Reviewerly/exHarmony huggingface dataset:
A Holistic Approach to Undesired Content Detection in the Real World

We present a holistic approach to building a robust and useful natural language classification system for real-world content moderation. The success of such a system relies on a chain of carefully designed and executed steps, including the design of content taxonomies and labeling instructions, data quality control, an active learning pipeline to capture rare events, and a variety of methods to make the model robust and to avoid overfitting. Our moderation system is trained to detect a broad set of categories of undesired content, including sexual content, hateful content, violence, self-harm, and harassment. This approach generalizes to a wide range of different content taxonomies and can be used to create high-quality content classifiers that outperform off-the-shelf models.
Mutual-Enhanced Incongruity Learning Network for Multi-Modal Sarcasm Detection

Sarcasm is a sophisticated linguistic phenomenon that is prevalent on today's social media platforms. Multi-modal sarcasm detection aims to identify whether a given sample with multi-modal information (i.e., text and image) is sarcastic. This task's key lies in capturing both inter- and intra-modal incongruities within the same context. Although existing methods have achieved compelling success, they are disturbed by irrelevant information extracted from the whole image and text, or overlooking some important information due to the incomplete input. To address these limitations, we propose a Mutual-enhanced Incongruity Learning Network for multi-modal sarcasm detection, named MILNet. In particular, we design a local semantic-guided incongruity learning module and a global incongruity learning module. Moreover, we introduce a mutual enhancement module to take advantage of the underlying consistency between the two modules to boost the performance. Extensive experiments on a widely-used dataset demonstrate the superiority of our model over cutting-edge methods.
Recognition of human activity using GRU deep learning algorithm

Abstract Human activity recognition (HAR) is a challenging issue in several fields, such as medical diagnosis. Recent advances in the accuracy of deep learning have contributed to solving the HAR issues. Thus, it is necessary to implement deep learning algorithms that have high performance and greater accuracy. In this paper, a gated recurrent unit (GRU) algorithm is proposed to classify human activities. This algorithm is applied to the Wireless Sensor Data Mining (WISDM) dataset gathered from many individuals with six classes of various activities – walking, sitting, downstairs, jogging, standing, and upstairs. The proposed algorithm is tested and trained via a hyper-parameter tuning method with TensorFlow framework to achieve high accuracy. Experiments are conducted to evaluate the performance of the GRU algorithm using receiver operating characteristic (ROC) curves and confusion matrices. The results demonstrate that the GRU algorithm provides high performance in the recognition of human activities. The GRU algorithm achieves a testing accuracy of 97.08%. The rate of testing loss for the GRU is 0.221, while the precision, sensitivity, and F1-score for the GRU are 97.11%, 97.09%, and 97.10%, respectively. Experimentally, the area under the ROC curves (AUC S ) is 100%.
YOLOV: Making Still Image Object Detectors Great at Video Object Detection

Video object detection (VID) is challenging because of the high variation of object appearance as well as the diverse deterioration in some frames. On the positive side, the detection in a certain frame of a video, compared with that in a still image, can draw support from other frames. Hence, how to aggregate features across different frames is pivotal to VID problem. Most of existing aggregation algorithms are customized for two-stage detectors. However, these detectors are usually computationally expensive due to their two-stage nature. This work proposes a simple yet effective strategy to address the above concerns, which costs marginal overheads with significant gains in accuracy. Concretely, different from traditional two-stage pipeline, we select important regions after the one-stage detection to avoid processing massive low-quality candidates. Besides, we evaluate the relationship between a target frame and reference frames to guide the aggregation. We conduct extensive experiments and ablation studies to verify the efficacy of our design, and reveal its superiority over other state-of-the-art VID approaches in both effectiveness and efficiency. Our YOLOX-based model can achieve promising performance (e.g., 87.5% AP50 at over 30 FPS on the ImageNet VID dataset on a single 2080Ti GPU), making it attractive for large-scale or real-time applications. The implementation is simple, we have made the demo codes and models available at https://github.com/YuHengsss/YOLOV.
User Interface Information System for Using Account Services (Joint Account) WEB-Based

Rekber Services (Joint Account) is a method of payment or transactions made online. This method is considered safer than having to transfer directly to the seller's account. Abroad, the account is known as Escrow Service. Not only abroad, the Indonesian banking world is also known as ES (Escrow Service).
Learn from Relational Correlations and Periodic Events for Temporal Knowledge Graph Reasoning

Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the timeline, has been widely studied to alleviate incompleteness issues in TKG, which is composed of a series of KG snapshots at different timestamps. Two types of information, i.e., intra-snapshot structural information and inter-snapshot temporal interactions, mainly contribute to the learned representations for reasoning in previous models. However, these models fail to leverage (1) semantic correlations between relationships for the former information and (2) the periodic temporal patterns along the timeline for the latter one. Thus, such insufficient mining manners hinder expressive ability, leading to sub-optimal performances. To address these limitations, we propose a novel reasoning model, termed RPC, which sufficiently mines the information underlying the Relational correlations and Periodic patterns via two novel Correspondence units, i.e., relational correspondence unit (RCU) and periodic correspondence unit (PCU). Concretely, relational graph convolutional network (RGCN) and RCU are used to encode the intra-snapshot graph structural information for entities and relations, respectively. Besides, the gated recurrent units (GRU) and PCU are designed for sequential and periodic inter-snapshot temporal interactions, separately. Moreover, the model-agnostic time vectors are generated by time2vector encoders to guide the time-dependent decoder for fact scoring. Extensive experiments on six benchmark datasets show that RPC outperforms the state-of-the-art TKGR models, and also demonstrate the effectiveness of two novel strategies in our model.
Towards Generalist Biomedical AI

Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly encode, integrate, and interpret this data at scale can potentially enable impactful applications ranging from scientific discovery to care delivery. To enable the development of these models, we first curate MultiMedBench, a new multimodal biomedical benchmark. MultiMedBench encompasses 14 diverse tasks such as medical question answering, mammography and dermatology image interpretation, radiology report generation and summarization, and genomic variant calling. We then introduce Med-PaLM Multimodal (Med-PaLM M), our proof of concept for a generalist biomedical AI system. Med-PaLM M is a large multimodal generative model that flexibly encodes and interprets biomedical data including clinical language, imaging, and genomics with the same set of model weights. Med-PaLM M reaches performance competitive with or exceeding the state of the art on all MultiMedBench tasks, often surpassing specialist models by a wide margin. We also report examples of zero-shot generalization to novel medical concepts and tasks, positive transfer learning across tasks, and emergent zero-shot medical reasoning. To further probe the capabilities and limitations of Med-PaLM M, we conduct a radiologist evaluation of model-generated (and human) chest X-ray reports and observe encouraging performance across model scales. In a side-by-side ranking on 246 retrospective chest X-rays, clinicians express a pairwise preference for Med-PaLM M reports over those produced by radiologists in up to 40.50% of cases, suggesting potential clinical utility. While considerable work is needed to validate these models in real-world use cases, our results represent a milestone towards the development of generalist biomedical AI systems.
Progressively Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

Unsupervised image-to-image translation has received considerable attention due to the recent remarkable advancements in generative adversarial networks (GANs). In image-to-image translation, state-of-the-art methods use unpaired image data to learn mappings between the source and target domains. However, despite their promising results, existing approaches often fail in challenging conditions, particularly when images have various target instances and a translation task involves significant transitions in shape and visual artifacts when translating low-level information rather than high-level semantics. To tackle the problem, we propose a novel framework called Progressive Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization (PRO-U-GAT-IT) for the unsupervised image-to-image translation task. In contrast to existing attention-based models that fail to handle geometric transitions between the source and target domains, our model can translate images requiring extensive and holistic changes in shape. Experimental results show the superiority of the proposed approach compared to the existing state-of-the-art models on different datasets.
Autonomous quantum error correction and fault-tolerant quantum computation with squeezed cat qubits

Abstract We propose an autonomous quantum error correction scheme using squeezed cat (SC) code against excitation loss in continuous-variable systems. Through reservoir engineering, we show that a structured dissipation can stabilize a two-component SC while autonomously correcting the errors. The implementation of such dissipation only requires low-order nonlinear couplings among three bosonic modes or between a bosonic mode and a qutrit. While our proposed scheme is device independent, it is readily implementable with current experimental platforms such as superconducting circuits and trapped-ion systems. Compared to the stabilized cat, the stabilized SC has a much lower dominant error rate and a significantly enhanced noise bias. Furthermore, the bias-preserving operations for the SC have much lower error rates. In combination, the stabilized SC leads to substantially better logical performance when concatenating with an outer discrete-variable code. The surface-SC scheme achieves more than one order of magnitude increase in the threshold ratio between the loss rate κ 1 and the engineered dissipation rate κ 2 . Under a practical noise ratio κ 1 / κ 2 = 10 −3 , the repetition-SC scheme can reach a 10 −15 logical error rate even with a small mean excitation number of 4, which already suffices for practically useful quantum algorithms.
Prompted Opinion Summarization with GPT-3.5

