pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of the Evolution of Advanced Transformer-Based Language Models: Experiments on Opinion Mining
Authors: Nour Eddine Zekaoui, Siham Yousfi, Maryem Rhanoui, Mounia Mikram
Date: 2023-08-07 00:00:00
Tasks: Emotion Recognition, Opinion Mining, Sentiment Analysis, Sentiment Classification, Text Classification

Opinion mining, also known as sentiment analysis, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information in textual material. This can include determining the overall sentiment of a piece of text (e.g., positive or negative), as well as identifying specific emotions or opinions expressed in the text, that involves the use of advanced machine and deep learning techniques. Recently, transformer-based language models make this task of human emotion analysis intuitive, thanks to the attention mechanism and parallel computation. These advantages make such models very powerful on linguistic tasks, unlike recurrent neural networks that spend a lot of time on sequential processing, making them prone to fail when it comes to processing long text. The scope of our paper aims to study the behaviour of the cutting-edge Transformer-based language models on opinion mining and provide a high-level comparison between them to highlight their key particularities. Additionally, our comparative study shows leads and paves the way for production engineers regarding the approach to focus on and is useful for researchers as it provides guidelines for future research subjects.
UTILIZING FEDERATED LEARNING AND META LEARNING FOR FEW-SHOT LEARNING ON EDGE DEVICES
Authors: Anthony Breitzman, Nancy Tinkham, Ning Wang
Date: 2022-10-25 00:00:00
Proceeding: Rowan University 2022 10
Tasks: Federated Learning, Few-Shot Learning, Meta-Learning

The efficient and effective handling of few-shot learning tasks on mobile devices is challenging due to the small training set issue and the physical limitations in power and computational resources on these devices. In this thesis, we propose a solution that combines federated learning and meta-learning to handle independent few-shot learning tasks on multiple devices (or clients) and the server. In particular, we utilize the Prototypical Networks to perform meta-learning on all devices to learn multiple independent few-shot learning models and to combine the models in a centralized data distributed architecture using federated learning which can be reused by the clients subsequently. We perform extensive experiments to (1) compare three different federated learning approaches, namely Federated Averaging (FedAvg), Federated Proximal (FedProx), and Federated Personalization (FedPer) on our proposed framework, and (2) explore the effect of data heterogeneity issue on the few-shot learning performance. Our empirical results show that our proposed approach is feasible and is able to improve the devices' individual prediction performance and improve significantly on the global model (on the server) using any of the federated learning approaches when the few-shot learning tasks are on the same datasets. However, the data heterogeneity problem still affects the prediction performance of our proposed solution no matter which federated learning approach we used.
Choir Transformer: Generating Polyphonic Music with Relative Attention on Transformer
Authors: Jiuyang Zhou, Hong Zhu, Xingping Wang
Date: 2023-08-01 00:00:00
Tasks: Music Generation, Rhythm

Polyphonic music generation is still a challenge direction due to its correct between generating melody and harmony. Most of the previous studies used RNN-based models. However, the RNN-based models are hard to establish the relationship between long-distance notes. In this paper, we propose a polyphonic music generation neural network named Choir Transformer[ https://github.com/Zjy0401/choir-transformer], with relative positional attention to better model the structure of music. We also proposed a music representation suitable for polyphonic music generation. The performance of Choir Transformer surpasses the previous state-of-the-art accuracy of 4.06%. We also measures the harmony metrics of polyphonic music. Experiments show that the harmony metrics are close to the music of Bach. In practical application, the generated melody and rhythm can be adjusted according to the specified input, with different styles of music like folk music or pop music and so on.
A Cost Analysis of Generative Language Models and Influence Operations
Authors: Micah Musser
Date: 2023-08-07 00:00:00

Despite speculation that recent large language models (LLMs) are likely to be used maliciously to improve the quality or scale of influence operations, uncertainty persists regarding the economic value that LLMs offer propagandists. This research constructs a model of costs facing propagandists for content generation at scale and analyzes (1) the potential savings that LLMs could offer propagandists, (2) the potential deterrent effect of monitoring controls on API-accessible LLMs, and (3) the optimal strategy for propagandists choosing between multiple private and/or open source LLMs when conducting influence operations. Primary results suggest that LLMs need only produce usable outputs with relatively low reliability (roughly 25%) to offer cost savings to propagandists, that the potential reduction in content generation costs can be quite high (up to 70% for a highly reliable model), and that monitoring capabilities have sharply limited cost imposition effects when alternative open source models are available. In addition, these results suggest that nation-states -- even those conducting many large-scale influence operations per year -- are unlikely to benefit economically from training custom LLMs specifically for use in influence operations.
SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator
Authors: Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov
Date: 2023-08-07 00:00:00

