Reviewerly/exHarmony huggingface dataset:
Disruption prediction for future tokamaks using parameter-based transfer learning

Abstract Tokamaks are the most promising way for nuclear fusion reactors. Disruption in tokamaks is a violent event that terminates a confined plasma and causes unacceptable damage to the device. Machine learning models have been widely used to predict incoming disruptions. However, future reactors, with much higher stored energy, cannot provide enough unmitigated disruption data at high performance to train the predictor before damaging themselves. Here we apply a deep parameter-based transfer learning method in disruption prediction. We train a model on the J-TEXT tokamak and transfer it, with only 20 discharges, to EAST, which has a large difference in size, operation regime, and configuration with respect to J-TEXT. Results demonstrate that the transfer learning method reaches a similar performance to the model trained directly with EAST using about 1900 discharge. Our results suggest that the proposed method can tackle the challenge in predicting disruptions for future tokamaks like ITER with knowledge learned from existing tokamaks.
Undercover investigation

The use of the method of investigating and documenting criminal activity through undercover officers is a new method aimed at actively countering organized crime. The method is still not widely used in the Republic of Bulgaria.
Diverse Demonstrations Improve In-context Compositional Generalization

In-context learning has shown great success in i.i.d semantic parsing splits, where the training and test sets are drawn from the same distribution.In this setup, models are typically prompted with demonstrations that are similar to the input utterance.However, in the setup of compositional generalization, where models are tested on outputs with structures that are absent from the training set, selecting similar demonstrations is insufficient, as often no example will be similar enough to the input.In this work, we propose a method to select diverse demonstrations that aims to collectively cover all of the structures required in the output program, in order to encourage the model to generalize to new structures from these demonstrations.We empirically show that combining diverse demonstrations with in-context learning substantially improves performance across three compositional generalization semantic parsing datasets in the pure in-context learning setup and when combined with finetuning. 1 * Equal contribution 1 Our code is available at: https://github.com/itayle/diverse-demonstrations Question: What is the most populous state through which the mississippi runs?Q: What are the major cities in states through which the mississippi runs?A: major(city(loc_2( state(traverse_1(riverid('mississippi')))) )) Q: What are the cities in states through which the mississippi runs?A: city(loc_2( state(traverse_1(riverid('mississippi'))) )) Q: What is the most populous state through which the mississippi runs?(Output) most_populous( state(traverse_1(riverid('mississippi'))) ) (a) Similarity-Based Prompting Q: What are the major cities in states through which the mississippi runs?A: major(city(loc_2( state(traverse_1(riverid('mississippi')))) )) Q: What rivers flow through the state with the largest population?A: river(traverse_2( largest_one(population_1(state (all))))) Q: What is the most populous state through which the mississippi runs?(Output) largest_one(population_1(state(traverse_1(riverid('mississippi'))) )) (b) Diversity-Based Prompting (Ours)
SemEval-2023 Task 10: Explainable Detection of Online Sexism

Online sexism is a widespread and harmful phenomenon. Automated tools can assist the detection of sexism at scale. Binary detection, however, disregards the diversity of sexist content, and fails to provide clear explanations for why something is sexist. To address this issue, we introduce SemEval Task 10 on the Explainable Detection of Online Sexism (EDOS). We make three main contributions: i) a novel hierarchical taxonomy of sexist content, which includes granular vectors of sexism to aid explainability; ii) a new dataset of 20,000 social media comments with fine-grained labels, along with larger unlabelled datasets for model adaptation; and iii) baseline models as well as an analysis of the methods, results and errors for participant submissions to our task.
Probing Pretrained Models of Source Codes

Deep learning models are widely used for solving challenging code processing tasks, such as code generation or code summarization.Traditionally, a specific model architecture was carefully built to solve a particular code processing task.However, recently general pretrained models such as CodeBERT or CodeT5 have been shown to outperform task-specific models in many applications.While pretrained models are known to learn complex patterns from data, they may fail to understand some properties of source code.To test diverse aspects of code understanding, we introduce a set of diagnostic probing tasks.We show that pretrained models of code indeed contain information about code syntactic structure, the notions of identifiers, and namespaces, but they may fail to recognize more complex code properties such as semantic equivalence.We also investigate how probing results are affected by using code-specific pretraining objectives, varying the model size, or finetuning.
Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation

Recently, aspect sentiment quad prediction (ASQP) has become a popular task in the field of aspect-level sentiment analysis.Previous work utilizes a predefined template to paraphrase the original sentence into a structure target sequence, which can be easily decoded as quadruplets of the form (aspect category, aspect term, opinion term, sentiment polarity).The template involves the four elements in a fixed order.However, we observe that this solution contradicts with the order-free property of the ASQP task, since there is no need to fix the template order as long as the quadruplet is extracted correctly.Inspired by the observation, we study the effects of template orders and find that some orders help the generative model achieve better performance.It is hypothesized that different orders provide various views of the quadruplet.Therefore, we propose a simple but effective method to identify the most proper orders, and further combine multiple proper templates as data augmentation to improve the ASQP task.Specifically, we use the pre-trained language model to select the orders with minimal entropy.By fine-tuning the pre-trained language model with these template orders, our approach improves the performance of quad prediction, and outperforms state-ofthe-art methods significantly in low-resource settings 1 .
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning

Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context.Moreover, it can be expensive to retrain well-established retrievers such as search engines that are originally developed for nonconversational queries.To facilitate their use, we develop a query rewriting model CONQRR that rewrites a conversational question in the context into a standalone question.It is trained with a novel reward function to directly optimize towards retrieval using reinforcement learning and can be adapted to any off-theshelf retriever.CONQRR achieves state-ofthe-art results on a recent open-domain CQA dataset containing conversations from three different sources, and is effective for two different off-the-shelf retrievers.Our extensive analysis also shows the robustness of CON-QRR to out-of-domain dialogues as well as to zero query rewriting supervision.
Entity-centered Cross-document Relation Extraction

Relation Extraction (RE) is a fundamental task of information extraction, which has attracted a large amount of research attention.Previous studies focus on extracting the relations within a sentence or document, while currently researchers begin to explore cross-document RE.However, current cross-document RE methods directly utilize text snippets surrounding the target entities in multiple given documents, which brings considerable noisy and non-relevant sentences.Moreover, they utilize all the text paths in a document bag in a coarse-grained way, without considering the connections between these text paths.In this paper, we aim to address both of these shortages and push the stateof-the-art for cross-document RE.First, we focus on input construction for our RE model and propose an entity-based document-context filter to retain useful information in the given documents by using the bridge entities in the text paths.Second, we propose a cross-document RE model based on cross-path entity relation attention, which allows the entity relations across text paths to interact with each other.We compare our cross-document RE method with the state-of-the-art methods in the dataset CodRED.Our method outperforms them by at least 10% in F1, thus demonstrating its effectiveness.
HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold

Conventional event detection models under supervised learning settings suffer from the inability of transfer to newly-emerged event types owing to lack of sufficient annotations.A commonly-adapted solution is to follow a identify-then-classify manner, which first identifies the triggers and then converts the classification task via a few-shot learning paradigm.However, these methods still fall far short of expectations due to: (i) insufficient learning of discriminative representations in low-resource scenarios, and (ii) trigger misidentification caused by the overlap of the learned representations of triggers and non-triggers.To address the problems, in this paper, we propose a novel Hybrid Contrastive Learning method with a Task-Adaptive Threshold (abbreviated as HCL-TAT), which enables discriminative representation learning with a two-view contrastive loss (support-support and prototype-query), and devises an easily-adapted threshold to alleviate misidentification of triggers.Extensive experiments on the benchmark dataset FewEvent demonstrate the superiority of our method to achieve better results compared to the state-ofthe-arts.All the code and data of this paper are available at https://github.com/CCIIPLab/HCL-TAT.
CLE-ViT: Contrastive Learning Encoded Transformer for Ultra-Fine-Grained Visual Categorization

