Reviewerly/exHarmony huggingface dataset:
HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification

Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex label hierarchy.Recently, the pretrained language models (PLM) have been widely adopted in HTC through a finetuning paradigm.However, in this paradigm, there exists a huge gap between the classification tasks with sophisticated label hierarchy and the masked language model (MLM) pretraining tasks of PLMs and thus the potential of PLMs cannot be fully tapped.To bridge the gap, in this paper, we propose HPT, a Hierarchy-aware Prompt Tuning method to handle HTC from a multi-label MLM perspective.Specifically, we construct a dynamic virtual template and label words that take the form of soft prompts to fuse the label hierarchy knowledge and introduce a zero-bounded multi-label cross-entropy loss to harmonize the objectives of HTC and MLM.Extensive experiments show HPT achieves state-of-the-art performances on 3 popular HTC datasets and is adept at handling the imbalance and low resource situations.
Graph-based Model Generation for Few-Shot Relation Extraction

Few-shot relation extraction (FSRE) has been a challenging problem since it only has a handful of training instances.Existing models follow a 'one-for-all' scheme where one general large model performs all individual N-way-Kshot tasks in FSRE, which prevents the model from achieving the optimal point on each task.In view of this, we propose a model generation framework that consists of one general model for all tasks and many tiny task-specific models for each individual task.The general model generates and passes the universal knowledge to the tiny models which will be further fine-tuned when performing specific tasks.In this way, we decouple the complexity of the entire task space from that of all individual tasks while absorbing the universal knowledge.Extensive experimental results on two public datasets demonstrate that our framework reaches a new state-of-the-art performance for FRSE tasks.
Retrieval-Augmented Generative Question Answering for Event Argument Extraction

Event argument extraction has long been studied as a sequential prediction problem with extractive-based methods, tackling each argument in isolation.Although recent work proposes generation-based methods to capture cross-argument dependency, they require generating and post-processing a complicated target sequence (template).Motivated by these observations and recent pretrained language models' capabilities of learning from demonstrations.We propose a retrieval-augmented generative QA model (R-GQA) for event argument extraction.It retrieves the most similar QA pair and augments it as prompt to the current example's context, then decodes the arguments as answers.Our approach outperforms substantially prior methods across various settings (i.e.fully supervised, domain transfer, and fewshot learning).Finally, we propose a clusteringbased sampling strategy (JointEnc) and conduct a thorough analysis of how different strategies influence the few-shot learning performance. 1
BBTv2: Towards a Gradient-Free Future with Large Language Models

Most downstream adaptation methods tune all or part of the parameters of pre-trained models (PTMs) through gradient descent, where the tuning cost increases linearly with the growth of the model size.By contrast, gradient-free methods only require the forward computation of the PTM to tune the prompt, retaining the benefits of efficient tuning and deployment.Though, past work on gradient-free tuning often introduces gradient descent to seek a good initialization of prompt and lacks versatility across tasks and PTMs.In this paper, we present BBTv2, an improved version of Black-Box Tuning (Sun et al., 2022b), to drive PTMs for few-shot learning.We prepend continuous prompts to every layer of the PTM and propose a divide-and-conquer gradient-free algorithm to optimize the prompts at different layers alternately.Extensive experiments across various tasks and PTMs show that BBTv2 can achieve comparable performance to full model tuning and state-of-the-art parameter-efficient methods (e.g., Adapter, LoRA, BitFit, etc.) under few-shot settings while maintaining much fewer tunable parameters.
Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature

Lay summarisation aims to jointly summarise and simplify a given text, thus making its content more comprehensible to non-experts.Automatic approaches for lay summarisation can provide significant value in broadening access to scientific literature, enabling a greater degree of both interdisciplinary knowledge sharing and public understanding when it comes to research findings.However, current corpora for this task are limited in their size and scope, hindering the development of broadly applicable data-driven approaches.Aiming to rectify these issues, we present two novel lay summarisation datasets, PLOS (large-scale) and eLife (medium-scale), each of which contains biomedical journal articles alongside expert-written lay summaries.We provide a thorough characterisation of our lay summaries, highlighting differing levels of readability and abstractiveness between datasets that can be leveraged to support the needs of different applications.Finally, we benchmark our datasets using mainstream summarisation approaches and perform a manual evaluation with domain experts, demonstrating their utility and casting light on the key challenges of this task.Our code and datasets are available at https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation.
Autoregressive Structured Prediction with Language Models

