OpenRaiser/Intern-Atlas huggingface dataset:
AGENTiGraph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Data
Venue: arXiv cs.AI 2024
Year: 2024

Large Language Models~(LLMs) have demonstrated capabilities across various applications but face challenges such as hallucination, limited reasoning abilities, and factual inconsistencies, especially when tackling complex, domain-specific tasks like question answering~(QA). While Knowledge Graphs~(KGs) have been shown to help mitigate these issues, research on the integration of LLMs with background KGs remains limited. In particular, user accessibility and the flexibility of the underlying KG have not been thoroughly explored. We introduce AGENTiGraph (Adaptive Generative ENgine for Task-based Interaction and Graphical Representation), a platform for knowledge management through natural language interaction. It integrates knowledge extraction, integration, and real-time visualization. AGENTiGraph employs a multi-agent architecture to dynamically interpret user intents, manage tasks, and integrate new knowledge, ensuring adaptability to evolving user requirements and data contexts. Our approach demonstrates superior performance in knowledge graph interactions, particularly for complex domain-specific tasks. Experimental results on a dataset of 3,500 test cases show AGENTiGraph significantly outperforms state-of-the-art zero-shot baselines, achieving 95.12\% accuracy in task classification and 90.45\% success rate in task execution. User studies corroborate its effectiveness in real-world scenarios. To showcase versatility, we extended AGENTiGraph to legislation and healthcare domains, constructing specialized KGs capable of answering complex queries in legal and medical contexts.
Temporal Hyperproperties for Population Protocols
Venue: arXiv cs.LO 2024
Year: 2024

Hyperproperties are properties over sets of traces (or runs) of a system, as opposed to properties of just one trace. They were introduced in 2010 and have been much studied since, in particular via an extension of the temporal logic LTL called HyperLTL. Most verification efforts for HyperLTL are restricted to finite-state systems, usually defined as Kripke structures. In this paper we study hyperproperties for an important class of infinite-state systems. We consider population protocols, a popular distributed computing model in which arbitrarily many identical finite-state agents interact in pairs. Population protocols are a good candidate for studying hyperproperties because the main decidable verification problem, well-specification, is a hyperproperty. We first show that even for simple (monadic) formulas, HyperLTL verification for population protocols is undecidable. We then turn our attention to immediate observation population protocols, a simpler and well-studied subclass of population protocols. We show that verification of monadic HyperLTL formulas without the next operator is decidable in 2-EXPSPACE, but that all extensions make the problem undecidable.
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study
Venue: arXiv cs.LG 2024
Year: 2024

The development of intelligent Industrial Internet of Things (IIoT) systems promises to revolutionize operational and maintenance practices, driving improvements in operational efficiency. Anomaly detection within IIoT architectures plays a crucial role in preventive maintenance and spotting irregularities in industrial components. However, due to limited message and processing capacity, traditional Machine Learning (ML) faces challenges in deploying anomaly detection models in resource-constrained environments like LoRaWAN. On the other hand, Federated Learning (FL) solves this problem by enabling distributed model training, addressing privacy concerns, and minimizing data transmission. This study explores using FL for anomaly detection in industrial and civil construction machinery architectures that use IIoT prototypes with LoRaWAN communication. The process leverages an optimized autoencoder neural network structure and compares federated models with centralized ones. Despite uneven data distribution among machine clients, FL demonstrates effectiveness, with a mean F1 score (of 94.77), accuracy (of 92.30), TNR (of 90.65), and TPR (92.93), comparable to centralized models, considering airtime of trainning messages of 52.8 min. Local model evaluations on each machine highlight adaptability. At the same time, the performed analysis identifies message requirements, minimum training hours, and optimal round/epoch configurations for FL in LoRaWAN, guiding future implementations in constrained industrial environments.
Findings of the WMT 2024 Shared Task on Chat Translation
Venue: arXiv cs.CL 2024
Year: 2024

This paper presents the findings from the third edition of the Chat Translation Shared Task. As with previous editions, the task involved translating bilingual customer support conversations, specifically focusing on the impact of conversation context in translation quality and evaluation. We also include two new language pairs: English-Korean and English-Dutch, in addition to the set of language pairs from previous editions: English-German, English-French, and English-Brazilian Portuguese. We received 22 primary submissions and 32 contrastive submissions from eight teams, with each language pair having participation from at least three teams. We evaluated the systems comprehensively using both automatic metrics and human judgments via a direct assessment framework. The official rankings for each language pair were determined based on human evaluation scores, considering performance in both translation directions--agent and customer. Our analysis shows that while the systems excelled at translating individual turns, there is room for improvement in overall conversation-level translation quality.
Transformer Layer Injection: A Novel Approach for Efficient Upscaling of Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