An explanation method called SurvBeX is proposed to interpret predictions of the machine learning survival black-box models. The main idea behind the method is to use the modified Beran estimator as the surrogate explanation model. Coefficients, incorporated into Beran estimator, can be regarded as values of the feature impacts on the black-box model prediction. Following the well-known LIME method, many points are generated in a local area around an example of interest. For every generated example, the survival function of the black-box model is computed, and the survival function of the surrogate model (the Beran estimator) is constructed as a function of the explanation coefficients. In order to find the explanation coefficients, it is proposed to minimize the mean distance between the survival functions of the black-box model and the Beran estimator produced by the generated examples. Many numerical experiments with synthetic and real survival data demonstrate the SurvBeX efficiency and compare the method with the well-known method SurvLIME. The method is also compared with the method SurvSHAP. The code implementing SurvBeX is available at: https://github.com/DanilaEremenko/SurvBeX
TinyLVLM-eHub: Towards Comprehensive and Efficient Evaluation for Large Vision-Language Models
Authors: Wenqi Shao, Meng Lei, Yutao Hu, Peng Gao, Kaipeng Zhang, Fanqing Meng, Peng Xu, Siyuan Huang, Hongsheng Li, Yu Qiao, Ping Luo
Date: 2023-08-07 00:00:00
Tasks: Hallucination, Object Hallucination, Visual Reasoning

Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated significant progress in tackling complex multimodal tasks. Among these cutting-edge developments, Google's Bard stands out for its remarkable multimodal capabilities, promoting comprehensive comprehension and reasoning across various domains. This work presents an early and holistic evaluation of LVLMs' multimodal abilities, with a particular focus on Bard, by proposing a lightweight variant of LVLM-eHub, named Tiny LVLM-eHub. In comparison to the vanilla version, Tiny LVLM-eHub possesses several appealing properties. Firstly, it provides a systematic assessment of six categories of multimodal capabilities, including visual perception, visual knowledge acquisition, visual reasoning, visual commonsense, object hallucination, and embodied intelligence, through quantitative evaluation of $42$ standard text-related visual benchmarks. Secondly, it conducts an in-depth analysis of LVLMs' predictions using the ChatGPT Ensemble Evaluation (CEE), which leads to a robust and accurate evaluation and exhibits improved alignment with human evaluation compared to the word matching approach. Thirdly, it comprises a mere $2.1$K image-text pairs, facilitating ease of use for practitioners to evaluate their own offline LVLMs. Through extensive experimental analysis, this study demonstrates that Bard outperforms previous LVLMs in most multimodal capabilities except object hallucination, to which Bard is still susceptible. Tiny LVLM-eHub serves as a baseline evaluation for various LVLMs and encourages innovative strategies aimed at advancing multimodal techniques. Our project is publicly available at \url{https://github.com/OpenGVLab/Multi-Modality-Arena}.
Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation
Authors: McKell Woodland, Nihil Patel, Mais Al Taie, Joshua P. Yung, Tucker J. Netherton, Ankit B. Patel, Kristy K. Brock
Date: 2023-08-07 00:00:00
Tasks: Dimensionality Reduction, Image Segmentation, Medical Image Segmentation, Out-of-Distribution Detection, Semantic Segmentation

Clinically deployed segmentation models are known to fail on data outside of their training distribution. As these models perform well on most cases, it is imperative to detect out-of-distribution (OOD) images at inference to protect against automation bias. This work applies the Mahalanobis distance post hoc to the bottleneck features of a Swin UNETR model that segments the liver on T1-weighted magnetic resonance imaging. By reducing the dimensions of the bottleneck features with principal component analysis, OOD images were detected with high performance and minimal computational load.
SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention
Authors: Efimia Panagiotaki, Daniele De Martini, Georgi Pramatarov, Matthew Gadd, Lars Kunze
Date: 2023-08-07 00:00:00
Tasks: Graph Attention, Graph Neural Network, Inductive Bias, Pose Estimation

This paper proposes a Graph Neural Network(GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates. Semantic and morphological features of the environment serve as key reference points for registration, enabling accurate lidar-based pose estimation. Our novel lightweight static graph structure informs our attention-based node aggregation network by identifying semantic-instance relationships, acting as an inductive bias to significantly reduce the computational burden of pointcloud registration. By connecting candidate nodes and exploiting cross-graph attention, we identify confidence scores for all potential registration correspondences and estimate the displacement between pointcloud scans. Our pipeline enables introspective analysis of the model's performance by correlating it with the individual contributions of local structures in the environment, providing valuable insights into the system's behaviour. We test our method on the KITTI odometry dataset, achieving competitive accuracy compared to benchmark methods and a higher track smoothness while relying on significantly fewer network parameters.
Safe Multimodal Communication in Human-Robot Collaboration
Authors: Davide Ferrari, Andrea Pupa, Alberto Signoretti, Cristian Secchi
Date: 2023-08-07 00:00:00

