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Found 82 papers, 32 papers with code
EMAC+: Embodied Multimodal Agent for Collaborative Planning with VLM+LLM
Although LLMs demonstrate proficiency in several text-based reasoning and planning tasks, their implementation in robotics control is constrained by significant deficiencies: (1) LLM agents are designed to work mainly with textual inputs rather than visual conditions; (2) Current multimodal agents treat LLMs as static planners, which separates their reasoning from environment dynamics, resulting in actions that do not take domain-specific knowledge into account; and (3) LLMs are not designed to learn from visual interactions, which makes it harder for them to make better policies for specific domains.
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning
Retrieval-Augmented Generation (RAG) systems empower large language models (LLMs) with external knowledge, yet struggle with efficiency-accuracy trade-offs when scaling to large knowledge graphs.
SOCIA: An End-to-End Agentic Framework for Automated Cyber-Physical-Social Simulator Generation
This paper introduces SOCIA (Simulation Orchestration for Cyber-physical-social Intelligence and Agents), a novel end-to-end framework leveraging Large Language Model (LLM)-based multi-agent systems to automate the generation of high-fidelity Cyber-Physical-Social (CPS) simulators.
Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks
By releasing Massive-STEPS, we aim to facilitate reproducible and equitable research in human mobility and POI recommendation.
Evaluating the Bias in LLMs for Surveying Opinion and Decision Making in Healthcare
Generative agents have been increasingly used to simulate human behaviour in silico, driven by large language models (LLMs).
Enforcing Consistency and Fairness in Multi-level Hierarchical Classification with a Mask-based Output Layer
Traditional Multi-level Hierarchical Classification (MLHC) classifiers often rely on backbone models with $n$ independent output layers.
Long Context Modeling with Ranked Memory-Augmented Retrieval
Effective long-term memory management is crucial for language models handling extended contexts.
Beyond Single Pass, Looping Through Time: KG-IRAG with Iterative Knowledge Retrieval
Graph Retrieval-Augmented Generation (GraphRAG) has proven highly effective in enhancing the performance of Large Language Models (LLMs) on tasks that require external knowledge.
RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration Exemplars
This paper proposes a low-cost, tuning-free method using in-context learning (ICL) to enhance LLM alignment.
PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN
We thoroughly evaluate our PAR-AdvGAN method with a large-scale experiment, demonstrating its superior performance over various state-of-the-art black-box adversarial attacks, as well as the original AdvGAN. Moreover, PAR-AdvGAN significantly accelerates the adversarial example generation, i. e., achieving the speeds of up to 335. 5 frames per second on Inception-v3 model, outperforming the gradient-based transferable attack algorithms.
Leveraging Taxonomy and LLMs for Improved Multimodal Hierarchical Classification
Multi-level Hierarchical Classification (MLHC) tackles the challenge of categorizing items within a complex, multi-layered class structure.
ODEStream: A Buffer-Free Online Learning Framework with ODE-based Adaptor for Streaming Time Series Forecasting
Addressing the challenges of irregularity and concept drift in streaming time series is crucial in real-world predictive modelling.
Exploring Capabilities of Time Series Foundation Models in Building Analytics
The growing integration of digitized infrastructure with Internet of Things (IoT) networks has transformed the management and optimization of building energy consumption.
GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems
Traditional Point-of-Interest (POI) recommendation systems often lack transparency, interpretability, and scrutability due to their reliance on dense vector-based user embeddings.
SensorLLM: Human-Intuitive Alignment of Multivariate Sensor Data with LLMs for Activity Recognition
This alignment enables LLMs to interpret numerical patterns, channel-specific signals, and variable-length inputs--without requiring human annotation.
WorkR: Occupation Inference for Intelligent Task Assistance
Occupation information can be utilized by digital assistants to provide occupation-specific personalized task support, including interruption management, task planning, and recommendations.
Long-term Fairness in Ride-Hailing Platform
The recent studies that focus on fairness in ride-hailing exploit traditional optimisation methods and the Markov Decision Process to balance efficiency and fairness.
AuditNet: A Conversational AI-based Security Assistant [DEMO]
In the age of information overload, professionals across various fields face the challenge of navigating vast amounts of documentation and ever-evolving standards.
REVEAL-IT: REinforcement learning with Visibility of Evolving Agent poLicy for InTerpretability
Understanding the agent's learning process in complicated environments or tasks is more challenging.
ViLCo-Bench: VIdeo Language COntinual learning Benchmark
This framework addresses challenges including memory complexity from long video clips, natural language complexity from open queries, and text-video misalignment.
BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics
Buildings play a crucial role in human well-being, influencing occupant comfort, health, and safety.
CAPRI-FAIR: Integration of Multi-sided Fairness in Contextual POI Recommendation Framework
Experiments show that a linear scoring model for provider fairness in re-scoring items offers the best balance between performance and long-tail exposure, sometimes without much precision loss.
ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation
Spatiotemporal data is prevalent in a wide range of edge devices, such as those used in personal communication and financial transactions.
Promoting Two-sided Fairness in Dynamic Vehicle Routing Problem
Dynamic Vehicle Routing Problem (DVRP), is an extension of the classic Vehicle Routing Problem (VRP), which is a fundamental problem in logistics and transportation.
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings
The increasing demand for sustainable energy solutions has driven the integration of digitalized buildings into the power grid, leveraging Internet-of-Things (IoT) technologies to enhance energy efficiency and operational performance.
Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational Search
Instruments such as eye-tracking devices have contributed to understanding how users interact with screen-based search engines.
Characterizing Information Seeking Processes with Multiple Physiological Signals
Information access systems are getting complex, and our understanding of user behavior during information seeking processes is mainly drawn from qualitative methods, such as observational studies or surveys.
Large Language Models for Next Point-of-Interest Recommendation
Our results show that the proposed framework outperforms the state-of-the-art models in all three datasets.
Prompt Mining for Language-based Human Mobility Forecasting
Specifically, the framework includes a prompt generation stage based on the information entropy of prompts and a prompt refinement stage to integrate mechanisms such as the chain of thought.
SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embedding
Our framework offers a simple but comprehensive way to understand the underlying patterns and trends in dynamic KG, thereby enhancing the accuracy of predictions and the relevance of recommendations.
Measuring Misogyny in Natural Language Generation: Preliminary Results from a Case Study on two Reddit Communities
Generic `toxicity' classifiers continue to be used for evaluating the potential for harm in natural language generation, despite mounting evidence of their shortcomings.
Utilizing Language Models for Energy Load Forecasting
Energy load forecasting plays a crucial role in optimizing resource allocation and managing energy consumption in buildings and cities.
ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality
This paper explores the intersection of technology and sleep pattern comprehension, presenting a cutting-edge two-stage framework that harnesses the power of Large Language Models (LLMs).
Human Mobility Question Answering (Vision Paper)
The aim of the task is to let the intelligent system learn from mobility data and answer related questions.
MAPLE: Mobile App Prediction Leveraging Large Language Model Embeddings
This study introduces a novel prediction model, Mobile App Prediction Leveraging Large Language Model Embeddings (MAPLE), which employs Large Language Models (LLMs) and installed app similarity to overcome these challenges.
Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
In traditional deep learning algorithms, one of the key assumptions is that the data distribution remains constant during both training and deployment.
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
Counterfactuals operationalised through algorithmic recourse have become a powerful tool to make artificial intelligence systems explainable.
Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction
In this paper, we propose a novel Hypergraph Convolutional Network that allows the representation of non-pairwise relationships among diagnosis codes in a hypergraph to capture the hidden feature structures so that fine-grained patient similarity can be calculated for personalized mortality risk prediction.
Designing and Evaluating Presentation Strategies for Fact-Checked Content
We address this research gap by exploring the critical design elements in fact-checking reports and investigating whether credibility and presentation-based design improvements can enhance users' ability to interpret the report accurately.
Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRs
Existing approaches focus on exploiting the variable correlations in patient medical records to impute missing values and establishing time-decay mechanisms to deal with such irregularity.
Continually learning out-of-distribution spatiotemporal data for robust energy forecasting
One of the primary reasons for this is the shift in distribution of occupancy patterns, with many people working or learning from home.
Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)
During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training.
Message Passing Neural Networks for Traffic Forecasting
A road network, in the context of traffic forecasting, is typically modeled as a graph where the nodes are sensors that measure traffic metrics (such as speed) at that location.
Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study
In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data.
Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness
Group fairness is achieved by equalising prediction distributions between protected sub-populations; individual fairness requires treating similar individuals alike.
Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting
Further analysis also shows that each pair of sensors also has a unique dynamic.
Multiple-level Point Embedding for Solving Human Trajectory Imputation with Prediction
Sparsity is a common issue in many trajectory datasets, including human mobility data.
Detecting Change Intervals with Isolation Distributional Kernel
Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis.
SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time Series
Ordinary Differential Equations (ODE) based models have become popular as foundation models for solving many time series problems.
Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values
Our model can capture both long- and short-term temporal patterns within each patient journey and effectively handle the high degree of missingness in EHR data without any imputation data generation.
PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
In this novel task, the numerical input and output are transformed into prompts and the forecasting task is framed in a sentence-to-sentence manner, making it possible to directly apply language models for forecasting purposes.
Leveraging Language Foundation Models for Human Mobility Forecasting
In this paper, we propose a novel pipeline that leverages language foundation models for temporal sequential pattern mining, such as for human mobility forecasting tasks.
COCOA: Cross Modality Contrastive Learning for Sensor Data
Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations of data.
Modeling Long-term Dependencies and Short-term Correlations in Patient Journey Data with Temporal Attention Networks for Health Prediction
Building models for health prediction based on Electronic Health Records (EHR) has become an active research area.
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data
Unlike existing reviews of SSRL that have pre-dominately focused upon methods in the fields of CV or NLP for a single modality, we aim to provide the first comprehensive review of multimodal self-supervised learning methods for temporal data.
Measuring disentangled generative spatio-temporal representation
Disentangled representation learning offers useful properties such as dimension reduction and interpretability, which are essential to modern deep learning approaches.
Event-Aware Multimodal Mobility Nowcasting
As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality.
MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction
Furthermore, unlike existing methods, we introduce a location prediction branch in MobTCast as an auxiliary task to model the geographical context and predict the next location.
PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series
Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis.
Spatio-temporal Disentangled representation learning for mobility prediction
In this work, we propose a VAE-based architecture for learning the disentangled representation from real spatio-temporal data for mobility forecasting.
CoSEM: Contextual and Semantic Embedding for App Usage Prediction
App usage prediction is important for smartphone system optimization to enhance user experience.
Evolutionary Ensemble Learning for Multivariate Time Series Prediction
In this framework, a specific pipeline is encoded as a candidate solution and a multi-objective evolutionary algorithm is applied under different population sizes to produce multiple Pareto optimal sets (POSs).
MoParkeR : Multi-objective Parking Recommendation
Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only.
Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas.
Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification
The pre-trained feature encoder is then fine-tuned in the downstream phase to perform cough classification.
Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables
We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia.
Drone-as-a-Service Composition Under Uncertainty
Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts.
Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding
Change Point Detection (CPD) methods identify the times associated with changes in the trends and properties of time series data in order to describe the underlying behaviour of the system.
Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors
This paper investigates the Cyber-Physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators.
Generative Adversarial Networks for Spatio-temporal Data: A Survey
Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area.
Multi-officer Routing for Patrolling High Risk Areas Jointly Learned from Check-ins, Crime and Incident Response Data
In this paper, we formulate the dynamic crime patrol planning problem for multiple police officers using check-ins, crime, incident response data, and POI information.
flexgrid2vec: Learning Efficient Visual Representations Vectors
We compare the performance of flexgrid2vec with a set of state-of-the-art visual representation learning models on binary and multi-class image classification tasks.
G-CREWE: Graph CompREssion With Embedding for Network Alignment
In this paper, we propose a framework, called G-CREWE (Graph CompREssion With Embedding) to solve the network alignment problem.
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence
Federated learning provides a compelling framework for learning models from decentralized data, but conventionally, it assumes the availability of labeled samples, whereas on-device data are generally either unlabeled or cannot be annotated readily through user interaction.
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data
Extracting informative and meaningful temporal segments from high-dimensional wearable sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as Human Activity Recognition (HAR), trajectory prediction, gesture recognition, and lifelogging.
Ranked #3 on
Change Point Detection
on TSSB
(Covering metric)
An Ambient-Physical System to Infer Concentration in Open-plan Workplace
One of the core challenges in open-plan workspaces is to ensure a good level of concentration for the workers while performing their tasks.
Transfer Learning for Thermal Comfort Prediction in Multiple Cities
The main purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the best utilisation of energy usage.
Relation Embedding for Personalised POI Recommendation
Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services.
COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference
This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments.
DeepObfuscator: Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones
Our experiments on CelebA and LFW datasets show that the quality of the reconstructed images from the obfuscated features of the raw image is dramatically decreased from 0. 9458 to 0. 3175 in terms of multi-scale structural similarity.
Modelling Regional Crime Risk using Directed Graph of Check-ins
The mobility dynamics inferred from Foursquare helps us understanding urban social events like crime In this paper, we propose a directed graph from the aggregated movement between regions using Foursquare data.
Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network
Deep learning has been extended to a number of new domains with critical success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and its variants are not commonly solved using such techniques.