In this paper, we propose Transformer Layer Injection (TLI), a novel method for efficiently upscaling large language models (LLMs) while minimizing computational costs and maintaining model performance. Model scale is a key factor in enhancing the quality of machine learning models, and TLI addresses the challenge of scaling by reducing initial loss, minimizing fine-tuning requirements, and preserving model complexity. Our approach improves upon the conventional Depth Up-Scaling (DUS) technique by injecting new layers into every set of K layers, enabling hidden representations to pass through transformer blocks with minimal disruption. We compare TLI with existing approaches, including Mixture of Experts (MoE) and DUS, and validate its efficiency through experiments on small LLMs (LLama3 1B, 3B, and 8B). Results show that TLI achieves better initialization, requires fewer training steps, and delivers superior accuracy on tasks such as KoBEST and KMCQA, with models performing effectively even without additional training. TLI is demonstrated to be both data-efficient and cost-effective, significantly outperforming existing methods. Its scalability and simplicity make it a promising solution for upscaling transformer-based models, with potential applications in scaling models from 10B to 405B parameters.
Leveraging Structure Knowledge and Deep Models for the Detection of Abnormal Handwritten Text
Venue: arXiv cs.CV 2024
Year: 2024

Currently, the destruction of the sequence structure in handwritten text has become one of the main bottlenecks restricting the recognition task. The typical situations include additional specific markers (the text swapping modification) and the text overlap caused by character modifications like deletion, replacement, and insertion. In this paper, we propose a two-stage detection algorithm that combines structure knowledge and deep models for the above mentioned text. Firstly, different structure prototypes are roughly located from handwritten text images. Based on the detection results of the first stage, in the second stage, we adopt different strategies. Specifically, a shape regression network trained by a novel semi-supervised contrast training strategy is introduced and the positional relationship between the characters is fully employed. Experiments on two handwritten text datasets show that the proposed method can greatly improve the detection performance. The new dataset is available at https://github.com/Wukong90.
Robotic Arm Platform for Multi-View Image Acquisition and 3D Reconstruction in Minimally Invasive Surgery
Venue: arXiv cs.RO 2024
Year: 2024

Minimally invasive surgery (MIS) offers significant benefits such as reduced recovery time and minimised patient trauma, but poses challenges in visibility and access, making accurate 3D reconstruction a significant tool in surgical planning and navigation. This work introduces a robotic arm platform for efficient multi-view image acquisition and precise 3D reconstruction in MIS settings. We adapted a laparoscope to a robotic arm and captured ex-vivo images of several ovine organs across varying lighting conditions (operating room and laparoscopic) and trajectories (spherical and laparoscopic). We employed recently released learning-based feature matchers combined with COLMAP to produce our reconstructions. The reconstructions were evaluated against high-precision laser scans for quantitative evaluation. Our results show that whilst reconstructions suffer most under realistic MIS lighting and trajectory, many versions of our pipeline achieve close to sub-millimetre accuracy with an average of 1.05 mm Root Mean Squared Error and 0.82 mm Chamfer distance. Our best reconstruction results occur with operating room lighting and spherical trajectories. Our robotic platform provides a tool for controlled, repeatable multi-view data acquisition for 3D generation in MIS environments which we hope leads to new datasets for training learning-based models.
Evidence of Cognitive Deficits andDevelopmental Advances in Generative AI: A Clock Drawing Test Analysis
Venue: arXiv cs.AI 2024
Year: 2024

Generative AI's rapid advancement sparks interest in its cognitive abilities, especially given its capacity for tasks like language understanding and code generation. This study explores how several recent GenAI models perform on the Clock Drawing Test (CDT), a neuropsychological assessment of visuospatial planning and organization. While models create clock-like drawings, they struggle with accurate time representation, showing deficits similar to mild-severe cognitive impairment (Wechsler, 2009). Errors include numerical sequencing issues, incorrect clock times, and irrelevant additions, despite accurate rendering of clock features. Only GPT 4 Turbo and Gemini Pro 1.5 produced the correct time, scoring like healthy individuals (4/4). A follow-up clock-reading test revealed only Sonnet 3.5 succeeded, suggesting drawing deficits stem from difficulty with numerical concepts. These findings may reflect weaknesses in visual-spatial understanding, working memory, or calculation, highlighting strengths in learned knowledge but weaknesses in reasoning. Comparing human and machine performance is crucial for understanding AI's cognitive capabilities and guiding development toward human-like cognitive functions.
ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification
Venue: arXiv cs.LG 2024
Year: 2024

