pwc-archive/papers-with-abstracts huggingface dataset:
SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning
Authors: Dong Chen, Shuai Zheng, Muhao Xu, Zhenfeng Zhu, Yao Zhao
Date: 2024-03-11 00:00:00
Tasks: Graph Neural Network, Graph Representation Learning, Node Classification, Representation Learning

In the domain of dynamic graph representation learning (DGRL), the efficient and comprehensive capture of temporal evolution within real-world networks is crucial. Spiking Neural Networks (SNNs), known as their temporal dynamics and low-power characteristic, offer an efficient solution for temporal processing in DGRL task. However, owing to the spike-based information encoding mechanism of SNNs, existing DGRL methods employed SNNs face limitations in their representational capacity. Given this issue, we propose a novel framework named Spike-induced Graph Neural Network (SiGNN) for learning enhanced spatialtemporal representations on dynamic graphs. In detail, a harmonious integration of SNNs and GNNs is achieved through an innovative Temporal Activation (TA) mechanism. Benefiting from the TA mechanism, SiGNN not only effectively exploits the temporal dynamics of SNNs but also adeptly circumvents the representational constraints imposed by the binary nature of spikes. Furthermore, leveraging the inherent adaptability of SNNs, we explore an in-depth analysis of the evolutionary patterns within dynamic graphs across multiple time granularities. This approach facilitates the acquisition of a multiscale temporal node representation.Extensive experiments on various real-world dynamic graph datasets demonstrate the superior performance of SiGNN in the node classification task.
Quality check of a sample partition using multinomial distribution
Authors: Soumita Modak
Date: 2024-04-11 00:00:00

In this paper, we advocate a novel measure for the purpose of checking the quality of a cluster partition for a sample into several distinct classes, and thus, determine the unknown value for the true number of clusters prevailing the provided set of data. Our objective leads us to the development of an approach through applying the multinomial distribution to the distances of data members, clustered in a group, from their respective cluster representatives. This procedure is carried out independently for each of the clusters, and the concerned statistics are combined together to design our targeted measure. Individual clusters separately possess the category-wise probabilities which correspond to different positions of its members in the cluster with respect to a typical member, in the form of cluster-centroid, medoid or mode, referred to as the corresponding cluster representative. Our method is robust in the sense that it is distribution-free, since this is devised irrespective of the parent distribution of the underlying sample. It fulfills one of the rare coveted qualities, present in the existing cluster accuracy measures, of having the capability to investigate whether the assigned sample owns any inherent clusters other than a single group of all members or not. Our measure's simple concept, easy algorithm, fast runtime, good performance, and wide usefulness, demonstrated through extensive simulation and diverse case-studies, make it appealing.
A solution for the mean parametrization of the von Mises-Fisher distribution
Authors: Marcel Nonnenmacher, Maneesh Sahani
Date: 2024-04-10 00:00:00
Tasks: Form

The von Mises-Fisher distribution as an exponential family can be expressed in terms of either its natural or its mean parameters. Unfortunately, however, the normalization function for the distribution in terms of its mean parameters is not available in closed form, limiting the practicality of the mean parametrization and complicating maximum-likelihood estimation more generally. We derive a second-order ordinary differential equation, the solution to which yields the mean-parameter normalizer along with its first two derivatives, as well as the variance function of the family. We also provide closed-form approximations to the solution of the differential equation. This allows rapid evaluation of both densities and natural parameters in terms of mean parameters. We show applications to topic modeling with mixtures of von Mises-Fisher distributions using Bregman Clustering.
Auditing health-related recommendations in social media: A Case Study of Abortion on YouTube
Authors: Mohammed Lahsaini, Mohamed Lechiakh, Alexandre Maurer
Date: 2024-04-11 00:00:00
Tasks: Misinformation

