January 2022

AbstractCoastal storms often cause damages and losses in occupied areas. Under climate change conditions (i.e. sea-level rise and increased frequency of extreme sea levels) and increasing human occupation, the consequences of coastal storms will be amplified if no adaptation actions are implemented. The selection of the best possible coastal management measures to reduce risks at coastal areas, considering costs, effectiveness and acceptance, will be mandatory in the future. This work presents a generic approach to model disaster risk reduction measures at coastal areas, including climate change effects. The proposed methodology is adaptable to any coastal region and can be used to test (and improve) management options at a broad number of coastal areas. It can also be used to define a timeframe for the implementation of the defined measures since not all risk reduction measures, under a climate change scenario, need to be implemented at the same time. This would help to optimise implementation costs while reducing the risk to the occupation and people.
3D CNN Architectures and Attention Mechanisms for Deepfake Detection
Authors: Roy Ritaban , Joshi Indu , Das Abhijit , Dantcheva Antitza 
Published: January 2022

AbstractManipulated images and videos have become increasingly realistic due to the tremendous progress of deep convolutional neural networks (CNNs). While technically intriguing, such progress raises a number of social concerns related to the advent and spread of fake information and fake news. Such concerns necessitate the introduction of robust and reliable methods for fake image and video detection. Toward this in this work, we study the ability of state-of-the-art video CNNs including 3D ResNet, 3D ResNeXt, and I3D in detecting manipulated videos. In addition, and toward a more robust detection, we investigate the effectiveness of attention mechanisms in this context. Such mechanisms are introduced in CNN architectures in order to ensure that robust features are being learnt. We test two attention mechanisms, namely SE-block and Non-local networks. We present related experimental results on videos tampered by four manipulation techniques, as included in the FaceForensics++ dataset. We investigate three scenarios, where the networks are trained to detect (a) all manipulated videos, (b) each manipulation technique individually, as well as (c) the veracity of videos pertaining to manipulation techniques not included in the train set.
Face Morphing Attack Detection Methods
Authors: Scherhag Ulrich , Rathgeb Christian , Busch Christoph 
Published: January 2022

AbstractMorphing attacks pose a serious threat to face recognition systems, especially in the border control scenario. In order to guarantee a secure operation of face recognition algorithms in the future, it is necessary to be able to reliably detect morphed facial images and thus be able to reject them during enrolment or verification. This chapter provides an overview of morphing attack detection algorithms and metrics to measure and compare their performance. Different concepts of morphing attack detection are introduced and state-of-the-art detection methods are evaluated in a comprehensive cross-database experiments considering various realistic image post-processings.
Practical Evaluation of Face Morphing Attack Detection Methods
Authors: Spreeuwers Luuk , Schils Maikel , Veldhuis Raymond , Kelly Una 
Published: January 2022

AbstractFace morphing is a technique to combine facial images of two (or more) subjects such that the result resembles both subjects. In a morphing attack, this is exploited by, e.g., applying for a passport with the morphed image. Both subjects who contributed to the morphed image can then travel using this passport. Many state-of-the-art face recognition systems are vulnerable to morphing attacks. Morphing attack detection (MAD) methods are developed to mitigate this threat. MAD methods published in literature are often trained on a limited number of or even a single dataset where all morphed faces are created using the same procedure. The resulting MAD methods work well for these specific datasets, with reported detection rates of over 99%, but their performance collapses for face morphs created using other procedures. Often even simple image manipulations, like adding noise or smoothing cause a serious degradation in performance of the MAD methods. In addition, more advanced tools exist to manipulate the face morphs, like manual retouching or morphing artifacts can be concealed by printing and scanning a photograph (as used in the passport application process in many countries). Furthermore, datasets for training and testing MAD methods are often created by morphing images from arbitrary subjects including even male-female morphs and morphs between subjects with different skin color. Although this may result in a large number of morphed faces, the created morphs are often not convincing and certainly don’t represent a best effort attack by a criminal. A far more realistic attack would include careful selection of subjects that look alike and create high quality morphs from images of these subjects using careful (manual) post-processing. In this chapter we therefore argue that for robust evaluation of MAD methods, we require datasets with morphed images created using a large number of different morphing methods, including various ways to conceal the morphing artifacts by, e.g., adding noise, smoothing, printing and scanning, various ways of pre- and post-processing, careful selection of the subjects and multiple facial datasets. We also show the sensitivity of various MAD methods to the mentioned variations and the effect of training MAD methods on multiple datasets.
Facial Retouching and Alteration Detection
Authors: Majumdar Puspita , Agarwal Akshay , Vatsa Mayank , Singh Richa 
Published: January 2022

