Reviewerly/exHarmony huggingface dataset:
UPV at CheckThat! 2021: Mitigating Cultural Differences for Identifying Multilingual Check-worthy Claims

Identifying check-worthy claims is often the first step of automated fact-checking systems. Tackling this task in a multilingual setting has been understudied. Encoding inputs with multilingual text representations could be one approach to solve the multilingual check-worthiness detection. However, this approach could suffer if cultural bias exists within the communities on determining what is check-worthy.In this paper, we propose a language identification task as an auxiliary task to mitigate unintended bias.With this purpose, we experiment joint training by using the datasets from CLEF-2021 CheckThat!, that contain tweets in English, Arabic, Bulgarian, Spanish and Turkish. Our results show that joint training of language identification and check-worthy claim detection tasks can provide performance gains for some of the selected languages.
Towards robustness under occlusion for face recognition

In this paper, we evaluate the effects of occlusions in the performance of a face recognition pipeline that uses a ResNet backbone. The classifier was trained on a subset of the CelebA-HQ dataset containing 5,478 images from 307 classes, to achieve top-1 error rate of 17.91%. We designed 8 different occlusion masks which were applied to the input images. This caused a significant drop in the classifier performance: its error rate for each mask became at least two times worse than before. In order to increase robustness under occlusions, we followed two approaches. The first is image inpainting using the pre-trained pluralistic image completion network. The second is Cutmix, a regularization strategy consisting of mixing training images and their labels using rectangular patches, making the classifier more robust against input corruptions. Both strategies revealed effective and interesting results were observed. In particular, the Cutmix approach makes the network more robust without requiring additional steps at the application time, though its training time is considerably longer. Our datasets containing the different occlusion masks as well as their inpainted counterparts are made publicly available to promote research on the field.
A Format for a Plagiarism-Proof Online Examination for Calculus and Linear Algebra Using Microsoft Excel

As educational systems move from onsite to online due to the COVID-19 pandemic, teachers face the difficulty of designing online examination formats which minimise opportunities for dishonesty. In this paper, we expose our design of such a format: a protected Microsoft Excel spreadsheet containing short-answer questions, which was implemented in a calculus module taught by us. This format allows examiners to randomise questions with the aim that each student receives each question with different numerical details, making plagiarism impossible, while keeping the marking effort very low.
Creating Multi-Scripts Sentiment Analysis Lexicons for Algerian, Moroccan and Tunisian Dialects

In this article, we tackle the issue of sentiment analysis in three Maghrebi dialects used in social networks. More precisely, we are interested by analysing sentiments in Algerian, Moroccan and Tunisian corpora. To do this, we built automatically three lexicons of sentiments, one for each dialect. Each lexicon is composed of words with their polarities, a dialect word could be written in Arabic or in Latin scripts. These lexicons may include French or English words as well as words in Arabic dialect and standard Arabic. The semantic orientation of a word represented by an embedding vector is determined automatically by calculating its distance with several embedding seed words. The embedding vectors are trained on three large corpora collected from YouTube. The proposed approach is evaluated by using few existing annotated corpora in Tunisian and Moroccan dialects. For the Algerian dialect, in addition to a small corpus we found in the literature, we collected and annotated one composed of 10k comments extracted from Youtube. This corpus represents a valuable resource which is proposed for free.
Temporal Knowledge Graph Completion using Box Embeddings

Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where each fact is additionally associated with a time stamp. Current approaches for TKGC primarily build on existing embedding models which are developed for (static) knowledge graph completion, and extend these models to incorporate time, where the idea is to learn latent representations for entities, relations, and timestamps and then use the learned representations to predict missing facts at various time steps. In this paper, we propose BoxTE, a box embedding model for TKGC, building on the static knowledge graph embedding model BoxE. We show that BoxTE is fully expressive, and possesses strong inductive capacity in the temporal setting. We then empirically evaluate our model and show that it achieves state-of-the-art results on several TKGC benchmarks.
Macroscopic limits of chaotic eigenfunctions

We give an overview of the interplay between the behavior of high energy eigenfunctions of the Laplacian on a compact Riemannian manifold and the dynamical properties of the geodesic flow on that manifold. This includes the Quantum Ergodicity theorem, the Quantum Unique Ergodicity conjecture, entropy bounds, and uniform lower bounds on mass of eigenfunctions. The above results belong to the domain of quantum chaos and use microlocal analysis, which is a theory behind the classical/quantum, or particle/wave, correspondence in physics. We also discuss the toy model of quantum cat maps and the challenges it poses for Quantum Unique Ergodicity.
Knowledge is reward: Learning optimal exploration by predictive reward cashing

