Reviewerly/exHarmony huggingface dataset:
Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors

Robustness of machine learning models on ever-changing real-world data is critical, especially for applications affecting human wellbeing such as content moderation.New kinds of abusive language continually emerge in online discussions in response to current events (e.g., COVID-19), and the deployed abuse detection systems should be updated regularly to remain accurate.In this paper, we show that general abusive language classifiers tend to be fairly reliable in detecting out-of-domain explicitly abusive utterances but fail to detect new types of more subtle, implicit abuse.Next, we propose an interpretability technique, based on the Testing Concept Activation Vector (TCAV) method from computer vision, to quantify the sensitivity of a trained model to the humandefined concepts of explicit and implicit abusive language, and use that to explain the generalizability of the model on new data, in this case, COVID-related anti-Asian hate speech.Extending this technique, we introduce a novel metric, Degree of Explicitness, for a single instance and show that the new metric is beneficial in suggesting out-of-domain unlabeled examples to effectively enrich the training data with informative, implicitly abusive texts.
The Flash-Friendly File System (F2FS)

Abstract The Flash-Friendly File System (F2FS) is used not just by removable media but also by mobile devices and more. In this chapter, we look under the hood to better understand the structure of and recognize this file system. From a forensic perspective, we look for deleted files to see if we can retrieve them.
Reconstruction of Radar Pulse Repetition Pattern via Semantic Coding of Intercepted Pulse Trains

The timing structure of multiple successive radar pulses constructs a high-dimensional pattern for intercepted pulse trains, which is called as pulse repetition interval (PRI) pattern. By treating the radar as a machine that uses differently permuted pulses to communicate with surroundings, this pattern acts as the grammatical structure of its language. Compared with the discrete PRI set that is conventionally used for pulse train description, PRI pattern contains richer and more condensed structural information about radar pulse train, which is helpful for pulse deinterleaving and radar recognition. This article introduces the semantic coding theory to reveal and reconstruct PRI pattern from the intercepted radar pulse train. We first define the coding complexity of a pulse train, which is divided into two parts: 1) the complexity of encoding a PRI pattern dictionary with each element consisting of several successive PRIs; and 2) encoding the intercepted pulse train based on this dictionary. The coding complexity is then minimized by optimizing the components in dictionary, and the PRI timing patterns are finally obtained from the dictionary when the minimization is reached. The effectiveness of semantic coding model and PRI pattern reconstruction method is verified in the simulation part.
Nozza@LT-EDI-ACL2022: Ensemble Modeling for Homophobia and Transphobia Detection

In this paper, we describe our approach for the task of homophobia and transphobia detection in English social media comments.The dataset consists of YouTube comments, and it has been released for the shared task on Homophobia/Transphobia Detection in social media comments.Given the high class imbalance, we propose a solution based on data augmentation and ensemble modeling.We fine-tuned different large language models (BERT, RoBERTa, and HateBERT) and used the weighted majority vote on their predictions.Our proposed model obtained 0.48 and 0.94 for macro and weighted F1-score, respectively, ranking at the third position.
How does the pre-training objective affect what large language models learn about linguistic properties?

Several pre-training objectives, such as masked language modeling (MLM), have been proposed to pre-train language models (e.g.BERT) with the aim of learning better language representations.However, to the best of our knowledge, no previous work so far has investigated how different pre-training objectives affect what BERT learns about linguistics properties.We hypothesize that linguistically motivated objectives such as MLM should help BERT to acquire better linguistic knowledge compared to other non-linguistically motivated objectives that are not intuitive or hard for humans to guess the association between the input and the label to be predicted.To this end, we pre-train BERT with two linguistically motivated objectives and three non-linguistically motivated ones.We then probe for linguistic characteristics encoded in the representation of the resulting models.We find strong evidence that there are only small differences in probing performance between the representations learned by the two different types of objectives.These surprising results question the dominant narrative of linguistically informed pre-training. 1
Cow Face Recognition for a Small Sample Based on Siamese DB Capsule Network

