Reviewerly/exHarmony huggingface dataset:
Blockchain Technology: Energy Efficiency and Ethical Compliance

Blockchain technology has the ability to disrupt almost every industry due to its ability to create a decentralized tamper-proof ledger network and carry out transactions without the need of a trusted third party intermediary. One of the major drawback of blockchain technology is the energy consumption surrounding its application. The problem has been discussed extensively, with proposals focusing on strengthening its consensus protocol and also integrating renewable energy sources to minimize its carbon footprint. To boost adaptability of blockchain in all sectors, the technology itself needs to look for less energy-intensive alternatives to boost its ethical and industrial compliance. As the energy sector is currently undergoing a revamp from its age-long one-way power network towards a decentralized grid system with distributed generation and storage. Adoption of blockchain technology must work hand in hand with the energy industry in order to make itself energy efficient and competitive in the long term.
Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation

Haze is a term that is widely used in image processing to refer to natural and human-activity-emitted aerosols. It causes light scattering and absorption, which reduce the visibility of captured images. This reduction hinders the proper operation of many photographic and computer-vision applications, such as object recognition/localization. Accordingly, haze removal, which is also known as image dehazing or defogging, is an apposite solution. However, existing dehazing algorithms unconditionally remove haze, even when haze occurs occasionally. Therefore, an approach for haze density estimation is highly demanded. This paper then proposes a model that is known as the haziness degree evaluator to predict haze density from a single image without reference to a corresponding haze-free image, an existing georeferenced digital terrain model, or training on a significant amount of data. The proposed model quantifies haze density by optimizing an objective function comprising three haze-relevant features that result from correlation and computation analysis. This objective function is formulated to maximize the image’s saturation, brightness, and sharpness while minimizing the dark channel. Additionally, this study describes three applications of the proposed model in hazy/haze-free image classification, dehazing performance assessment, and single image dehazing. Extensive experiments on both real and synthetic datasets demonstrate its efficacy in these applications.
A cascade model-aware generative adversarial example detection method

Deep Neural Networks (DNNs) are demonstrated to be vulnerable to adversarial examples, which are elaborately crafted to fool learning models. Since the accuracy and robustness of DNNs are at odds for the adversarial training method, the adversarial example detection algorithms check whether the specific example is adversarial, which is promising to solve the issue of the adversarial example. However, among the existing methods, model-aware detection methods do not generalize well, while the detection accuracies of the generative-based methods are lower compared to the model-aware methods. In this paper, we propose a cascade model-aware generative adversarial example detection method, namely CMAG. CMAG consists of two first-order reconstructors and a second-order reconstructor, which can illustrate what the model sees to the human by reconstructing the logit and feature maps of the last convolution layer. Experimental results demonstrate that our method is effective and is more interpretable compared to some state-of-the-art methods.
Automated Search Oriented to Key Recovery on Ciphers with Linear Key Schedule

Automatic modelling to search distinguishers with high probability covering as many rounds as possible, such as MILP, SAT/SMT, CP models, has become a very popular cryptanalysis topic today. In those models, the optimizing objective is usually the probability or the number of rounds of the distinguishers. If we want to recover the secret key for a round-reduced block cipher, there are usually two phases, i.e., finding an efficient distinguisher and performing key-recovery attack by extending several rounds before and after the distinguisher. The total number of attacked rounds is not only related to the chosen distinguisher, but also to the extended rounds before and after the distinguisher. In this paper, we try to combine the two phases in a uniform automatic model.Concretely, we apply this idea to automate the related-key rectangle attacks on SKINNY and ForkSkinny. We propose some new distinguishers with advantage to perform key-recovery attacks. Our key-recovery attacks on a few versions of round-reduced SKINNY and ForkSkinny cover 1 to 2 more rounds than the best previous attacks.
Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks

End-to-end approaches for sequence tasks are becoming increasingly popular.Yet for complex sequence tasks, like speech translation, systems that cascade several models trained on sub-tasks have shown to be superior, suggesting that the compositionality of cascaded systems simplifies learning and enables sophisticated search capabilities.In this work, we present an end-to-end framework that exploits compositionality to learn searchable hidden representations at intermediate stages of a sequence model using decomposed sub-tasks.These hidden intermediates can be improved using beam search to enhance the overall performance and can also incorporate external models at intermediate stages of the network to re-score or adapt towards out-of-domain data.One instance of the proposed framework is a Multi-Decoder model for speech translation that extracts the searchable hidden intermediates from a speech recognition sub-task.The model demonstrates the aforementioned benefits and outperforms the previous state-of-theart by around +6 and +3 BLEU on the two test sets of Fisher-CallHome and by around +3 and +4 BLEU on the English-German and English-French test sets of MuST-C. 1
Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation

Federated learning is a setting where agents, each with access to their own data source, combine models learned from local data to create a global model. If agents are drawing their data from different distributions, though, federated learning might produce a biased global model that is not optimal for each agent. This means that agents face a fundamental question: should they join the global model or stay with their local model? In this work, we show how this situation can be naturally analyzed through the framework of coalitional game theory. Motivated by these considerations, we propose the following game: there are heterogeneous players with different model parameters governing their data distribution and different amounts of data they have noisily drawn from their own distribution. Each player's goal is to obtain a model with minimal expected mean squared error (MSE) on their own distribution. They have a choice of fitting a model based solely on their own data, or combining their learned parameters with those of some subset of the other players. Combining models reduces the variance component of their error through access to more data, but increases the bias because of the heterogeneity of distributions. In this work, we derive exact expected MSE values for problems in linear regression and mean estimation. We use these values to analyze the resulting game in the framework of hedonic game theory; we study how players might divide into coalitions, where each set of players within a coalition jointly constructs a single model. In a case with arbitrarily many players that each have either a "small" or "large" amount of data, we constructively show that there always exists a stable partition of players into coalitions.
The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing

In selective prediction, a classifier is allowed to abstain from making predictions on lowconfidence examples.Though this setting is interesting and important, selective prediction has rarely been examined in natural language processing (NLP) tasks.To fill this void in the literature, we study in this paper selective prediction for NLP, comparing different models and confidence estimators.We further propose a simple error regularization trick that improves confidence estimation without substantially increasing the computation budget.We show that recent pre-trained transformer models simultaneously improve both model accuracy and confidence estimation effectiveness.We also find that our proposed regularization improves confidence estimation and can be applied to other relevant scenarios, such as using classifier cascades for accuracyefficiency trade-offs.
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning

Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on characterizing and understanding its impact on backdooring attacks in federated learning through comprehensive experiments using synthetic and the LEAF benchmarks. The initial impression driven by our experimental results suggests that data heterogeneity is the dominant factor in the effectiveness of attacks and it may be a redemption for defending against backdooring as it makes the attack less efficient, more challenging to design effective attack strategies, and the attack result also becomes less predictable. However, with further investigations, we found data heterogeneity is more of a curse than a redemption as the attack effectiveness can be significantly boosted by simply adjusting the client-side backdooring timing. More importantly, data heterogeneity may result in overfitting at the local training of benign clients, which can be utilized by attackers to disguise themselves and fool skewed-feature based defenses. In addition, effective attack strategies can be made by adjusting attack data distribution. Finally, we discuss the potential directions of defending the curses brought by data heterogeneity. The results and lessons learned from our extensive experiments and analysis offer new insights for designing robust federated learning methods and systems.
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction

Detecting what emotions are expressed in text is a well-studied problem in natural language processing.However, research on finergrained emotion analysis such as what causes an emotion is still in its infancy.We present solutions that tackle both emotion recognition and emotion cause detection in a joint fashion.Considering that common-sense knowledge plays an important role in understanding implicitly expressed emotions and the reasons for those emotions, we propose novel methods that combine common-sense knowledge via adapted knowledge models with multi-task learning to perform joint emotion classification and emotion cause tagging.We show performance improvement on both tasks when including common-sense reasoning and a multitask framework.We provide a thorough analysis to gain insights into model performance.
Continual Learning by Using Information of Each Class Holistically

Continual learning (CL) incrementally learns a sequence of tasks while solving the catastrophic forgetting (CF) problem. Existing methods mainly try to deal with CF directly. In this paper, we propose to avoid CF by considering the features of each class holistically rather than only the discriminative information for classifying the classes seen so far. This latter approach is prone to CF because the discriminative information for old classes may not be sufficiently discriminative for the new class to be learned. Consequently, in learning each new task, the network parameters for previous tasks have to be revised, which causes CF. With the holistic consideration, after adding new tasks, the system can still do well for previous tasks. The proposed technique is called Per-class Continual Learning (PCL). PCL has two key novelties. (1) It proposes a one-class learning based technique for CL, which considers features of each class holistically and represents a new approach to solving the CL problem. (2) It proposes a method to extract discriminative information after training to further improve the accuracy. Empirical evaluation shows that PCL markedly outperforms the state-of-the-art baselines for one or more classes per task. More tasks also result in more gains.
Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning

Commonsense reasoning research has so far been limited to English.We aim to evaluate and improve popular multilingual language models (ML-LMs) to help advance commonsense reasoning (CSR) beyond English.We collect the Mickey corpus, consisting of 561k sentences in 11 different languages, which can be used for analyzing and improving ML-LMs.We propose Mickey Probe, a languageagnostic probing task for fairly evaluating the common sense of popular ML-LMs across different languages.In addition, we also create two new datasets, X-CSQA and X-CODAH, by translating their English versions to 15 other languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense reasoning.To improve the performance beyond English, we propose a simple yet effective method -multilingual contrastive pretraining (MCP).It significantly enhances sentence representations, yielding a large performance gain on both benchmarks (e.g., +2.7% accuracy for X-CSQA over XLM-R L ) 1 . Birds have [mask]. LAMA Probe Where do adults usually use glue sticks?A) school B) drawer C) office CommonsenseQA SWAG/CODAHThe chef drops the piece of shrimp in the fryer.→
Neural-Symbolic Integration: A Compositional Perspective

Despite significant progress in the development of neural-symbolic frameworks, the question of how to integrate a neural and a symbolic system in a compositional manner remains open. Our work seeks to fill this gap by treating these two systems as black boxes to be integrated as modules into a single architecture, without making assumptions on their internal structure and semantics. Instead, we expect only that each module exposes certain methods for accessing the functions that the module implements: the symbolic module exposes a deduction method for computing the function's output on a given input, and an abduction method for computing the function's inputs for a given output; the neural module exposes a deduction method for computing the function's output on a given input, and an induction method for updating the function given input-output training instances. We are, then, able to show that a symbolic module --- with any choice for syntax and semantics, as long as the deduction and abduction methods are exposed --- can be cleanly integrated with a neural module, and facilitate the latter's efficient training, achieving empirical performance that exceeds that of previous work.
Adversarial Learning for Discourse Rhetorical Structure Parsing

Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the notorious lack of training data.Although recent top-down DRS parsers can better leverage global document context and have achieved certain success, the performance is still far from perfect.To our knowledge, all previous DRS parsers make local decisions for either bottomup node composition or top-down split point ranking at each time step, and largely ignore DRS parsing from the global view point.Obviously, it is not sufficient to build an entire DRS tree only through these local decisions.In this work, we present our insight on evaluating the pros and cons of the entire DRS tree for global optimization.Specifically, based on recent well-performing top-down frameworks, we introduce a novel method to transform both gold standard and predicted constituency trees into tree diagrams with two color channels.After that, we learn an adversarial bot between gold and fake tree diagrams to estimate the generated DRS trees from a global perspective.We perform experiments on both RST-DT and CDTB corpora and use the original Parseval for performance evaluation.The experimental results show that our parser can substantially improve the performance when compared with previous state-of-the-art parsers.
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data

Weak supervision has shown promising results in many natural language processing tasks, such as Named Entity Recognition (NER).Existing work mainly focuses on learning deep NER models only with weak supervision, i.e., without any human annotation, and shows that by merely using weakly labeled data, one can achieve good performance, though still underperforms fully supervised NER with manually/strongly labeled data.In this paper, we consider a more practical scenario, where we have both a small amount of strongly labeled data and a large amount of weakly labeled data.Unfortunately, we observe that weakly labeled data does not necessarily improve, or even deteriorate the model performance (due to the extensive noise in the weak labels) when we train deep NER models over a simple or weighted combination of the strongly labeled and weakly labeled data.To address this issue, we propose a new multi-stage computational framework -NEEDLE with three essential ingredients: (1) weak label completion, (2) noise-aware loss function, and (3) final finetuning over the strongly labeled data.Through experiments on E-commerce query NER and Biomedical NER, we demonstrate that NEE-DLE can effectively suppress the noise of the weak labels and outperforms existing methods.In particular, we achieve new SOTA F1-scores on 3 Biomedical NER datasets: BC5CDRchem 93.74, BC5CDR-disease 90.69, NCBIdisease 92.28.
Vector Semantic Representations as Descriptors for Visual Place Recognition