There is a strong link between the general concept of intelligence and the ability to collect and use information. The theory of Bayes-adaptive exploration offers an attractive optimality framework for training machines to perform complex information gathering tasks. However, the computational complexity of the resulting optimal control problem has limited the diffusion of the theory to mainstream deep AI research. In this paper we exploit the inherent mathematical structure of Bayes-adaptive problems in order to dramatically simplify the problem by making the reward structure denser while simultaneously decoupling the learning of exploitation and exploration policies. The key to this simplification comes from the novel concept of cross-value (i.e. the value of being in an environment while acting optimally according to another), which we use to quantify the value of currently available information. This results in a new denser reward structure that "cashes in" all future rewards that can be predicted from the current information state. In a set of experiments we show that the approach makes it possible to learn challenging information gathering tasks without the use of shaping and heuristic bonuses in situations where the standard RL algorithms fail.
Auto White-Balance Correction for Mixed-Illuminant Scenes

Auto white balance (AWB) is applied by camera hardware at capture time to remove the color cast caused by the scene illumination. The vast majority of white-balance algorithms assume a single light source illuminates the scene; however, real scenes often have mixed lighting conditions. This paper presents an effective AWB method to deal with such mixed-illuminant scenes. A unique departure from conventional AWB, our method does not require illuminant estimation, as is the case in traditional camera AWB modules. Instead, our method proposes to render the captured scene with a small set of predefined white-balance settings. Given this set of rendered images, our method learns to estimate weighting maps that are used to blend the rendered images to generate the final corrected image. Through extensive experiments, we show this proposed method produces promising results compared to other alternatives for single- and mixed-illuminant scene color correction. Our source code and trained models are available at https://github.com/mahmoudnafifi/mixedillWB.
Neural Unification for Logic Reasoning over Natural Language

Automated Theorem Proving (ATP) deals with the development of computer programs being able to show that some conjectures (queries) are a logical consequence of a set of axioms (facts and rules). There exists several successful ATPs where conjectures and axioms are formally provided (e.g. formalised as First Order Logic formulas). Recent approaches, such as (Clark et al., 2020), have proposed transformer-based architectures for deriving conjectures given axioms expressed in natural language (English). The conjecture is verified through a binary text classifier, where the transformers model is trained to predict the truth value of a conjecture given the axioms. The RuleTaker approach of (Clark et al., 2020) achieves appealing results both in terms of accuracy and in the ability to generalize, showing that when the model is trained with deep enough queries (at least 3 inference steps), the transformers are able to correctly answer the majority of queries (97.6%) that require up to 5 inference steps. In this work we propose a new architecture, namely the Neural Unifier, and a relative training procedure, which achieves state-of-the-art results in term of generalisation, showing that mimicking a well-known inference procedure, the backward chaining, it is possible to answer deep queries even when the model is trained only on shallow ones. The approach is demonstrated in experiments using a diverse set of benchmark data.
Compressed Oblivious Encoding for Homomorphically Encrypted Search

Fully homomorphic encryption (FHE) enables a simple, attractive framework for secure search. Compared to other secure search systems, no costly setup procedure is necessary; it is sufficient for the client merely to upload the encrypted database to the server. Confidentiality is provided because the server works only on the encrypted query and records. While the search functionality is enabled by the full homomorphism of the encryption scheme. For this reason, researchers have been paying increasing attention to this problem. Since Akavia et al. (CCS 2018) presented a framework for secure search on FHE encrypted data and gave a working implementation called SPiRiT, several more efficient realizations have been proposed. In this paper, we identify the main bottlenecks of this framework and show how to significantly improve the performance of FHE-base secure search. In particular, 1. To retrieve $\ell$ matching items, the existing framework needs to repeat the protocol $\ell$ times sequentially. In our new framework, all matching items are retrieved in parallel in a single protocol execution. 2. The most recent work by Wren et al. (CCS 2020) requires $O(n)$ multiplications to compute the first matching index. Our solution requires no homomorphic multiplication, instead using only additions and scalar multiplications to encode all matching indices. 3. Our implementation and experiments show that to fetch 16 matching records, our system gives an 1800X speed-up over the state of the art in fetching the query results resulting in a 26X speed-up for the full search functionality.
Sister Help: Data Augmentation for Frame-Semantic Role Labeling

