Reviewerly/exHarmony huggingface dataset:
Beyond prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations

Recent work has demonstrated that pre-trained language models (PLMs) are zero-shot learners.However, most existing zero-shot methods involve heavy human engineering or complicated self-training pipelines, hindering their application to new situations.In this work, we show that zero-shot text classification can be improved simply by clustering texts in the embedding spaces of PLMs.Specifically, we fit the unlabeled texts with a Bayesian Gaussian Mixture Model after initializing cluster positions and shapes using class names.Despite its simplicity, this approach achieves superior or comparable performance on both topic and sentiment classification datasets and outperforms prior works significantly on unbalanced datasets.We further explore the applicability of our clustering approach by evaluating it on 14 datasets with more diverse topics, text lengths, and numbers of classes.Our approach achieves an average of 20% absolute improvement over prompt-based zero-shot learning.Finally, we compare different PLM embedding spaces and find that texts are well-clustered by topics even if the PLM is not explicitly pre-trained to generate meaningful sentence embeddings.This work indicates that PLM embeddings can categorize texts without task-specific fine-tuning, thus providing a new way to analyze and utilize their knowledge and zero-shot learning ability 1 .
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models

In this paper, we focus on the robustness evaluation of Chinese Question Matching (QM) models.Most of the previous work on analyzing robustness issues focus on just one or a few types of artificial adversarial examples.Instead, we argue that a comprehensive evaluation should be conducted on natural texts, which takes into account the fine-grained linguistic capabilities of QM models.For this purpose, we create a Chinese dataset namely DuQM which contains natural questions with linguistic perturbations to evaluate the robustness of QM models.DuQM contains 3 categories and 13 subcategories with 32 linguistic perturbations.The extensive experiments demonstrate that DuQM has a better ability to distinguish different models.Importantly, the detailed breakdown of evaluation by the linguistic phenomenon in DuQM helps us easily diagnose the strength and weakness of different models.Additionally, our experiment results show that the effect of artificial adversarial examples does not work on natural texts.Our baseline codes and a leaderboard are now publicly available.
Controllable Dialogue Simulation with In-context Learning

Building dialogue systems requires a large corpus of annotated dialogues.Such datasets are usually created via crowdsourcing, which is expensive and time-consuming.In this paper, we propose DIALOGIC 1 , a novel dialogue simulation method based on large language model in-context learning to automate dataset creation.Seeded with a few annotated dialogues, DIA-LOGIC automatically selects in-context examples for demonstration and prompts GPT-3 to generate new dialogues and annotations in a controllable way.Our method can rapidly expand a small set of dialogue data with minimum or zero human involvement and parameter update and is thus much more cost-efficient and time-saving than crowdsourcing.Experimental results on the MultiWOZ dataset demonstrate that training a model on the simulated dialogues leads to even better performance than using the same amount of human-generated dialogues under the challenging low-resource settings, with as few as 85 dialogues as a seed.When enough data is available, our method can still serve as an effective data augmentation method.Human evaluation results also show that our simulated dialogues have near-human fluency and annotation accuracy.The code and data are available at https://github.com/Leezekun/dialogic. User([hotel]type is guest house, area is center): I need a place to stay.It should be a guest house in the center of town .
Leveraging QA Datasets to Improve Generative Data Augmentation

The ability of generative language models (GLMs) to generate text has improved considerably in the last few years, enabling their use for generative data augmentation.In this work, we propose CONDA, an approach to further improve GLMs' ability to generate synthetic data by reformulating data generation as context generation for a given question-answer (QA) pair and leveraging QA datasets for training context generators.Then, we cast downstream tasks into the same question answering format and adapt the fine-tuned context generators to the target task domain.Finally, we use the fine-tuned GLM to generate relevant contexts, which are in turn used as synthetic training data for their corresponding tasks.We perform extensive experiments on multiple classification datasets and demonstrate substantial improvements in performance for both few-and zeroshot settings.Our analysis reveals that QA datasets that require high-level reasoning abilities (e.g., abstractive and common-sense QA datasets) tend to give the best boost in performance in both few-shot and zero-shot settings.
Grafting Pre-trained Models for Multimodal Headline Generation

