OpenRaiser/Intern-Atlas huggingface dataset:
Comparando Estratégias de Roteamento em Redes Quânticas Oportunísticas
Venue: arXiv cs.NI 2023
Year: 2023

This paper presents a comparative analysis of three routing strategies in opportunistic quantum networks. Quantum communication networks face unique challenges, such as the fragility of qubits and the need to create and maintain pairs of entangled states for reliable transmission. In this context, efficient and reliable routing is crucial to maximize the fidelity of the established routes, minimize the creation of new entangled pairs, and reduce the need for route recalculation. The routing strategies are compared based on the fidelity of the chosen routes, the number of entangled pairs created, and the number of route recalculations. The results obtained provide valuable information for the design and optimization of opportunistic quantum networks, contributing to advances in the efficiency and reliability of quantum communications.
An Empirical Study of Benchmarking Chinese Aspect Sentiment Quad Prediction
Venue: arXiv cs.CL 2023
Year: 2023

Aspect sentiment quad prediction (ASQP) is a critical subtask of aspect-level sentiment analysis. Current ASQP datasets are characterized by their small size and low quadruple density, which hinders technical development. To expand capacity, we construct two large Chinese ASQP datasets crawled from multiple online platforms. The datasets hold several significant characteristics: larger size (each with 10,000+ samples) and rich aspect categories, more words per sentence, and higher density than existing ASQP datasets. Moreover, we are the first to evaluate the performance of Generative Pre-trained Transformer (GPT) series models on ASQP and exhibit potential issues. The experiments with state-of-the-art ASQP baselines underscore the need to explore additional techniques to address ASQP, as well as the importance of further investigation into methods to improve the performance of GPTs.
CDGraph: Dual Conditional Social Graph Synthesizing via Diffusion Model
Venue: arXiv cs.SI 2023
Year: 2023

The social graphs synthesized by the generative models are increasingly in demand due to data scarcity and concerns over user privacy. One of the key performance criteria for generating social networks is the fidelity to specified conditionals, such as users with certain membership and financial status. While recent diffusion models have shown remarkable performance in generating images, their effectiveness in synthesizing graphs has not yet been explored in the context of conditional social graphs. In this paper, we propose the first kind of conditional diffusion model for social networks, CDGraph, which trains and synthesizes graphs based on two specified conditions. We propose the co-evolution dependency in the denoising process of CDGraph to capture the mutual dependencies between the dual conditions and further incorporate social homophily and social contagion to preserve the connectivity between nodes while satisfying the specified conditions. Moreover, we introduce a novel classifier loss, which guides the training of the diffusion process through the mutual dependency of dual conditions. We evaluate CDGraph against four existing graph generative methods, i.e., SPECTRE, GSM, EDGE, and DiGress, on four datasets. Our results show that the generated graphs from CDGraph achieve much higher dual-conditional validity and lower discrepancy in various social network metrics than the baselines, thus demonstrating its proficiency in generating dual-conditional social graphs.
Modeling the Uncertainty with Maximum Discrepant Students for Semi-supervised 2D Pose Estimation
Venue: arXiv cs.CV 2023
Year: 2023

Semi-supervised pose estimation is a practically challenging task for computer vision. Although numerous excellent semi-supervised classification methods have emerged, these methods typically use confidence to evaluate the quality of pseudo-labels, which is difficult to achieve in pose estimation tasks. For example, in pose estimation, confidence represents only the possibility that a position of the heatmap is a keypoint, not the quality of that prediction. In this paper, we propose a simple yet efficient framework to estimate the quality of pseudo-labels in semi-supervised pose estimation tasks from the perspective of modeling the uncertainty of the pseudo-labels. Concretely, under the dual mean-teacher framework, we construct the two maximum discrepant students (MDSs) to effectively push two teachers to generate different decision boundaries for the same sample. Moreover, we create multiple uncertainties to assess the quality of the pseudo-labels. Experimental results demonstrate that our method improves the performance of semi-supervised pose estimation on three datasets.
Generating Unbiased Pseudo-labels via a Theoretically Guaranteed Chebyshev Constraint to Unify Semi-supervised Classification and Regression
Venue: arXiv cs.CV 2023
Year: 2023