While FrameNet is widely regarded as a rich resource of semantics in natural language processing, a major criticism concerns its lack of coverage and the relative paucity of its labeled data compared to other commonly used lexical resources such as PropBank and VerbNet. This paper reports on a pilot study to address these gaps. We propose a data augmentation approach, which uses existing frame-specific annotation to automatically annotate other lexical units of the same frame which are unannotated. Our rule-based approach defines the notion of a sister lexical unit and generates frame-specific augmented data for training. We present experiments on frame-semantic role labeling which demonstrate the importance of this data augmentation: we obtain a large improvement to prior results on frame identification and argument identification for FrameNet, utilizing both full-text and lexicographic annotations under FrameNet. Our findings on data augmentation highlight the value of automatic resource creation for improved models in frame-semantic parsing.
Towards Defensive Autonomous Driving: Collecting and Probing Driving Demonstrations of Mixed Qualities

Designing or learning an autonomous driving policy is undoubtedly a challenging task as the policy has to maintain its safety in all corner cases. In order to secure safety in autonomous driving, the ability to detect hazardous situations, which can be seen as an out-of-distribution (OOD) detection problem, becomes crucial. However, most conventional datasets only provide expert driving demonstrations, although some non-expert or uncommon driving behavior data are needed to implement a safety guaranteed autonomous driving platform. To this end, we present a novel dataset called the R3 Driving Dataset, composed of driving data with different qualities. The dataset categorizes abnormal driving behaviors into eight categories and 369 different detailed situations. The situations include dangerous lane changes and near-collision situations. To further enlighten how these abnormal driving behaviors can be detected, we utilize different uncertainty estimation and anomaly detection methods to the proposed dataset. From the results of the proposed experiment, it can be inferred that by using both uncertainty estimation and anomaly detection, most of the abnormal cases in the proposed dataset can be discriminated. The dataset of this paper can be downloaded from https://rllab-snu.github.io/projects/R3-Driving-Dataset/doc.html.
Unsupervised domain adaptation with non-stochastic missing data

We consider unsupervised domain adaptation (UDA) for classification problems in the presence of missing data in the unlabelled target domain. More precisely, motivated by practical applications, we analyze situations where distribution shift exists between domains and where some components are systematically absent on the target domain without available supervision for imputing the missing target components. We propose a generative approach for imputation. Imputation is performed in a domain-invariant latent space and leverages indirect supervision from a complete source domain. We introduce a single model performing joint adaptation, imputation and classification which, under our assumptions, minimizes an upper bound of its target generalization error and performs well under various representative divergence families (H-divergence, Optimal Transport). Moreover, we compare the target error of our Adaptation-imputation framework and the "ideal" target error of a UDA classifier without missing target components. Our model is further improved with self-training, to bring the learned source and target class posterior distributions closer. We perform experiments on three families of datasets of different modalities: a classical digit classification benchmark, the Amazon product reviews dataset both commonly used in UDA and real-world digital advertising datasets. We show the benefits of jointly performing adaptation, classification and imputation on these datasets.
Revisiting Tri-training of Dependency Parsers

We compare two orthogonal semi-supervised learning techniques, namely tri-training and pretrained word embeddings, in the task of dependency parsing. We explore language-specific FastText and ELMo embeddings and multilingual BERT embeddings. We focus on a low resource scenario as semi-supervised learning can be expected to have the most impact here. Based on treebank size and available ELMo models, we select Hungarian, Uyghur (a zero-shot language for mBERT) and Vietnamese. Furthermore, we include English in a simulated low-resource setting. We find that pretrained word embeddings make more effective use of unlabelled data than tri-training but that the two approaches can be successfully combined.
Context-NER : Contextual Phrase Generation at Scale

Named Entity Recognition (NER) has seen significant progress in recent years, with numerous state-of-the-art (SOTA) models achieving high performance. However, very few studies have focused on the generation of entities' context. In this paper, we introduce CONTEXT-NER, a task that aims to generate the relevant context for entities in a sentence, where the context is a phrase describing the entity but not necessarily present in the sentence. To facilitate research in this task, we also present the EDGAR10-Q dataset, which consists of annual and quarterly reports from the top 1500 publicly traded companies. The dataset is the largest of its kind, containing 1M sentences, 2.8M entities, and an average of 35 tokens per sentence, making it a challenging dataset. We propose a baseline approach that combines a phrase generation algorithm with inferencing using a 220M language model, achieving a ROUGE-L score of 27% on the test split. Additionally, we perform a one-shot inference with ChatGPT, which obtains a 30% ROUGE-L, highlighting the difficulty of the dataset. We also evaluate models such as T5 and BART, which achieve a maximum ROUGE-L of 49% after supervised finetuning on EDGAR10-Q. We also find that T5-large, when pre-finetuned on EDGAR10-Q, achieve SOTA results on downstream finance tasks such as Headline, FPB, and FiQA SA, outperforming vanilla version by 10.81 points. To our surprise, this 66x smaller pre-finetuned model also surpasses the finance-specific LLM BloombergGPT-50B by 15 points. We hope that our dataset and generated artifacts will encourage further research in this direction, leading to the development of more sophisticated language models for financial text analysis
Pre-trained Gaussian processes for Bayesian optimization