Classifying nodes in a graph is a common problem. The ideal classifier must adapt to any imbalances in the class distribution. It must also use information in the clustering structure of real-world graphs. Existing Graph Neural Networks (GNNs) have not addressed both problems together. We propose the Enhanced Cluster-aware Graph Network (ECGN), a novel method that addresses these issues by integrating cluster-specific training with synthetic node generation. Unlike traditional GNNs that apply the same node update process for all nodes, ECGN learns different aggregations for different clusters. We also use the clusters to generate new minority-class nodes in a way that helps clarify the inter-class decision boundary. By combining cluster-aware embeddings with a global integration step, ECGN enhances the quality of the resulting node embeddings. Our method works with any underlying GNN and any cluster generation technique. Experimental results show that ECGN consistently outperforms its closest competitors by up to 11% on some widely studied benchmark datasets.
Adaptive Ankle Torque Control for Bipedal Humanoid Walking on Surfaces with Unknown Horizontal and Vertical Motion
Venue: arXiv cs.RO 2024
Year: 2024

Achieving stable bipedal walking on surfaces with unknown motion remains a challenging control problem due to the hybrid, time-varying, partially unknown dynamics of the robot and the difficulty of accurate state and surface motion estimation. Surface motion imposes uncertainty on both system parameters and non-homogeneous disturbance in the walking robot dynamics. In this paper, we design an adaptive ankle torque controller to simultaneously address these two uncertainties and propose a step-length planner to minimize the required control torque. Typically, an adaptive controller is used for a continuous system. To apply adaptive control on a hybrid system such as a walking robot, an intermediate command profile is introduced to ensure a continuous error system. Simulations on a planar bipedal robot, along with comparisons against a baseline controller, demonstrate that the proposed approach effectively ensures stable walking and accurate tracking under unknown, time-varying disturbances.
An incremental exact algorithm for the hyper-rectangular clustering problem with axis-parallel clusters
Venue: arXiv cs.DM 2024
Year: 2024

We address the problem of clustering a set of points in $\mathbb{R}^d$ with axis-parallel clusters. Previous exact approaches to this problem are mostly based on integer programming formulations and can only solve to optimality instances of small size. In this work we propose an adaptive exact strategy which takes advantage of the capacity to solve small instances to optimality of previous approaches. Our algorithm starts by solving an instance with a small subset of points and iteratively adds more points if these are not covered by the obtained solution. We prove that as soon as a solution covers the whole set of point from the instance, then the solution is actually an optimal solution for the original problem. We compare the efficiency of the new method against the existing ones with an exhaustive computational experimentation in which we show that the new approach is able to solve to optimality instances of higher orders of magnitude.
Experimental Validation of User Experience-focused Dynamic Onboard Service Orchestration for Software Defined Vehicles
Venue: arXiv cs.DC 2024
Year: 2024

In response to the growing need for dynamic software features in automobiles, Software Defined Vehicles (SDVs) have emerged as a promising solution. They integrate dynamic onboard service management to handle the large variety of user-requested services during vehicle operation. Allocating onboard resources efficiently in this setting is a challenging task, as it requires a balance between maximizing user experience and guaranteeing mixed-criticality Quality-of-Service (QoS) network requirements. Our previous research introduced a dynamic resource-based onboard service orchestration algorithm. This algorithm considers real-time invehicle and V2X network health, along with onboard resource constraints, to globally select degraded modes for onboard applications. It maximizes the overall user experience at all times while being embeddable onboard for on-the-fly decisionmaking. A key enabler of this approach is the introduction of the Automotive eXperience Integrity Level (AXIL), a metric expressing runtime priority for non-safety-critical applications. While initial simulation results demonstrated the algorithm's effectiveness, a comprehensive performance assessment would greatly contribute in validating its industrial feasibility. In this current work, we present experimental results obtained from a dedicated test bench. These results illustrate, validate, and assess the practicality of our proposed solution, providing a solid foundation for the continued advancement of dynamic onboard service orchestration in SDVs.
A Simulated Annealing Approach to Identical Parallel Machine Scheduling
Venue: arXiv cs.DC 2024
Year: 2024