In recent years, NLP has moved towards the application of language models to a more diverse set of tasks.However, applying language models to structured prediction, e.g., predicting parse trees, taggings, and coreference chains, is not straightforward.Prior work on language model-based structured prediction typically flattens the target structure into a string to easily fit it into the language modeling framework.Such flattening limits the accessibility of structural information and can lead to inferior performance compared to approaches that overtly model the structure.In this work, we propose to construct a conditional language model over sequences of structure-building actions, rather than over strings in a way that makes it easier for the model to pick up on intra-structure dependencies.Our method sets the new state of the art on named entity recognition, end-to-end relation extraction, and coreference resolution.
Open science

<h2>Summary</h2> The term ‘open science' refers to a range of methods, tools, platforms and practices that aim to make scientific research more accessible, transparent, reproducible and reliable. This includes, for example, sharing code, data and research materials, embracing new publishing formats such as registered reports and preprints, pursuing replication studies and reanalyses, optimising statistical approaches to improve evidence assessment and re-evaluating institutional incentives. The ongoing shift towards open science practices is partly due to mounting evidence that studies across disciplines suffer from biases, underpowered designs and irreproducible or non-replicable results. It also stems from a general desire amongst many researchers to reduce hyper-competitivity in science and instead promote collaborative research that benefits science and society.
MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery

We propose a novel teacher-student model for semi-supervised multi-organ segmentation. In teacher-student model, data augmentation is usually adopted on unlabeled data to regularize the consistent training between teacher and student. We start from a key perspective that fixed relative locationsand variable sizes of different organs can provide distribution information where a multi-organ CT scan is drawn. Thus, we treat the prior anatomy as a strong tool to guide the data augmentation and reduce the mismatch between labeled and unlabeled images for semi-supervised learning. More specifically, we propose a data augmentation strategy based on partition-and-recovery N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> cubes cross-and within-labeled and unlabeled images. Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch). For within-branch, we further propose to refine the quality of pseudo labels by blending the learned representations from small cubes to incorporate local attributes. Our method is termed as MagicNet, since it treats the CT volume as a magic-cube and N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -cube partition-and-recovery process matches with the rule of playing a magic-cube. Extensive experiments on two public CT multi-organ datasets demonstrate the effectiveness of MagicNet, and noticeably outperforms state-of-the-art semi-supervised medical image segmentation approaches, with + 7% DSC improvement on MACT dataset with 10% labeled images. Code is avaiable at https://github.com/DeepMed-Lab-ECNU/MagicNet.
MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation

In this work, we propose an ID-preserving talking head generation framework, which advances previous methods in two aspects. First, as opposed to interpolating from sparse flow, we claim that dense landmarks are crucial to achieving accurate geometry-aware flow fields. Second, inspired by face-swapping methods, we adaptively fuse the source identity during synthesis, so that the network better preserves the key characteristics of the image portrait. Although the proposed model surpasses prior generation fidelity on established benchmarks, personalized fine-tuning is still needed to further make the talking head generation qualified for real usage. However, this process is rather computationally demanding that is unaffordable to standard users. To alleviate this, we propose a fast adaptation model using a metalearning approach. The learned model can be adapted to a high-quality personalized model as fast as 30 seconds. Last but not least, a spatial-temporal enhancement module is proposed to improve the fine details while ensuring temporal coherency. Extensive experiments prove the significant superiority of our approach over the state of the arts in both one-shot and personalized settings.
Frame-Event Alignment and Fusion Network for High Frame Rate Tracking

Most existing RGB-based trackers target low frame rate benchmarks of around 30 frames per second. This setting restricts the tracker's functionality in the real world, especially for fast motion. Event-based cameras as bioinspired sensors provide considerable potential for high frame rate tracking due to their high temporal resolution. However, event-based cameras cannot offer fine-grained texture information like conventional cameras. This unique complementarity motivates us to combine conventional frames and events for high frame rate object tracking under various challenging conditions. In this paper, we propose an end-to-end network consisting of multi-modality alignment and fusion modules to effectively combine meaningful information from both modalities at different measurement rates. The alignment module is responsible for cross-style and cross-frame-rate alignment between frame and event modalities under the guidance of the moving cues furnished by events. While the fusion module is accountable for emphasizing valuable features and suppressing noise information by the mutual complement between the two modalities. Extensive experiments show that the proposed approach outper-forms state-of-the-art trackers by a significant margin in high frame rate tracking. With the FE240Hz dataset, our approach achieves high frame rate tracking up to 240Hz.
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors

In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector. Existing end-to-end methods, e.g. MOTR [43] and TrackFormer [20] are inferior to their tracking-by-detection counterparts mainly due to their poor detection performance. We aim to improve MOTR by elegantly incorporating an extra object detector. We first adopt the anchor formulation of queries and then use an extra object detector to generate proposals as anchors, providing detection prior to MOTR. The simple modification greatly eases the conflict between joint learning detection and association tasks in MOTR. MOTRv2 keeps the query propogation feature and scales well on large-scale benchmarks. MOTRv2 ranks the 1st place (73.4% HOTA on DanceTrack) in the 1st Multiple People Tracking in Group Dance Challenge. Moreover, MOTRv2 reaches state-of-the-art performance on the BDD100K dataset. We hope this simple and effective pipeline can provide some new insights to the end-to-end MOT community. Code is available at https://github.com/megvii-research/MOTRv2.
A Blockchain-Based Traceability Model for Grain and Oil Food Supply Chain

The structure of the grain-and-oil-food-supply chain has the characteristics of complexity, cross-regionality, a long cycle, and numerous participants, making it difficult to maintain the safety of supply. In recent years, some phenomena have emerged in the field of grain procurement and sale, such as topping the new with the old, rotating grains, the pressure of grades and prices, and counterfeit oil food, which have seriously threatened grain-and-oil-food security. Blockchain technology has the advantage of decentralization and non-tampering Therefore, this study analyzes the characteristics of traceability data in the grain-and-oil-food-supply chain, and presents a blockchain-based traceability model for the grain-and-oil-food-supply chain. Firstly, a new method combining blockchain and machine learning is proposed to enhance the authenticity and reliability of blockchain source data by constructing anomalous data-processing models. In addition, a lightweight blockchain-storage method and a data-recovery mechanism are proposed to reduce the pressure on supply-chain-data storage and improve fault tolerance. The results indicate that the average query delay of public data is 0.42 s, the average query delay of private data is 0.88 s, and the average data-recovery delay is 1.2 s. Finally, a blockchain-based grain-and-oil-food-supply-chain traceability system is designed and built using Hyperledger Fabric. Compared with the existing grain-and-oil-food-supply chain, the model constructed achieves multi-source heterogeneous data uploading, lightweight storage, data recovery, and traceability in the supply chain, which are of great significance for ensuring the safety of grain-and-oil food in China.
Software Composition Analysis for Vulnerability Detection: An Empirical Study on Java Projects

Software composition analysis (SCA) tools are proposed to detect potential vulnerabilities introduced by open-source software (OSS) imported as third-party libraries (TPL). With the increasing complexity of software functionality, SCA tools may encounter various scenarios during the dependency resolution process, such as diverse formats of artifacts, diverse dependency imports, and diverse dependency specifications. However, there still lacks a comprehensive evaluation of SCA tools for Java that takes into account the above scenarios. This could lead to a confined interpretation of comparisons, improper use of tools, and hinder further improvements of the tools. To fill this gap, we proposed an Evaluation Model which consists of Scan Modes, Scan Methods, and SCA Scope for Maven (SSM), for comprehensive assessments of the dependency resolving capabilities and effectiveness of SCA tools. Based on the Evaluation Model, we first qualitatively examined 6 SCA tools' capabilities. Next, the accuracy of dependency and vulnerability is quantitatively evaluated with a large-scale dataset (21,130 Maven modules with 73,499 unique dependencies) under two Scan Modes (i.e., build scan and pre-build scan). The results show that most tools do not fully support SSM, which leads to compromised accuracy. For dependency detection, the average F1-score is 0.890 and 0.692 for build and pre-build respectively, and for vulnerability accuracy, the average F1-score is 0.475. However, proper support for SSM reduces dependency detection false positives by 34.24% and false negatives by 6.91%. This further leads to a reduction of 18.28% in false positives and 8.72% in false negatives in vulnerability reports.
A Demonstration of SQUEGE: A CRPG Sub-Quest Generator

Scripting the plot in a computer role-playing game requires a large number of scripts that are difficult to program, track and maintain. Game adventures often include simple plots, called side-quests, that are independent from the main plot. Side-quests are important, as they add value to the open-world appeal of the game (e.g., for acquiring experience or resources), but they still need scripts. We have designed a tool to aid in the rapid creation of side-quests. The game designer provides the game setting and a list of objects in the setting. Our tool uses this information to create an outline for the side-quests. Then we use ScriptEase, a generative design pattern tool, to generate scripts from the side-quest outlines for the Neverwinter Nights game. A game designer can also adapt these outlines after the generation process, to add value such as humour to the side-quests.
Internet Explorers: An Nsf Sponsored Internship