Ultra-fine-grained visual classification (ultra-FGVC) targets at classifying sub-grained categories of fine-grained objects. This inevitably requires discriminative representation learning within a limited training set. Exploring intrinsic features from the object itself, e.g., predicting the rotation of a given image, has demonstrated great progress towards learning discriminative representation. Yet none of these works consider explicit supervision for learning mutual information at instance level. To this end, this paper introduces CLE-ViT, a novel contrastive learning encoded transformer, to address the fundamental problem in ultra-FGVC. The core design is a self-supervised module that performs self-shuffling and masking and then distinguishes these altered images from other images. This drives the model to learn an optimized feature space that has a large inter-class distance while remaining tolerant to intra-class variations. By incorporating this self-supervised module, the network acquires more knowledge from the intrinsic structure of the input data, which improves the generalization ability without requiring extra manual annotations. CLE-ViT demonstrates strong performance on 7 publicly available datasets, demonstrating its effectiveness in the ultra-FGVC task. The code is available at https://github.com/Markin-Wang/CLEViT.
Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic information. Prior works mainly localize regions corresponding to the sharing attributes. When various visual appearances correspond to the same attribute, the sharing attributes inevitably introduce semantic ambiguity, hampering the exploration of accurate semantic-visual interactions. In this paper, we deploy the dual semantic-visual transformer module (DSVTM) to progressively model the correspondences between attribute prototypes and visual features, constituting a progressive semantic-visual mutual adaption (PSVMA) network for semantic disambiguation and knowledge transferability improvement. Specifically, DSVTM devises an instance-motivated semantic encoder that learns instance-centric prototypes to adapt to different images, enabling the recast of the unmatched semantic-visual pair into the matched one. Then, a semantic-motivated instance decoder strengthens accurate cross-domain interactions between the matched pair for semantic-related instance adaption, en-couraging the generation of unambiguous visual representations. Moreover, to mitigate the bias towards seen classes in GZSL, a debiasing loss is proposed to pursue response consistency between seen and unseen predictions. The PSVMA consistently yields superior performances against other state-of-the-art methods. Code will be available at: https://github.com/ManLiuCoder/PSVMA.
Optical microscope algorithm: A new metaheuristic inspired by microscope magnification for solving engineering optimization problems

A novel optimization algorithm called the optical microscope algorithm (OMA) is developed and applied in this study. Drawing inspiration from the magnification capabilities of an optical microscope on the target object, OMA uses the naked eye for initial observation and simulates the magnification process through an objective lens and an eyepiece. The performance of OMA, which is user friendly and does not require predefined operating parameters, is validated through two experiments: (1) OMA is compared to nine well-known metaheuristic algorithms using constraint handling with 50 benchmark functions involving multiple dimensions. The results indicate that OMA consistently outperforms all other algorithms and requires a short computational time. (2) OMA is applied to solve engineering problems, including structural optimization and multiple resources leveling in the multiple projects scheduling (MRLMP). In those cases, OMA not only demonstrates superiority but also requires the fewest evaluations of objective functions. The novel OMA, which is robust, easy to implement, and uses fewer control parameters, can be deployed to solve for various numerical optimization problems. The source code of OMA is publicly accessible at https://www.mathworks.com/matlabcentral/fileexchange/134541-optical-microscope-algorithm-oma.
Discriminative Feature Learning with Co-occurrence Attention Network for Vehicle ReID

Vehicle Re-Identification (ReID) aims to find images of the same vehicle from different videos. It remains a challenging task in the video analysis field due to the huge appearance discrepancy of the same vehicle in cross-view matching and the subtle difference of different similar vehicles in same-view matching. In this paper, we propose a Co-occurrence Attention Net (CAN) to deal with these two challenges. Specifically, CAN consists of two branches, a main branch and an aware branch. The main branch is in charge of extracting global features that are consistent in most views. This feature encodes holistic information such as color and pose, however, it can not handle cross/same-view hard cases, as shown in Fig.1. Therefore, the aware branch is designed to focus on the local details and viewpoint information, which can become an important complement for those hard cases. Considering that the positions of local areas such as wheels and logos change with the viewpoint, Aware Attention Module is introduced to find the hidden relationship among local areas and seamlessly combine the viewpoint information simultaneously. Then, CAN is trained by a partition-and-reunion-based loss, which can narrow the intra-class distance and increase the inter-class distance. Further, an adaptive co-occurrence view emphasize strategy is adopted to fully utilize the learned features. Experimental results on three widely used datasets including VeRi-776, VehicleID and VERI-Wild demonstrate the effectiveness of our method and competitive performance with other state-of-the-art methods.
The systemic impact of deplatforming on social media

Abstract Deplatforming, or banning malicious accounts from social media, is a key tool for moderating online harms. However, the consequences of deplatforming for the wider social media ecosystem have been largely overlooked so far, due to the difficulty of tracking banned users. Here, we address this gap by studying the ban-induced platform migration from Twitter to Gettr. With a matched dataset of 15M Gettr posts and 12M Twitter tweets, we show that users active on both platforms post similar content as users active on Gettr but banned from Twitter, but the latter have higher retention and are 5 times more active. Our results suggest that increased Gettr use is not associated with a substantial increase in user toxicity over time. In fact, we reveal that matched users are more toxic on Twitter, where they can engage in abusive cross-ideological interactions, than Gettr. Our analysis shows that the matched cohort are ideologically aligned with the far-right, and that the ability to interact with political opponents may be part of Twitter’s appeal to these users. Finally, we identify structural changes in the Gettr network preceding the 2023 Brasília insurrections, highlighting the risks that poorly regulated social media platforms may pose to democratic life.
Explainable Artificial Intelligence in Alzheimer’s Disease Classification: A Systematic Review

