Reviewerly/exHarmony huggingface dataset:
When Age-Invariant Face Recognition Meets Face Age Synthesis: A  Multi-Task Learning Framework

To minimize the effects of age variation in face recognition, previous work either extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features, called age-invariant face recognition (AIFR), or removes age variation by transforming the faces of different age groups into the same age group, called face age synthesis (FAS); however, the former lacks visual results for model interpretation while the latter suffers from artifacts compromising downstream recognition. Therefore, this paper proposes a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn age-invariant identity-related representation while achieving pleasing face synthesis. Specifically, we first decompose the mixed face feature into two uncorrelated components -- identity- and age-related feature -- through an attention mechanism, and then decorrelate these two components using multi-task training and continuous domain adaption. In contrast to the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS, with a weight-sharing strategy to improve the age smoothness of synthesized faces. In addition, we collect and release a large cross-age face dataset with age and gender annotations to advance the development of the AIFR and FAS. Extensive experiments on five benchmark cross-age datasets demonstrate the superior performance of our proposed MTLFace over existing state-of-the-art methods for AIFR and FAS. We further validate MTLFace on two popular general face recognition datasets, showing competitive performance for face recognition in the wild. The source code and dataset are available at~\url{https://github.com/Hzzone/MTLFace}.
FOREST FIRE RISK MANAGEMENT INFORMATION SYSTEMS IN MONTENEGRO

In times of increasingly pronounced climate change, wildfires are among the key threats to forest ecosystems and pose a global environmental and economic problem.Montenegro's forests are particularly endangered due to its geographical position and the increasingly adverse climate effects.Wildfires occur as a causal link between climatic and meteorological conditions, humidity of forest cover, and plant vegetation with social activities in the area concerned (revised Forestry Development Strategy, 2018).Increasing attention is being paid to improving risk assessment and early warning using modern information systems to mitigate the wildfire risk preventively.Research on the capability and capacity of responsible institutions in Montenegro for establishing an information system is particularly important for wildfire risk management.The research showed that Montenegro's capabilities and capacities for risk assessment and early warning of wildfires are limited.The research showed that the system could be improved by establishing a single national wildfire information system.Various forms of early warning and wildfire risk management systems applied, including the Macedonian Forest Fire Information System (MKFFIS), Croatian's Integral Forest Fire Monitoring System (in Croatian IPNAS), and others, have already shown a high level of efficiency.Through a systemic approach, a large part of the tools used by these systems could be realistically developed and subsequently upgraded, taking into account their modularity in terms of software and hardware.
Cryptocurrencies” Phenomenon and Geopolitical Risks

This exploratory study attempts to capture a new and emerging phenomenology, namely the relationship between cryptocurrencies, as an expression of global digitization, as a new business model -a process catalyzed against the backdrop of the Covid-19 pandemic -and the geopolitical, tension-ridden and sanctioned world in which we live.It is from this context that the aim of the study derives, namely to create a theoretical framework that captures the "crypto phenomenon" in an interdisciplinary approach.Being an incipient phenomenon, but booming during the pandemic period (2020-2022), the results of this study are based on an extensive and critical literature review, in order to synthesize, on the one hand, the phenomenology related to cryptocurrencies at the business/geopolitical interface, and, on the other hand, to propose in the literature a convincing and comprehensive phrase defining the new theoretical framework, namely "cryptogeopolitics".The results attest to this double relationship: 1. geopolitical risks have major effects on the crypto market (due to high speculation and increased volatility of cryptocurrencies) and 2. the inverse relationship, where cryptocurrencies become a geopolitical tool or can trigger a geopolitical crisis.Thus, the "added value" of this study lies both in the theorization of the concept of "cryptogeopolitics", filling a gap in the literature, and in the interdisciplinary approach to the business/geopolitics interface.The applicability is evident, both in the theoretical realm, by conceptualizing a current phenomenology, and in the practical realm, given the emergence of the cyber phase of humanity.
Causal Inference for Time series Analysis: Problems, Methods and  Evaluation