Dairy cow face recognition using Neural Networks has several hurdles.For example, there are only a few instances of each individual.The positions and angles of the individuals in the image fluctuate considerably, the differences between individuals are not apparent, and the number of individuals that the network has not been trained on is enormous, etc.In this paper, an enhanced Siamese Neural Network is used to overcome these barriers.First, a combination of Dense Block and Capsule Network is employed as a feature extractor to keep the spatial information of features while expanding the feature extraction capabilities of the Convolutional Neural Network.Second, image pairings are processed through the Siamese Neural Network to obtain bivariate features.Finally, image recognition is achieved via the correlation analysis of bivariate features.We conduct comparison experiments with different networks on a small cow face dataset.The experimental results demonstrate that Siamese DB Capsule Network can learn abstract knowledge about distinct individuals and can be extended to unfamiliar cows for zero-shot learning.
Face Recognition With Masks Based on Spatial Fine-Grained Frequency Domain Broadening

Along with social distancing, wearing masks is an effective method of preventing the transmission of COVID-19 in the ongoing pandemic. However, masks occlude a large number of facial features, preventing facial recognition. The recognition rate of existing methods may be significantly reduced by the presence of masks. In this paper, we propose a method to effectively solve the problem of the lack of facial feature information needed to perform facial recognition on people wearing masks. The proposed approach uses image super-resolution technology to perform image preprocessing along with a deep bilinear module to improve EfficientNet. It also combines feature enhancement with frequency domain broadening, fuses the spatial features and frequency domain features of the unoccluded areas of the face, and classifies the fused features. The features of the unoccluded area are increased to improve the accuracy of recognition of masked faces. The results of a cross-validation show that the proposed approach achieved an accuracy of 98% on the RMFRD dataset, as well as a higher recognition rate and faster speed than previous methods. In addition, we also performed an experimental evaluation in an actual facial recognition system and achieved an accuracy of 99%, which demonstrates the effectiveness and practicability of the proposed method.
IDPT: Interconnected Dual Pyramid Transformer for Face Super-Resolution

Face Super-resolution (FSR) task works for generating high-resolution (HR) face images from the corresponding low-resolution (LR) inputs, which has received a lot of attentions because of the wide application prospects. However, due to the diversity of facial texture and the difficulty of reconstructing detailed content from degraded images, FSR technology is still far away from being solved. In this paper, we propose a novel and effective face super-resolution framework based on Transformer, namely Interconnected Dual Pyramid Transformer (IDPT). Instead of straightly stacking cascaded feature reconstruction blocks, the proposed IDPT designs the pyramid encoder/decoder Transformer architecture to extract coarse and detailed facial textures respectively, while the relationship between the dual pyramid Transformers is further explored by a bottom pyramid feature extractor. The pyramid encoder/decoder structure is devised to adapt various characteristics of textures in different spatial spaces hierarchically. A novel fusing modulation module is inserted in each spatial layer to guide the refinement of detailed texture by the corresponding coarse texture, while fusing the shallow-layer coarse feature and corresponding deep-layer detailed feature simultaneously. Extensive experiments and visualizations on various datasets demonstrate the superiority of the proposed method for face super-resolution tasks.
Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval (Extended Abstract)

Dense Retrieval (DR) has achieved state-of-the-art first-stage ranking effectiveness. However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space. Therefore, we present RepCONC, a novel retrieval model that learns discrete Representations via CONstrained Clustering. RepCONC jointly trains dual-encoders and the Product Quantization (PQ) method to learn discrete document representations and enables fast approximate NNS with compact indexes. It models quantization as a constrained clustering process, which requires the document embeddings to be uniformly clustered around the quantization centroids. We theoretically demonstrate the importance of the uniform clustering constraint and derive an efficient approximate solution for constrained clustering by reducing it to an instance of the optimal transport problem. Extensive experiments on two popular ad-hoc retrieval benchmarks show that RepCONC substantially outperforms a wide range of existing retrieval models in terms of retrieval effectiveness, memory efficiency, and time efficiency.
Towards Verifiable Federated Learning

Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models. Nevertheless, due to the nature of open participation by self-interested entities, it needs to guard against potential misbehaviours by legitimate FL participants. FL verification techniques are promising solutions for this problem. They have been shown to effectively enhance the reliability of FL networks and build trust among participants. Verifiable FL has become an emerging topic of research that has attracted significant interest from the academia and the industry alike. Currently, there is no comprehensive survey on the field of verifiable federated learning, which is interdisciplinary in nature and can be challenging for researchers to enter into. In this paper, we bridge this gap by reviewing works focusing on verifiable FL. We propose a novel taxonomy for verifiable FL covering both centralised and decentralised settings, summarise the commonly adopted performance evaluation approaches, and discuss promising directions towards a versatile verifiable FL framework.
Relational Triple Extraction: One Step is Enough