AbstractOn the social media platforms, the filters for digital retouching and face beautification have become a common trend. With the availability of easy-to-use image editing tools, the generation of altered images has become an effortless task. Apart from this, advancements in the Generative Adversarial Network (GAN) leads to creation of realistic facial images and alteration of facial images based on the attributes. While the majority of these images are created for fun and beautification purposes, they may be used with malicious intent for negative applications such as deepnude or spreading visual fake news. Therefore, it is important to detect digital alterations in images and videos. This chapter presents a comprehensive survey of existing algorithms for retouched and altered image detection. Further, multiple experiments are performed to highlight the open challenges of alteration detection.
Face Manipulation Detection in Remote Operational Systems
Authors: Pic Marc Michel , Mahfoudi Gaël , Trabelsi Anis , Dugelay Jean-Luc 
Published: January 2022

AbstractIn this chapter, we present the various categories of Face Manipulation and their use within different remote operational systems. We then use the example of remote identity document onboarding systems to illustrate how each category can be used in practice to compromise such a system. After a definition of the different Face Manipulation categories and the common algorithms used to produce them, we go through the various manipulation detection algorithms and common image and video forgery datasets. We then introduce some known counter-forensics methods that can be used by an attacker to avoid detection. Knowing the detection methods and the counter-forensic, we present how we can build up a safer system by using the correct methods at the correct time. But also how knowledge about the tampering process could be used to design the user experience to make the systems harder to compromise. We complete this review by the standardisation effort and legal aspect on the matter. And we conclude by discussing the remaining challenges and perspectives for better use of nowadays detection methods in practical usage.
Promises, Social, and Ethical Challenges with Biometrics in Remote Identity Onboarding
Authors: Laas-Mikko Katrin , Kalvet Tarmo , Derevski Robert , Tiits Marek 
Published: January 2022

AbstractIssuance of identity documents has commonly relied on face-to-face customer onboarding. Checking a person's physical presence and appearance has been an essential part of identity enrolling procedures to avoid the risk of identity forgery. Yet, several weaknesses, including face morphing attacks, have been identified in document issuing processes. In the context of the COVID-19 pandemic, increasing international mobility, and a greater focus on user convenience, established onboarding rules and procedures have been disrupted. Solutions are being sought which would eliminate the barriers that stem from physical distance while offering at least equal or even better onboarding processes than in-person identity verification. Recently, novel remote onboarding solutions have appeared on the market. They vary from human-assisted video identification procedures to biometric-based automated verification procedures. The main social and ethical issues with biometrics in remote identity onboarding are (1) the risk of harming integrity of personal identity and misuse of it; (2) the risk of privacy invasion and function creep; (3) ethical issues that are raising from algorithmically driven actions and decisions; and (4) public perception and social acceptance of technology. These non-technical requirements need to be addressed in developing identity verification technologies based on biometrical algorithms and security techniques.
The Threat of Deepfakes to Computer and Human Visions
Authors: Korshunov Pavel , Marcel Sébastien 
Published: January 2022

AbstractDeepfake videos, where a person’s face is automatically swapped with a face of someone else, are becoming easier to generate with more realistic results. The concern for the impact of the widespread deepfake videos on the societal trust in video recordings is growing. In this chapter, we demonstrate how dangerous deepfakes are for both human and computer visions by showing how well these videos can fool face recognition algorithms and naïve human subjects. We also show how well the state-of-the-art deepfake detection algorithms can detect deepfakes and whether they can outperform humans.
Talking Faces: Audio-to-Video Face Generation
Authors: Wang Yuxin , Song Linsen , Wu Wayne , Qian Chen , He Ran , Loy Chen Change 
Published: January 2022

AbstractTalking face generation aims at synthesizing coherent and realistic face sequences given an input speech. The task enjoys a wide spectrum of downstream applications, such as teleconferencing, movie dubbing, and virtual assistant. The emergence of deep learning and cross-modality research has led to many interesting works that address talking face generation. Despite great research efforts in talking face generation, the problem remains challenging due to the need for fine-grained control of face components and the generalization to arbitrary sentences. In this chapter, we first discuss the definition and underlying challenges of the problem. Then, we present an overview of recent progress in talking face generation. In addition, we introduce some widely used datasets and performance metrics. Finally, we discuss open questions, potential future directions, and ethical considerations in this task.
Detection of AI-Generated Synthetic Faces
Authors: Gragnaniello Diego , Marra Francesco , Verdoliva Luisa 
Published: January 2022