Abstract The unprecedented growth of computational capabilities in recent years has allowed Artificial Intelligence (AI) models to be developed for medical applications with remarkable results. However, a large number of Computer Aided Diagnosis (CAD) methods powered by AI have limited acceptance and adoption in the medical domain due to the typical blackbox nature of these AI models. Therefore, to facilitate the adoption of these AI models among the medical practitioners, the models' predictions must be explainable and interpretable. The emerging field of explainable AI (XAI) aims to justify the trustworthiness of these models' predictions. This work presents a systematic review of the literature reporting Alzheimer's disease (AD) detection using XAI that were communicated during the last decade. Research questions were carefully formulated to categorise AI models into different conceptual approaches (e.g., Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, Global, Local etc.) and frameworks (Local Interpretable Model-Agnostic Explanation or LIME, SHapley Additive exPlanations or SHAP, Gradient-weighted Class Activation Mapping or GradCAM, Layer-wise Relevance Propagation or LRP, etc.) of XAI. This categorisation provides broad coverage of the interpretation spectrum from intrinsic (e.g., Model-Specific, Ante-hoc models) to complex patterns (e.g., Model-Agnostic, Post-hoc models) and by taking local explanations to a global scope. Additionally, different forms of interpretations providing in-depth insight into the factors that support the clinical diagnosis of AD are also discussed. Finally, limitations, needs and open challenges of XAI research are outlined with possible prospects of their usage in AD detection.
Automatic Prompt Optimization with “Gradient Descent” and Beam Search

Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort. We propose a simple and nonparametric solution to this problem, Prompt Optimization with Textual Gradients (ProTeGi), which is inspired by numerical gradient descent to automatically improve prompts, assuming access to training data and an LLM API. The algorithm uses minibatches of data to form natural language “gradients” that criticize the current prompt, much like how numerical gradients point in the direction of error ascent. The natural language gradients are then “propagated” into the prompt by editing the prompt in the opposite semantic direction of the gradient. These gradient descent steps are guided by a beam search and bandit selection procedure which significantly improves algorithmic efficiency. Preliminary results across three benchmark NLP tasks and the novel problem of LLM jailbreak detection suggest that Automatic Prompt Optimization can outperform prior prompt editing techniques and improve an initial prompt’s performance by up to 31%, by using data to rewrite vague task descriptions into more precise annotation instructions.
SeMask: Semantically Masked Transformers for Semantic Segmentation

Finetuning a pretrained backbone in the encoder part of an image transformer network has been the traditional approach for the semantic segmentation task. However, such an approach leaves out the semantic context that an image provides during the encoding stage. This paper argues that incorporating semantic information of the image into pretrained hierarchical transformer-based backbones while finetuning improves the performance considerably. To achieve this, we propose SeMask, a simple and effective framework that incorporates semantic information into the encoder with the help of a semantic attention operation. In addition, we use a lightweight semantic decoder during training to provide supervision to the intermediate semantic prior maps at every stage. Our experiments demonstrate that incorporating semantic priors enhances the performance of the established hierarchical encoders with a slight increase in the number of FLOPs. We provide empirical proof by integrating SeMask into Swin Transformer and Mix Transformer backbones as our encoder paired with different decoders. Our framework achieves impressive performance of 58.25% mIoU on the ADE20K dataset with SeMask Swin-L backbone and improvements of over 3% in the mIoU metric on the Cityscapes dataset. The code is publicly available on https://github.com/Picsart-AI-Research/SeMask-Segmentation.
A Modified Gravitational Search Algorithm for Discrete Optimization Problem

This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational Search Algorithm (DGSA) for discrete optimization problems. In DGSA, an agent’s position is updated based on its direction and velocity. Both the direction and velocity determine the candidates of integer values for the position update of an agent and then the selection is done randomly. Unimodal test functions are used to evaluate the performance of the proposed DGSA. The experimental result shows that the FDGSA able to find better solutions and converges faster compared to the Binary Gravitational Search Algorithm.