The new industrial settings are characterized by the presence of human and robots that work in close proximity, cooperating in performing the required job. Such a collaboration, however, requires to pay attention to many aspects. Firstly, it is crucial to enable a communication between this two actors that is natural and efficient. Secondly, the robot behavior must always be compliant with the safety regulations, ensuring always a safe collaboration. In this paper, we propose a framework that enables multi-channel communication between humans and robots by leveraging multimodal fusion of voice and gesture commands while always respecting safety regulations. The framework is validated through a comparative experiment, demonstrating that, thanks to multimodal communication, the robot can extract valuable information for performing the required task and additionally, with the safety layer, the robot can scale its speed to ensure the operator's safety.
AgentBench: Evaluating LLMs as Agents
Authors: Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, Jie Tang
Date: 2023-08-07 00:00:00
Tasks: Decision Making, Instruction Following

Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. As a result, there has been an urgent need to evaluate LLMs as agents on challenging tasks in interactive environments. We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting. Our extensive test over 27 API-based and open-sourced (OSS) LLMs shows that, while top commercial LLMs present a strong ability of acting as agents in complex environments, there is a significant disparity in performance between them and OSS competitors. We identify the typical reasons of failures in environments and LLMs, showing that poor long-term reasoning, decision-making, and instruction following abilities are the main obstacles for developing usable LLM agents. Training on code and high quality multi-turn alignment data could improve agent performance. Datasets, environments, and an integrated evaluation package for AgentBench are released at \url{https://github.com/THUDM/AgentBench}.
SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples
Authors: Michael Färber, David Lamprecht, Johan Krause, Linn Aung, Peter Haase
Date: 2023-08-07 00:00:00
Tasks: Recommendation Systems

We present SemOpenAlex, an extensive RDF knowledge graph that contains over 26 billion triples about scientific publications and their associated entities, such as authors, institutions, journals, and concepts. SemOpenAlex is licensed under CC0, providing free and open access to the data. We offer the data through multiple channels, including RDF dump files, a SPARQL endpoint, and as a data source in the Linked Open Data cloud, complete with resolvable URIs and links to other data sources. Moreover, we provide embeddings for knowledge graph entities using high-performance computing. SemOpenAlex enables a broad range of use-case scenarios, such as exploratory semantic search via our website, large-scale scientific impact quantification, and other forms of scholarly big data analytics within and across scientific disciplines. Additionally, it enables academic recommender systems, such as recommending collaborators, publications, and venues, including explainability capabilities. Finally, SemOpenAlex can serve for RDF query optimization benchmarks, creating scholarly knowledge-guided language models, and as a hub for semantic scientific publishing.
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
Authors: Felix Chalumeau, Bryan Lim, Raphael Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Arthur Flajolet, Thomas Pierrot, Antoine Cully
Date: 2023-08-07 00:00:00
Tasks: continuous-control, Continuous Control, Diversity, reinforcement-learning, Reinforcement Learning (RL)

QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax. The library serves as a versatile tool for optimization purposes, ranging from black-box optimization to continuous control. QDax offers implementations of popular QD, Neuroevolution, and Reinforcement Learning (RL) algorithms, supported by various examples. All the implementations can be just-in-time compiled with Jax, facilitating efficient execution across multiple accelerators, including GPUs and TPUs. These implementations effectively demonstrate the framework's flexibility and user-friendliness, easing experimentation for research purposes. Furthermore, the library is thoroughly documented and tested with 95\% coverage.
Detecting Spells in Fantasy Literature with a Transformer Based Artificial Intelligence
Authors: Marcel Moravek, Alexander Zender, Andreas Müller
Date: 2023-08-07 00:00:00
Tasks: token-classification, Token Classification

