OpenRaiser/Intern-Atlas huggingface dataset:
Private Blockchain-based Procurement and Asset Management System with QR Code
Venue: arXiv cs.CR 2024
Year: 2024

The developed system aims to incorporate a private blockchain technology in the procurement process for the supply office. The procurement process includes the canvassing, purchasing, delivery and inspection of items, inventory, and disposal. The blockchain-based system includes a distributed ledger technology, peer-to-peer network, Proof-of-Authority consensus mechanism, and SHA3-512 cryptographic hash function algorithm. This will ensure trust and proper accountability to the custodian of the property while safeguarding sensitive information in the procurement records. The extreme prototyping model will be used as software development life cycle. It is mostly used for web-based applications and has an increased user involvement. The prototype version of the system allows the users get a better understanding of the system being developed. It also reduces the time and cost, has quicker user feedback, missing and difficult functions can be recognized, and confusing processes can be addressed on an early stage. The implementation of a private blockchain technology has an increased privacy, enhanced security, improved efficiency, and reduced complexity over traditional blockchain network. The use of SHA3-512 as cryptographic hash function algorithm is much faster than its predecessors when cryptography is handled by hardware components. Furthermore, it is not vulnerable to length extension attacks making it reliable in terms of security of data. The study recommends the use of private blockchain-based technology with the procurement and asset management system in the supply office. The procurement records will be protected against tampering using this technology. This will promote trust and confidence of the stakeholders. The implementation of blockchain technology in developing a system served as advancement and innovation in terms of securing data.
Open (Clinical) LLMs are Sensitive to Instruction Phrasings
Venue: arXiv cs.CL 2024
Year: 2024

Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are phrased. This issue is especially concerning in healthcare, as clinicians are unlikely to be experienced prompt engineers and the potential consequences of inaccurate outputs are heightened in this domain. This raises a practical question: How robust are instruction-tuned LLMs to natural variations in the instructions provided for clinical NLP tasks? We collect prompts from medical doctors across a range of tasks and quantify the sensitivity of seven LLMs -- some general, others specialized -- to natural (i.e., non-adversarial) instruction phrasings. We find that performance varies substantially across all models, and that -- perhaps surprisingly -- domain-specific models explicitly trained on clinical data are especially brittle, compared to their general domain counterparts. Further, arbitrary phrasing differences can affect fairness, e.g., valid but distinct instructions for mortality prediction yield a range both in overall performance, and in terms of differences between demographic groups.
FairyLandAI: Personalized Fairy Tales utilizing ChatGPT and DALLE-3
Venue: arXiv cs.AI 2024
Year: 2024

In the diverse world of AI-driven storytelling, there is a unique opportunity to engage young audiences with customized, and personalized narratives. This paper introduces FairyLandAI an innovative Large Language Model (LLM) developed through OpenAI's API, specifically crafted to create personalized fairytales for children. The distinctive feature of FairyLandAI is its dual capability: it not only generates stories that are engaging, age-appropriate, and reflective of various traditions but also autonomously produces imaginative prompts suitable for advanced image generation tools like GenAI and Dalle-3, thereby enriching the storytelling experience. FairyLandAI is expertly tailored to resonate with the imaginative worlds of children, providing narratives that are both educational and entertaining and in alignment with the moral values inherent in different ages. Its unique strength lies in customizing stories to match individual children's preferences and cultural backgrounds, heralding a new era in personalized storytelling. Further, its integration with image generation technology offers a comprehensive narrative experience that stimulates both verbal and visual creativity. Empirical evaluations of FairyLandAI demonstrate its effectiveness in crafting captivating stories for children, which not only entertain but also embody the values and teachings of diverse traditions. This model serves as an invaluable tool for parents and educators, supporting them in imparting meaningful moral lessons through engaging narratives. FairyLandAI represents a pioneering step in using LLMs, particularly through OpenAI's API, for educational and cultural enrichment, making complex moral narratives accessible and enjoyable for young, imaginative minds.
StyleSplat: 3D Object Style Transfer with Gaussian Splatting
Venue: arXiv cs.CV 2024
Year: 2024