Recommendation algorithms (RS) used by social media, like YouTube, significantly shape our information consumption across various domains, especially in healthcare. Hence, algorithmic auditing becomes crucial to uncover their potential bias and misinformation, particularly in the context of controversial topics like abortion. We introduce a simple yet effective sock puppet auditing approach to investigate how YouTube recommends abortion-related videos to individuals with different backgrounds. Our framework allows for efficient auditing of RS, regardless of the complexity of the underlying algorithms
M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
Authors: Jiachen Zhu, Yichao Wang, Jianghao Lin, Jiarui Qin, Ruiming Tang, Weinan Zhang, Yong Yu
Date: 2024-04-11 00:00:00
Tasks: counterfactual, Counterfactual Inference

We primarily focus on the field of multi-scenario recommendation, which poses a significant challenge in effectively leveraging data from different scenarios to enhance predictions in scenarios with limited data. Current mainstream efforts mainly center around innovative model network architectures, with the aim of enabling the network to implicitly acquire knowledge from diverse scenarios. However, the uncertainty of implicit learning in networks arises from the absence of explicit modeling, leading to not only difficulty in training but also incomplete user representation and suboptimal performance. Furthermore, through causal graph analysis, we have discovered that the scenario itself directly influences click behavior, yet existing approaches directly incorporate data from other scenarios during the training of the current scenario, leading to prediction biases when they directly utilize click behaviors from other scenarios to train models. To address these problems, we propose the Multi-Scenario Causal-driven Adaptive Network M-scan). This model incorporates a Scenario-Aware Co-Attention mechanism that explicitly extracts user interests from other scenarios that align with the current scenario. Additionally, it employs a Scenario Bias Eliminator module utilizing causal counterfactual inference to mitigate biases introduced by data from other scenarios. Extensive experiments on two public datasets demonstrate the efficacy of our M-scan compared to the existing baseline models.
Can Large Language Models Assess Serendipity in Recommender Systems?
Authors: Yu Tokutake, Kazushi Okamoto
Date: 2024-04-11 00:00:00
Tasks: Binary Classification, Recommendation Systems

Serendipity-oriented recommender systems aim to counteract over-specialization in user preferences. However, evaluating a user's serendipitous response towards a recommended item can be challenging because of its emotional nature. In this study, we address this issue by leveraging the rich knowledge of large language models (LLMs), which can perform a variety of tasks. First, this study explored the alignment between serendipitous evaluations made by LLMs and those made by humans. In this investigation, a binary classification task was given to the LLMs to predict whether a user would find the recommended item serendipitously. The predictive performances of three LLMs on a benchmark dataset in which humans assigned the ground truth of serendipitous items were measured. The experimental findings reveal that LLM-based assessment methods did not have a very high agreement rate with human assessments. However, they performed as well as or better than the baseline methods. Further validation results indicate that the number of user rating histories provided to LLM prompts should be carefully chosen to avoid both insufficient and excessive inputs and that the output of LLMs that show high classification performance is difficult to interpret.
Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment
Authors: Xinyu Zhu, Lilin Zhang, Ning Yang
Date: 2024-04-11 00:00:00
Tasks: Attribute, Fairness, Representation Learning

Personalized fairness in recommendations has been attracting increasing attention from researchers. The existing works often treat a fairness requirement, represented as a collection of sensitive attributes, as a hyper-parameter, and pursue extreme fairness by completely removing information of sensitive attributes from the learned fair embedding, which suffer from two challenges: huge training cost incurred by the explosion of attribute combinations, and the suboptimal trade-off between fairness and accuracy. In this paper, we propose a novel Adaptive Fair Representation Learning (AFRL) model, which achieves a real personalized fairness due to its advantage of training only one model to adaptively serve different fairness requirements during inference phase. Particularly, AFRL treats fairness requirements as inputs and can learn an attribute-specific embedding for each attribute from the unfair user embedding, which endows AFRL with the adaptability during inference phase to determine the non-sensitive attributes under the guidance of the user's unique fairness requirement. To achieve a better trade-off between fairness and accuracy in recommendations, AFRL conducts a novel Information Alignment to exactly preserve discriminative information of non-sensitive attributes and incorporate a debiased collaborative embedding into the fair embedding to capture attribute-independent collaborative signals, without loss of fairness. Finally, the extensive experiments conducted on real datasets together with the sound theoretical analysis demonstrate the superiority of AFRL.
Detection of financial opportunities in micro-blogging data with a stacked classification system
Authors: Francisco de Arriba-Pérez, Silvia García-Méndez, José A. Regueiro-Janeiro, Francisco J. González-Castaño
Date: 2024-03-29 00:00:00
Tasks: Decision Making