Time series data is a collection of chronological observations which is generated by several domains such as medical and financial fields. Over the years, different tasks such as classification, forecasting, and clustering have been proposed to analyze this type of data. Time series data has been also used to study the effect of interventions over time. Moreover, in many fields of science, learning the causal structure of dynamic systems and time series data is considered an interesting task which plays an important role in scientific discoveries. Estimating the effect of an intervention and identifying the causal relations from the data can be performed via causal inference. Existing surveys on time series discuss traditional tasks such as classification and forecasting or explain the details of the approaches proposed to solve a specific task. In this paper, we focus on two causal inference tasks, i.e., treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task. Furthermore, we curate a list of commonly used evaluation metrics and datasets for each task and provide in-depth insight. These metrics and datasets can serve as benchmarks for research in the field.
What Makes a Good and Useful Summary? Incorporating Users in Automatic Summarization Research

Automatic text summarization has enjoyed great progress over the years and is used in numerous applications, impacting the lives of many.Despite this development, there is little research that meaningfully investigates how the current research focus in automatic summarization aligns with users' needs.To bridge this gap, we propose a survey methodology that can be used to investigate the needs of users of automatically generated summaries.Importantly, these needs are dependent on the target group.Hence, we design our survey in such a way that it can be easily adjusted to investigate different user groups.In this work we focus on university students, who make extensive use of summaries during their studies.We find that the current research directions of the automatic summarization community do not fully align with students' needs.Motivated by our findings, we present ways to mitigate this mismatch in future research on automatic summarization: we propose research directions that impact the design, the development and the evaluation of automatically generated summaries.
Learning to Fuse Asymmetric Feature Maps in Siamese Trackers

Recently, Siamese-based trackers have achieved promising performance in visual tracking. Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW-XCorr) to obtain multi-channel correlation information from the two feature maps (target and search region). However, DW-XCorr has several limitations within Siamese-based tracking: it can easily be fooled by distractors, has fewer activated channels, and provides weak discrimination of object boundaries. Further, DW-XCorr is a handcrafted parameter-free module and cannot fully benefit from offline learning on large-scale data. We propose a learnable module, called the asymmetric convolution (ACM), which learns to better capture the semantic correlation information in offline training on large-scale data. Different from DW-XCorr and its predecessor(XCorr), which regard a single feature map as the convolution kernel, our ACM decomposes the convolution operation on a concatenated feature map into two mathematically equivalent operations, thereby avoiding the need for the feature maps to be of the same size (width and height)during concatenation. Our ACM can incorporate useful prior information, such as bounding-box size, with standard visual features. Furthermore, ACM can easily be integrated into existing Siamese trackers based on DW-XCorror XCorr. To demonstrate its generalization ability, we integrate ACM into three representative trackers: SiamFC, SiamRPN++, and SiamBAN. Our experiments reveal the benefits of the proposed ACM, which outperforms existing methods on six tracking benchmarks. On the LaSOT test set, our ACM-based tracker obtains a significant improvement of 5.8% in terms of success (AUC), over the baseline.
Deep Graph Neural Networks with Shallow Subgraph Samplers

While Graph Neural Networks (GNNs) are powerful models for learning representations on graphs, most state-of-the-art models do not have significant accuracy gain beyond two to three layers. Deep GNNs fundamentally need to address: 1). expressivity challenge due to oversmoothing, and 2). computation challenge due to neighborhood explosion. We propose a simple "deep GNN, shallow sampler" design principle to improve both the GNN accuracy and efficiency -- to generate representation of a target node, we use a deep GNN to pass messages only within a shallow, localized subgraph. A properly sampled subgraph may exclude irrelevant or even noisy nodes, and still preserve the critical neighbor features and graph structures. The deep GNN then smooths the informative local signals to enhance feature learning, rather than oversmoothing the global graph signals into just "white noise". We theoretically justify why the combination of deep GNNs with shallow samplers yields the best learning performance. We then propose various sampling algorithms and neural architecture extensions to achieve good empirical results. On the largest public graph dataset, ogbn-papers100M, we achieve state-of-the-art accuracy with an order of magnitude reduction in hardware cost.
Target Guided Emotion Aware Chat Machine

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem by proposing a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post and leverage target information for generating more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence  Perspective with Transformers

Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth. In this work, we revisit the problem from a sequence-to-sequence correspondence perspective to replace cost volume construction with dense pixel matching using position information and attention. This approach, named STereo TRansformer (STTR), has several advantages: It 1) relaxes the limitation of a fixed disparity range, 2) identifies occluded regions and provides confidence estimates, and 3) imposes uniqueness constraints during the matching process. We report promising results on both synthetic and real-world datasets and demonstrate that STTR generalizes across different domains, even without fine-tuning.
syrapropa at SemEval-2020 Task 11: BERT-based Models Design For  Propagandistic Technique and Span Detection

This paper describes the BERT-based models proposed for two subtasks in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. We first build the model for Span Identification (SI) based on SpanBERT, and facilitate the detection by a deeper model and a sentence-level representation. We then develop a hybrid model for the Technique Classification (TC). The hybrid model is composed of three submodels including two BERT models with different training methods, and a feature-based Logistic Regression model. We endeavor to deal with imbalanced dataset by adjusting cost function. We are in the seventh place in SI subtask (0.4711 of F1-measure), and in the third place in TC subtask (0.6783 of F1-measure) on the development set.
Local Temperature Scaling for Probability Calibration

For semantic segmentation, label probabilities are often uncalibrated as they are typically only the by-product of a segmentation task. Intersection over Union (IoU) and Dice score are often used as criteria for segmentation success, while metrics related to label probabilities are not often explored. However, probability calibration approaches have been studied, which match probability outputs with experimentally observed errors. These approaches mainly focus on classification tasks, but not on semantic segmentation. Thus, we propose a learning-based calibration method that focuses on multi-label semantic segmentation. Specifically, we adopt a convolutional neural network to predict local temperature values for probability calibration. One advantage of our approach is that it does not change prediction accuracy, hence allowing for calibration as a post-processing step. Experiments on the COCO, CamVid, and LPBA40 datasets demonstrate improved calibration performance for a range of different metrics. We also demonstrate the good performance of our method for multi-atlas brain segmentation from magnetic resonance images.
Quantum Computation for Pricing the Collateralized Debt Obligations

Collateralized debt obligation (CDO) has been one of the most commonly used structured financial products and is intensively studied in quantitative finance. By setting the asset pool into different tranches, it effectively works out and redistributes credit risks and returns to meet the risk preferences for different tranche investors. The copula models of various kinds are normally used for pricing CDOs, and the Monte Carlo simulations are required to get their numerical solution. Here we implement two typical CDO models, the single-factor Gaussian copula model and Normal Inverse Gaussian copula model, and by applying the conditional independence approach, we manage to load each model of distribution in quantum circuits. We then apply quantum amplitude estimation as an alternative to Monte Carlo simulation for CDO pricing. We demonstrate the quantum computation results using IBM Qiskit. Our work addresses a useful task in finance instrument pricing, significantly broadening the application scope for quantum computing in finance.
Rethinking Importance Weighting for Deep Learning under Distribution  Shift

Under distribution shift (DS) where the training data distribution differs from the test one, a powerful technique is importance weighting (IW) which handles DS in two separate steps: weight estimation (WE) estimates the test-over-training density ratio and weighted classification (WC) trains the classifier from weighted training data. However, IW cannot work well on complex data, since WE is incompatible with deep learning. In this paper, we rethink IW and theoretically show it suffers from a circular dependency: we need not only WE for WC, but also WC for WE where a trained deep classifier is used as the feature extractor (FE). To cut off the dependency, we try to pretrain FE from unweighted training data, which leads to biased FE. To overcome the bias, we propose an end-to-end solution dynamic IW that iterates between WE and WC and combines them in a seamless manner, and hence our WE can also enjoy deep networks and stochastic optimizers indirectly. Experiments with two representative types of DS on three popular datasets show that our dynamic IW compares favorably with state-of-the-art methods.
Faithful Embeddings for Knowledge Base Queries

The deductive closure of an ideal knowledge base (KB) contains exactly the logical queries that the KB can answer. However, in practice KBs are both incomplete and over-specified, failing to answer some queries that have real-world answers. \emph{Query embedding} (QE) techniques have been recently proposed where KB entities and KB queries are represented jointly in an embedding space, supporting relaxation and generalization in KB inference. However, experiments in this paper show that QE systems may disagree with deductive reasoning on answers that do not require generalization or relaxation. We address this problem with a novel QE method that is more faithful to deductive reasoning, and show that this leads to better performance on complex queries to incomplete KBs. Finally we show that inserting this new QE module into a neural question-answering system leads to substantial improvements over the state-of-the-art.
Cross-corpora experiments of automatic proficiency assessment and error detection for spoken English

The growing demand for learning English as a second language has led to an increasing interest in automatic approaches for assessing spoken language proficiency.One of the most significant challenges in this field is the lack of publicly available annotated spoken data.Another common issue is the lack of consistency and coherence in human assessment.To tackle both problems, in this paper we address the task of automatically predicting the scores of spoken test responses of Englishas-a-second-language learners by training neural models on written data and using the presence of grammatical errors as a feature, as they can be considered consistent indicators of proficiency through their distribution and frequency.Specifically, we train a feature extractor on EF-CAMDAT, a large written corpus containing error annotations and proficiency levels assigned by human experts, in order to extract information related to grammatical errors and, in turn, we use the resulting model for inference on the CLC-FCE corpus, on the ICNALE corpus, and on the spoken section of the TLT-school corpus, a collection of proficiency tests taken by Italian students.The work investigates the impact of the feature extractor on spoken proficiency assessment as well as the written-to-spoken approach.We find that our error-based approach can be beneficial for assessing spoken proficiency.The results obtained on the considered datasets are discussed and evaluated with appropriate metrics.
SemEval-2022 Task 9: R2VQ – Competence-based Multimodal Question Answering

In this task, we identify a challenge that is reflective of linguistic and cognitive competencies that humans have when speaking and reasoning.Particularly, given the intuition that textual and visual information mutually inform each other for semantic reasoning, we formulate a Competence-based Question Answering challenge, designed to involve rich semantic annotation and aligned text-video objects.The task is to answer questions from a collection of English language cooking recipes and videos, where each question belongs to a "question family" reflecting a specific reasoning competence.The data and task result is publicly available. 1
Opportunities for Human-centered Evaluation of Machine Translation Systems

Machine translation models are embedded in larger user-facing systems.Although model evaluation has matured, evaluation at the systems level is still lacking.We review literature from both the translation studies and HCI communities about who uses machine translation and for what purposes.We emphasize an important difference in evaluating machine translation models versus the physical and cultural systems in which they are embedded.We then propose opportunities for improved measurement of user-facing translation systems.We pay particular attention to the need for design and evaluation to aid engendering trust and enhancing user agency in future machine translation systems.
TeamOtter at SemEval-2022 Task 5: Detecting Misogynistic Content in Multimodal Memes

We describe our system for the SemEval 2022 task on detecting misogynous content in memes.This is a pressing problem and we explore various methods ranging from traditional machine learning to deep learning models such as multimodal transformers.We propose a multimodal BERT architecture that uses information from both image and text.We further incorporate common world knowledge from pretrained CLIP and Urban dictionary.We also provide qualitative analysis to support out model.Our best performing model achieves an F1 score of 0.679 on Task A (Rank 5) and 0.680 on Task B (Rank 13) of the hidden test set.Our code is available at https://github.com/paridhimaheshwari2708/MAMI.
Exploring Text Representations for Generative Temporal Relation Extraction

Sequence-to-sequence models are appealing because they allow both encoder and decoder to be shared across many tasks by formulating those tasks as text-to-text problems.Despite recently reported successes of such models, we find that engineering input/output representations for such text-to-text models is challenging.On the Clinical TempEval 2016 relation extraction task, the most natural choice of output representations, where relations are spelled out in simple predicate logic statements, did not lead to good performance.We explore a variety of input/output representations, with the most successful prompting one event at a time, and achieving results competitive with standard pairwise temporal relation extraction systems.
Text Style Transfer via Optimal Transport

Text style transfer (TST) is a well-known task whose goal is to convert the style of the text (e.g., from formal to informal) while preserving its content.Recently, it has been shown that both syntactic and semantic similarities between the source and the converted text are important for TST.However, the interaction between these two concepts has not been modeled.In this work, we propose a novel method based on Optimal Transport for TST to simultaneously incorporate syntactic and semantic information into similarity computation between the source and the converted text.We evaluate the proposed method in both supervised and unsupervised settings.Our analysis reveal the superiority of the proposed model in both settings.
LyS_ACoruña at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsing

This paper addressed the problem of structured sentiment analysis using a bi-affine semantic dependency parser, large pre-trained language models, and publicly available translation models.For the monolingual setup, we considered: (i) training on a single treebank, and (ii) relaxing the setup by training on treebanks coming from different languages that can be adequately processed by cross-lingual language models.For the zero-shot setup and a given target treebank, we relied on: (i) a word-level translation of available treebanks in other languages to get noisy, unlikely-grammatical, but annotated data (we release as much of it as licenses allow), and (ii) merging those translated treebanks to obtain training data.In the post-evaluation phase, we also trained cross-lingual models that simply merged all the English treebanks and did not use word-level translations, and yet obtained better results.According to the official results, we ranked 8th and 9th in the monolingual and cross-lingual setups.