Both semi-supervised classification and regression are practically challenging tasks for computer vision. However, semi-supervised classification methods are barely applied to regression tasks. Because the threshold-to-pseudo label process (T2L) in classification uses confidence to determine the quality of label. It is successful for classification tasks but inefficient for regression tasks. In nature, regression also requires unbiased methods to generate high-quality labels. On the other hand, T2L for classification often fails if the confidence is generated by a biased method. To address this issue, in this paper, we propose a theoretically guaranteed constraint for generating unbiased labels based on Chebyshev's inequality, combining multiple predictions to generate superior quality labels from several inferior ones. In terms of high-quality labels, the unbiased method naturally avoids the drawback of T2L. Specially, we propose an Unbiased Pseudo-labels network (UBPL network) with multiple branches to combine multiple predictions as pseudo-labels, where a Feature Decorrelation loss (FD loss) is proposed based on Chebyshev constraint. In principle, our method can be used for both classification and regression and can be easily extended to any semi-supervised framework, e.g. Mean Teacher, FixMatch, DualPose. Our approach achieves superior performance over SOTAs on the pose estimation datasets Mouse, FLIC and LSP, as well as the classification datasets CIFAR10/100 and SVHN.
When fairness is an abstraction: Equity and AI in Swedish compulsory education
Venue: arXiv cs.CY 2023
Year: 2023

Artificial intelligence experts often question whether AI is fair. They view fairness as a property of AI systems rather than of sociopolitical and economic systems. This paper emphasizes the need to be fair in the social, political, and economic contexts within which an educational system operates and uses AI. Taking Swedish decentralized compulsory education as the context, this paper examines whether and how the use of AI envisaged by national authorities and edtech companies exacerbates unfairness. A qualitative content analysis of selected Swedish policy documents and edtech reports was conducted using the concept of relevant social groups to understand how different groups view the risks and benefits of AI for fairness. Three groups that view efficiency as a key value of AI are identified, and interpreted as economical, pedagogical and accessibility-related. By separating fairness from social justice, this paper challenges the notion of fairness as the formal equality of opportunities.
Sentiment Analysis through LLM Negotiations
Venue: arXiv cs.CL 2023
Year: 2023

A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round under the framework of in-context learning. This framework suffers the key disadvantage that the single-turn output generated by a single LLM might not deliver the perfect decision, just as humans sometimes need multiple attempts to get things right. This is especially true for the task of sentiment analysis where deep reasoning is required to address the complex linguistic phenomenon (e.g., clause composition, irony, etc) in the input. To address this issue, this paper introduces a multi-LLM negotiation framework for sentiment analysis. The framework consists of a reasoning-infused generator to provide decision along with rationale, a explanation-deriving discriminator to evaluate the credibility of the generator. The generator and the discriminator iterate until a consensus is reached. The proposed framework naturally addressed the aforementioned challenge, as we are able to take the complementary abilities of two LLMs, have them use rationale to persuade each other for correction. Experiments on a wide range of sentiment analysis benchmarks (SST-2, Movie Review, Twitter, yelp, amazon, IMDB) demonstrate the effectiveness of proposed approach: it consistently yields better performances than the ICL baseline across all benchmarks, and even superior performances to supervised baselines on the Twitter and movie review datasets.
End-to-End assessment of AR-assisted neurosurgery systems
Venue: arXiv cs.HC 2023
Year: 2023

Augmented Reality (AR) has emerged as a significant advancement in surgical procedures, offering a solution to the challenges posed by traditional neuronavigation methods. These conventional techniques often necessitate surgeons to split their focus between the surgical site and a separate monitor that displays guiding images. Over the years, many systems have been developed to register and track the hologram at the targeted locations, each employed its own evaluation technique. On the other hand, hologram displacement measurement is not a straightforward task because of various factors such as occlusion, Vengence-Accomodation Conflict, and unstable holograms in space. In this study, we explore and classify different techniques for assessing an AR-assisted neurosurgery system and propose a new technique to systematize the assessment procedure. Moreover, we conduct a deeper investigation to assess surgeon error in the pre- and intra-operative phases of the surgery based on the respective feedback given. We found that although the system can undergo registration and tracking errors, physical feedback can significantly reduce the error caused by hologram displacement. However, the lack of visual feedback on the hologram does not have a significant effect on the user 3D perception.
Depth-guided Free-space Segmentation for a Mobile Robot
Venue: arXiv cs.RO 2023
Year: 2023