Micro-blogging sources such as the Twitter social network provide valuable real-time data for market prediction models. Investors' opinions in this network follow the fluctuations of the stock markets and often include educated speculations on market opportunities that may have impact on the actions of other investors. In view of this, we propose a novel system to detect positive predictions in tweets, a type of financial emotions which we term "opportunities" that are akin to "anticipation" in Plutchik's theory. Specifically, we seek a high detection precision to present a financial operator a substantial amount of such tweets while differentiating them from the rest of financial emotions in our system. We achieve it with a three-layer stacked Machine Learning classification system with sophisticated features that result from applying Natural Language Processing techniques to extract valuable linguistic information. Experimental results on a dataset that has been manually annotated with financial emotion and ticker occurrence tags demonstrate that our system yields satisfactory and competitive performance in financial opportunity detection, with precision values up to 83%. This promising outcome endorses the usability of our system to support investors' decision making.
An efficient domain-independent approach for supervised keyphrase extraction and ranking
Authors: Sriraghavendra Ramaswamy
Date: 2024-03-24 00:00:00
Tasks: Deep Learning, Keyphrase Extraction, Word Embeddings

We present a supervised learning approach for automatic extraction of keyphrases from single documents. Our solution uses simple to compute statistical and positional features of candidate phrases and does not rely on any external knowledge base or on pre-trained language models or word embeddings. The ranking component of our proposed solution is a fairly lightweight ensemble model. Evaluation on benchmark datasets shows that our approach achieves significantly higher accuracy than several state-of-the-art baseline models, including all deep learning-based unsupervised models compared with, and is competitive with some supervised deep learning-based models too. Despite the supervised nature of our solution, the fact that does not rely on any corpus of "golden" keywords or any external knowledge corpus means that our solution bears the advantages of unsupervised solutions to a fair extent.
Improving Retrieval for RAG based Question Answering Models on Financial Documents
Authors: Spurthi Setty, Harsh Thakkar, Alyssa Lee, Eden Chung, Natan Vidra
Date: 2024-03-23 00:00:00
Tasks: Chunking, Question Answering, RAG, Re-Ranking, Retrieval, Retrieval-augmented Generation, Text Retrieval

The effectiveness of Large Language Models (LLMs) in generating accurate responses relies heavily on the quality of input provided, particularly when employing Retrieval Augmented Generation (RAG) techniques. RAG enhances LLMs by sourcing the most relevant text chunk(s) to base queries upon. Despite the significant advancements in LLMs' response quality in recent years, users may still encounter inaccuracies or irrelevant answers; these issues often stem from suboptimal text chunk retrieval by RAG rather than the inherent capabilities of LLMs. To augment the efficacy of LLMs, it is crucial to refine the RAG process. This paper explores the existing constraints of RAG pipelines and introduces methodologies for enhancing text retrieval. It delves into strategies such as sophisticated chunking techniques, query expansion, the incorporation of metadata annotations, the application of re-ranking algorithms, and the fine-tuning of embedding algorithms. Implementing these approaches can substantially improve the retrieval quality, thereby elevating the overall performance and reliability of LLMs in processing and responding to queries.
Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers
Authors: Kunal Sawarkar, Abhilasha Mangal, Shivam Raj Solanki
Date: 2024-03-22 00:00:00
Tasks: Information Retrieval, Open-Domain Question Answering, Question Answering, RAG, Retrieval, Retrieval-augmented Generation, Zero-shot Text Search

