Reviewerly/exHarmony huggingface dataset:
An Improved CycleGAN Based Model For Low-light Image Enhancement

The low-light image enhancement is a challenging and hot research issue in the image processing field. In order to enhance the quality of low-light images to obtain full structure and details, many low-light image enhancement algorithms have been proposed and deep learning-based methods have achieved great success in this field. However, most of the deep learning methods require paired training data, which is difficult to obtain. And the overall visual quality of the enhanced image is still not very satisfying. To deal with these problems, an unsupervised low-light image enhancement model based on an improved Cycle-Consistent Generative Adversarial Networks (CycleGAN) is proposed in this paper. In the proposed model, a low-light enhancement generator of the CycleGAN network is constructed based on an improved U-Net structure, and the adaptive instance normalization (AdaIN) is designed to learn the style of the normal light image. In particular, a detail enhancement method based on multi-layer guided filtering is added to the proposed model, which can improve the quality and visual pleasantness of image enhancement. In addition, a joint training strategy based on structural similarity is presented, to strengthen the constraints on generating more realistic and natural images. At last, extensive experiments are conducted and the results show that the proposed method can accomplish the task of transferring low-light images to normal light and outperform the state-of-the-art approaches in various metrics of visual quality.
Frequency-oriented Efficient Transformer for All-in-one Weather-degraded Image Restoration

Adverse weather conditions, such as rain, raindrop, snow and haze, consistently degrade images in an unpredictable manner, thereby rendering existing task-specific and task-aligned methods inadequate in addressing this formidable problem. To this end, we investigate the application of Transformer in image restoration and introduce an efficient frequency-oriented method called AIRFormer, which is designed to restore weather-degraded images comprehensively and holistically. Specifically, we identify that the initial self-attention mechanism exhibits distinctive properties akin to a low-pass filter. Therefore, we construct a frequency-guided Transformer encoder by incorporating wavelet-based prior information to guide the extraction of image features. Additionally, considering the non-specific frequency characteristics of self-attention in the later stages, we develop a frequency-refined Transformer decoder that incorporates learnable task-specific queries across spatial dimensions, channel dimensions, and wavelet domains. To facilitate the training of our proposed method, we curate a comprehensive benchmark dataset named AIR40K that, encompasses a wide range of challenging scenarios. Extensive experimental evaluations demonstrate the superiority of our AIRFormer over both task-aligned and all-in-one methods across 15 publicly available datasets. Notably, AIRFormer achieves the best trade-off between the inference time and quality of reconstructed image, comparing with existing methods such as TransWeather and Restormer. The source code, dataset and pre-trained models will be available at https://github.com/chdwyb/AIRFormer.
Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning

We demonstrate transfer learning-assisted neural network models for optical matrix multipliers with scarce measurement data. Our approach uses &lt; 10% of experimental data needed for best performance and outperforms analytical models for a Mach-Zehnder interferometer mesh.
Small-footprint photonic reservoir computing chip based on speckle patterns in a multimode waveguide

We propose Si-photonic reservoir computing based on speckle patterns in a 250 x 920 pm 2 -footprint snake-crawling-shaped multimode waveguide. Good performance of the NMSE as low as 0.048 for the Santa-Fe time-series prediction is experimentally obtained.
Artificial intelligence and digital pathology: clinical promise and deployment considerations

Artificial intelligence (AI) presents an opportunity in anatomic pathology to provide quantitative objective support to a traditionally subjective discipline, thereby enhancing clinical workflows and enriching diagnostic capabilities. AI requires access to digitized pathology materials, which, at present, are most commonly generated from the glass slide using whole-slide imaging. Models are developed collaboratively or sourced externally, and best practices suggest validation with internal datasets most closely resembling the data expected in practice. Although an array of AI models that provide operational support for pathology practices or improve diagnostic quality and capabilities has been described, most of them can be categorized into one or more discrete types. However, their function in the pathology workflow can vary, as a single algorithm may be appropriate for screening and triage, diagnostic assistance, virtual second opinion, or other uses depending on how it is implemented and validated. Despite the clinical promise of AI, the barriers to adoption have been numerous, to which inclusion of new stakeholders and expansion of reimbursement opportunities may be among the most impactful solutions.
Machine translation in language acquisition: A study on <scp>EFL</scp> students' perceptions and practices in <scp>Saudi Arabia and South Korea</scp>

