Reviewerly/exHarmony huggingface dataset:
From multi-label learning to cross-domain transfer: a model-agnostic approach

In multi-label learning it has been widely assumed in the literature that, to obtain best accuracy, the dependence among the labels should be explicitly modeled. This premise led to a proliferation of methods offering techniques to learn and predict labels together (joint modeling). Even though it is now acknowledged that in many contexts a model of dependence is not required for optimal performance, such models continue to outperform independent models in some of those very contexts, suggesting alternative explanations for their performance beyond label dependence. In this article we turn the original premise of multi-label learning on its head, and approach the problem of joint-modeling specifically under the absence of any measurable dependence among task labels. The insights from this study allow us to design a method for cross-domain transfer learning which, unlike most contemporary methods of this type, is model-agnostic (any base model class can be considered) and does not require any access to source data. The results we obtain have important implications and we provide clear directions for future work, both in the areas of multi-label and transfer learning.
Mixed Reality Takes to the Skies

Airbus to team up with Microsoft to explore HoloLens 2, a reality headset that uses holograms to help speed up aircraft production
Robust NLP for Finance (RobustFin)

Natural language processing (NLP) technologies have been widely applied in business domains such as e-commerce and customer service, but their adoption in the financial sector has been constrained by industry-specific performance standards and regulatory restrictions. This challenge has created new opportunities for core research in related areas. Recent advancements in NLP, such as the advent of large language models, has encouraged adoption in the finance sector. However, compared to other domains, finance has stricter requirements for robustness, explainability, and generalizability. Given this background, we propose to organize the first Robust NLP for Finance (RobustFin) workshop at KDD '23 to encourage the study of and research on robustness and explainability technologies with regard to financial NLP. The goal of the workshop is to extend the applications of NLP in finance, while motivating further research in robust NLP.
When to Pre-Train Graph Neural Networks? From Data Generation Perspective!

In recent years, graph pre-training has gained significant attention, focusing on acquiring transferable knowledge from unlabeled graph data to improve downstream performance. Despite these recent endeavors, the problem of negative transfer remains a major concern when utilizing graph pre-trained models to downstream tasks. Previous studies made great efforts on the issue of what to pre-train and how to pre-train by designing a variety of graph pre-training and fine-tuning strategies. However, there are cases where even the most advanced "pre-train and fine-tune" paradigms fail to yield distinct benefits. This paper introduces a generic framework W2PGNN to answer the crucial question of when to pre-train (.e., in what situations could we take advantage of graph pre-training) before performing effortful pre-training or fine-tuning. We start from a new perspective to explore the complex generative mechanisms from the pre-training data to downstream data. In particular, W2PGNN first fits the pre-training data into graphon bases, each element of graphon basis (i.e., a graphon) identifies a fundamental transferable pattern shared by a collection of pre-training graphs. All convex combinations of graphon bases give rise to a generator space, from which graphs generated form the solution space for those downstream data that can benefit from pre-training. In this manner, the feasibility of pre-training can be quantified as the generation probability of the downstream data from any generator in the generator space. W2PGNN offers three broad applications: providing the application scope of graph pre-trained models, quantifying the feasibility of pre-training, and assistance in selecting pre-training data to enhance downstream performance. We provide a theoretically sound solution for the first application and extensive empirical justifications for the latter two applications.
Removing Camouflage and Revealing Collusion: Leveraging Gang-crime Pattern in Fraudster Detection

As one of the major threats to the healthy development of various online platforms, fraud has become increasingly committed in the form of gangs since collusive fraudulent activities are much easier to obtain illicit benefits with lower exposure risk. To detect fraudsters in a gang, spatio-temporal graph neural network models have been widely applied to detect both temporal and spatial collusive patterns. However, a closer peek into real-world records of fraudsters can reveal that fraud gangs usually conduct community-level camouflage, specified by two types, i.e., temporal and spatial camouflage. Such camouflage can disguise gangs as benign communities by concealing collusive patterns and thus deceiving many existing graph neural network models. In the meantime, many existing graph neural network models suffer from the challenge of extreme sample imbalance caused by rare fraudsters hidden among massive users. To handle all these challenges, in this paper, we propose a generative adversarial network framework, named Adversarial Camouflage Detector, to detect fraudsters. Concretely, this ACD framework consists of four modules, in charge of community division, camouflage identification, fraudster detection, and camouflage generation, respectively. The first three modules form up a discriminator that uses spatio-temporal graph neural networks as the foundation model and enhance fraudster detection by amplifying the gangs' collusive patterns through automatically identifying and removing camouflage. Meanwhile, the camouflage generation module plays as the generator role that generates fraudsters samples by competing against the discriminator to alleviate the challenge of sample imbalance and increase the model robustness. The experimental result shows that our proposed method outperforms other methods on real-world datasets.
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications

Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be drawn for pre-training graph models on large graphs in the hope of benefiting downstream graph applications, which has also been explored by several recent studies. However, no existing study has ever investigated the pre-training of text plus graph models on large heterogeneous graphs with abundant textual information (a.k.a. large graph corpora) and then fine-tuning the model on different related downstream applications with different graph schemas. To address this problem, we propose a framework of graph-aware language model pre-training (GaLM) on a large graph corpus, which incorporates large language models and graph neural networks, and a variety of fine-tuning methods on downstream applications. We conduct extensive experiments on Amazon's real internal datasets and large public datasets. Comprehensive empirical results and in-depth analysis demonstrate the effectiveness of our proposed methods along with lessons learned.
Robust Estimation of Exposure Ratios in Multi-Exposure Image Stacks

Merging multi-exposure image stacks into a high dynamic range (HDR) image requires knowledge of accurate exposure times. When exposure times are inaccurate, for example, when they are extracted from a camera's EXIF metadata, the reconstructed HDR images reveal banding artifacts at smooth gradients. To remedy this, we propose to estimate exposure ratios directly from the input images. We derive the exposure time estimation as an optimization problem, in which pixels are selected from pairs of exposures to minimize estimation error caused by camera noise. When pixel values are represented in the logarithmic domain, the problem can be solved efficiently using a linear solver. We demonstrate that the estimation can be easily made robust to pixel misalignment caused by camera or object motion by collecting pixels from multiple spatial tiles. The proposed automatic exposure estimation and alignment eliminates banding artifacts in popular datasets and is essential for applications that require physically accurate reconstructions, such as measuring the modulation transfer function of a display. The code for the method is available.
Figurative Language Processing: A Linguistically Informed Feature Analysis of the Behavior of Language Models and Humans

Recent years have witnessed a growing interest in investigating what Transformer-based language models (TLMs) actually learn from the training data.This is especially relevant for complex tasks such as the understanding of non-literal meaning.In this work, we probe the performance of three black-box TLMs and two intrinsically transparent white-box models on figurative language classification of sarcasm, similes, idioms, and metaphors.We conduct two studies on the classification results to provide insights into the inner workings of such models.With our first analysis on feature importance, we identify crucial differences in model behavior.With our second analysis using an online experiment with human participants, we inspect different linguistic characteristics of the four figurative language types.
Event-Centric Query Expansion in Web Search

In search engines, query expansion (QE) is a crucial technique to improve search experience.Previous studies often rely on longterm search log mining, which leads to slow updates and is sub-optimal for time-sensitive news searches.In this work, we present Event-Centric Query Expansion (EQE), a novel QE system that addresses these issues by mining the best expansion from a significant amount of potential events rapidly and accurately.This system consists of four stages, i.e., event collection, event reformulation, semantic retrieval and online ranking.Specifically, we first collect and filter news headlines from websites.Then we propose a generation model that incorporates contrastive learning and prompt-tuning techniques to reformulate these headlines to concise candidates.Additionally, we fine-tune a dual-tower semantic model to function as an encoder for event retrieval and explore a two-stage contrastive training approach to enhance the accuracy of event retrieval.Finally, we rank the retrieved events and select the optimal one as QE, which is then used to improve the retrieval of event-related documents.Through offline analysis and online A/B testing, we observe that the EQE system significantly improves many metrics compared to the baseline.The system has been deployed in Tencent QQ Browser Search and served hundreds of millions of users.The dataset and baseline codes are available at https: //open-event-hub.github.io/eqe.
Free Lunch for Efficient Textual Commonsense Integration in Language Models

Recent years have witnessed the emergence of textual commonsense knowledge bases, aimed at providing more nuanced and contextrich knowledge.The integration of external commonsense into language models has been shown to be a key enabler in advancing the state-of-the-art for a wide range of NLP tasks.However, incorporating textual commonsense descriptions is computationally expensive, as compared to encoding conventional symbolic knowledge.In this paper, we propose a method to improve its efficiency without modifying the model.We group training samples with similar commonsense descriptions into a single batch, thus reusing the encoded description across multiple samples.One key observation is that the upper bound of batch partitioning can be reduced to the classic graph k-cut problem.Consequently, we propose a spectral clusteringbased algorithm to solve this problem.Extensive experiments illustrate that the proposed batch partitioning approach effectively reduces the computational cost while preserving performance.The efficiency improvement is more pronounced on larger datasets and on devices with more memory capacity, attesting to its practical utility for large-scale applications.
Attention at SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS)

In this paper, we have worked on explainability and understanding of the decisions made by models in the form of classification tasks.The task is divided into 3 subtasks.The first task consists of determining Binary Sexism Detection.The second task describes the Category of Sexism.The third task describes a more Fine-grained Category of Sexism.Our work explores solving these tasks as a classification problem by fine-tuning transformer-based architecture.We have performed several experiments with our architecture, including combining multiple transformers, using domain adaptive pretraining on the unlabelled dataset provided by Reddit and Gab, Joint learning, and taking different layers of transformers as input to a classification head.Our system (with team name 'Attention') was able to achieve a macro F1 score of 0.839 for task A, 0.5835 macro F1 score for task B and 0.3356 macro F1 score for task C at the Codalab SemEval Competition.Later we improved the accuracy of Task B to 0.6228 and Task C to 0.3693 in the test set.
Augmentation Invariant Discrete Representation for Generative Spoken Language Modeling

Generative Spoken Language Modeling research focuses on optimizing speech Language Models (LMs) using raw audio recordings without accessing any textual supervision.Such speech LMs usually operate over discrete units obtained from quantizing internal representations of self-supervised models.Although such units show impressive modeling results, their robustness capabilities have not been extensively investigated.This work focuses on improving the invariance of discrete input representations to non-spoken augmentations for generative spoken language modeling.First, we formally define how to measure the robustness of such representations to various signal variations that do not alter the spoken information (e.g., time-stretch).Next, we empirically demonstrate how current state-of-the-art representation models lack robustness to such variations.To overcome this, we propose an effective and efficient method to learn invariant discrete speech representation for generative spoken language modeling.The proposed approach is based on applying a set of signal transformations to the speech signal and optimizing the model using an iterative pseudolabeling scheme.Our method significantly improves over the evaluated baselines when considering encoding and modeling metrics.We additionally evaluate our method on the speechto-speech translation task, considering Spanish-English and French-English translations, and show the proposed approach outperforms the evaluated baselines.
Exploring Automatically Perturbed Natural Language Explanations in Relation Extraction

Previous research has demonstrated that natural language explanations provide valuable inductive biases that guide models, thereby improving the generalization ability and data efficiency.In this paper, we undertake a systematic examination of the effectiveness of these explanations.Remarkably, we find that corrupted explanations with diminished inductive biases can achieve competitive or superior performance compared to the original explanations.Our findings furnish novel insights into the characteristics of natural language explanations in the following ways: (1) the impact of explanations varies across different training styles and datasets, with previously believed improvements primarily observed in frozen language models.(2) While previous research has attributed the effect of explanations solely to their inductive biases, our study shows that the effect persists even when the explanations are completely corrupted.We propose that the main effect is due to the provision of additional context space.(3) Utilizing the proposed automatic perturbed context, we were able to attain comparable results to annotated explanations, but with a significant increase in computational efficiency, 20-30 times faster.
Learning to Immunize Images for Tamper Localization and Self-Recovery

Digital images are vulnerable to nefarious tampering attacks such as content addition or removal that severely alter the original meaning. It is somehow like a person without protection that is open to various kinds of viruses. Image immunization (Imuge) is a technology of protecting the images by introducing trivial perturbation, so that the protected images are immune to the viruses in that the tampered contents can be auto-recovered. This paper presents Imuge+, an enhanced scheme for image immunization. By observing the invertible relationship between image immunization and the corresponding self-recovery, we employ an invertible neural network to jointly learn image immunization and recovery respectively in the forward and backward pass. We also introduce an efficient attack layer that involves both malicious tamper and benign image post-processing, where a novel distillation-based JPEG simulator is proposed for improved JPEG robustness. Our method achieves promising results in real-world tests where experiments show accurate tamper localization as well as high-fidelity content recovery. Additionally, we show superior performance on tamper localization compared to state-of-the-art schemes based on passive forensics.
The Difficulties of Determining Main Idea on Students Reading Comprehension