Extracting relational triples from unstructured text is an essential task in natural language processing and knowledge graph construction. Existing approaches usually contain two fundamental steps: (1) finding the boundary positions of head and tail entities; (2) concatenating specific tokens to form triples. However, nearly all previous methods suffer from the problem of error accumulation, i.e., the boundary recognition error of each entity in step (1) will be accumulated into the final combined triples. To solve the problem, in this paper, we introduce a fresh perspective to revisit the triple extraction task and propose a simple but effective model, named DirectRel. Specifically, the proposed model first generates candidate entities through enumerating token sequences in a sentence, and then transforms the triple extraction task into a linking problem on a ``head -&gt; tail" bipartite graph. By doing so, all triples can be directly extracted in only one step. Extensive experimental results on two widely used datasets demonstrate that the proposed model performs better than the state-of-the-art baselines.
Automated Eruption Forecasting at Frequently Active Volcanoes Using Bayesian Networks Learned From Monitoring Data and Expert Elicitation: Application to Mt Ruapehu, Aotearoa, New Zealand

Volcano observatory best practice recommends using probabilistic methods to forecast eruptions to account for the complex natural processes leading up to an eruption and communicating the inherent uncertainties in appropriate ways. Bayesian networks (BNs) are an artificial intelligence technology to model complex systems with uncertainties. BNs consist of a graphical presentation of the system that is being modelled and robust statistics to describe the joint probability distribution of all variables. They have been applied successfully in many domains including risk assessment to support decision-making and modelling multiple data streams for eruption forecasting and volcanic hazard and risk assessment. However, they are not routinely or widely employed in volcano observatories yet. BNs provide a flexible framework to incorporate conceptual understanding of a volcano, learn from data when available and incorporate expert elicitation in the absence of data. Here we describe a method to build a BN model to support decision-making. The method is built on the process flow of risk management by the International Organization for Standardization. We have applied the method to develop a BN model to forecast the probability of eruption for Mt Ruapehu, Aotearoa New Zealand in collaboration with the New Zealand volcano monitoring group (VMG). Since 2014, the VMG has regularly estimated the probability of volcanic eruptions at Mt Ruapehu that impact beyond the crater rim. The BN model structure was built with expert elicitation based on the conceptual understanding of Mt Ruapehu and with a focus on making use of the long eruption catalogue and the long-term monitoring data. The model parameterisation was partly done by data learning, complemented by expert elicitation. The retrospective BN model forecasts agree well with the VMG elicitations. The BN model is now implemented as a software tool to automatically calculate daily forecast updates.
Learning Dynamic Generative Attention for Single Image Super-Resolution

Attention mechanisms have achieved great success for image super-resolution as they can effectively improve the feature representation ability. However, most attention-based methods produce the static attention weights, which are applied identically for all input samples. This popular attention strategy is difficult to automatically adapt the content variations of each individual input, hence hindering further improvements of the magnification performance. To explore towards resolving this challenge, we propose a variational hybrid network with newly dynamic attention mechanisms for image super-resolution tasks. Specifically, we design a multi-scale variational encoder network to transform the curvature map of an input image into the latent space. This is made possible for randomly generated latent variables to reflect the valuable high-frequency information and recalibrate the main network. We utilize these latent variables to further generate controllable attention weights, which modulate not only frequency parameters of convolutional kernels but also spatial characteristics of feature maps for boosting representation power. Moreover, a curvature-domain loss is designed to help the main network to concentrate more on high-frequency geometric structures. Experimental results have revealed that our method can generate more realistic and visually pleasing high-resolution images in comparison to state-of-the-art methods.
Effective data management strategy and RDD weight cache replacement strategy in Spark

