OpenRaiser/Intern-Atlas huggingface dataset:
Learning to Learn in Interactive Constraint Acquisition
Venue: arXiv cs.AI 2023
Year: 2023

Constraint Programming (CP) has been successfully used to model and solve complex combinatorial problems. However, modeling is often not trivial and requires expertise, which is a bottleneck to wider adoption. In Constraint Acquisition (CA), the goal is to assist the user by automatically learning the model. In (inter)active CA, this is done by interactively posting queries to the user, e.g., asking whether a partial solution satisfies their (unspecified) constraints or not. While interac tive CA methods learn the constraints, the learning is related to symbolic concept learning, as the goal is to learn an exact representation. However, a large number of queries is still required to learn the model, which is a major limitation. In this paper, we aim to alleviate this limitation by tightening the connection of CA and Machine Learning (ML), by, for the first time in interactive CA, exploiting statistical ML methods. We propose to use probabilistic classification models to guide interactive CA to generate more promising queries. We discuss how to train classifiers to predict whether a candidate expression from the bias is a constraint of the problem or not, using both relation-based and scope-based features. We then show how the predictions can be used in all layers of interactive CA: the query generation, the scope finding, and the lowest-level constraint finding. We experimentally evaluate our proposed methods using different classifiers and show that our methods greatly outperform the state of the art, decreasing the number of queries needed to converge by up to 72%.
Deep-Dispatch: A Deep Reinforcement Learning-Based Vehicle Dispatch Algorithm for Advanced Air Mobility
Venue: arXiv cs.AI 2023
Year: 2023

Near future air taxi operations with electric vertical take-off and landing (eVTOL) aircraft will be constrained by the need for frequent recharging of eVTOLs, limited takeoff and landing pads in vertiports, and subject to time-varying demand and electricity prices, making the eVTOL dispatch problem unique and particularly challenging to solve. Previously, we have developed optimization models to address this problem. Such optimization models however suffer from prohibitively high computational run times when the scale of the problem increases, making them less practical for real world implementation. To overcome this issue, we have developed two deep reinforcement learning-based eVTOL dispatch algorithms, namely single-agent and multi-agent deep Q-learning eVTOL dispatch algorithms, where the objective is to maximize operating profit. An eVTOL-based passenger transportation simulation environment was built to assess the performance of our algorithms across $36$ numerical cases with varying number of eVTOLs, vertiports, and demand. The results indicate that the multi-agent eVTOL dispatch algorithm can closely approximate the optimal dispatch policy with significantly less computational expenses compared to the benchmark optimization model. The multi-agent algorithm was found to outperform the single-agent counterpart with respect to both profits generated and training time.
The Implementation of Arduino Microcontroller Boards in Science: A Bibliometric Analysis from 2008 to 2022
Venue: arXiv cs.DL 2023
Year: 2023

The name "Arduino" made its international debut in 2005, marking the age of Arduino as one of the most user-friendly and cost-effective microcontroller boards (MCBs) for novices. The science implementation of Arduino boards in automation, networking and data acquisition has been increasing steadily. This study provides a thorough Bibliometric analysis from 1122 papers focused on the Scopus database of published microcontroller research, from the first year the Arduino keyword appeared in 2008 until 2022. Various science articles indexed by Scopus and referring to the use of Arduino MCBs are selected. The Bibliometric analysis explores comprehensive and general key attributes that form a trend from the Scopus articles based on authors, titles, publication years, keywords, citations, affiliations, abstracts, funding information, and languages. The generated data is visualized and analyzed to find patterns that appear within the time span. This study found a significant increase in the number of articles on Arduino boards in Biology, Physics, Chemistry, Science, and STEM category of the paper. Despite using only the Scopus database, this study opens up to view the direction of the growing application of Arduino boards in Science. The use of Bibliometric analysis maps the scientific implementation of Arduino boards as an extensive guide for future collaborations in education and industry.
Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models
Venue: arXiv cs.LG 2023
Year: 2023

The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH4 as a classification problem and (ii) predict the intensity of CH4 as a regression problem.
On Computing Makespan-Optimal Solutions for Generalized Sliding-Tile Puzzles
Venue: arXiv cs.RO 2023
Year: 2023