This study used a descriptive-qualitative method using three data collection procedures to collect research data. The instruments used in this present research are observation, interviews, and documentation. This study has two research objectives. The first goal is to comprehend the challenges English students at an institute in East Java, Indonesia, face in identifying the main ideas in student reading comprehension. The second goal is to comprehend the factors that affect English language learners’ struggles in identifying the main ideas of their reading comprehension. Based on observations, interviews, and documentation, there are two difficulties in determining the main idea in students’ reading comprehension that English learners face: the first is that there is a difference between the main idea and the topic sentence, and the second is an inductive paragraph. Based on the research results of the study, some factors influence the difficulty in determining the main ideas for English learners, among which are a lack of vocabulary, reading style, and material themes.&#x0D; &#x0D; Keywords: Main Idea; Topic Sentence; Reading Comprehension.
Strategies and Challenges in Teaching Speaking by English Tutors: A Study in Madani Super Camp English Classes

Speaking is an important skill needed in communication and requires good training as well as professional and experienced English tutors. The speaking tutors will encounter various difficulties and need to employ effective teaching strategies to overcome them. This study aimed to find out the types of challenges faced by the English tutors in the Madani Super Camp (MSC) Course Narmada in teaching speaking and the types of learning strategies applied by the tutors to overcome the challenges in teaching speaking. This qualitative descriptive research involved eight English teachers in the MSC Course Narmada as respondents. The data were obtained by using closed-ended questions and semi-structured interviews, which were transcribed descriptively. In addition, the data analysis used is data reduction, data display, and conclusion drawing. The results of the research showed that the tutors faced five challenges in teaching English, including a lack of student discipline, additional work, a lack of self-confidence, a lack of technical teaching, and controlling the classroom and class management. The researcher reveals the research findings related to the learning strategies applied by the tutors to overcome the challenges in teaching speaking ability, including discussion and classroom interaction, storytelling, classroom practise, games, and picture description.&#x0D; &#x0D; Keywords: Tutors’ Challenges; Tutors’ Strategies; Teaching Speaking.
Chapter 5. Boolean Connectives and Deep Learning: Three Interpretations

In this paper, we motivate three approaches for integrating symbolic logic systems and deep learning methods. First, we consider whether the hidden layers of neural networks can be used to represent and reason about Boolean functions via so-called Tractable Circuits. Second, we discuss a method for encoding domain knowledge into the training and outputs of neural networks via so-called MultiplexNets. Finally, we show how we can instantiate deep learning architectures that perform exact function learning, via so-called Signal Perceptrons.
Chapter 11. Autodidactic and Coachable Neural Architectures

The prediction made by a learned model is rarely the end outcome of interest to a given agent. In most real-life scenarios, a certain policy is applied on the model’s prediction and on some relevant context to reach a decision. It is the (possibly temporally distant) effects of this decision that bring value to the agent. Moreover, it is those effects, and not the model’s prediction, that need to be evaluated as far as the agent’s satisfaction is concerned. The formalization of such scenarios naturally raises certain questions: How should a learned model be integrated with a policy to reach decisions? How should the learned model be trained and evaluated in the presence of such a policy? How is the training affected in terms of the type of access that one has on the policy? How can the policy be represented and updated in a way that is cognitively compatible with a human, so that it offers an explainable layer of reasoning on top of the learned model? This chapter offers a high-level overview of past work on the integration of modular reasoning with autodidactic learning and with user-driven coaching, as it applies on neural-symbolic architectures that combine sequentially a neural module with an arbitrary symbolically represented (and possibly non-differentiable) policy. In this context, the chapter offers responses to the questions above when the policy can be reasoned with only in a deductive manner, or in a deductive and an abductive manner. It further discusses how the policy can be learned / updated in an elaboration-tolerant and cognitively-light manner through machine coaching, and highlights the connections of the dialectical coaching process with the central role that argumentation plays in human reasoning.
Enhancement Ability to Write Complex Procedure Texts through Quantum Writing Method Students of Class XI SMA 3 Sidrap.

Learning Language Indonesia in Senior High School 3 Sidrap Which is done by the Teacher does not yet show quality, so students feel fed up, and not enough active in learning, and student learning outcomes are still low. This research aims to know the performance model And know increasing learning outcomes through the application of the Quantum method of Writing. Study This uses the method of Study Class Action ( PTK ) which is carried out in two cycles. Each cycle consists of one meeting, and each cycle consists of four stages viz planning, implementation, observation, And reflection. Data results study in getting by using student activity observation sheets as well tests. The research results obtained are an increase in value significant from cycle I to cycle II. Still, students in cycle I are still in the less category, in cycle II are in a good and very good position. This clearly shows a difference. In cycle I there are only eight students who completed KKM, while in cycle II 29 students Already completed KKM. With thereby, 72.4% of student experience enhancement from cycle I to cycle II.