With the dramatic increase in internet users and their demand for real-time network performance, Spark has distributed computing environment has emerged. It is widely used due to its high-performance caching mechanism and high scalability. In the face of the unpredictability of data access patterns in the current big data environment, the data shuffling phase is prone to the problems of under-utilization of Spark cluster resources, high computational latency, and high task processing latency. Based on this, this paper proposes an intermediate data management strategy based on the data shuffling phase. Firstly, the size of the data generated in the data shuffling phase of the Spark platform is predicted by random sampling. The strength division strategy obtains the skewed data degree to obtain the part with excessive skew deviation. Finally, the adaptive data management strategy is applied to perform the corresponding computation tasks by the data deviation. In addition, to improve the response time, memory usage, and computation latency of Spark applications, an adaptive cache replacement algorithm based on RDD partition weights is proposed, which takes into account the influence of four weight factors such as computation cost, usage times, partition size and life cycle of RDDs by reasonably calculating the RDD partition weight values. Compared with the current mainstream baseline algorithms, the data management algorithm based on the data mash-up phase proposed in this paper can effectively reduce resource usage and computational response latency. The RDD-based partition weighted adaptive cache replacement algorithm proposed in this paper can fully use memory resources and effectively reduce the problem of resource wastage.
TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding

Data augmentation is an effective approach to tackle over-fitting.Many previous works have proposed different data augmentations strategies for NLP, such as noise injection, word replacement, back-translation etc.Though effective, they missed one important characteristic of language-compositionality, meaning of a complex expression is built from its sub-parts.Motivated by this, we propose a compositional data augmentation approach for natural language understanding called TreeMix.Specifically, TreeMix leverages constituency parsing tree to decompose sentences into constituent sub-structures and the Mixup data augmentation technique to recombine them to generate new sentences.Compared with previous approaches, TreeMix introduces greater diversity to the samples generated and encourages models to learn compositionality of NLP data.Extensive experiments on text classification and SCAN demonstrate that TreeMix outperforms current state-of-the-art data augmentation methods.
Non-Autoregressive Machine Translation: It’s Not as Fast as it Seems

Efficient machine translation models are commercially important as they can increase inference speeds, and reduce costs and carbon emissions.Recently, there has been much interest in non-autoregressive (NAR) models, which promise faster translation.In parallel to the research on NAR models, there have been successful attempts to create optimized autoregressive models as part of the WMT shared task on efficient translation.In this paper, we point out flaws in the evaluation methodology present in the literature on NAR models and we provide a fair comparison between a stateof-the-art NAR model and the autoregressive submissions to the shared task.We make the case for consistent evaluation of NAR models, and also for the importance of comparing NAR models with other widely used methods for improving efficiency.We run experiments with a connectionist-temporal-classification-based (CTC) NAR model implemented in C++ and compare it with AR models using wall clock times.Our results show that, although NAR models are faster on GPUs, with small batch sizes, they are almost always slower under more realistic usage conditions.We call for more realistic and extensive evaluation of NAR models in future work.
Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework

Variational Autoencoder (VAE) is an effective framework to model the interdependency for non-autoregressive neural machine translation (NAT).One of the prominent VAE-based NAT frameworks, LaNMT, achieves great improvements to vanilla models, but still suffers from two main issues which lower down the translation quality: (1) mismatch between training and inference circumstances and (2) inadequacy of latent representations.In this work, we target on addressing these issues by proposing posterior consistency regularization.Specifically, we first perform stochastic data augmentation on the input samples to better adapt the model for inference circumstance, and then conduct consistency training on posterior latent variables to construct a more robust latent representations without any expansion on latent size.Experiments on En<->De and En<->Ro benchmarks confirm the effectiveness of our methods with about 1.5/0.7 and 0.8/0.3BLEU points improvement to the baseline model with about 12.6× faster than autoregressive Transformer.
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation

The past several years have witnessed Variational Auto-Encoder's superiority in various text generation tasks.However, due to the sequential nature of the text, auto-regressive decoders tend to ignore latent variables and then reduce to simple language models, known as the KL vanishing problem, which would further deteriorate when VAE is combined with Transformer-based structures.To ameliorate this problem, we propose DELLA, a novel variational Transformer framework.DELLA learns a series of layer-wise latent variables with each inferred from those of lower layers and tightly coupled with the hidden states by low-rank tensor product.In this way, DELLA forces these posterior latent variables to be fused deeply with the whole computation path and hence incorporate more information.We theoretically demonstrate that our method can be regarded as entangling latent variables to avoid posterior information decrease through layers, enabling DELLA to get higher nonzero KL values even without any annealing or thresholding tricks.Experiments on four unconditional and three conditional generation tasks show that DELLA could better alleviate KL vanishing and improve both quality and diversity compared to several strong baselines.
MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)

Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications.Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.