Transformer architectures and models have made significant progress in language-based tasks. In this area, is BERT one of the most widely used and freely available transformer architecture. In our work, we use BERT for context-based phrase recognition of magic spells in the Harry Potter novel series. Spells are a common part of active magic in fantasy novels. Typically, spells are used in a specific context to achieve a supernatural effect. A series of investigations were conducted to see if a Transformer architecture could recognize such phrases based on their context in the Harry Potter saga. For our studies a pre-trained BERT model was used and fine-tuned utilising different datasets and training methods to identify the searched context. By considering different approaches for sequence classification as well as token classification, it is shown that the context of spells can be recognised. According to our investigations, the examined sequence length for fine-tuning and validation of the model plays a significant role in context recognition. Based on this, we have investigated whether spells have overarching properties that allow a transfer of the neural network models to other fantasy universes as well. The application of our model showed promising results and is worth to be deepened in subsequent studies.
FFF: Fragments-Guided Flexible Fitting for Building Complete Protein Structures
Authors: WeiJie Chen, Xinyan Wang, Yuhang Wang
Date: 2023-08-07 00:00:00
Tasks: Protein Structure Prediction

Cryo-electron microscopy (cryo-EM) is a technique for reconstructing the 3-dimensional (3D) structure of biomolecules (especially large protein complexes and molecular assemblies). As the resolution increases to the near-atomic scale, building protein structures de novo from cryo-EM maps becomes possible. Recently, recognition-based de novo building methods have shown the potential to streamline this process. However, it cannot build a complete structure due to the low signal-to-noise ratio (SNR) problem. At the same time, AlphaFold has led to a great breakthrough in predicting protein structures. This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure. In this paper, we propose a new method named FFF that bridges protein structure prediction and protein structure recognition with flexible fitting. First, a multi-level recognition network is used to capture various structural features from the input 3D cryo-EM map. Next, protein structural fragments are generated using pseudo peptide vectors and a protein sequence alignment method based on these extracted features. Finally, a complete structural model is constructed using the predicted protein fragments via flexible fitting. Based on our benchmark tests, FFF outperforms the baseline methods for building complete protein structures.
Segmentation Framework for Heat Loss Identification in Thermal Images: Empowering Scottish Retrofitting and Thermographic Survey Companies
Authors: Md Junayed Hasan, Eyad Elyan, Yijun Yan, Jinchang Ren, Md Mostafa Kamal Sarker
Date: 2023-08-07 00:00:00
Tasks: Region Proposal, Transfer Learning

Retrofitting and thermographic survey (TS) companies in Scotland collaborate with social housing providers to tackle fuel poverty. They employ ground-level infrared (IR) camera-based-TSs (GIRTSs) for collecting thermal images to identi-fy the heat loss sources resulting from poor insulation. However, this identifica-tion process is labor-intensive and time-consuming, necessitating extensive data processing. To automate this, an AI-driven approach is necessary. Therefore, this study proposes a deep learning (DL)-based segmentation framework using the Mask Region Proposal Convolutional Neural Network (Mask RCNN) to validate its applicability to these thermal images. The objective of the framework is to au-tomatically identify, and crop heat loss sources caused by weak insulation, while also eliminating obstructive objects present in those images. By doing so, it min-imizes labor-intensive tasks and provides an automated, consistent, and reliable solution. To validate the proposed framework, approximately 2500 thermal imag-es were collected in collaboration with industrial TS partner. Then, 1800 repre-sentative images were carefully selected with the assistance of experts and anno-tated to highlight the target objects (TO) to form the final dataset. Subsequently, a transfer learning strategy was employed to train the dataset, progressively aug-menting the training data volume and fine-tuning the pre-trained baseline Mask RCNN. As a result, the final fine-tuned model achieved a mean average precision (mAP) score of 77.2% for segmenting the TO, demonstrating the significant po-tential of proposed framework in accurately quantifying energy loss in Scottish homes.
MedMine: Examining Pre-trained Language Models on Medication Mining
Authors: Haifa Alrdahi, Lifeng Han, Hendrik Šuvalov, Goran Nenadic
Date: 2023-08-07 00:00:00
Tasks: Data Augmentation, Ensemble Learning, Language Modeling, Language Modelling, Large Language Model

Automatic medication mining from clinical and biomedical text has become a popular topic due to its real impact on healthcare applications and the recent development of powerful language models (LMs). However, fully-automatic extraction models still face obstacles to be overcome such that they can be deployed directly into clinical practice for better impacts. Such obstacles include their imbalanced performances on different entity types and clinical events. In this work, we examine current state-of-the-art pre-trained language models (PLMs) on such tasks, via fine-tuning including the monolingual model Med7 and multilingual large language model (LLM) XLM-RoBERTa. We compare their advantages and drawbacks using historical medication mining shared task data sets from n2c2-2018 challenges. We report the findings we get from these fine-tuning experiments such that they can facilitate future research on addressing them, for instance, how to combine their outputs, merge such models, or improve their overall accuracy by ensemble learning and data augmentation. MedMine is part of the M3 Initiative \url{https://github.com/HECTA-UoM/M3}