Accurate indoor free-space segmentation is a challenging task due to the complexity and the dynamic nature that indoor environments exhibit. We propose an indoors free-space segmentation method that associates large depth values with navigable regions. Our method leverages an unsupervised masking technique that, using positive instances, generates segmentation labels based on textural homogeneity and depth uniformity. Moreover, we generate superpixels corresponding to areas of higher depth and align them with features extracted from a Dense Prediction Transformer (DPT). Using the estimated free-space masks and the DPT feature representation, a SegFormer model is fine-tuned on our custom-collected indoor dataset. Our experiments demonstrate sufficient performance in intricate scenarios characterized by cluttered obstacles and challenging identification of free space.
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Recently, large-scale pre-trained models such as Segment-Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP) have demonstrated remarkable success and revolutionized the field of computer vision. These foundation vision models effectively capture knowledge from a large-scale broad data with their vast model parameters, enabling them to perform zero-shot segmentation on previously unseen data without additional training. While they showcase competence in 2D tasks, their potential for enhancing 3D scene understanding remains relatively unexplored. To this end, we present a novel framework that adapts various foundational models for the 3D point cloud segmentation task. Our approach involves making initial predictions of 2D semantic masks using different large vision models. We then project these mask predictions from various frames of RGB-D video sequences into 3D space. To generate robust 3D semantic pseudo labels, we introduce a semantic label fusion strategy that effectively combines all the results via voting. We examine diverse scenarios, like zero-shot learning and limited guidance from sparse 2D point labels, to assess the pros and cons of different vision foundation models. Our approach is experimented on ScanNet dataset for 3D indoor scenes, and the results demonstrate the effectiveness of adopting general 2D foundation models on solving 3D point cloud segmentation tasks.
Post Turing: Mapping the landscape of LLM Evaluation
Venue: arXiv cs.CL 2023
Year: 2023

In the rapidly evolving landscape of Large Language Models (LLMs), introduction of well-defined and standardized evaluation methodologies remains a crucial challenge. This paper traces the historical trajectory of LLM evaluations, from the foundational questions posed by Alan Turing to the modern era of AI research. We categorize the evolution of LLMs into distinct periods, each characterized by its unique benchmarks and evaluation criteria. As LLMs increasingly mimic human-like behaviors, traditional evaluation proxies, such as the Turing test, have become less reliable. We emphasize the pressing need for a unified evaluation system, given the broader societal implications of these models. Through an analysis of common evaluation methodologies, we advocate for a qualitative shift in assessment approaches, underscoring the importance of standardization and objective criteria. This work serves as a call for the AI community to collaboratively address the challenges of LLM evaluation, ensuring their reliability, fairness, and societal benefit.
Semantic Modelling of Organizational Knowledge as a Basis for Enterprise Data Governance 4.0 -- Application to a Unified Clinical Data Model
Venue: arXiv cs.AI 2023
Year: 2023

Individuals and organizations cope with an always-growing amount of data, which is heterogeneous in its contents and formats. An adequate data management process yielding data quality and control over its lifecycle is a prerequisite to getting value out of this data and minimizing inherent risks related to multiple usages. Common data governance frameworks rely on people, policies, and processes that fall short of the overwhelming complexity of data. Yet, harnessing this complexity is necessary to achieve high-quality standards. The latter will condition any downstream data usage outcome, including generative artificial intelligence trained on this data. In this paper, we report our concrete experience establishing a simple, cost-efficient framework that enables metadata-driven, agile and (semi-)automated data governance (i.e. Data Governance 4.0). We explain how we implement and use this framework to integrate 25 years of clinical study data at an enterprise scale in a fully productive environment. The framework encompasses both methodologies and technologies leveraging semantic web principles. We built a knowledge graph describing avatars of data assets in their business context, including governance principles. Multiple ontologies articulated by an enterprise upper ontology enable key governance actions such as FAIRification, lifecycle management, definition of roles and responsibilities, lineage across transformations and provenance from source systems. This metadata model is the keystone to data governance 4.0: a semi-automatised data management process that considers the business context in an agile manner to adapt governance constraints to each use case and dynamically tune it based on business changes.
Preference Elicitation with Soft Attributes in Interactive Recommendation
Venue: arXiv cs.IR 2023
Year: 2023

Preference elicitation plays a central role in interactive recommender systems. Most preference elicitation approaches use either item queries that ask users to select preferred items from a slate, or attribute queries that ask them to express their preferences for item characteristics. Unfortunately, users often wish to describe their preferences using soft attributes for which no ground-truth semantics is given. Leveraging concept activation vectors for soft attribute semantics, we develop novel preference elicitation methods that can accommodate soft attributes and bring together both item and attribute-based preference elicitation. Our techniques query users using both items and soft attributes to update the recommender system's belief about their preferences to improve recommendation quality. We demonstrate the effectiveness of our methods vis-a-vis competing approaches on both synthetic and real-world datasets.
A Comprehensive Study on Model Initialization Techniques Ensuring Efficient Federated Learning
Venue: arXiv cs.LG 2023
Year: 2023