Retrieval-Augmented Generation (RAG) is a prevalent approach to infuse a private knowledge base of documents with Large Language Models (LLM) to build Generative Q\&A (Question-Answering) systems. However, RAG accuracy becomes increasingly challenging as the corpus of documents scales up, with Retrievers playing an outsized role in the overall RAG accuracy by extracting the most relevant document from the corpus to provide context to the LLM. In this paper, we propose the 'Blended RAG' method of leveraging semantic search techniques, such as Dense Vector indexes and Sparse Encoder indexes, blended with hybrid query strategies. Our study achieves better retrieval results and sets new benchmarks for IR (Information Retrieval) datasets like NQ and TREC-COVID datasets. We further extend such a 'Blended Retriever' to the RAG system to demonstrate far superior results on Generative Q\&A datasets like SQUAD, even surpassing fine-tuning performance.
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation
Authors: Shaowei Wei, Zhengwei Wu, Xin Li, Qintong Wu, Zhiqiang Zhang, Jun Zhou, Lihong Gu, Jinjie Gu
Date: 2024-03-22 00:00:00
Tasks: Clustering, Contrastive Learning, Online Clustering, Recommendation Systems, Self-Supervised Learning, Sequential Recommendation

Sequential recommendation methods play a pivotal role in modern recommendation systems. A key challenge lies in accurately modeling user preferences in the face of data sparsity. To tackle this challenge, recent methods leverage contrastive learning (CL) to derive self-supervision signals by maximizing the mutual information of two augmented views of the original user behavior sequence. Despite their effectiveness, CL-based methods encounter a limitation in fully exploiting self-supervision signals for users with limited behavior data, as users with extensive behaviors naturally offer more information. To address this problem, we introduce a novel learning paradigm, named Online Self-Supervised Self-distillation for Sequential Recommendation ($S^4$Rec), effectively bridging the gap between self-supervised learning and self-distillation methods. Specifically, we employ online clustering to proficiently group users by their distinct latent intents. Additionally, an adversarial learning strategy is utilized to ensure that the clustering procedure is not affected by the behavior length factor. Subsequently, we employ self-distillation to facilitate the transfer of knowledge from users with extensive behaviors (teachers) to users with limited behaviors (students). Experiments conducted on four real-world datasets validate the effectiveness of the proposed method.
A Framework for Multi-Robot Pose Graph SLAM
Authors: Isaac Deutsch, Ming Liu and Roland Siegwart
Date: 2016-06-06 00:00:00
Proceeding: IEEE International Conference on Real-time Computing and Robotics (RCAR) 2016 6
Tasks: Scene Recognition

We introduce a software framework for realtime multi-robot collaborative SLAM. Rather than building a complete SLAM system, our framework is designed to enable collaborative mapping for existing (single-robot) SLAM systems in a convenient fashion. The framework aggregates local pose graphs obtained from its multiple robots into a global pose graph, which it then feeds back to the robots to increase their mapping and localization effectiveness. The framework can potentially work with various SLAM algorithms, as long as they provide a pose graph with an image associated with each node, and absolute scaling. The merging of pose graphs is purely visual-based and does not require well-defined initial robot positions nor environment markers. To handle network delays, we propose a graph correction scheme that avoids using mutexes (and thus avoids modifying the existing SLAM system) by assuming local graph consistency. Furthermore, we propose a simple image feature filtering method that uses an associated depth image to filter image features unsuitable for scene recognition. We demonstrate the framework’s functionality with several interior datasets that we have collected using three robots.
Precise spiking motifs in neurobiological and neuromorphic data
Authors: Antoine Grimaldi, Amélie Gruel, Camille Besnainou, Jean-Nicolas Jérémie, Jean Martinet, Laurent U Perrinet
Date: 2024-04-11 00:00:00