Abstract Background Language learners are taking advantage of the increased accuracy afforded by machine translation (MT) tools like Google Translate. There is a growing debate as to whether these tools support or hinder English as a foreign language (EFL) education. Some EFL students are drawn to MT tools to save time and energy when communicating, but how this use relates to second language acquisition needs to be clarified. Objective This study investigated the perceptions and practices of MT tools among university EFL students in Saudi Arabia and South Korea. Methods The study surveyed Saudi ( n = 310) and South Korean ( n = 160) university students to explore the use of MT tools in EFL learning. It used mean scores, correlation analysis, and structural equation modelling to understand the perceived benefits and limitations of MT tools and their influence on the use of translation websites. The findings were triangulated using content analysis of open‐ended items. Results and Conclusion Findings indicate students showed a high level of acceptance and utilization of MT tools. English avoidance behaviour ( B = 0.43), benefits ( B = 0.32), and foreign language proficiency ( B = −0.19) predicted 61% of actual use of MT tools like Google Translate. Further, students frequently used MT tools despite the reported concerns about accuracy mistakes. Saudi students listed a more comprehensive array of why they use MT tools than Korean students, while Saudis and Koreans frequently used MT tools for individual word and sentence translations. Implications Students should edit MT output when writing and are encouraged to use the voice recording and other functions of MT tools to learn language skills. Instructors must prevent overuse and dependency on MT tools, especially among low‐proficiency students who want to avoid using English.
Data-Explainable Website Fingerprinting with Network Simulation

Website fingerprinting (WF) attacks allow an adversary to associate a website with the encrypted traffic patterns produced when accessing it, thus threatening to destroy the client-server unlinkability promised by anonymous communication networks. Explainable WF is an open problem in which we need to improve our understanding of (1) the machine learning models used to conduct WF attacks; and (2) the WF datasets used as inputs to those models. This paper focuses on explainable datasets; that is, we develop an alternative to the standard practice of gathering low-quality WF datasets using synthetic browsers in large networks without controlling for natural network variability. In particular, we demonstrate how network simulation can be used to produce explainable WF datasets by leveraging the simulator's high degree of control over network operation. Through a detailed investigation of the effect of network variability on WF performance, we find that: (1) training and testing WF attacks in networks with distinct levels of congestion increases the false-positive rate by as much as 200%; (2) augmenting the WF attacks by training them across several networks with varying degrees of congestion decreases the false-positive rate by as much as 83%; and (3) WF classifiers trained on completely simulated data can achieve greater than 80% accuracy when applied to the real world.
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundamental challenge for this task is to learn a representation map that enables effective discrimination of anomalies. Reconstruction-based methods still dominate, but the representation learning with anomalies might hurt the performance with its large abnormal loss. On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection. In this paper, we propose DCdetector, a multi-scale dual attention contrastive representation learning model. DCdetector utilizes a novel dual attention asymmetric design to create the permutated environment and pure contrastive loss to guide the learning process, thus learning a permutation invariant representation with superior discrimination abilities. Extensive experiments show that DCdetector achieves state-of-the-art results on multiple time series anomaly detection benchmark datasets. Code is publicly available at https://github.com/DAMO-DI-ML/KDD2023-DCdetector.
Hierarchical Projection Enhanced Multi-behavior Recommendation

Various types of user behaviors are recorded in most real-world recommendation scenarios. To fully utilize the multi-behavior information, the exploration of multiplex interaction among them is essential. Many multi-task learning based multi-behavior methods are proposed recently to use multiple types of supervision signals and perform information transfer among them. Despite the great successes, these methods fail to design prediction tasks comprehensively, leading to insufficient utilization of multi-behavior correlative information. Besides, these methods are either based on the weighting of expert information extracted from the coupled input or modeling of information transfer between multiple behavior levels through task-specific extractors, which are usually accompanied by negative transfer phenomenon1. To address the above problems, we propose a multi-behavior recommendation framework, called Hierarchical Projection Enhanced Multi-behavior Recommendation (HPMR). The key module, Projection-based Transfer Network (PTN), uses the projection mechanism to "explicitly" model the correlations of upstream and downstream behaviors, refines the upstream behavior representations, and fully uses the refined representations to enhance the learning of downstream tasks. Offline experiments on public and industrial datasets and online A/B test further verify the effectiveness of HPMR in modeling the associations from upstream to downstream and alleviating the negative transfer. The source code and datasets are available at https://github.com/MC-CV/HPMR.
Learning Behavior-oriented Knowledge Tracing

Exploring how learners' knowledge states evolve during the learning activities is a critical task in online learning systems, which can facilitate personalized services downstream, such as course recommendation. Most of existing methods have devoted great efforts to analyzing learners' knowledge states according to their responses (i.e., right or wrong) to different questions. However, the significant effect of learners' learning behaviors (e.g., answering speed, the number of attempts) is omitted, which can reflect their knowledge acquisition deeper and ensure the reliability of the response. In this paper, we propose a Learning Behavior-oriented Knowledge Tracing (LBKT) model, with the goal of explicitly exploring the learning behavior effects on learners' knowledge states. Specifically, we first analyze and summarize several dominated learning behaviors including Speed, Attempts and Hints in the learning process. As the characteristics of different learning behaviors vary greatly, we separately estimate their various effects on learners' knowledge acquisition in a quantitative manner. Then, considering that different learning behaviors are closely dependent with each other, we assess the fused effect of multiple learning behaviors by capturing their complex dependent patterns. Finally, we integrate the forgetting factor with learners' knowledge acquisition to comprehensively update their changing knowledge states in learning. Extensive experimental results on several public datasets demonstrate that our model generates better performance prediction for learners against existing methods. Moreover, LBKT shows good interpretability in tracking learners' knowledge state by incorporating the learning behavior effects. Our codes are available at https://github.com/xbh0720/LBKT.
MUSTIE: Multimodal Structural Transformer for Web Information Extraction

The task of web information extraction is to extract target fields of an object from web pages, such as extracting the name, genre and actor from a movie page.Recent sequential modeling approaches have achieved state-of-the-art results on web information extraction.However, most of these methods only focus on extracting information from textual sources while ignoring the rich information from other modalities such as image and web layout.In this work, we propose a novel MUltimodal Structural Transformer (MUST) that incorporates multiple modalities for web information extraction.Concretely, we develop a structural encoder that jointly encodes the multimodal information based on the HTML structure of the web layout, where high-level DOM nodes, low-level text, and image tokens are introduced to represent the entire page.Structural attention patterns are designed to learn effective cross-modal embeddings for all DOM nodes and low-level tokens.An extensive set of experiments has been conducted on WebSRC and Common Crawl benchmarks.Experimental results demonstrate the superior performance of MUST over several state-of-the-art baselines.
Chunk-based Nearest Neighbor Machine Translation

Semi-parametric models, which augment generation with retrieval, have led to impressive results in language modeling and machine translation, due to their ability to retrieve fine-grained information from a datastore of examples.One of the most prominent approaches, kNN-MT, exhibits strong domain adaptation capabilities by retrieving tokens from domain-specific datastores (Khandelwal et al., 2021).However, kNN-MT requires an expensive retrieval operation for every single generated token, leading to a very low decoding speed (around 8 times slower than a parametric model).In this paper, we introduce a chunk-based kNN-MT model which retrieves chunks of tokens from the datastore, instead of a single token.We propose several strategies for incorporating the retrieved chunks into the generation process, and for selecting the steps at which the model needs to search for neighbors in the datastore.Experiments on machine translation in two settings, static and "on-the-fly" domain adaptation, show that the chunk-based kNN-MT model leads to significant speed-ups (up to 4 times) with only a small drop in translation quality. 1
A Distributional Lens for Multi-Aspect Controllable Text Generation

Multi-aspect controllable text generation is a more challenging and practical task than single-aspect control. Existing methods achieve complex multi-aspect control by fusing multiple controllers learned from single-aspect, but suffer from attribute degeneration caused by the mutual interference of these controllers. To address this, we provide observations on attribute fusion from a distributional perspective and propose to directly search for the intersection areas of multiple attribute distributions as their combination for generation. Our method first estimates the attribute space with an autoencoder structure. Afterward, we iteratively approach the intersections by jointly minimizing distances to points representing different attributes. Finally, we map them to attribute-relevant sentences with a prefix-tuning-based decoder. Experiments on the three-aspect control task, including sentiment, topic, and detoxification aspects, reveal that our method outperforms several strong baselines on attribute relevance and text quality and achieves the SOTA. Further analysis also supplies some explanatory support for the effectiveness of our approach.
Reasoning Like Program Executors