In the $15$-puzzle game, $15$ labeled square tiles are reconfigured on a $4\times 4$ board through an escort, wherein each (time) step, a single tile neighboring it may slide into it, leaving the space previously occupied by the tile as the new escort. We study a generalized sliding-tile puzzle (GSTP) in which (1) there are $1+$ escorts and (2) multiple tiles can move synchronously in a single time step. Compared with popular discrete multi-agent/robot motion models, GSTP provides a more accurate model for a broad array of high-utility applications, including warehouse automation and autonomous garage parking, but is less studied due to the more involved tile interactions. In this work, we analyze optimal GSTP solution structures, establishing that computing makespan-optimal solutions for GSTP is NP-complete and developing polynomial time algorithms yielding makespans approximating the minimum with expected/high probability constant factors, assuming randomized start and goal configurations.
Behaviour Description Database for AVs in Singapore
Venue: arXiv cs.RO 2023
Year: 2023

A Technical Reference for Autonomous Vehicles (AVs), with part 1 focusing on basic behaviour guidelines (TR68-1) is published with the intent to be a reference for evaluation of appropriated behaviour on Autonomous Vehicles for Singapore. This is based on applicability from Basic Theory of Driving (BTD) and Final Theory of Driving (FTD) which are the traffic code/rules for human driving. This report contains a consolidation of current guidelines from TR68-1, BTD and FTD. It will allow an initial identification of missing guidelines for AV behaviour on roads; however, it is difficult to identify conflicting rules or gaps in guidance without going into identified traffic situations. Identified situations for analysis were chosen from Centre of Excellence for Testing & Research of Autonomous Vehicle (CETRAN) assessment experience for further investigation. The outcome of the report proposes additional behaviour characteristics and guidelines to situations identified to close the gap between assessors and developers on expected AV behaviour. These recommendations could improve current guidelines for AV behavioural in assessment and generally for the local AV ecosystem for urban tropical roads in Singapore. These recommendations could also serve as inputs for future TR 68-1 revisions where a sample set of reference situations can help to define clearer expectations or requirements for AV behaviour in those situations. This will help Singapore push forward in better definition of the expected AV behaviour for AV systems.
ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton
Venue: arXiv cs.AI 2023
Year: 2023

Agent forecasting systems have been explored to investigate agent patterns and improve decision-making in various domains, e.g., pedestrian predictions and marketing bidding. Badminton represents a fascinating example of a multifaceted turn-based sport, requiring both sophisticated tactic developments and alternate-dependent decision-making. Recent deep learning approaches for player tactic forecasting in badminton show promising performance partially attributed to effective reasoning about rally-player interactions. However, a critical obstacle lies in the unclear functionality of which features are learned for simulating players' behaviors by black-box models, where existing explainers are not equipped with turn-based and multi-output attributions. To bridge this gap, we propose a turn-based feature attribution approach, ShuttleSHAP, for analyzing forecasting models in badminton based on variants of Shapley values. ShuttleSHAP is a model-agnostic explainer that aims to quantify contribution by not only temporal aspects but also player aspects in terms of multifaceted cues. Incorporating the proposed analysis tool into the state-of-the-art turn-based forecasting model on the benchmark dataset reveals that it is, in fact, insignificant to reason about past strokes, while conventional sequential models have greater impacts. Instead, players' styles influence the models for the future simulation of a rally. On top of that, we investigate and discuss the causal analysis of these findings and demonstrate the practicability with local analysis.
A Multimodal Approach for Advanced Pest Detection and Classification
Venue: arXiv cs.CV 2023
Year: 2023

This paper presents a novel multi modal deep learning framework for enhanced agricultural pest detection, combining tiny-BERT's natural language processing with R-CNN and ResNet-18's image processing. Addressing limitations of traditional CNN-based visual methods, this approach integrates textual context for more accurate pest identification. The R-CNN and ResNet-18 integration tackles deep CNN issues like vanishing gradients, while tiny-BERT ensures computational efficiency. Employing ensemble learning with linear regression and random forest models, the framework demonstrates superior discriminate ability, as shown in ROC and AUC analyses. This multi modal approach, blending text and image data, significantly boosts pest detection in agriculture. The study highlights the potential of multi modal deep learning in complex real-world scenarios, suggesting future expansions in diversity of datasets, advanced data augmentation, and cross-modal attention mechanisms to enhance model performance.
Soft Alignment of Modality Space for End-to-end Speech Translation
Venue: arXiv cs.CL 2023
Year: 2023

End-to-end Speech Translation (ST) aims to convert speech into target text within a unified model. The inherent differences between speech and text modalities often impede effective cross-modal and cross-lingual transfer. Existing methods typically employ hard alignment (H-Align) of individual speech and text segments, which can degrade textual representations. To address this, we introduce Soft Alignment (S-Align), using adversarial training to align the representation spaces of both modalities. S-Align creates a modality-invariant space while preserving individual modality quality. Experiments on three languages from the MuST-C dataset show S-Align outperforms H-Align across multiple tasks and offers translation capabilities on par with specialized translation models.
Liquid Leak Detection Using Thermal Images
Venue: arXiv cs.CV 2023
Year: 2023