This paper studies the application of the simulated annealing metaheuristic on the identical parallel machine scheduling problem, a variant of the broader optimal job scheduling problem. In the identical parallel machine scheduling problem, $n$ jobs are to be assigned among $m$ machines. Furthermore, each job takes a certain amount of time that remains constant across machines. The goal of the paper is to schedule $n$ jobs on $m$ machines and minimize the maximum runtime of all machines. Both exact and heuristic methods have been applied to the problem, and the proposed algorithm falls in the heuristic category, making use of the simulated annealing metaheuristic. Compared to exact algorithms, simulated annealing was found to yield near-optimal solutions in comparable or less time for all problem cases.
Simulation-based inference with scattering representations: scattering is all you need
Venue: arXiv cs.LG 2024
Year: 2024

We demonstrate the successful use of scattering representations without further compression for simulation-based inference (SBI) with images (i.e. field-level), illustrated with a cosmological case study. Scattering representations provide a highly effective representational space for subsequent learning tasks, although the higher dimensional compressed space introduces challenges. We overcome these through spatial averaging, coupled with more expressive density estimators. Compared to alternative methods, such an approach does not require additional simulations for either training or computing derivatives, is interpretable, and resilient to covariate shift. As expected, we show that a scattering only approach extracts more information than traditional second order summary statistics.
Improving Digital Mentorship: Insights and Recommendations from the Re:Coded Community Platform Case Study
Venue: arXiv cs.HC 2024
Year: 2024

With the rapid growth of technology, emerging IT professionals increasingly require mentorship to secure positions in the field. Recognizing this need, ReCoded has enhanced the skill set of their tech Bootcamp graduates by introducing the community platform -- "ReCoded's Mentorship Platform". To improve the user experience of the volunteer mentors at ReCoded, this thesis investigates optimizing mentors' interactions with digital mentorship platforms and provides suggestions for enhancing these interactions. Multiple third-party collaborators have powered the mentorship platform at ReCoded. This thesis examines the platform powered by StellarUp as a case study. The insights obtained may inform the UX design of any subsequent mentorship platforms considered by ReCoded. This thesis adopted a user-centric approach to solving a UX question. The study identified challenges in the mentors' user journey and their needs by engaging with users via interviews, usability tests, and eye-tracking methods. Three principal issues emerged: platform navigation, the onboarding process, and the seamless integration of external tools. Solutions were derived from desk research for onboarding, card sorting techniques for navigation, and competitive analyses for tool integration. Throughout the research, feedback from 23 participants was gathered, ensuring a holistic understanding and actionable recommendations for developing a user-friendly and efficient mentorship platform.
Heterogeneous Graph Generation: A Hierarchical Approach using Node Feature Pooling
Venue: arXiv cs.SI 2024
Year: 2024

Heterogeneous graphs are present in various domains, such as social networks, recommendation systems, and biological networks. Unlike homogeneous graphs, heterogeneous graphs consist of multiple types of nodes and edges, each representing different entities and relationships. Generating realistic heterogeneous graphs that capture the complex interactions among diverse entities is a difficult task due to several reasons. The generator has to model both the node type distribution along with the feature distribution for each node type. In this paper, we look into solving challenges in heterogeneous graph generation, by employing a two phase hierarchical structure, wherein the first phase creates a skeleton graph with node types using a prior diffusion based model and in the second phase, we use an encoder and a sampler structure as generator to assign node type specific features to the nodes. A discriminator is used to guide training of the generator and feature vectors are sampled from a node feature pool. We conduct extensive experiments with subsets of IMDB and DBLP datasets to show the effectiveness of our method and also the need for various architecture components.
An Online Self-learning Graph-based Lateral Controller for Self-Driving Cars
Venue: arXiv cs.RO 2024
Year: 2024

The hype around self-driving cars has been growing over the past years and has sparked much research. Several modules in self-driving cars are thoroughly investigated to ensure safety, comfort, and efficiency, among which the controller is crucial. The controller module can be categorized into longitudinal and lateral controllers in which the task of the former is to follow the reference velocity, and the latter is to reduce the lateral displacement error from the reference path. Generally, a tuned controller is not sufficient to perform in all environments. Thus, a controller that can adapt to changing conditions is necessary for autonomous driving. Furthermore, these controllers often depend on vehicle models that also need to adapt over time due to varying environments. This paper uses graphs to present novel techniques to learn the vehicle model and the lateral controller online. First, a heterogeneous graph is presented depicting the current states of and inputs to the vehicle. The vehicle model is then learned online using known physical constraints in conjunction with the processing of the graph through a Graph Neural Network structure. Next, another heterogeneous graph - depicting the transition from current to desired states - is processed through another Graph Neural Network structure to generate the steering command on the fly. Finally, the performance of this self-learning model-based lateral controller is evaluated and shown to be satisfactory on an open-source autonomous driving platform called CARLA.