Reasoning over natural language is a longstanding goal for the research community.However, studies have shown that existing language models are inadequate in reasoning.To address the issue, we present POET, a novel reasoning pre-training paradigm.Through pretraining language models with programs and their execution results, POET empowers language models to harvest the reasoning knowledge possessed by program executors via a data-driven approach.POET is conceptually simple and can be instantiated by different kinds of program executors.In this paper, we showcase two simple instances POET-Math and POET-Logic, in addition to a complex instance, POET-SQL.Experimental results on six benchmarks demonstrate that POET can significantly boost model performance in natural language reasoning, such as numerical reasoning, logical reasoning, and multi-hop reasoning.POET opens a new gate on reasoningenhancement pre-training, and we hope our analysis would shed light on the future research of reasoning like program executors.
A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling

Document-level relation extraction (RE) aimsto identify relations between entities across multiple sentences.Most previous methods focused on document-level RE under full supervision.However, in real-world scenario, it is expensive and difficult to completely label all relations in a document because the number of entity pairs in document-level RE grows quadratically with the number of entities.To solve the common incomplete labeling problem, we propose a unified positive-unlabeled learning frameworkshift and squared ranking loss positive-unlabeled (SSR-PU) learning.We use positive-unlabeled (PU) learning on documentlevel RE for the first time.Considering that labeled data of a dataset may lead to prior shift of unlabeled data, we introduce a PU learning under prior shift of training data.Also, using none-class score as an adaptive threshold, we propose squared ranking loss and prove its Bayesian consistency with multi-label ranking metrics.Extensive experiments demonstrate that our method achieves an improvement of about 14 F1 points relative to the previous baseline with incomplete labeling.In addition, it outperforms previous state-of-the-art results under both fully supervised and extremely unlabeled settings as well.
Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems

Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals.However, such systems rely on costly manually labeled dialogs which are not available in practical scenarios.In this paper, we present our models for Track 2 of the SereTOD 2022 challenge, which is the first challenge of building semisupervised and reinforced TOD systems on a large-scale real-world Chinese TOD dataset MobileCS.We build a knowledge-grounded dialog model to formulate dialog history and local KB as input and predict the system response.And we perform semi-supervised pretraining both on the labeled and unlabeled data.Our system achieves the first place both in the automatic evaluation and human interaction, especially with higher BLEU (+7.64) and Success (+13.6%)than the second place. 1
ZeroGen: Efficient Zero-shot Learning via Dataset Generation

There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs).In this paper, we study a flexible and efficient zero-short learning method, ZEROGEN.Given a zero-shot task, we first generate a dataset from scratch using PLMs in an unsupervised manner.Then, we train a tiny task model (e.g., LSTM) under the supervision of the synthesized dataset.This approach allows highly efficient inference as the final task model only has orders of magnitude fewer parameters comparing to PLMs (e.g., GPT2-XL).Apart from being annotation-free and efficient, we argue that ZEROGEN can also provide useful insights from the perspective of datafree model-agnostic knowledge distillation, and unreferenced text generation evaluation.Experiments and analysis on different NLP tasks, namely, text classification, question answering, and natural language inference, show the effectiveness of ZEROGEN.
A Fine-grained Interpretability Evaluation Benchmark for Neural NLP

While there is increasing concern about the interpretability of neural models, the evaluation of interpretability remains an open problem, due to the lack of proper evaluation datasets and metrics.In this paper, we present a novel benchmark to evaluate the interpretability of both neural models and saliency methods.This benchmark covers three representative NLP tasks: sentiment analysis, textual similarity and reading comprehension, each provided with both English and Chinese annotated data.In order to precisely evaluate the interpretability, we provide token-level rationales that are carefully annotated to be sufficient, compact and comprehensive.We also design a new metric, i.e., the consistency between the rationales before and after perturbations, to uniformly evaluate the interpretability on different types of tasks.Based on this benchmark, we conduct experiments on three typical models with three saliency methods, and unveil their strengths and weakness in terms of interpretability.We will release this benchmark 1 and hope it can facilitate the research in building trustworthy systems.