Recent advancements in radiance fields have opened new avenues for creating high-quality 3D assets and scenes. Style transfer can enhance these 3D assets with diverse artistic styles, transforming creative expression. However, existing techniques are often slow or unable to localize style transfer to specific objects. We introduce StyleSplat, a lightweight method for stylizing 3D objects in scenes represented by 3D Gaussians from reference style images. Our approach first learns a photorealistic representation of the scene using 3D Gaussian splatting while jointly segmenting individual 3D objects. We then use a nearest-neighbor feature matching loss to finetune the Gaussians of the selected objects, aligning their spherical harmonic coefficients with the style image to ensure consistency and visual appeal. StyleSplat allows for quick, customizable style transfer and localized stylization of multiple objects within a scene, each with a different style. We demonstrate its effectiveness across various 3D scenes and styles, showcasing enhanced control and customization in 3D creation.
Representation Debiasing of Generated Data Involving Domain Experts
Venue: arXiv cs.HC 2024
Year: 2024

Biases in Artificial Intelligence (AI) or Machine Learning (ML) systems due to skewed datasets problematise the application of prediction models in practice. Representation bias is a prevalent form of bias found in the majority of datasets. This bias arises when training data inadequately represents certain segments of the data space, resulting in poor generalisation of prediction models. Despite AI practitioners employing various methods to mitigate representation bias, their effectiveness is often limited due to a lack of thorough domain knowledge. To address this limitation, this paper introduces human-in-the-loop interaction approaches for representation debiasing of generated data involving domain experts. Our work advocates for a controlled data generation process involving domain experts to effectively mitigate the effects of representation bias. We argue that domain experts can leverage their expertise to assess how representation bias affects prediction models. Moreover, our interaction approaches can facilitate domain experts in steering data augmentation algorithms to produce debiased augmented data and validate or refine the generated samples to reduce representation bias. We also discuss how these approaches can be leveraged for designing and developing user-centred AI systems to mitigate the impact of representation bias through effective collaboration between domain experts and AI.
IT Enabling Factors in a new Industry Design: Open Banking and Digital Economy
Venue: arXiv cs.CY 2024
Year: 2024

The fourth industrial revolution promotes the integration of Information Technology (IT) and strategic resources. New IT demands and uses have been leading to changes in business processes and corporate governance. Lately, the financial industry has adopted a new integrated banking model known as Open Banking (OB) and the advent of cryptocurrencies has led to the Digital Economy (DE) materialization. Considering these facts, this paper expects to point out through literature review some IT enabling factors that allow the conception of a new industry design (or governance) specifically in the financial industry illustrated by the cases of the Open Banking and Digital Economy. This paper is structured mostly on literature review, accompanied by results, discussions, and finally, conclusions are presented. It was found five potential enabling factors. Keywords: Digital Economy, Information Technology (IT), Open Banking.
On when is Reservoir Computing with Cellular Automata Beneficial?
Venue: arXiv cs.NE 2024
Year: 2024

Reservoir Computing with Cellular Automata (ReCA) is a relatively novel and promising approach. It consists of 3 steps: an encoding scheme to inject the problem into the CA, the CA iterations step itself and a simple classifying step, typically a linear classifier. This paper demonstrates that the ReCA concept is effective even in arguably the simplest implementation of a ReCA system. However, we also report a failed attempt on the UCR Time Series Classification Archive where ReCA seems to work, but only because of the encoding scheme itself, not in any part due to the CA. This highlights the need for ablation testing, i.e., comparing internally with sub-parts of one model, but also raises an open question on what kind of tasks ReCA is best suited for.
Focused State Recognition Using EEG with Eye Movement-Assisted Annotation
Venue: arXiv cs.HC 2024
Year: 2024

With the rapid advancement in machine learning, the recognition and analysis of brain activity based on EEG and eye movement signals have attained a high level of sophistication. Utilizing deep learning models for learning EEG and eye movement features proves effective in classifying brain activities. A focused state indicates intense concentration on a task or thought. Distinguishing focused and unfocused states can be achieved through eye movement behaviors, reflecting variations in brain activities. By calculating binocular focusing point disparity in eye movement signals and integrating relevant EEG features, we propose an annotation method for focused states. The resulting comprehensive dataset, derived from raw data processed through a bio-acquisition device, includes both EEG features and focused labels annotated by eye movements. Extensive training and testing on several deep learning models, particularly the Transformer, yielded a 90.16% accuracy on the subject-dependent experiments. The validity of this approach was demonstrated, with cross-subject experiments, key frequency band and brain region analyses confirming its generalizability and providing physiological explanations.
Research on fusing topological data analysis with convolutional neural network
Venue: arXiv cs.CV 2024
Year: 2024