Multimodal headline utilizes both video frames and transcripts to generate the natural language title of the videos.Due to a lack of large-scale, manually annotated data, the task of annotating grounded headlines for video is labor intensive and impractical.Previous researches on pretrained language models and video-language models have achieved significant progress in related downstream tasks.However, none of them can be directly applied to multimodal headline architecture where we need both multimodal encoder and sentence decoder.A major challenge in simply gluing language model and video-language model is the modality balance, which is aimed at combining visual-language complementary abilities.In this paper, we propose a novel approach to graft the video encoder from the pre-trained video-language model on the generative pre-trained language model.We also present a consensus fusion mechanism for the integration of different components, via inter/intra modality relation.Empirically, experiments show that the grafted model achieves strong results on a brand-new dataset collected from real-world applications.
Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition

It has been shown that named entity recognition (NER) could benefit from incorporating the long-distance structured information captured by dependency trees.However, dependency trees built by tools usually have a certain percentage of errors.Under such circumstances, how to better use relevant structured information while ignoring irrelevant or wrong structured information from the dependency trees to improve NER performance is still a challenging research problem.In this paper, we propose the Attention and Edge-Label guided Graph Convolution Network (AELGCN) model.Then, we integrate it into BiLSTM-CRF to form BiLSTM-AELGCN-CRF model.We design an edge-aware node joint update module and introduce a node-aware edge update module to explore hidden structured information entirely and solve the wrong dependency label information to some extent.After two modules, we apply attention-guided GCN, which automatically learns how to attend to the relevant structured information selectively.We conduct extensive experiments on several standard datasets across four languages and achieve better results than previous approaches.Through experimental analysis, it is found that our proposed model can better exploit the structured information on the dependency tree to improve the recognition of long entities.
Towards a smarter IoT environment with Ethereum Virtual Machine enabling ledgers

Distributed Ledger Technology (DLT), from the initial goal of moving digital assets, allows more advanced approaches as smart contracts executed on distributed computational enabling nodes such as Ethereum Virtual Machines (EVM) initially available only on the Ethereum ledger. Since the release of different EVM-based ledgers, the use cases to incentive the integration of smart contracts on other domains, such as IoT environments, increased. In this paper, we analyze the most IoT environment expedient quantitative metrics of various popular EVM-enabling ledgers to provide an overview of potential EVM-enabling characteristics.
Improving the matching of deformable objects by learning to detect keypoints

We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence. By leveraging true correspondences acquired by matching annotated image pairs with a specified descriptor extractor, we train an end-to-end convolutional neural network (CNN) to find keypoint locations that are more appropriate to the considered descriptor. For that, we apply geometric and photometric warpings to images to generate a supervisory signal, allowing the optimization of the detector. Experiments demonstrate that our method enhances the Mean Matching Accuracy of numerous descriptors when used in conjunction with our detection method, while outperforming the state-of-the-art keypoint detectors on real images of non-rigid objects by 20 p.p. We also apply our method on the complex real-world task of object retrieval where our detector performs on par with the finest keypoint detectors currently available for this task. The source code and trained models are publicly available at https://github.com/verlab/LearningToDetect_PRL_2023
“John is 50 years old, can his son be 65?” Evaluating NLP Models’ Understanding of Feasibility

Himanshu Gupta, Neeraj Varshney, Swaroop Mishra, Kuntal Kumar Pal, Saurabh Arjun Sawant, Kevin Scaria, Siddharth Goyal, Chitta Baral. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Rank and pairs of Rank and Dimension of Kernel of Z<sub> <i>p</i> </sub>Z<sub> <i>p</i> <sup>2</sup> </sub>-linear codes

A code <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> is called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z<sub>p</sub>Z<sub>p<sup>2</sup></sub></i> -linear if it is the Gray image of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z<sub>p</sub>Z<sub>p<sup>2</sup></sub></i> -additive code. For any prime number <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> larger than 3, the bounds of the rank of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z<sub>p</sub>Z<sub>p<sup>2</sup></sub></i> -linear codes are given. For each value of the rank and the pairs of rank and the dimension of the kernel of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z<sub>p</sub>Z<sub>p<sup>2</sup></sub></i> -linear codes, we give detailed construction of the corresponding codes. As an example, the rank and the dimension of the kernel of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sub> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">25</sub> -linear codes are studied.
Derivative Based Extended Regular Expression Matching Supporting Intersection, Complement and Lookarounds

