Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive Black-box Defense against Trojan Attacks (TrojDef)

Trojan backdoor is a poisoning attack against neural network (NN) classifiers in which adversaries try to exploit the (highly desirable) model reuse property to implant Trojans into model parameters for backdoor breaches through a poisoned training process. To misclassify an input to a target class, the attacker activates the backdoor by augmenting the input with a predefined trigger that is only known to her/him. Most of the proposed defenses against Trojan attacks assume a white-box setup, in which the defender either has access to the inner state of NN or is able to run backpropagation through it. In this work, we propose a more practical black-box defense, dubbed TrojDef. In a black-box setup, the defender can only run forward-pass of the NN. TrojDef is motivated by the Trojan poisoned training, in which the model is trained on both benign and Trojan inputs. TrojDef tries to identify and filter out Trojan inputs (i.e., inputs augmented with the Trojan trigger) by monitoring the changes in the prediction confidence when the input is repeatedly perturbed by random noise. We derive a function based on the prediction outputs which is called the prediction confidence bound to decide whether the input example is Trojan or not. The intuition is that Trojan inputs are more stable as the misclassification only depends on the trigger, while benign inputs will suffer when augmented with noise due to the perturbation of the classification features. Through mathematical analysis, we show that if the attacker is perfect in injecting the backdoor, the Trojan infected model will be trained to learn the appropriate prediction confidence bound, which is used to distinguish Trojan and benign inputs under arbitrary perturbations. However, because the attacker might not be perfect in injecting the backdoor, we introduce a nonlinear transform to the prediction confidence bound to improve the detection accuracy in practical settings. Extensive empirical evaluations show that TrojDef significantly outperforms the-state-of-the-art defenses and is highly stable under different settings, even when the classifier architecture, the training process, or the hyperparameters change.
Guidelines to Develop Trustworthy Conversational Agents for Children

Conversational agents (CAs) embodied in speakers or chatbots are becoming very popular in some countries, and despite their adult-centred design, they have become part of children’s lives, generating a need for children-centric trustworthy systems. This paper presents a literature review t o identify the main opportunities, challenges and risks brought by CAs when used by children. We then consider relevant ethical guidelines for AI and adapt them to this particular system and population, using a Delphi methodology with a set of experts from different disciplines. From this analysis, we propose specific guidelines to help CAs developers improve their design towards trustworthiness and children.
The Unusual Active Galaxy H1821+643 and the Elusive Nature of FRI Quasars

The moderate spin estimate for the black hole at the center of the cool core cluster H1821+643 motivates the completion of a story about this object’s origin and evolution that was in the making since the work by Blundell & Rawlings over two decades ago as the first example of a massive black hole accreting at near-Eddington rates with an FRI jet. This elusive combination of properties was explained in our 2010 model where we showed it to be part of a small parameter space that includes X-shaped radio galaxies. As an accreting black hole that never experienced a counterrotating phase, H1821+643 is constrained by theory to produce a jet for spin values a satisfying 0.1 < a < ∼ 0.7 and an FRI jet for a slightly smaller range. The feedback from such a black hole is not subject to a tilted jet and is why star formation rates remain high in this cluster environment. The prediction is that H1821+643 is within millions of years of becoming jetless.
FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures

Fuzzing has been an important approach for finding bugs and vulnerabilities in programs. Many fuzzers deployed in industry run daily and can generate an overwhelming number of crashes. Diagnosing such crashes can be very challenging and time-consuming. Existing fuzzers typically employ heuristics such as code coverage or call stack hashes to weed out duplicate reporting of bugs. While these heuristics are cheap, they are often imprecise and end up still reporting many “unique” crashes corresponding to the same bug. In this paper, we present FuzzerAid that uses fault signatures to group crashes reported by the fuzzers. Fault signature is a small executable program and consists of a selection of necessary statements from the original program that can reproduce a bug. In our approach, we first generate a fault signature using a given crash. We then execute the fault signature with other crash inducing inputs. If the failure is reproduced, we classify the crashes into the group labeled with the fault signature; if not, we generate a new fault signature. After all the crash inducing inputs are classified, we further merge the fault signatures of the same root cause into a group. We implemented our approach in a tool called FuzzerAid and evaluated it on 3020 crashes generated from 15 real-world bugs and 4 large open source projects. Our evaluation shows that we are able to correctly group 99.1% of the crashes and reported only 17 (+2) “unique” bugs, outperforming the state-of-the-art fuzzers.
Fermi‐Bose Mixtures in Three‐Dimensional Optical Lattices

In this article, we summarize our recent results on Fermi‐Bose mixtures in 3D optical lattices. We present the first measurements on Fermi‐Bose quantum gas mixtures in a 3D optical lattice as well as the first realization of ultracold heteronuclear molecules in this lattice by means of Feshbach‐tuning and rf‐association.
4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement

Image enhancement aims at improving the aesthetic visual quality of photos by retouching the color and tone, and is an essential technology for professional digital photography. Recent years deep learning-based image enhancement algorithms have achieved promising performance and attracted increasing popularity. However, typical efforts attempt to construct a uniform enhancer for all pixels’ color transformation. It ignores the pixel differences between different content (e.g., sky, ocean, etc.) that are significant for photographs, causing unsatisfactory results. In this paper, we propose a novel learnable context-aware 4-dimensional lookup table (4D LUT), which achieves content-dependent enhancement of different contents in each image via adaptively learning of photo context. In particular, we first introduce a lightweight context encoder and a parameter encoder to learn a context map for the pixel-level category and a group of image-adaptive coefficients, respectively. Then, the context-aware 4D LUT is generated by integrating multiple basis 4D LUTs via the coefficients. Finally, the enhanced image can be obtained by feeding the source image and context map into fused context-aware 4D LUT via quadrilinear interpolation. Compared with traditional 3D LUT, i.e., RGB mapping to RGB, which is usually used in camera imaging pipeline systems or tools, 4D LUT, i.e., RGBC(RGB+Context) mapping to RGB, enables finer control of color transformations for pixels with different content in each image, even though they have the same RGB values. Experimental results demonstrate that our method outperforms other state-of-the-art methods in widely-used benchmarks.
Lattice-Based Shape Tracking and Servoing of Elastic Objects

In this article, we propose a general unified tracking-servoing approach for controlling the shape of elastic deformable objects using robotic arms. Our approach works by forming a lattice around the object, binding the object to the lattice, and tracking and servoing the lattice instead of the object. This makes our approach have full control over the deformation of elastic deformable objects of any general form (linear, thin-shell, and volumetric) in 3-D space. Furthermore, it decouples the runtime complexity of the approach from the objects' geometric complexity. Our approach is based on the as-rigid-as-possible deformation model. It requires no mechanical parameter of the object to be known and can drive the object toward desired shapes through large deformations. The inputs to our approach are the point cloud of the object's surface in its rest shape and the point cloud captured by a 3-D camera in each frame. Overall, our approach is more broadly applicable than existing approaches. We validate the efficiency of our approach through numerous experiments with elastic deformable objects of various shapes and materials (paper, rubber, plastic, and foam).
A negative imaginary approach to hybrid integrator-gain system control

In this paper, we show that a hybrid integrator-gain system (HIGS) is a nonlinear negative imaginary (NNI) system. We prove that the positive feedback interconnection of a linear negative imaginary (NI) system and a HIGS is asymptotically stable. We apply the HIGS to a MEMS nanopositioner, as an example of a linear NI system, in a single-input single-output framework. We analyze the stability and the performance of the closed-loop interconnection in both time and frequency domains through simulations and demonstrate the applicability of HIGS as an NNI controller to a linear NI system.
Communication Efficient Distributed Learning Over Wireless Channels

Vertically distributed learning exploits the local features collected by multiple learning workers to form a better global model. However, data exchange between the workers and the model aggregator for parameter training incurs a heavy communication burden, especially when the learning system is built upon capacity-constrained wireless networks. In this letter, we propose a novel hierarchical distributed learning framework, where each worker separately learns a low-dimensional embedding of their local observed data. Then, they perform communication-efficient distributed max-pooling to efficiently transmit the synthesized input to the aggregator. For data exchange over a shared wireless channel, we propose an opportunistic carrier sensing-based protocol to implement the max-pooling of the output of all the workers. Our simulation experiments show that the proposed learning framework is able to achieve almost the same model accuracy as the learning model using the concatenation of all the raw outputs from the learning workers while significantly reducing the communication load.
Natural Policy Gradients In Reinforcement Learning Explained

Traditional policy gradient methods are fundamentally flawed. Natural gradients converge quicker and better, forming the foundation of contemporary Reinforcement Learning such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). This lecture note aims to clarify the intuition behind natural policy gradients, focusing on the thought process and the key mathematical constructs.
Volumetric video streaming: Current approaches and implementations

The rise of capturing systems for objects and scenes in 3D with increased fidelity and immersion has led to the popularity of volumetric video contents that can be seen from any position and angle in 6 degrees of freedom navigation. Such contents need large volumes of data to accurately represent the real world. Thus, novel optimization solutions and delivery systems are needed to enable volumetric video streaming over bandwidth-limited networks. In this chapter, we discuss theoretical approaches to volumetric video streaming optimization, through compression solutions, as well as network and user adaptation, for high-end and low-powered devices. Moreover, we present an overview of existing end-to-end systems, and we point to the future of volumetric video streaming.
Learning to Predict on Octree for Scalable Point Cloud Geometry Coding