Convolutional Neural Network (CNN) struggle to capture the multi-dimensional structural information of complex high-dimensional data, which limits their feature learning capability. This paper proposes a feature fusion method based on Topological Data Analysis (TDA) and CNN, named TDA-CNN. This method combines numerical distribution features captured by CNN with topological structure features captured by TDA to improve the feature learning and representation ability of CNN. TDA-CNN divides feature extraction into a CNN channel and a TDA channel. CNN channel extracts numerical distribution features, and the TDA channel extracts topological structure features. The two types of features are fused to form a combined feature representation, with the importance weights of each feature adaptively learned through an attention mechanism. Experimental validation on datasets such as Intel Image, Gender Images, and Chinese Calligraphy Styles by Calligraphers demonstrates that TDA-CNN improves the performance of VGG16, DenseNet121, and GoogleNet networks by 17.5%, 7.11%, and 4.45%, respectively. TDA-CNN demonstrates improved feature clustering and the ability to recognize important features. This effectively enhances the model's decision-making ability.
ViPro: Enabling and Controlling Video Prediction for Complex Dynamical Scenarios using Procedural Knowledge
Venue: arXiv cs.CV 2024
Year: 2024

We propose a novel architecture design for video prediction in order to utilize procedural domain knowledge directly as part of the computational graph of data-driven models. On the basis of new challenging scenarios we show that state-of-the-art video predictors struggle in complex dynamical settings, and highlight that the introduction of prior process knowledge makes their learning problem feasible. Our approach results in the learning of a symbolically addressable interface between data-driven aspects in the model and our dedicated procedural knowledge module, which we utilize in downstream control tasks.
Info Overload: A Cooperative Evacuation Game
Venue: arXiv cs.HC 2024
Year: 2024

Info Overload is a minigame within a larger collection aimed at increasing awareness and preparation for an evacuation in the event of a wildfire. The game relies on experiential two-player cooperative gameplay and is played on a mobile device (a phone or tablet).
VDB-GPDF: Online Gaussian Process Distance Field with VDB Structure
Venue: arXiv cs.RO 2024
Year: 2024

Robots reason about the environment through dedicated representations. Popular choices for dense representations exploit Truncated Signed Distance Functions (TSDF) and Octree data structures. However, TSDF provides a projective or non-projective signed distance obtained directly from depth measurements that overestimate the Euclidean distance. Octrees, despite being memory efficient, require tree traversal and can lead to increased runtime in large scenarios. Other representations based on the Gaussian Process (GP) distance fields are appealing due to their probabilistic and continuous nature, but the computational complexity is a concern. In this paper, we present an online efficient mapping framework that seamlessly couples GP distance fields and the fast-access OpenVDB data structure. The key aspect is a latent Local GP Signed Distance Field (L-GPDF) contained in a local VDB structure that allows fast queries of the Euclidean distance, surface properties and their uncertainties for arbitrary points in the field of view. Probabilistic fusion is then performed by merging the inferred values of these points into a global VDB structure that is efficiently maintained over time. After fusion, the surface mesh is recovered, and a global GP Signed Distance Field (G-GPDF) is generated and made available for downstream applications to query accurate distance and gradients. A comparison with the state-of-the-art frameworks shows superior efficiency and accuracy of the inferred distance field and comparable reconstruction performance. https://github.com/UTS-RI/VDB_GPDF
Fine-Grained Optimality of Partially Dynamic Shortest Paths and More
Venue: arXiv cs.DS 2024
Year: 2024

Single Source Shortest Paths ($\textrm{SSSP}$) is among the most well-studied problems in computer science. In the incremental (resp. decremental) setting, the goal is to maintain distances from a fixed source in a graph undergoing edge insertions (resp. deletions). A long line of research culminated in a near-optimal deterministic $(1 + \varepsilon)$-approximate data structure with $m^{1 + o(1)}$ total update time over all $m$ updates by Bernstein, Probst Gutenberg and Saranurak [FOCS 2021]. However, there has been remarkably little progress on the exact $\textrm{SSSP}$ problem beyond Even and Shiloach's algorithm [J. ACM 1981] for unweighted graphs. For weighted graphs, there are no exact algorithms beyond recomputing $\textrm{SSSP}$ from scratch in $\widetilde{O}(m^2)$ total update time, even for the simpler Single-Source Single-Target Shortest Path problem ($\textrm{stSP}$). Despite this lack of progress, known (conditional) lower bounds only rule out algorithms with amortized update time better than $m^{1/2 - o(1)}$ in dense graphs. In this paper, we give a tight (conditional) lower bound: any partially dynamic exact $\textrm{stSP}$ algorithm requires $m^{2 - o(1)}$ total update time for any sparsity $m$. We thus resolve the complexity of partially dynamic shortest paths, and separate the hardness of exact and approximate shortest paths, giving evidence as to why no non-trivial exact algorithms have been obtained while fast approximation algorithms are known. Moreover, we give tight bounds on the complexity of combinatorial algorithms for several path problems that have been studied in the static setting since early sixties: Node-weighted shortest paths (studied alongside edge-weighted shortest paths), bottleneck paths (early work dates back to 1960), and earliest arrivals (early work dates back to 1958).
Fast and Robust Phase Retrieval via Deep Expectation-Consistent Approximation
Venue: arXiv cs.CV 2024
Year: 2024