This paper presents a comprehensive solution to address the critical challenge of liquid leaks in the oil and gas industry, leveraging advanced computer vision and deep learning methodologies. Employing You Only Look Once (YOLO) and Real-Time Detection Transformer (RT DETR) models, our project focuses on enhancing early identification of liquid leaks in key infrastructure components such as pipelines, pumps, and tanks. Through the integration of surveillance thermal cameras and sensors, the combined YOLO and RT DETR models demonstrate remarkable efficacy in the continuous monitoring and analysis of visual data within oil and gas facilities. YOLO's real-time object detection capabilities swiftly recognize leaks and their patterns, while RT DETR excels in discerning specific leak-related features, particularly in thermal images. This approach significantly improves the accuracy and speed of leak detection, ultimately mitigating environmental and financial risks associated with liquid leaks.
Centroidal State Estimation and Control for Hardware-constrained Humanoid Robots
Venue: arXiv cs.RO 2023
Year: 2023

We introduce novel methods for state estimation, feedforward and feedback control, which specifically target humanoid robots with hardware limitations. Our method combines a five-mass model with approximate dynamics of each mass. It enables acquiring an accurate assessment of the centroidal state and Center of Pressure, even when direct forms of force or contact sensing are unavailable. Upon this, we develop a feedforward scheme that operates on the centroidal state, accounting for insufficient joint tracking capabilities. Finally, we implement feedback mechanisms, which compensate for the lack in Degrees of Freedom that our NimbRo-OP2X robot has. The whole approach allows for reactive stepping to maintain balance despite these limitations, which was verified on hardware during RoboCup 2023, in Bordeaux, France.
MISA: Unveiling the Vulnerabilities in Split Federated Learning
Venue: arXiv cs.LG 2023
Year: 2023

\textit{Federated learning} (FL) and \textit{split learning} (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. \textit{Split federated learning} (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of robustness. In this paper, we present a novel poisoning attack known as MISA. It poisons both the top and bottom models, causing a \textbf{\underline{misa}}lignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention.
SinMPI: Novel View Synthesis from a Single Image with Expanded Multiplane Images
Venue: arXiv cs.CV 2023
Year: 2023

Single-image novel view synthesis is a challenging and ongoing problem that aims to generate an infinite number of consistent views from a single input image. Although significant efforts have been made to advance the quality of generated novel views, less attention has been paid to the expansion of the underlying scene representation, which is crucial to the generation of realistic novel view images. This paper proposes SinMPI, a novel method that uses an expanded multiplane image (MPI) as the 3D scene representation to significantly expand the perspective range of MPI and generate high-quality novel views from a large multiplane space. The key idea of our method is to use Stable Diffusion to generate out-of-view contents, project all scene contents into an expanded multiplane image according to depths predicted by monocular depth estimators, and then optimize the multiplane image under the supervision of pseudo multi-view data generated by a depth-aware warping and inpainting module. Both qualitative and quantitative experiments have been conducted to validate the superiority of our method to the state of the art. Our code and data are available at https://github.com/TrickyGo/SinMPI.
VECOM: Variation Resilient Encoding and Offset Compensation Schemes for Reliable ReRAM Based DNN Accelerator
Venue: arXiv cs.ET 2023
Year: 2023

Resistive Random Access Memory (ReRAM) based Processing In Memory (PIM) Accelerator has emerged as a promising computing architecture for memory intensive applications, such as Deep Neural Networks (DNNs). However, due to its immaturity, ReRAM devices often suffer from various reliability issues, which hinder the practicality of the PIM architecture and lead to a severe degradation in DNN accuracy. Among various reliability issues, device variation and offset current from High Resistance State (HRS) cell have been considered as major problems in a ReRAM based PIM architecture. Due to these problems, the throughput of the ReRAM based PIM is reduced as fewer wordlines are activated. In this paper, we propose VECOM, a novel approach that includes a variation resilient encoding technique and an offset compensation scheme for a robust ReRAM based PIM architecture. The first technique (i.e., VECOM encoding) is built based on the analysis of the weight pattern distribution of DNN models, along with the insight into the ReRAM's variation property. The second technique, VECOM offset compensation, tolerates offset current in PIM by mapping the conductance of each Multi level Cell (MLC) level added with a specific offset conductance. Experimental results in various DNN models and datasets show that the proposed techniques can increase the throughput of the PIM architecture by up to 9.1 times while saving 50% of energy consumption without any software overhead. Additionally, VECOM is also found to endure low R ratio ReRAM cell (up to 7) with a negligible accuracy drop.
MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts
Venue: arXiv cs.NE 2023
Year: 2023