Large language models have shown impressive performance across a wide variety of tasks, including text summarization.In this paper, we show that this strong performance extends to opinion summarization.We explore several pipeline methods for applying GPT-3.5 to summarize a large collection of user reviews in a prompted fashion.To handle arbitrarily large numbers of user reviews, we explore recursive summarization as well as methods for selecting salient content to summarize through supervised clustering or extraction.On two datasets, an aspect-oriented summarization dataset of hotel reviews (SPACE) and a generic summarization dataset of Amazon and Yelp reviews (Few-Sum), we show that GPT-3.5 models achieve very strong performance in human evaluation.We argue that standard evaluation metrics do not reflect this, and introduce three new metrics targeting faithfulness, factuality, and genericity to contrast these different methods.
RACE: Retrieval-augmented Commit Message Generation

Commit messages are important for software development and maintenance.Many neural network-based approaches have been proposed and shown promising results on automatic commit message generation.However, the generated commit messages could be repetitive or redundant.In this paper, we propose RACE, a new retrieval-augmented neural commit message generation method, which treats the retrieved similar commit as an exemplar and leverages it to generate an accurate commit message.As the retrieved commit message may not always accurately describe the content/intent of the current code diff, we also propose an exemplar guider, which learns the semantic similarity between the retrieved and current code diff and then guides the generation of commit message based on the similarity.We conduct extensive experiments on a large public dataset with five programming languages.Experimental results show that RACE can outperform all baselines.Furthermore, RACE can boost the performance of existing Seq2Seq models in commit message generation.
Transformer Language Models without Positional Encodings Still Learn Positional Information

Causal transformer language models (LMs), such as GPT-3, typically require some form of positional encoding, such as positional embeddings.However, we show that LMs without any explicit positional encoding are still competitive with standard models, and that this phenomenon is robust across different datasets, model sizes, and sequence lengths.Probing experiments reveal that such models acquire an implicit notion of absolute positions throughout the network, effectively compensating for the missing information.We conjecture that causal attention enables the model to infer the number of predecessors that each token can attend to, thereby approximating its absolute position.Our findings indicate that causal LMs might derive positional awareness not only from the explicit positioning mechanism, but also from the effects of the causal mask.
Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion

Few-shot knowledge graph completion (FKGC) aims to infer unknown fact triples of a relation using its few-shot reference entity pairs.Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities.Such practice, however, ignores the inter-entity interaction, resulting in low-discrimination representations for entity pairs, especially when these entity pairs are associated with 1-to-N, N-to-1, and N-to-N relations.To address this issue, this paper proposes a novel FKGC model, named Cross-Interaction Attention Network (CIAN) to investigate the inter-entity interaction between head and tail entities.Specifically, we first explore the interactions within entities by computing the attention between the task relation and each entity neighbor, and then model the interactions between head and tail entities by letting an entity to attend to the neighborhood of its paired entity.In this way, CIAN can figure out the relevant semantics between head and tail entities, thereby generating more discriminative representations for entity pairs.Extensive experiments on two public datasets show that CIAN outperforms several state-of-the-art methods.
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation

A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity.We introduce a novel approach for dataset creation based on worker and AI collaboration, which brings together the generative strength of language models and the evaluative strength of humans.Starting with an existing dataset, MultiNLI for natural language inference (NLI), our approach uses dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instructs GPT-3 to compose new examples with similar patterns.Machine generated examples are then automatically filtered, and finally revised and labeled by human crowdworkers.The resulting dataset, WANLI, consists of 107,885 NLI examples and presents unique empirical strengths over existing NLI datasets.Remarkably, training a model on WANLI improves performance on eight out-of-domain test sets we consider, including by 11% on HANS and 9% on Adversarial NLI, compared to training on the 4× larger MultiNLI.Moreover, it continues to be more effective than MultiNLI augmented with other NLI datasets.Our results demonstrate the promise of leveraging natural language generation techniques and re-imagining the role of humans in the dataset creation process. Seed MultiNLI example Generated WANLI ExampleLabel & Reasoning P: 5 percent probability that each part will be defect free.H: Each part has a 95 percent chance of having a defect.P: 1 percent of the seats were vacant.H: 99 percent of the seats were occupied. EntailmentSet complements P: The artisans, tradespeople, and providers of entertainment (reputable and not so reputable) lived downtown on the reclaimed marshlands north and east, in the area still known as Shitamachi.H: The only place where artisans, tradespeople and entertainers could live was in the marshlands to the north and east.P: At the time of the Revolution, the old port of Marseille was a great center of shipbuilding and commerce.H: The only place where ships were built was in the old port of Marseille. Neutral Hypothesis introduces exclusivityP: To the south, in the Sea of Marmara, lie the woods and beaches of the Princes' Islands.H: In the north is the Sea of Marmara where there are mountains to climb.P: From the park's southern entrance, follow the avenue south to the Hotel de Ville.H: From the park's northern entrance, follow the avenue north to the Hotel de
Physical-World Optical Adversarial Attacks on 3D Face Recognition