Advancement in the field of machine learning is unavoidable, but something of major concern is preserving the privacy of the users whose data is being used for training these machine learning algorithms. Federated learning(FL) has emerged as a promising paradigm for training machine learning models in a distributed and privacy-preserving manner which enables one to collaborate and train a global model without sharing local data. But starting this learning process on each device in the right way, called ``model initialization" is critical. The choice of initialization methods used for models plays a crucial role in the performance, convergence speed, communication efficiency, privacy guarantees of federated learning systems, etc. In this survey, we dive deeper into a comprehensive study of various ways of model initialization techniques in FL.Unlike other studies, our research meticulously compares, categorizes, and delineates the merits and demerits of each technique, examining their applicability across diverse FL scenarios. We highlight how factors like client variability, data non-IIDness, model caliber, security considerations, and network restrictions influence FL model outcomes and propose how strategic initialization can address and potentially rectify many such challenges. The motivation behind this survey is to highlight that the right start can help overcome challenges like varying data quality, security issues, and network problems. Our insights provide a foundational base for experts looking to fully utilize FL, also while understanding the complexities of model initialization.
Resist Label Noise with PGM for Graph Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

While robust graph neural networks (GNNs) have been widely studied for graph perturbation and attack, those for label noise have received significantly less attention. Most existing methods heavily rely on the label smoothness assumption to correct noisy labels, which adversely affects their performance on heterophilous graphs. Further, they generally perform poorly in high noise-rate scenarios. To address these problems, in this paper, we propose a novel probabilistic graphical model (PGM) based framework LNP. Given a noisy label set and a clean label set, our goal is to maximize the likelihood of labels in the clean set. We first present LNP-v1, which generates clean labels based on graphs only in the Bayesian network. To further leverage the information of clean labels in the noisy label set, we put forward LNP-v2, which incorporates the noisy label set into the Bayesian network to generate clean labels. The generative process can then be used to predict labels for unlabeled nodes. We conduct extensive experiments to show the robustness of LNP on varying noise types and rates, and also on graphs with different heterophilies. In particular, we show that LNP can lead to inspiring performance in high noise-rate situations.
RigLSTM: Recurrent Independent Grid LSTM for Generalizable Sequence Learning
Venue: arXiv cs.LG 2023
Year: 2023

Sequential processes in real-world often carry a combination of simple subsystems that interact with each other in certain forms. Learning such a modular structure can often improve the robustness against environmental changes. In this paper, we propose recurrent independent Grid LSTM (RigLSTM), composed of a group of independent LSTM cells that cooperate with each other, for exploiting the underlying modular structure of the target task. Our model adopts cell selection, input feature selection, hidden state selection, and soft state updating to achieve a better generalization ability on the basis of the recent Grid LSTM for the tasks where some factors differ between training and evaluation. Specifically, at each time step, only a fraction of cells are activated, and the activated cells select relevant inputs and cells to communicate with. At the end of one time step, the hidden states of the activated cells are updated by considering the relevance between the inputs and the hidden states from the last and current time steps. Extensive experiments on diversified sequential modeling tasks are conducted to show the superior generalization ability when there exist changes in the testing environment. Source code is available at \url{https://github.com/ziyuwwang/rig-lstm}.
Using General Value Functions to Learn Domain-Backed Inventory Management Policies
Venue: arXiv cs.LG 2023
Year: 2023

We consider the inventory management problem, where the goal is to balance conflicting objectives such as availability and wastage of a large range of products in a store. We propose a reinforcement learning (RL) approach that utilises General Value Functions (GVFs) to derive domain-backed inventory replenishment policies. The inventory replenishment decisions are modelled as a sequential decision making problem, which is challenging due to uncertain demand and the existence of aggregate (cross-product) constraints. In existing literature, GVFs have primarily been used for auxiliary task learning. We use this capability to train GVFs on domain-critical characteristics such as prediction of stock-out probability and wastage quantity. Using this domain expertise for more effective exploration, we train an RL agent to compute the inventory replenishment quantities for a large range of products (up to 6000 in the reported experiments), which share aggregate constraints such as the total weight/volume per delivery. Additionally, we show that the GVF predictions can be used to provide additional domain-backed insights into the decisions proposed by the RL agent. Finally, since the environment dynamics are fully transferred, the trained GVFs can be used for faster adaptation to vastly different business objectives (for example, due to the start of a promotional period or due to deployment in a new customer environment).