Octree-based point cloud representation and compression have been adopted by the MPEG G-PCC standard. However, it only uses handcrafted methods to predict the probability that a leaf node is non-empty, which is then used for entropy coding. We propose a novel approach for predicting such probabilities for geometry coding, which applies a denoising neural network to a “noisy” context cube that includes both neighboring decoded voxels as well as uncoded voxels. We further propose a convolution-based model to upsample the decoded point cloud at a coarse resolution on the decoder side. Integration of the two approaches significantly improves the rate-distortion performance for geometry coding compared to the original G-PCC standard and other baseline methods for dense point clouds. The proposed octree-based entropy coding approach is naturally scalable, which is desirable for dynamic rate adaptation in point cloud streaming systems.
Optimized Precoding for MU-MIMO With Fronthaul Quantization

One of the first widespread uses of multi-user multiple-input multiple-output (MU-MIMO) is in 5G networks, where each base station has an advanced antenna system (AAS) that is connected to the baseband unit (BBU) with a capacity-constrained fronthaul. In the AAS configuration, multiple passive antenna elements and radio units are integrated into a single box. This paper considers precoded downlink transmission over a single-cell MU-MIMO system. We study optimized linear precoding for AAS with a limited-capacity fronthaul, which requires the precoding matrix to be quantized. We propose a new precoding design that is aware of the fronthaul quantization and minimizes the mean-squared error at the receiver side. We compute the precoding matrix using a sphere decoding (SD) approach. We also propose a heuristic low-complexity approach to quantized precoding. This heuristic is computationally efficient enough for massive MIMO systems. The numerical results show that our proposed precoding significantly outperforms quantization-unaware precoding and other previous approaches in terms of the sum rate. The performance loss for our heuristic method compared to quantization-aware precoding is insignificant considering the complexity reduction, which makes the heuristic method feasible for real-time applications. We consider both perfect and imperfect channel state information (CSI).
Continual Learning for Steganalysis

To detect the existing steganographic algorithms, recent steganalysis methods usually train a Convolutional Neural Network (CNN) model on the dataset consisting of corresponding paired cover/stego-images. However, it is inefficient and impractical for those steganalysis tools to completely retrain the CNN model to make it effective against both the existing steganographic algorithms and a new emerging steganographic algorithm. Thus, existing steganalysis models usually lack dynamic extensibility for new steganographic algorithms, which limits their application in real-world scenarios. To address this issue, we propose an accurate parameter importance estimation (APIE) based-continual learning scheme for steganalysis. In this scheme, when a steganalysis model is trained on the new image dataset generated by the new steganographic algorithm, its network parameters are effectively and efficiently updated with sufficient consideration of their importance evaluated in the previous training process. This approach can guide the steganalysis model to learn the patterns of the new steganographic algorithm without significantly degrading the detectability against the previous steganographic algorithms. Experimental results demonstrate the proposed scheme has promising extensibility for new emerging steganographic algorithms.
Ferromagnetism in nanoscale BiFeO3

A remarkably high saturation magnetization of ∼0. 4μB∕Fe along with room temperature ferromagnetic hysteresis loop has been observed in nanoscale (4–40nm) multiferroic BiFeO3 which in bulk form exhibits weak magnetization (∼0.02μB∕Fe) and an antiferromagnetic order. The magnetic hysteresis loops exhibit exchange bias and vertical asymmetry which could be because of spin pinning at the boundaries between ferromagnetic and antiferromagnetic domains. Interestingly, both the calorimetric and dielectric permittivity data in nanoscale BiFeO3 exhibit characteristic features at the magnetic transition point. These features establish the formation of a true ferromagnetic-ferroelectric system with a coupling between the respective order parameters in nanoscale BiFeO3.
Lemma Hunting: Automatic Spelling Normalization for German CMC Corpora

This paper presents and evaluates a method for automatic orthographic normalization and the treatment of out-of-vocabulary words (OOV) in German social media data. The system uses a cascade of spellchecking operations including casing-, sound-and keyboard-based letter permutations, as well as letter context likelihoods, and combines partial and root spellchecking with compound analysis and heuristic inflection analysis in novel ways. The system also handles contractions, elisions and some tokenization errors. In addition, pattern-based recognition of foreign words and abbreviations is attempted, supported by jargon-informed lexicon expansion. Contextual Constraint Grammar (CG) disambiguation is used to resolve possible ambiguity. For Twitter data, F-scores of 87.3 and 77.1 were achieved for the identification and correct lemmatization, respectively, of German spelling errors and non-standard abbreviations. 77.6% of foreign words were recognized with 86.5% precision and 1/3 POS errors.
Improved Opinion Role Labelling in Parliamentary Debates