The success rate of current adversarial attacks remains low on real-world 3D face recognition tasks because the 3D-printing attacks need to meet the requirement that the generated points should be adjacent to the surface, which limits the adversarial example’ searching space. Additionally, they have not considered unpredictable head movements or the non-homogeneous nature of skin reflectance in the real world. To address the real-world challenges, we propose a novel structured-light attack against structured-light-based 3D face recognition. We incorporate the 3D reconstruction process and skin's reflectance in the optimization process to get the end-to-end attack and present 3D transform invariant loss and sensitivity maps to improve robustness. Our attack enables adversarial points to be placed in any position and is resilient to random head movements while maintaining the perturbation unnoticeable. Experiments show that our new method can attack point-cloud-based and depth-image-based 3D face recognition systems with a high success rate, using fewer perturbations than previous physical 3D adversarial attacks.
Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection

Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. Specifically, UDA methods try to align the source and target representations to improve generalization on the target domain. Further, UDA methods work under the assumption that the source data is accessible during the adaptation process. However, in real-world scenarios, the labelled source data is often restricted due to privacy regulations, data transmission constraints, or proprietary data concerns. The Source-Free Domain Adaptation (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data. In this paper, we explore the SFDA setting for the task of adaptive object detection. To this end, we propose a novel training strategy for adapting a source-trained object detector to the target domain without source data. More precisely, we design a novel contrastive loss to enhance the target representations by exploiting the objects relations for a given target domain input. These object instance relations are modelled using an Instance Relation Graph (IRG) network, which are then used to guide the contrastive representation learning. In addition, we utilize a student-teacher to effectively distill knowledge from source-trained model to target domain. Extensive experiments on multiple object detection benchmark datasets show that the proposed approach is able to efficiently adapt source-trained object detectors to the target domain, outperforming state-of-the-art domain adaptive detection methods. Code and models are provided in https://viudomain.github.io/irg-sfda-web/.
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing

Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data. As a good start and the only existing outcome of zero-shot image captioning (IC), ZeroCap abandons supervised training and sequentially searches every word in the caption using the knowledge of large-scale pre-trained models. Though effective, its autoregressive generation and gradient-directed searching mechanism limit the diversity of captions and inference speed, respectively. Moreover, ZeroCap does not consider the controllability issue of zero-shot IC. To move forward, we propose a framework for Controllable Zero-shot IC, named ConZIC. The core of ConZIC is a novel sampling-based non-autoregressive language model named Gibbs-BERT, which can generate and continuously polish every word. Extensive quantitative and qualitative results demonstrate the superior performance of our proposed ConZIC for both zero-shot IC and controllable zero-shot IC. Especially, ConZIC achieves about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5\times$</tex> generation speed than ZeroCap, and about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.5\times$</tex> diversity scores, with accurate generation given different control signals. Our code is available at https://github.com/joeyz0z/ConZIC.
DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction

Document-level relation extraction (DocRE) is the task of identifying all relations between each entity pair in a document. Evidence, defined as sentences containing clues for the relationship between an entity pair, has been shown to help DocRE systems focus on relevant texts, thus improving relation extraction. However, evidence retrieval (ER) in DocRE faces two major issues: high memory consumption and limited availability of annotations. This work aims at addressing these issues to improve the usage of ER in DocRE. First, we propose DREEAM, a memory-efficient approach that adopts evidence information as the supervisory signal, thereby guiding the attention modules of the DocRE system to assign high weights to evidence. Second, we propose a self-training strategy for DREEAM to learn ER from automatically-generated evidence on massive data without evidence annotations. Experimental results reveal that our approach exhibits state-of-the-art performance on the DocRED benchmark for both DocRE and ER. To the best of our knowledge, DREEAM is the first approach to employ ER self-training.