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 1692 Internet Explorers: An NSF Sponsored Internship Lawrence J. Genalo, Krishna S. Athreya, Ann K. Dieterich Iowa State University 1. Introduction Summer research internships were extended to twenty high school girls who had completed their junior year with the primary goal of increasing middle school girls' participation in science, engineering, and mathematics (SEM). The interns spent eight weeks during the summer of 1997 on the Iowa State University campus where they researched science and engineering topics, learned programming methods and developed SEM units for the Internet. The summer research interns were directed by a team of four female undergraduate students who have demonstrated ability in computers, were trained in courseware development, and were majoring in SEM-related fields. A female graduate student in secondary education, and also a practicing middle school science teacher, worked as a consultant to the team to provide expertise in curricular content, educational delivery, and impact on the targeted middle school students. Another female graduate student in secondary education, specializing in assessment, worked to assess the program and to provide valuable “self discovery” information for the interns through Learning Styles Inventories and the Myers-Briggs Test. A secondary goal was the enhancement of the undergraduate and graduate women's own professional development. 2. Project Results Twenty-nine internet explorations covering human biology, natural sciences, and technology were created and can be viewed at http://ecss.eng.iastate.edu/explorer/. The four undergraduate students provided the training and day-to-day assistance for the interns in the use of computer technology to design and develop the explorations package. The interns learned quickly and made significant use of the internet and their programming abilities to produce the explorations. A special dimension was added to the undergraduate school experience of the undergraduate mentors. End-of-project reports from the undergraduates showed their experience in serving as mentors to be very positive. A middle school teacher/graduate student helped aim the explorations at the target audience of middle school girls. She also arranged for visits by members of the target audience to
Experimental Real-Time Heuristic Search Results in a Video Game

In real-time domains such as video games, a planning algo- rithm has a strictly bounded time before it must return the next action for the agent to execute. We introduce a realistic video game benchmark domain that is useful for evaluating real-time heuristic search algorithms. Unlike previous bench- marks such as grid pathfinding and the sliding tile puzzle, this new domain includes dynamics and induces a directed graph. Using both the previous and new domains, we investigate sev- eral enhancements to a leading real-time search algorithm, LSS-LRTA*. We show experimentally that 1) it is not dif- ficult to outperform A* when optimizing goal achievement time, 2) it is better to plan after each action than to commit to multiple actions or to use a dynamically sized lookahead, 3) A*-based lookahead can cause undesirable actions to be selected, and 4) on-line de-biasing of the heuristic can lead to improved performance. We hope that this new domain and results will stimulate further research on applying real-time search to dynamic real-time domains.
Emotion-Based Interactive Storytelling with Artificial Intelligence

Artificial Intelligence (AI) techniques have been widely used in video games to control non-playable characters. More recently, AI has been applied to automated story generation and game-mastering: managing the player’s experience in an interactive narrative on-the-fly. Such methods allow the narrative to be generated dynamically, in response to the player’s in-game actions. As a result, it is more difficult for the human game designers to ensure that each possible narrative trajectory will elicit desired emotional response from the player. We tackle this problem by computationally predicting the player’s emotional response to a narrative segment. We use the predictions within an AI experience manager to shape the narrative dynamically during the game to keep the player on an author-supplied target emotional curve.
Planning Paths with Fewer Turns on Grid Maps

In this paper, we consider the problem of planning any-angle paths with small numbers of turns on grid maps. We propose a novel heuristic search algorithm called Link* that returns paths containing fewer turns at the cost of slightly longer path lengths. Experimental results demonstrate that Link* can produce paths with fewer turns than other any-angle path planning algorithms while still maintaining comparable path lengths. Because it produces this type of path, artificial agents can take advantage of Link* when the cost of turns is expensive.
ReACT: Real-Time Algorithm Configuration through Tournaments

The success or failure of a solver is oftentimes closely tied to the proper configuration of the solver's parameters. However, tuning such parameters by hand requires expert knowledge, is time consuming, and is error-prone. In recent years, automatic algorithm configuration tools have made significant advances and can nearly always find better parameters than those found through hand tuning. However, current approaches require significant offline computational resources, and follow a train-once methodology that is unable to later adapt to changes in the type of problem solved. To this end, this paper presents Real-time Algorithm Configuration through Tournaments (ReACT), a method that does not require any offline training to perform algorithm configuration. ReACT exploits the multi-core infrastructure available on most modern machines to create a system that continuously searches for improving parameterizations, while guaranteeing a particular level of performance. The experimental results show that, despite the simplicity of the approach, ReACT quickly finds a set of parameters that is better than the default parameters and is competitive with state-of-the-art algorithm configurators.