Regular expressions are widely used in software. Various regular expression engines support different combinations of extensions to classical regular constructs such as Kleene star, concatenation, nondeterministic choice (union in terms of match semantics). The extensions include e.g. anchors, lookarounds, counters, backreferences. The properties of combinations of such extensions have been subject of active recent research. In the current paper we present a symbolic derivatives based approach to finding matches to regular expressions that, in addition to the classical regular constructs, also support complement, intersection and lookarounds (both negative and positive lookaheads and lookbacks). The theory of computing symbolic derivatives and determining nullability given an input string is presented that shows that such a combination of extensions yields a match semantics that corresponds to an effective Boolean algebra, which in turn opens up possibilities of applying various Boolean logic rewrite rules to optimize the search for matches. In addition to the theoretical framework we present an implementation of the combination of extensions to demonstrate the efficacy of the approach accompanied with practical examples.
On Comparing Early and Late Fusion Methods

This paper presents a theoretical comparison of early and late fusion methods. An initial discussion on the conditions to apply early or late (soft or hard) fusion is introduced. The analysis show that, if large training sets are available, early fusion will be the best option. If training sets are limited we must do late fusion, either soft or hard. In this latter case, the complications inherent in optimally estimating the fusion function could be avoided in exchange for lower performance. The paper also includes a comparative review of the fusion state of the art methods with the following divisions: early sensor-level fusion; early feature-level fusion; late score-level fusion (late soft fusion); and late decision-level fusion (late hard fusion). The main strengths and weaknesses of the methods are discussed.
Recurrent Embedded Topic Model

In this paper we propose the Recurrent Embedded Topic Model (RETM) which is a modification of the Embedded Topic Modelling (ETM) by reusing the Continuous Bag of Words (CBOW) that the model had implemented and applying it to a recurrent neural network (LSTM), using the order of the words of the text, in the CBOW space as the recurrency of the LSTM, while calculating the topic–document distribution of the model. This approach is novel because the ETM and Latent Dirichlet Allocation (LDA) do not use the order of the words while calculating the topic proportions for each text, making worse predictions in the end. The RETM is a topic-modelling technique that vastly improves (by more than 15 times in train data and between 10% and 90% better based on test dataset values for perplexity) the quality of the topics that were calculated for the datasets used in this paper. This model is explained in detail throughout the paper and presents results on different use cases on how the model performs against ETM and LDA. The RETM can be used with better accuracy for any topic model-related problem.
Perancangan Aplikasi Ujian Online Pada Masa Pandemi Persiapan Ujian Nasional

This research was conducted to analyze and design a website which is a technology that is very influential on educational science during the current pandemic, so we need a technology that can support the course of learning and exams. One of them is about the technology of Exam Preparation Applications Using Web-Based Randomized Methods at SMA Negeri 1 Pantai Cermin. The exam itself is an activity that is carried out regularly to measure the achievement of student competencies after completing one or more basic competencies. The research method used consists of several stages of research, namely data collection, system analysis and software design. The data collection method used is observation and literature study. System analysis and design method used is Rapid Application Development (RAD) using Unified Modeling Language (UML) and user interface system design. The application is made on two sides, namely the admin application and the user application. The programming language used for software development is PHP and the server uses MySQL as the database. The result of this research is an online exam website-based application for preparation for the national exam and uses a random method that can later be used by teachers and students. The application of this application can provide convenience for teachers and students in the process of working on computerized exam practice questions and can immediately see exam results quickly.
Emerging Trends in Cybercrime Awareness in Nigeria

The study examined the current trend in cybercrime awareness and the relationship such trend has with cybercrime vulnerability or victimization.Selecting a sample of 1104 Internet users from Umuahia, Abia State, Nigeria, We found that: 1) awareness of information security was high in that about 2 in every 3 (68%) participants demonstrated a favorable awareness of information security and cybercrime.It was, however, revealed that such a high level of awareness could be partial and weak.2) most Internet users demonstrated the awareness of fraud-related cybercrime categories (39%), e-theft (15%), hacking (12%), and ATM theft (10%).However, they were rarely aware of sexually related offenses, cyber-terrorism, malware attacks, spam emails, and identity theft as their proportion hovered around 8% and below.3) Internet users significantly demonstrated more awareness of computer-assisted (M = 2.5; SD = 1.7) than that of computer-focused cybercrime categories (M = 2.2, SD = 1.3), t(1103) = 2.9, p=.000, r =.2.4) Internet users significantly demonstrated more awareness of property cybercrime (M = 2.54; SD = 1.6) than that of violent cybercrime categories (M = 1.82,SD = 1.2), t(1103) = 5.94, p=.000, r =.3.5) cybercrime awareness is positively correlated to cybercrime victimization experiences in that participants who demonstrated more awareness of cybercrime experienced significantly more cybercrime victimization (M = 1.66;SD = 1.7) than those who did not demonstrate awareness of cybercrime (M = .73,SD = 1.4), t(1103) = 7.55, p=.000, r =.52.
Synergetic quantum error mitigation by randomized compiling and zero-noise extrapolation for the variational quantum eigensolver