AbstractIn recent years there have been astonishing advances in AI-based synthetic media generation. Thanks to deep learning methods it is now possible to generate visual data with a high level of realism. This is especially true for human faces. Advanced deep learning tools allow one to easily change some specific attributes of a real face or even create brand new identities. Although this opens up a large number of new opportunities, just think of the entertainment industry, it also undermines the trustworthiness of media content and supports the spread of fake identities over the internet. In this context, there is a fundamental need to develop robust and automatic tools capable of distinguishing synthetic faces from real ones. The scientific community is making a huge research effort in this field, proposing several interesting approaches. However, a universal detector is yet to come. Fundamentally, the research in this field is like a cat and mouse game, with new detectors that are designed to deal with powerful synthetic face generators, while the latter keep improving to produce more and more realistic images. In this chapter we will present the most effective techniques proposed in the literature for the detection of synthetic faces. We will analyze their rationale, present real-world application scenarios , and compare different approaches in terms of accuracy and generalization ability.
Understanding the Basics of Aviation Noise
Authors: Gély Denis , Márki Ferenc 
Published: January 2022

AbstractThis chapter deals with the description of the physical mechanisms of noise, the noise perception and the annoyance induced by air traffic in the aeronautics domain. The authors introduce the basics of aviation noise, describe the main characteristics of the noise emitted by an aircraft in flight, recall the fundamental laws of the audition and noise perception and present the specific context for the annoyance due to aviation noise. This chapter presents and details, as simply as possible, the complex relationships between physical phenomena and noise perception in order to highlight the key notions in aviation noise issues. The readers will find answers to many usual and legitimate questions, for example what is the relationship between the perceived noise and the level of the physical noise related to the European ACARE goals which are expressed sometimes as EPNL reduction in dB or sometimes as perceived noise reduction in percent.
Perspective on 25 Years of European Aircraft Noise Reduction Technology Efforts and Shift Towards Global Research Aimed at Quieter Air Transport
Authors: Kors Eugene , Collin Dominique 
Published: January 2022

AbstractThis article provides a perspective on 25 years of European aircraft noise reduction technology efforts as well as the gradual shift towards a more global research effort aimed at quieter air transport activity. It covers the following aspects:

Introduction - Background and general context of noise from air transport operations

European context – ACARE Strategic Research Agendas and establishment of the 2020 and 2050 aviation environmental goals

Phased strategy towards 2020 targets and beyond

A coordinated European aviation noise research effort

Assessment of progress relative to ACARE noise reduction targets

Addressing the longer-term objectives – Noise and the ACARE SRIA

Community building

Lessons learned
The ‘Management’ of Migration – And of the Resulting Irregularities
Authors: Schweitzer Reinhard 
Published: January 2022

AbstractThis chapter lays out the conceptual and analytical frameworks for my analysis and embeds it in the existing literature on the internalisation of immigration control and the role of implementing actors and their everyday practices. It starts with a critical discussion of ‘migration management’ and the limits of state control over contemporary migration. After conceptualising contemporary migrant irregularity and what I call the micro-management of it, I develop the theoretical and conceptual framework of my study, which combines a critical understanding of internal bordering processes with Foucauldian conceptualisations of power and governmentality as well as crucial insights from organisation studies. At the end of the chapter I explain my analytical framework and its application in detail.
Long-range fermions and critical dualities
Authors: Chai Noam , Chakraborty Soumangsu , Goykhman Mikhail , Sinha Ritam 
Published: January 2022

Abstract
We construct long-range fermionic models with the Gross-Neveu and Gross­Neveu-Yukawa interaction, and argue that their critical regimes are equivalent. To this end, we calculate various CFT data in ϵ- and 1/N-expansion, and demonstrate their agreement in the overlapping regimes of validity.
Inconsistency of islands in theories with long-range gravity
Authors: Geng Hao , Karch Andreas , Perez-Pardavila Carlos , Raju Suvrat , Randall Lisa , Riojas Marcos , Shashi Sanjit 
Published: January 2022

Abstract
In ordinary gravitational theories, any local bulk operator in an entanglement wedge is accompanied by a long-range gravitational dressing that extends to the asymptotic part of the wedge. Islands are the only known examples of entanglement wedges that are disconnected from the asymptotic region of spacetime. In this paper, we show that the lack of an asymptotic region in islands creates a potential puzzle that involves the gravitational Gauss law, independently of whether or not there is a non-gravitational bath. In a theory with long-range gravity, the energy of an excitation localized to the island can be detected from outside the island, in contradiction with the principle that operators in an entanglement wedge should commute with operators from its complement. In several known examples, we show that this tension is resolved because islands appear in conjunction with a massive graviton. We also derive some additional consistency conditions that must be obeyed by islands in decoupled systems. Our arguments suggest that islands might not constitute consistent entanglement wedges in standard theories of massless gravity where the Gauss law applies.
On the finite index subgroups of Houghton’s groups
Authors: Cox Charles Garnet 
Published: January 2022