Choosing a set of benchmark problems is often a key component of any empirical evaluation of iterative optimization heuristics. In continuous, single-objective optimization, several sets of problems have become widespread, including the well-established BBOB suite. While this suite is designed to enable rigorous benchmarking, it is also commonly used for testing methods such as algorithm selection, which the suite was never designed around. We present the MA-BBOB function generator, which uses the BBOB suite as component functions in an affine combination. In this work, we describe the full procedure to create these affine combinations and highlight the trade-offs of several design decisions, specifically the choice to place the optimum uniformly at random in the domain. We then illustrate how this generator can be used to gain more low-level insight into the function landscapes through the use of exploratory landscape analysis. Finally, we show a potential use-case of MA-BBOB in generating a wide set of training and testing data for algorithm selectors. Using this setup, we show that the basic scheme of using a set of landscape features to predict the best algorithm does not lead to optimal results, and that an algorithm selector trained purely on the BBOB functions generalizes poorly to the affine combinations.
Graph Transformers for Large Graphs
Venue: arXiv cs.LG 2023
Year: 2023

Transformers have recently emerged as powerful neural networks for graph learning, showcasing state-of-the-art performance on several graph property prediction tasks. However, these results have been limited to small-scale graphs, where the computational feasibility of the global attention mechanism is possible. The next goal is to scale up these architectures to handle very large graphs on the scale of millions or even billions of nodes. With large-scale graphs, global attention learning is proven impractical due to its quadratic complexity w.r.t. the number of nodes. On the other hand, neighborhood sampling techniques become essential to manage large graph sizes, yet finding the optimal trade-off between speed and accuracy with sampling techniques remains challenging. This work advances representation learning on single large-scale graphs with a focus on identifying model characteristics and critical design constraints for developing scalable graph transformer (GT) architectures. We argue such GT requires layers that can adeptly learn both local and global graph representations while swiftly sampling the graph topology. As such, a key innovation of this work lies in the creation of a fast neighborhood sampling technique coupled with a local attention mechanism that encompasses a 4-hop reception field, but achieved through just 2-hop operations. This local node embedding is then integrated with a global node embedding, acquired via another self-attention layer with an approximate global codebook, before finally sent through a downstream layer for node predictions. The proposed GT framework, named LargeGT, overcomes previous computational bottlenecks and is validated on three large-scale node classification benchmarks. We report a 3x speedup and 16.8% performance gain on ogbn-products and snap-patents, while we also scale LargeGT on ogbn-papers100M with a 5.9% performance improvement.
Implementation of the IIIF Presentation API 3.0 based on Software Support: Use Case of an Incremental IIIF Deployment within a Citizen Science Project
Venue: arXiv cs.DL 2023
Year: 2023

As part of the Participatory Knowledge Practices in Analogue and Digital Image Archives (PIA) research project, we have been implementing Linked Open Usable Data (LOUD) standards including the International Image Interoperability Framework (IIIF) specifications to disseminate digital objects, their related metadata and streamline our processes. We have taken an incremental approach to IIIF deployment, first by installing the Simple Image Presentation Interface (SIPI), a IIIF Image API 3.0 server, followed by conceiving a workflow based on cookbook recipes created and vetted by the IIIF community for the generation of resources compatible with the IIIF Presentation API 3.0, one of the key components of our architecture. This workflow resulted in a monitoring exercise of this community-driven effort, principally to align the requirements of PIA and the IIIF Presentation API support of software clients.
Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues
Venue: arXiv cs.CV 2023
Year: 2023

Existing learning-based hyperspectral reconstruction methods show limitations in fully exploiting the information among the hyperspectral bands. As such, we propose to investigate the chromatic inter-dependencies in their respective hyperspectral embedding space. These embedded features can be fully exploited by querying the inter-channel correlations in a combinatorial manner, with the unique and complementary information efficiently fused into the final prediction. We found such independent modeling and combinatorial excavation mechanisms are extremely beneficial to uncover marginal spectral features, especially in the long wavelength bands. In addition, we have proposed a spatio-spectral attention block and a spectrum-fusion attention module, which greatly facilitates the excavation and fusion of information at both semantically long-range levels and fine-grained pixel levels across all dimensions. Extensive quantitative and qualitative experiments show that our method (dubbed CESST) achieves SOTA performance. Code for this project is at: https://github.com/AlexYangxx/CESST.