This paper presents a model for German Opin-ion Role Labelling (ORL), using the data from the IGGSA-STEPS 2014 and 2016 shared tasks. We frame the problem as a token classi-ﬁcation task and employ a simple transformer-based model that achieves new state-of-the-art results on the data. Then we investigate whether we can further improve our model by transferring knowledge from a related task, i.e., Semantic Role Labelling. Our results show that, despite the small size of our data, this transfer learning step yields further improvements for ORL, mostly regarding recall for target prediction. Finally, we present an error analysis, showing where knowledge transfer from SRL can help and what is still difﬁcult for German ORL.
Evaluation of Automatic Speech Recognition for Conversational Speech in Dutch, English and German: What Goes Missing?

As voice user interfaces and conversational agents grow in importance, automatic speech recognition (ASR) encounters increasingly free-form and informal input data. Conversa-tional speech is at once the most challenging and the most ecologically relevant type of data for speech recognition in this context. Here we evaluate the performance of several ASR engines on conversational speech in three languages, focusing on the fate of backchannels and other interactionally relevant elements of talk. We propose forms of error analysis based on ngram salience scoring that can complement default measures like word error rates (WER) and are more informative of ASR’s ability to live up to the task of accurately representing real-world interaction.
Overview of the GermEval 2022 Shared Task on Text Complexity Assessment of German Text

In this paper we present the GermEval 2022 shared task on Text Complexity Assessment of German text. Text forms an integral part of exchanging information and interacting with the world, correlating with quality and experience of life. Text complexity is one of the factors which affects a reader’s understanding of a text. The mapping of a body of text to a mathematical unit quantifying the degree of readability is the basis of complexity assessment. As readability might be influenced by representation, we only target the text complexity for readers in this task. We designed the task as text regression in which participants developed models to predict complexity of pieces of text for a German learner in a range from 1 to 7. The shared task is organized in two phases; the development and the test phases. Among 24 participants who registered for the shared task, ten teams submitted their results on the test data.
Modelling Cultural and Socio-Economic Dimensions of Political Bias in German Tweets

We introduce a new bi-dimensional classification scheme for political bias. In particular, we collaborate with political scientists and identify two important aspects: cultural and socio-economic positions. Using a dataset of tweets by German politicians, we show that the new scheme draws more distinctive boundaries that are easier to model for machine learning classifiers (F1 scores: 0.92 and 0.86), compared to one-dimensional approaches. We investigate the validity by applying the new classifiers to the whole dataset, including previously unseen data from other parties. Additional experiments highlight the importance of dataset size and balance, as well as the superior performance of transformer language models as opposed to older methods. Finally, an extensive error analysis confirms our hypothesis that lexical overlap, in combination with high attention values, is a reliable empirical predictor of misclassification for political bias.
Adapting GermaNet for the Semantic Web

GermaNet 1 (Hamp and Feldweg, 1997) is a lexical-semantic net that relates German nouns, verbs, and adjectives semantically. For this purpose, it groups lexical units that express the same concept into synsets and it de-ﬁnes semantic relations between them. Ger-maNet has been developed since 1997, and its most recent edition contains over 200,000 lexical units and about 160,000 synsets. The GermaNet resource is of high quality as all its entries have been manually entered with great care. GermaNet has been linked with the In-terLingual Index and with Wiktionary , and it is our goal to increase such linkage with other resources such as the Leipzig Corpora Collection and the DWDS-W¨orterbuch . For this purpose, GermaNet is converted to RDF, a format that facilitates the interlinking of data sources signiﬁcantly.
Do gender neutral affixes naturally reduce gender bias in static word embeddings?

In German, substituting gendered role nouns with gender neutral versions, known as gendergerechte Sprache , has rapidly been gaining ground, with the primary aim being the inclu-sion of non-male people. Its effectiveness, how-ever, has not been conclusively demonstrated. Previously, word embeddings have been shown to contain gender biases similar to natural language. They thus can be used to measure whether this practice impacts gender association of role nouns. Methods of debiasing pretrained word embeddings have been devised, but their effectiveness in German, especially compared to gendergerechte Sprache , has not been tested. In this paper, we systematically compare two methods of gender neutral affixation to a base corpus to examine the effect on gender bias of role nouns. We also compare the gender biases of analogy resolutions generated with embeddings trained on the base corpus, on the base corpus after undergoing an established post-hoc debiasing method, and the corpus after introduction of gender neutral affixation. Our results show a mixed picture: affixation leads to increased gender bias of role nouns, but decreased gender bias of generated analogy resolutions, even outperforming post-hoc debiasing methods.