Place recognition is the task of recognizing the current scene from a database of known places.The currently dominant algorithmic paradigm is to use (deep learning based) holistic feature vectors to describe each place and use fast vector query methods to find matchings.We propose a novel type of image descriptor, Vector Semantic Representations (VSR), that encodes the spatial semantic layout from a semantic segmentation together with appearance properties in a, for example, 4,096 dimensional vector for place recognition.We leverage operations from the established class of Vector Symbolic Architectures to combine symbolic (e.g.class label) and numeric (e.g.feature map response) information in a common vector representation.We evaluate the proposed semantic descriptor on 13 standard mobile robotic place recognition datasets and compare to six descriptors from the literature.VSR is on par with the best compared descriptor (NetVLAD) in terms of mean average precision and superior in terms of recall and worst-case average precision.This makes the approach particularly interesting for candidate selection.For a more detailed investigation, we discuss and evaluate recall integrity as additional criterion.Further, we demonstrate that the semantic descriptor is particularly well suited for combination with existing appearance descriptors indicating that semantics provide complementary information for image matching.
Consistency Regularization for Cross-Lingual Fine-Tuning

Fine-tuning pre-trained cross-lingual language models can transfer task-specific supervision from one language to the others.In this work, we propose to improve cross-lingual finetuning with consistency regularization.Specifically, we use example consistency regularization to penalize the prediction sensitivity to four types of data augmentations, i.e., subword sampling, Gaussian noise, code-switch substitution, and machine translation.In addition, we employ model consistency to regularize the models trained with two augmented versions of the same training set.Experimental results on the XTREME benchmark show that our method 1 significantly improves crosslingual fine-tuning across various tasks, including text classification, question answering, and sequence labeling.
Distance-based clustering challenges for unbiased benchmarking studies

Benchmark datasets with predefined cluster structures and high-dimensional biomedical datasets outline the challenges of cluster analysis: clustering algorithms are limited in their clustering ability in the presence of clusters defining distance-based structures resulting in a biased clustering solution. Data sets might not have cluster structures. Clustering yields arbitrary labels and often depends on the trial, leading to varying results. Moreover, recent research indicated that all partition comparison measures can yield the same results for different clustering solutions. Consequently, algorithm selection and parameter optimization by unsupervised quality measures (QM) are always biased and misleading. Only if the predefined structures happen to meet the particular clustering criterion and QM, can the clusters be recovered. Results are presented based on 41 open-source algorithms which are particularly useful in biomedical scenarios. Furthermore, comparative analysis with mirrored density plots provides a significantly more detailed benchmark than that with the typically used box plots or violin plots.
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records

One important challenge of applying deep learning to electronic health records (EHR) is the complexity of their multimodal structure. EHR usually contains a mixture of structured (codes) and unstructured (free-text) data with sparse and irregular longitudinal features -- all of which doctors utilize when making decisions. In the deep learning regime, determining how different modality representations should be fused together is a difficult problem, which is often addressed by handcrafted modeling and intuition. In this work, we extend state-of-the-art neural architecture search (NAS) methods and propose MUltimodal Fusion Architecture SeArch (MUFASA) to simultaneously search across multimodal fusion strategies and modality-specific architectures for the first time. We demonstrate empirically that our MUFASA method outperforms established unimodal NAS on public EHR data with comparable computation costs. In addition, MUFASA produces architectures that outperform Transformer and Evolved Transformer. Compared with these baselines on CCS diagnosis code prediction, our discovered models improve top-5 recall from 0.88 to 0.91 and demonstrate the ability to generalize to other EHR tasks. Studying our top architecture in depth, we provide empirical evidence that MUFASA's improvements are derived from its ability to both customize modeling for each modality and find effective fusion strategies.
Dialogue Response Selection with Hierarchical Curriculum Learning

We study the learning of a matching model for dialogue response selection.Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical curriculum learning framework that trains the matching model in an "easy-to-difficult" scheme.Our learning framework consists of two complementary curricula: (1) corpus-level curriculum (CC); and (2) instance-level curriculum (IC).In CC, the model gradually increases its ability in finding the matching clues between the dialogue context and a response candidate.As for IC, it progressively strengthens the model's ability in identifying the mismatching information between the dialogue context and a response candidate.Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics.