pwc-archive/papers-with-abstracts huggingface dataset:
Few-Shot Recognition and Classification Framework for Jamming Signal: A CGAN-Based Fusion CNN Approach
Authors: Xuhui Ding, Yue Zhang, Gaoyang Li, Xiaozheng Gao, Neng Ye, Dusit Niyato, Kai Yang
Date: 2023-11-09 00:00:00
Tasks: Generative Adversarial Network

Subject to intricate environmental variables, the precise classification of jamming signals holds paramount significance in the effective implementation of anti-jamming strategies within communication systems. In light of this imperative, we propose an innovative fusion algorithm based on conditional generative adversarial network (CGAN) and convolutional neural network (CNN), which aims to deal with the difficulty in applying deep learning (DL) algorithms due to the instantaneous nature of jamming signals in practical communication systems. Compared with previous methods, our algorithm embeds jamming category labels to constrain the range of generated signals in the frequency domain by using the CGAN model, which simultaneously captures potential label information while learning the distribution of signal data thus achieves an 8% improvement in accuracy even when working with a few-sample dataset. Real-world satellite communication scenarios are simulated by adopting hardware platform, and we validate our algorithm by using the resulting time-domain waveform data. The experimental results indicate that our algorithm still performs extremely well, which demonstrates significant potential for practical application in real-world communication scenarios.
Delay Doppler Transform
Authors: Xiang-Gen Xia
Date: 2023-11-09 00:00:00

This letter is to introduce delay Doppler transform (DDT) for a time domain signal. It is motivated by the recent studies in wireless communications over delay Doppler channels that have both time and Doppler spreads, such as, satellite communication channels. We present some simple properties of DDT as well. The DDT study may provide insights of delay Doppler channels.
An Analytical Model for Coordinated Multi-Satellite Joint Transmission System
Authors: Xiangyu Li, Bodong Shang
Date: 2023-11-09 00:00:00

Satellite communication is one of the key technologies that is enabling next-generation networks. However, nearest-satellite-supported downlink transmission may not meet a user's requirements due to limited signal strength, especially in emergent scenarios. In this paper, we investigate a coordinated multi-satellite joint transmission system from a system-level perspective, where a user can be served by multiple satellites to improve its quality-of-service (QoS). Furthermore, we analyze the coverage and rate of a typical user in the joint transmission system. Simulation and numerical results show that the introduced system achieves a higher coverage probability than the traditional nearest-satellite-supported network. Moreover, a user's ergodic rate can be maximized by selecting an appropriate number of serving satellites.
Constrained Independent Vector Analysis with Reference for Multi-Subject fMRI Analysis
Authors: Trung Vu, Francisco Laport, Hanlu Yang, Vince D. Calhoun, Tulay Adali
Date: 2023-11-08 00:00:00

Independent component analysis (ICA) is now a widely used solution for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. Independent vector analysis (IVA) generalizes ICA to multiple datasets, i.e., to multi-subject data, and in addition to higher-order statistical information in ICA, it leverages the statistical dependence across the datasets as an additional type of statistical diversity. As such, it preserves variability in the estimation of single-subject maps but its performance might suffer when the number of datasets increases. Constrained IVA is an effective way to bypass computational issues and improve the quality of separation by incorporating available prior information. Existing constrained IVA approaches often rely on user-defined threshold values to define the constraints. However, an improperly selected threshold can have a negative impact on the final results. This paper proposes two novel methods for constrained IVA: one using an adaptive-reverse scheme to select variable thresholds for the constraints and a second one based on a threshold-free formulation by leveraging the unique structure of IVA. We demonstrate that our solutions provide an attractive solution to multi-subject fMRI analysis both by simulations and through analysis of resting state fMRI data collected from 98 subjects -- the highest number of subjects ever used by IVA algorithms. Our results show that both proposed approaches obtain significantly better separation quality and model match while providing computationally efficient and highly reproducible solutions.
Is one brick enough to break the wall of spoken dialogue state tracking?
Authors: Lucas Druart, Valentin Vielzeuf, Yannick Estève
Date: 2023-11-03 00:00:00
Tasks: Dialogue State Tracking

In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's requests (\textit{a.k.a} dialogue state tracking) is key to a smooth interaction. Traditionally, TOD systems perform this update in three steps: transcription of the user's utterance, semantic extraction of the key concepts, and contextualization with the previously identified concepts. Such cascade approaches suffer from cascading errors and separate optimization. End-to-End approaches have been proven helpful up to the turn-level semantic extraction step. This paper goes one step further and provides (1) a novel approach for completely neural spoken DST, (2) an in depth comparison with a state of the art cascade approach and (3) avenues towards better context propagation. Our study highlights that jointly-optimized approaches are also competitive for contextually dependent tasks, such as Dialogue State Tracking (DST), especially in audio native settings. Context propagation in DST systems could benefit from training procedures accounting for the previous' context inherent uncertainty.
Are cascade dialogue state tracking models speaking out of turn in spoken dialogues?
Authors: Lucas Druart, Léo Jacqmin, Benoît Favre, Lina Maria Rojas-Barahona, Valentin Vielzeuf
Date: 2023-11-03 00:00:00
Tasks: Dialogue State Tracking

In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's needs is key to a smooth interaction. Traditionally TOD systems are composed of several modules that interact with one another. While each of these components is the focus of active research communities, their behavior in interaction can be overlooked. This paper proposes a comprehensive analysis of the errors of state of the art systems in complex settings such as Dialogue State Tracking which highly depends on the dialogue context. Based on spoken MultiWoz, we identify that errors on non-categorical slots' values are essential to address in order to bridge the gap between spoken and chat-based dialogue systems. We explore potential solutions to improve transcriptions and help dialogue state tracking generative models correct such errors.
Firing rate distributions in plastic networks of spiking neurons
Authors: Marina Vegué, Antoine Allard, Patrick Desrosiers
Date: 2023-11-09 00:00:00

In recurrent networks of leaky integrate-and-fire (LIF) neurons, mean-field theory has proven successful in describing various statistical properties of neuronal activity at equilibrium, such as firing rate distributions. Mean-field theory has been applied to networks in which either the synaptic weights are homogeneous across synapses and the number of incoming connections of individual neurons is heterogeneous, or vice versa. Here we extend the previous mean-field formalisms to treat networks in which these two sources of structural heterogeneity occur simultaneously, including networks whose synapses are subject to plastic, activity-dependent modulation. The plasticity in our model is mediated by the introduction of one spike trace per neuron: a chemical signal that is released every time the neuron emits a spike and which is degraded over time. The temporal evolution of the trace is controlled by its degradation rate $r_p$ and by the neuron's underlying firing rate $\nu$. When the ratio $\alpha=\nu / r_p$ tends to infinity, the trace can be rescaled to be a reliable estimation of the neuron's firing rate. In this regime, the value of any synaptic weight at equilibrium is a function of the pre- and post-synaptic firing rates, and this relation can be used in the mean-field formalism. The solution to the mean-field equations specifies the firing rate and synaptic weight distributions at equilibrium. These equations are exact in the limit of reliable traces but they already provide accurate results when the degradation rate lies within a reasonable range, as we show by comparison with simulations of the full neuronal dynamics in networks composed of excitatory and inhibitory LIF neurons. Overall, this work offers a way to explore and better understand the way in which plasticity shapes both activity and structure in neuronal networks.
Rooting capacity of hardwood cuttings of some fruit trees in relation to cutting pattern
Authors: Aram Akram Mohammed, Rasul Rafiq Aziz, Faraydwn Karim Ahmad, Ibrahim Maaroof Noori, Tariq Abubakr Ahmad
Date: 2023-11-08 00:00:00
Tasks: Relation

Study two cut patterns in hardwood cuttings of (Cydonia oblonga), (Punica granatum) and (Ficus carica). The cuttings have been cut either straight with different internode stub lengths [0 (just onto the basal node as control), 0.5, 1.0, 2.0 or 3.0 cm below the basal node], or slant with 45 degree angle for each length mentioned above (except the first length (0 cm). Effect of the basal cut directions on rooting percentage and other shoot and root characteristics were not significantly different, while the effect of slant cut pattern on one-side rooting at the basal margin observed in some quince cuttings but it was rarely observed in pomegranate and fig cuttings. Quince cuttings gave no different rooting percentage and other shoot and root characteristics significantly with different internode stub lengths. While, internode stub 1 and 2 cm in pomegranate cuttings, and 0 cm in fig cuttings gave the best rooting percentages 44.44% and 100%, respectively. Also, interaction effects of the two factors on rooting percentage and other shoot and root characteristics were just significantly different in pomegranate and fig cuttings. The best rooting capacity achieved in pomegranate cuttings (49.99%) in those were cut straightly at the base with 1 and 2 cm basal internode stub lengths, and fig cuttings straightly cut at the base with 0 and 1 cm basal internode stub lengths gave the highest rooting capacity (100%).
Phosphatidylserine transport in cell life and death
Authors: Alenka {Č}opi{č}, Thibaud Dieudonné, Guillaume Lenoir
Date: 2023-11-09 00:00:00

Phosphatidylserine (PS) is a negatively-charged glycerophospholipid found mainly in the plasma membrane (PM) and in the late secretory/endocytic compartments, where it regulates cellular activity and can mediate apoptosis. Export of PS from the endoplasmic reticulum, its site of synthesis, to other compartments, and its transbilayer asymmetry must therefore be precisely regulated. We review recent findings on non-vesicular transport of PS by lipid transfer proteins (LTPs) at membrane contact sites, on PS flip-flop between membrane leaflets by flippases and scramblases, and on PS nano-clustering at the PM. We also discuss emerging data on cooperation between scramblases and LTPs, how perturbation of PS distribution can lead to disease, and the specific role of PS in viral infection.
Intermediating DFMM Asset (IDA)
Authors: Arman Abgaryan, Utkarsh Sharma
Date: 2023-11-09 00:00:00

The Dynamic Function Market Maker (DFMM) introduced a fully automated framework for operating a multi-asset market, wherein an algorithmic accounting asset was used to connect different liquidity pools and ensure efficient rebalancing of risks, and internal accounting processes. In the DFMM design, this asset was not tradaeble; however, in this work, we explore the characteristics of this asset, if it were to be made tradeable. Named the Intermediating DFMM Asset (IDA), this asset serves as a unit of account in cross-chain finance, functioning as an intermediating asset for predictable budgeting, and efficient multichain transfers and settlements. Harnessing its robust liquidity as the key counterpart asset in DFMM, it achieves capital efficiency through the strategic repurposing of its asset base, while simultaneously mitigating risk via the dynamic optimisation of its multicollateral foundation. We outline key characteristics of the proposed asset, unique risk mitigation aspects enabled by the adopting AMM (DFMM), and control levers enabling the protocol's tactical asset and liability management toolkit to harmonise the asset's objectives with its real-world realisation, through a novel prudential market operation to incentivise productive use of a finite asset and dynamic AMM fee to ensure alignment of behaviours. The proposed design has the potential to harmonise the interests of diverse market participants, leading to synergetic reactions to informational flow, aiding IDA protocol in achieving its objectives.
Detecting Relevant Information in High-Volume Chat Logs: Keyphrase Extraction for Grooming and Drug Dealing Forensic Analysis
Authors: Jeovane Honório Alves, Horácio A. C. G. Pedroso, Rafael Honorio Venetikides, Joel E. M. Köster, Luiz Rodrigo Grochocki, Cinthia O. A. Freitas, Jean Paul Barddal
Date: 2023-09-15 00:00:00
Tasks: Keyphrase Extraction

The growing use of digital communication platforms has given rise to various criminal activities, such as grooming and drug dealing, which pose significant challenges to law enforcement and forensic experts. This paper presents a supervised keyphrase extraction approach to detect relevant information in high-volume chat logs involving grooming and drug dealing for forensic analysis. The proposed method, JointKPE++, builds upon the JointKPE keyphrase extractor by employing improvements to handle longer texts effectively. We evaluate JointKPE++ using BERT-based pre-trained models on grooming and drug dealing datasets, including BERT, RoBERTa, SpanBERT, and BERTimbau. The results show significant improvements over traditional approaches and demonstrate the potential for JointKPE++ to aid forensic experts in efficiently detecting keyphrases related to criminal activities.
FlaCGEC: A Chinese Grammatical Error Correction Dataset with Fine-grained Linguistic Annotation
Authors: Hanyue Du, Yike Zhao, Qingyuan Tian, Jiani Wang, Lei Wang, Yunshi Lan, Xuesong Lu
Date: 2023-09-26 00:00:00
Tasks: Diagnostic, Grammatical Error Correction

Chinese Grammatical Error Correction (CGEC) has been attracting growing attention from researchers recently. In spite of the fact that multiple CGEC datasets have been developed to support the research, these datasets lack the ability to provide a deep linguistic topology of grammar errors, which is critical for interpreting and diagnosing CGEC approaches. To address this limitation, we introduce FlaCGEC, which is a new CGEC dataset featured with fine-grained linguistic annotation. Specifically, we collect raw corpus from the linguistic schema defined by Chinese language experts, conduct edits on sentences via rules, and refine generated samples manually, which results in 10k sentences with 78 instantiated grammar points and 3 types of edits. We evaluate various cutting-edge CGEC methods on the proposed FlaCGEC dataset and their unremarkable results indicate that this dataset is challenging in covering a large range of grammatical errors. In addition, we also treat FlaCGEC as a diagnostic dataset for testing generalization skills and conduct a thorough evaluation of existing CGEC models.
An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach
Authors: Suhaima Jamal, Hayden Wimmer
Date: 2023-11-01 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, model, Spam detection

Phishing and spam detection is long standing challenge that has been the subject of much academic research. Large Language Models (LLM) have vast potential to transform society and provide new and innovative approaches to solve well-established challenges. Phishing and spam have caused financial hardships and lost time and resources to email users all over the world and frequently serve as an entry point for ransomware threat actors. While detection approaches exist, especially heuristic-based approaches, LLMs offer the potential to venture into a new unexplored area for understanding and solving this challenge. LLMs have rapidly altered the landscape from business, consumers, and throughout academia and demonstrate transformational potential for the potential of society. Based on this, applying these new and innovative approaches to email detection is a rational next step in academic research. In this work, we present IPSDM, our model based on fine-tuning the BERT family of models to specifically detect phishing and spam email. We demonstrate our fine-tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets. This work serves as an important first step towards employing LLMs to improve the security of our information systems.
Explainable Identification of Hate Speech towards Islam using Graph Neural Networks
Authors: Azmine Toushik Wasi
Date: 2023-11-02 00:00:00
Tasks: Decoder, Hate Speech Detection, Part-Of-Speech Tagging, Word Embeddings

Islamophobic language on online platforms fosters intolerance, making detection and elimination crucial for promoting harmony. Traditional hate speech detection models rely on NLP techniques like tokenization, part-of-speech tagging, and encoder-decoder models. However, Graph Neural Networks (GNNs), with their ability to utilize relationships between data points, offer more effective detection and greater explainability. In this work, we represent speeches as nodes and connect them with edges based on their context and similarity to develop the graph. This study introduces a novel paradigm using GNNs to identify and explain hate speech towards Islam. Our model leverages GNNs to understand the context and patterns of hate speech by connecting texts via pretrained NLP-generated word embeddings, achieving state-of-the-art performance and enhancing detection accuracy while providing valuable explanations. This highlights the potential of GNNs in combating online hate speech and fostering a safer, more inclusive online environment.
Adapting Fake News Detection to the Era of Large Language Models
Authors: Jinyan Su, Claire Cardie, Preslav Nakov
Date: 2023-11-02 00:00:00
Tasks: Articles, Fake News Detection

In the age of large language models (LLMs) and the widespread adoption of AI-driven content creation, the landscape of information dissemination has witnessed a paradigm shift. With the proliferation of both human-written and machine-generated real and fake news, robustly and effectively discerning the veracity of news articles has become an intricate challenge. While substantial research has been dedicated to fake news detection, this either assumes that all news articles are human-written or abruptly assumes that all machine-generated news are fake. Thus, a significant gap exists in understanding the interplay between machine-(paraphrased) real news, machine-generated fake news, human-written fake news, and human-written real news. In this paper, we study this gap by conducting a comprehensive evaluation of fake news detectors trained in various scenarios. Our primary objectives revolve around the following pivotal question: How to adapt fake news detectors to the era of LLMs? Our experiments reveal an interesting pattern that detectors trained exclusively on human-written articles can indeed perform well at detecting machine-generated fake news, but not vice versa. Moreover, due to the bias of detectors against machine-generated texts \cite{su2023fake}, they should be trained on datasets with a lower machine-generated news ratio than the test set. Building on our findings, we provide a practical strategy for the development of robust fake news detectors.
Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?
Authors: Ngoc Dang Nguyen, Wei Tan, Lan Du, Wray Buntine, Richard Beare, Changyou Chen
Date: 2023-11-02 00:00:00
Tasks: All, Low Resource Named Entity Recognition, Meta-Learning, Multi-class Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classification problem. However, this approach often overlooks the issues of imbalanced label distributions, particularly in low-resource settings, which is common in certain NER contexts, like biomedical NER (bioNER). To address these issues, we propose an innovative reformulation of the multi-class problem as a one-vs-all (OVA) learning problem and introduce a loss function based on the area under the receiver operating characteristic curve (AUC). To enhance the efficiency of our OVA-based approach, we propose two training strategies: one groups labels with similar linguistic characteristics, and another employs meta-learning. The superiority of our approach is confirmed by its performance, which surpasses traditional NER learning in varying NER settings.
Successor Features for Efficient Multisubject Controlled Text Generation
Authors: Meng Cao, Mehdi Fatemi, Jackie Chi Kit Cheung, Samira Shabanian
Date: 2023-11-03 00:00:00
Tasks: Computational Efficiency, Language Modelling, Text Generation

While large language models (LLMs) have achieved impressive performance in generating fluent and realistic text, controlling the generated text so that it exhibits properties such as safety, factuality, and non-toxicity remains challenging. % such as DExperts, GeDi, and rectification Existing decoding-based methods are static in terms of the dimension of control; if the target subject is changed, they require new training. Moreover, it can quickly become prohibitive to concurrently control multiple subjects. In this work, we introduce SF-GEN, which is grounded in two primary concepts: successor features (SFs) to decouple the LLM's dynamics from task-specific rewards, and language model rectification to proportionally adjust the probability of selecting a token based on the likelihood that the finished text becomes undesired. SF-GEN seamlessly integrates the two to enable dynamic steering of text generation with no need to alter the LLM's parameters. Thanks to the decoupling effect induced by successor features, our method proves to be memory-wise and computationally efficient for training as well as decoding, especially when dealing with multiple target subjects. To the best of our knowledge, our research represents the first application of successor features in text generation. In addition to its computational efficiency, the resultant language produced by our method is comparable to the SOTA (and outperforms baselines) in both control measures as well as language quality, which we demonstrate through a series of experiments in various controllable text generation tasks.