Abstract

Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement. This aids readers in considering broader implications for further research. In this article, we present a novel and challenging task of Suggestive Limitation Generation (SLG) for research papers. We compile a dataset called LimGen, encompassing 4068 research papers and their associated limitations from the ACL anthology. We investigate several approaches to harness large language models (LLMs) for producing suggestive limitations, by thoroughly examining the related challenges, practical insights, and potential opportunities. Our LimGen dataset and code can be accessed at https://github. com/arbmf/LimGen.

Abstract

This paper examines the enhancement of quadrotor efficiency through adaptive control to address the critical need for Fault-Tolerant Control (FTC) in quadrotors amidst operational uncertainties and component inefficiency. State-of-the-art adaptive FTC strategies often assume the uncertainties to be bounded by a constant a priori; however, imprecise knowledge of inertial system parameters lead to state-dependent uncertainties which do not follow such assumption. Remain unattended, statedependent uncertainties can lead to instability, especially under actuator faults. The proposed adaptive FTC offers actuator fault mitigation while tackling unknown (statedependent) uncertainties via suitably designed adaptive laws. Additionally, real-time fault detection and control allocation are used simultaneously to avoid conservative control application. The closed-loop system stability is studied analytically and the effectiveness of the proposed solution is verified on a realistic simulator in comparison to the state of the art.

Abstract

Understanding the structural organisation of 3D indoor scenes in terms of rooms is often accomplished via floorplan extraction. Robotic tasks such as planning and navigation require a semantic understanding of the scene as well. This is typically achieved via object-level semantic segmentation. However, such methods struggle to segment out topological regions like “kitchen” in the scene. In this work, we introduce a two-step pipeline. First, we extract a topological map, i.e., floorplan of the indoor scene using a novel multi-channel occupancy representation. Then, we generate CLIP-aligned features and semantic labels for every room instance based on the objects it contains using a self-attention transformer. Our language-topology alignment supports natural language querying, e.g. a “place to cook” locates the “kitchen”. We outperform the current state-of-the-art on room segmentation by ∼20% and room classification by ∼12%. Our detailed qualitative analysis and ablation studies provide insights into the problem of joint structural and semantic 3D scene understanding.

Abstract

Language models often exhibit undesirable behavior, e.g., generating toxic or gender-biased text. In the case of neural language models, an encoding of the undesirable behavior is often present in the model's representations. Thus, one natural (and common) approach to prevent the model from exhibiting undesirable behavior is to steer the model's representations in a manner that reduces the probability of it generating undesirable text. This paper investigates the formal and empirical properties of steering functions, i.e., transformation of the neural language model's representations that alter its behavior. First, we derive two optimal, in the least-squares sense, affine steering functions under different constraints. Our theory provides justification for existing approaches and offers a novel, improved steering approach. Second, we offer a series of experiments that demonstrate the empirical effectiveness of the methods in mitigating bias and reducing toxic generation.

Abstract

Named Entity Recognition (NER) is a use- ful component in Natural Language Process- ing (NLP) applications. It is used in various tasks such as Machine Translation, Summa- rization, Information Retrieval, and Question- Answering systems. The research on NER is centered around English and some other ma- jor languages, whereas limited attention has been given to Indian languages. We analyze the challenges and propose techniques that can be tailored for Multilingual Named Entity Recog- nition for Indian Languages. We present a hu- man annotated named entity corpora of ∼40K sentences for 4 Indian languages from two of the major Indian language families. Addition- ally,we present a multilingual model fine-tuned on our dataset, which achieves an F1 score of ∼0.80 on our dataset on average. We achieve comparable performance on completely unseen benchmark datasets for Indian languages which affirms the usability of our model.

Abstract

The task of automatically detecting syllable stress is a key module in computer-assisted language learning systems. There are numerous studies proposed in the literature for automatic syllable stress detection by using different knowledge-based prosodic features. Also, different statistical machine learning and deep learning models are explored for this task using knowledge-based features. However, the acoustic parameters considered to compute knowledge-based features might not always represent the stress phenomena, hence the knowledge based features are not always suitable for generalization and scalability. Recently, the rapidly emerging self-supervised learning based representations are outperforming the existing state-of-the-art knowledge-based features in all speech applications. Also, these representations allow the models to be built in an end-to-end fashion. In this work, we explore the use of self-supervised representations (Wav2Vec-2.0), for syllable stress detection and compare the performance with state-of-the-art knowledge-based features. Further, we use our recently proposed explicit representation learning framework, modeled by jointly optimizing variational autoencoder (VAE) and DNN for stress detection. We analyze the performance of representation learning framework with two different state-of-the-art classifiers, support vector machines (SVM) and simple deep neural network (DNN). We conduct experiments on two non-native English speakers’ datasets from ISLE corpus i.e., German (GER), and Italian (ITA). From the analysis study, it is observed that the classification accuracy for syllable stress detection using self-supervised representations significantly improved by 3.2% and 2.7% over knowledge-based features in GER and ITA, respectively. From the t-SNE plots, it is observed that the representations learned by explicit representation learning framework with VAE show better discrimination among stressed and unstressed syllables compared with representations learned implicitly with simple DNN.

Abstract

The maritime transportation industry is a prime target for cybersecurity attacks due to the inherent risks of transmitting sensitive information required for its smooth operation. The introduction of digital systems in maritime transport has made them susceptible to a range of cybersecurity threats. These attacks can lead to the unauthorized acquisition and misuse of vulnerable data, such as personal information of crew and passengers, vessel location, route, schedule, and other related information. To address this issue, a new lightweight authentication protocol has been proposed by utilizing drone technology in conjunction with the 5th generation mobile network (5G) communication. The effectiveness of the proposed protocol has been analyzed, which demonstrates its ability to withstand various security attacks while maintaining low communication and computation costs. Furthermore, it successfully meets the security and functionality requirements of anonymity and untraceability. A comprehensive simulation study and simulation using the network simulator (NS3) have been conducted to assess the impact on various network performance parameters for the proposed scheme. In addition, various experiments using machine-learning algorithms for Big data analytics show the efficiency of the proposed scheme in terms of accuracy, precision, recall and F1 score.

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Abstract

In a prior study, a novel deterministic compartmental model known as the SEIHRK model was introduced, shedding light on the pivotal role of test kits as an intervention strategy for mitigating epidemics. Particularly in heterogeneous networks, it was empirically demonstrated that strategically distributing a limited number of test kits among nodes with higher degrees substantially diminishes the outbreak size. The network's dynamics were explored under varying values of infection rate. In this research, we expand upon these findings to investigate the influence of migration on infection dynamics within distinct communities of the network. Notably, we observe that nodes equipped with test kits and those without tend to segregate into two separate clusters when coupling strength is low, but beyond a critical threshold coupling coefficient, they coalesce into a unified cluster. Building on this clustering phenomenon, we develop a reduced equation model and rigorously validate its accuracy through comprehensive simulations. We show that this property is observed in both complete and random graphs.