AbstractHoughton’s groups $$H_2, H_3, \ldots $$

H
2

,

H
3

,
…

are certain infinite permutation groups acting on a countably infinite set; they have been studied, among other things, for their finiteness properties. In this note we describe all of the finite index subgroups of each Houghton group, and their isomorphism types. Using the standard notation that d(G) denotes the minimal size of a generating set for G, we then show, for each $$n\in \{2, 3,\ldots \}$$

n
∈
{
2
,
3
,
…
}

and U of finite index in $$H_n$$

H
n

, that $$d(U)\in \{d(H_n), d(H_n)+1\}$$

d

(
U
)

∈
{
d

(

H
n

)

,
d

(

H
n

)

+
1
}

and characterise when each of these cases occurs.
Impfstoffe gegen Hepatitis E: Wo stehen wir?
Authors: Behrendt Patrick , Wedemeyer Heiner 
Published: January 2022

ZusammenfassungIn Europa ist aktuell kein Impfstoff gegen das Hepatitis-E-Virus (HEV) zugelassen. Demgegenüber steht in China bereits seit 10 Jahren mit HEV-239 (Hecolin®, Xiamen Innovax Biotech Co., Xiamen, China) ein Vakzin gegen den HEV-Genotyp 4 zur Verfügung. Herausforderungen für die Entwicklung von Impfstoffen ergeben sich v. a. aus den Unterschieden zwischen den Genotypen bezüglich Verbreitung, Übertragungswege und Risikogruppen. Weitere Hindernisse sind die Umhüllung von HEV im Blut durch Wirtsmembranen, die Replikation in verschiedenen Organen außerhalb der Leber sowie schwächere Immunantworten in vulnerablen Gruppen. In diesem Artikel wird der aktuelle Stand der verfügbaren und in fortgeschrittener präklinischer Evaluation befindlichen Vakzine gegen HEV mit Fokus auf Strategien der Impfstoffentwicklung dargestellt. Herausforderungen und Limitationen werden beschrieben.Aktuelle Impfkandidaten fokussieren auf proteinbasierte Immunisierungen mit dem Ziel der Induktion von schützenden, neutralisierenden Antikörperantworten. Das Ziel der HEV-239-Zulassungsstudie mit mehr als 100.000 Studienteilnehmern war die Verhinderung von akuten symptomatischen Infektionen. Es ist jedoch unklar, inwieweit asymptomatische Infektionen durch das Vakzin verhindert wurden und ob es in Risikopatienten für einen komplizierten Verlauf, wie Patienten mit Leberzirrhose, Immunsupprimierten und Schwangeren, effektiv genug wirkt. Effiziente In-vitro-Modelle ermöglichen zunehmend die Entwicklung von monoklonalen neutralisierenden Antikörpern zur passiven Immunisierung oder Therapie.Zukünftige Vakzine sollten neben einem sehr guten Sicherheitsprofil eine eindeutige Protektion gegenüber allen Genotypen demonstrieren. Die Entwicklung einer effizienten passiven Immunisierungsstrategie, insbesondere für immunsupprimierte Personen, ist wünschenswert.
Oblique decision tree induction by cross-entropy optimization based on the von Mises–Fisher distribution
Authors: Bollwein Ferdinand , Westphal Stephan 
Published: January 2022

AbstractOblique decision trees recursively divide the feature space by using splits based on linear combinations of attributes. Compared to their univariate counterparts, which only use a single attribute per split, they are often smaller and more accurate. A common approach to learn decision trees is by iteratively introducing splits on a training set in a top–down manner, yet determining a single optimal oblique split is in general computationally intractable. Therefore, one has to rely on heuristics to find near-optimal splits. In this paper, we adapt the cross-entropy optimization method to tackle this problem. The approach is motivated geometrically by the observation that equivalent oblique splits can be interpreted as connected regions on a unit hypersphere which are defined by the samples in the training data. In each iteration, the algorithm samples multiple candidate solutions from this hypersphere using the von Mises–Fisher distribution which is parameterized by a mean direction and a concentration parameter. These parameters are then updated based on the best performing samples such that when the algorithm terminates a high probability mass is assigned to a region of near-optimal solutions. Our experimental results show that the proposed method is well-suited for the induction of compact and accurate oblique decision trees in a small amount of time.