Bayesian optimization (BO) has become a popular strategy for global optimization of expensive real-world functions. Contrary to a common expectation that BO is suited to optimizing black-box functions, it actually requires domain knowledge about those functions to deploy BO successfully. Such domain knowledge often manifests in Gaussian process (GP) priors that specify initial beliefs on functions. However, even with expert knowledge, it is non-trivial to quantitatively define a prior. This is especially true for hyperparameter tuning problems on complex machine learning models, where landscapes of tuning objectives are often difficult to comprehend. We seek an alternative practice for setting these functional priors. In particular, we consider the scenario where we have data from similar functions that allow us to pre-train a tighter distribution a priori. In this work, we detail what pre-training entails for GPs using a KL divergence based loss function, and propose a new pre-training based BO framework named HyperBO. Theoretically, we show bounded posterior predictions and near-zero regrets for HyperBO without assuming the "ground truth" GP prior is known. To verify our approach in realistic model training setups, we collect a large multi-task hyperparameter tuning dataset by training tens of thousands of configurations of near-state-of-the-art deep learning models on popular image and text datasets, as well as a protein sequence dataset. Our results show that on average, HyperBO is able to locate good hyperparameters at least 3 times more efficiently than the best competing methods on both our new tuning dataset and classic multi-task BO benchmarks.
Deterministic transformations between unitary operations: Exponential  advantage with adaptive quantum circuits and the power of indefinite  causality

This work analyses the performance of quantum circuits and general processes to transform $k$ uses of an arbitrary unitary operation $U$ into another unitary operation $f(U)$. When the desired function $f$ a homomorphism, i.e., $f(UV)=f(U)f(V)$, it is known that optimal average fidelity is attainable by parallel circuits and indefinite causality does not provide any advantage. Here we show that the situation changes dramatically when considering anti-homomorphisms, i.e., $f(UV)=f(V)f(U)$. In particular, we prove that when $f$ is an anti-homomorphism, sequential circuits could exponentially outperform parallel ones and processes with indefinite causal order could outperform sequential ones. We presented explicit constructions on how to obtain such advantages for the unitary inversion task $f(U)=U^{-1}$ and the unitary transposition task $f(U)=U^T$. We also stablish a one-to-one connection between the problem of unitary estimation and parallel unitary transposition, allowing one to easily translate results from one field to the other. Finally, we apply our results to several concrete problem instances and present a method based on computer-assisted proofs to show optimality.
Learning logic programs through divide, constrain, and conquer

We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal, recursive, and large programs and supports predicate invention. Our experiments on three domains (classification, inductive general game playing, and program synthesis) show that our approach can increase predictive accuracies and reduce learning times.
Invertible Frowns: Video-to-Video Facial Emotion Translation

We present Wav2Lip-Emotion, a video-to-video translation architecture that modifies facial expressions of emotion in videos of speakers. Previous work modifies emotion in images, uses a single image to produce a video with animated emotion, or puppets facial expressions in videos with landmarks from a reference video. However, many use cases such as modifying an actor's performance in post-production, coaching individuals to be more animated speakers, or touching up emotion in a teleconference require a video-to-video translation approach. We explore a method to maintain speakers' lip movements, identity, and pose while translating their expressed emotion. Our approach extends an existing multi-modal lip synchronization architecture to modify the speaker's emotion using L1 reconstruction and pre-trained emotion objectives. We also propose a novel automated emotion evaluation approach and corroborate it with a user study. These find that we succeed in modifying emotion while maintaining lip synchronization. Visual quality is somewhat diminished, with a trade off between greater emotion modification and visual quality between model variants. Nevertheless, we demonstrate (1) that facial expressions of emotion can be modified with nothing other than L1 reconstruction and pre-trained emotion objectives and (2) that our automated emotion evaluation approach aligns with human judgements.
Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders

The ability of learning disentangled representations represents a major step for interpretable NLP systems as it allows latent linguistic features to be controlled. Most approaches to disentanglement rely on continuous variables, both for images and text. We argue that despite being suitable for image datasets, continuous variables may not be ideal to model features of textual data, due to the fact that most generative factors in text are discrete. We propose a Variational Autoencoder based method which models language features as discrete variables and encourages independence between variables for learning disentangled representations. The proposed model outperforms continuous and discrete baselines on several qualitative and quantitative benchmarks for disentanglement as well as on a text style transfer downstream application.