We propose a quantum error mitigation strategy for the variational quantum eigensolver (VQE) algorithm. We find, via numerical simulation, that very small amounts of coherent noise in VQE can cause substantially large errors that are difficult to suppress by conventional mitigation methods, and yet our proposed mitigation strategy is able to significantly reduce these errors. The proposed strategy is a combination of previously reported techniques, namely randomized compiling (RC) and zero-noise extrapolation (ZNE). Intuitively, randomized compiling turns coherent errors in the circuit into stochastic Pauli errors, which facilitates extrapolation to the zero-noise limit when evaluating the cost function. Our numerical simulation of VQE for small molecules shows that the proposed strategy can mitigate energy errors induced by various types of coherent noise by up to two orders of magnitude.
Improving Bot Response Contradiction Detection via Utterance Rewriting

Though chatbots based on large neural models can often produce fluent responses in open domain conversations, one salient error type is contradiction or inconsistency with the preceding conversation turns. Previous work has treated contradiction detection in bot responses as a task similar to natural language inference, e.g., detect the contradiction between a pair of bot utterances. However, utterances in conversations may contain co-references or ellipsis, and using these utterances as is may not always be sufficient for identifying contradictions. This work aims to improve the contradiction detection via rewriting all bot utterances to restore co-references and ellipsis. We curated a new dataset for utterance rewriting and built a rewriting model on it. We empirically demonstrate that this model can produce satisfactory rewrites to make bot utterances more complete. Furthermore, using rewritten utterances improves contradiction detection performance significantly, e.g., the AUPR and joint accuracy scores (detecting contradiction along with evidence) increase by 6.5% and 4.5% (absolute increase), respectively.
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization

Chieh-Yang Huang, Ting-Yao Hsu, Ryan Rossi, Ani Nenkova, Sungchul Kim, Gromit Yeuk-Yin Chan, Eunyee Koh, C Lee Giles, Ting-Hao Huang. Proceedings of the 16th International Natural Language Generation Conference. 2023.
Hand Gesture Recognition Using MediaPipe and CNN for Indian Sign Language and Conversion to Speech Format for Indian Regional Languages

In recent times, deep learning techniques have made remarkable progress across various domains and applications. However, Indian Sign Language (ISL) recognition, audio generation translation, still present significant challenges from a developmental perspective. In this paper, introduction of a novel method to create a broad framework for real-time ISL recognition, translation tasks. To enhance recognition accuracy, leveraged the power of the MediaPipe library and employ a hybrid Convolutional Neural Network model for extracting pose details and generating text. On the other hand, for producing sign gesture audio corresponding to spoken sentences, used GTTs, supporting the conversion of the audio into various Indian Regional Languages like Hindi, Kannada, Telugu, Marathi, etc. The proposed mechanism tackles the complexities present in earlier approaches and achieves an impressive accuracy of about 94%. Extensively tested the mechanism during its development phases, and evaluation metrics demonstrate convincing improvements over previous methods. In conclusion, this novel approach, combining MediaPipe, CNN, has significantly improved the recognition and production of sign language gestures. The model's performance on evaluation metrics showcases its potential to enhance communication and accessibility for individuals with hearing and speech impairments.
MixSynthFormer: A Transformer Encoder-like Structure with Mixed Synthetic Self-attention for Efficient Human Pose Estimation

Human pose estimation in videos has wide-ranging practical applications across various fields, many of which require fast inference on resource-scarce devices, necessitating the development of efficient and accurate algorithms. Previous works have demonstrated the feasibility of exploiting motion continuity to conduct pose estimation using sparsely sampled frames with transformer-based models. However, these methods only consider the temporal relation while neglecting spatial attention, and the complexity of dot product self-attention calculations in transformers are quadratically proportional to the embedding size. To address these limitations, we propose MixSynthFormer, a transformer encoder-like model with MLP-based mixed synthetic attention. By mixing synthesized spatial and temporal attentions, our model incorporates inter-joint and inter-frame importance and can accurately estimate human poses in an entire video sequence from sparsely sampled frames. Additionally, the flexible design of our model makes it versatile for other motion synthesis tasks. Our extensive experiments on 2D/3D pose estimation, body mesh recovery, and motion prediction validate the effectiveness and efficiency of MixSynthFormer. The code is available at https://github.com/ireneesun/MixSynthFormer.git