Why do neurons communicate through spikes? By definition, spikes are all-or-none neural events which occur at continuous times. In other words, spikes are on one side binary, existing or not without further details, and on the other can occur at any asynchronous time, without the need for a centralized clock. This stands in stark contrast to the analog representation of values and the discretized timing classically used in digital processing and at the base of modern-day neural networks. As neural systems almost systematically use this so-called event-based representation in the living world, a better understanding of this phenomenon remains a fundamental challenge in neurobiology in order to better interpret the profusion of recorded data. With the growing need for intelligent embedded systems, it also emerges as a new computing paradigm to enable the efficient operation of a new class of sensors and event-based computers, called neuromorphic, which could enable significant gains in computation time and energy consumption -- a major societal issue in the era of the digital economy and global warming. In this review paper, we provide evidence from biology, theory and engineering that the precise timing of spikes plays a crucial role in our understanding of the efficiency of neural networks.
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
Authors: Vahid Balazadeh, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis
Date: 2024-04-10 00:00:00
Tasks: Decision Making, Meta Reinforcement Learning, Multi-Armed Bandits, reinforcement-learning, Reinforcement Learning, Self-Driving Cars, Sequential Decision Making

We study the problem of online sequential decision-making given auxiliary demonstrations from experts who made their decisions based on unobserved contextual information. These demonstrations can be viewed as solving related but slightly different problems than what the learner faces. This setting arises in many application domains, such as self-driving cars, healthcare, and finance, where expert demonstrations are made using contextual information, which is not recorded in the data available to the learning agent. We model the problem as zero-shot meta-reinforcement learning with an unknown distribution over the unobserved contextual variables and a Bayesian regret minimization objective, where the unobserved variables are encoded as parameters with an unknown prior. We propose the Experts-as-Priors algorithm (ExPerior), an empirical Bayes approach that utilizes expert data to establish an informative prior distribution over the learner's decision-making problem. This prior distribution enables the application of any Bayesian approach for online decision-making, such as posterior sampling. We demonstrate that our strategy surpasses existing behaviour cloning, online, and online-offline baselines for multi-armed bandits, Markov decision processes (MDPs), and partially observable MDPs, showcasing the broad reach and utility of ExPerior in using expert demonstrations across different decision-making setups.
An Emotion Recognition Embedded System using a Lightweight Deep Learning Model
Authors: Mehdi Bazargani, Amir Tahmasebi, Mohammadreza Yazdchi, Zahra Baharlouei
Date: 2023-08-31 00:00:00
Proceeding: Journal of Medical Signals and Sensors 2023 8
Tasks: Deep Learning, EEG, Emotion Recognition

Diagnosing emotional states would improve human‑computer interaction (HCI) systems to be more effective in practice. Correlations between Electroencephalography (EEG) signals and emotions have been shown in various research; therefore, EEG signal‑based methods are the most accurate and informative. Methods: In this study, three Convolutional Neural Network (CNN) models, EEGNet, ShallowConvNet and DeepConvNet, which are appropriate for processing EEG signals, are applied to diagnose emotions. We use baseline removal preprocessing to improve classification accuracy. Each network is assessed in two setting ways: subject‑dependent and subject‑independent. We improve the selected CNN model to be lightweight and implementable on a Raspberry Pi processor. The emotional states are recognized for every three‑second epoch of received signals on the embedded system, which can be applied in real‑time usage in practice. Results: Average classification accuracies of 99.10% in the valence and 99.20% in the arousal for subject‑dependent and 90.76% in the valence and 90.94% in the arousal for subject independent were achieved on the well‑known DEAP dataset. Conclusion: Comparison of the results with the related works shows that a highly accurate and implementable model has been achieved for practice.