Accurately recovering images from phaseless measurements is a challenging and long-standing problem. In this work, we present "deepECpr," which combines expectation-consistent (EC) approximation with deep denoising networks to surpass state-of-the-art phase-retrieval methods in both speed and accuracy. In addition to applying EC in a non-traditional manner, deepECpr includes a novel stochastic damping scheme that is inspired by recent diffusion methods. Like existing phase-retrieval methods based on plug-and-play priors, regularization by denoising, or diffusion, deepECpr iterates a denoising stage with a measurement-exploitation stage. But unlike existing methods, deepECpr requires far fewer denoiser calls. We compare deepECpr to the state-of-the-art prDeep (Metzler et al., 2018), Deep-ITA (Wang et al., 2020), DOLPH (Shoushtari et al., 2023), and Diffusion Posterior Sampling (Chung et al., 2023) methods for noisy phase-retrieval of color, natural, and unnatural grayscale images on oversampled-Fourier and coded-diffraction-pattern measurements and find improvements in both PSNR and SSIM with significantly fewer denoiser calls.
Purrfect Pitch: Exploring Musical Interval Learning through Multisensory Interfaces
Venue: arXiv cs.HC 2024
Year: 2024

We introduce Purrfect Pitch, a system consisting of a wearable haptic device and a custom-designed learning interface for musical ear training. We focus on the ability to identify musical intervals (sequences of two musical notes), which is a perceptually ambiguous task that usually requires strenuous rote training. With our system, the user would hear a sequence of two tones while simultaneously receiving two corresponding vibrotactile stimuli on the back. Providing haptic feedback along the back makes the auditory distance between the two tones more salient, and the back-worn design is comfortable and unobtrusive. During training, the user receives multi-sensory feedback from our system and inputs their guessed interval value on our web-based learning interface. They see a green (otherwise red) screen for a correct guess with the correct interval value. Our study with 18 participants shows that our system enables novice learners to identify intervals more accurately and consistently than those who only received audio feedback, even after the haptic feedback is removed. We also share further insights on how to design a multisensory learning system.
Biased Backpressure Routing Using Link Features and Graph Neural Networks
Venue: arXiv cs.NI 2024
Year: 2024

To reduce the latency of Backpressure (BP) routing in wireless multi-hop networks, we propose to enhance the existing shortest path-biased BP (SP-BP) and sojourn time-based backlog metrics, since they introduce no additional time step-wise signaling overhead to the basic BP. Rather than relying on hop-distance, we introduce a new edge-weighted shortest path bias built on the scheduling duty cycle of wireless links, which can be predicted by a graph convolutional neural network based on the topology and traffic of wireless networks. Additionally, we tackle three long-standing challenges associated with SP-BP: optimal bias scaling, efficient bias maintenance, and integration of delay awareness. Our proposed solutions inherit the throughput optimality of the basic BP, as well as its practical advantages of low complexity and fully distributed implementation. Our approaches rely on common link features and introduces only a one-time constant overhead to previous SP-BP schemes, or a one-time overhead linear in the network size to the basic BP. Numerical experiments show that our solutions can effectively address the major drawbacks of slow startup, random walk, and the last packet problem in basic BP, improving the end-to-end delay of existing low-overhead BP algorithms under various settings of network traffic, interference, and mobility.
CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis
Venue: arXiv cs.AI 2024
Year: 2024

Single-cell RNA sequencing (scRNA-seq) data analysis is crucial for biological research, as it enables the precise characterization of cellular heterogeneity. However, manual manipulation of various tools to achieve desired outcomes can be labor-intensive for researchers. To address this, we introduce CellAgent (http://cell.agent4science.cn/), an LLM-driven multi-agent framework, specifically designed for the automatic processing and execution of scRNA-seq data analysis tasks, providing high-quality results with no human intervention. Firstly, to adapt general LLMs to the biological field, CellAgent constructs LLM-driven biological expert roles - planner, executor, and evaluator - each with specific responsibilities. Then, CellAgent introduces a hierarchical decision-making mechanism to coordinate these biological experts, effectively driving the planning and step-by-step execution of complex data analysis tasks. Furthermore, we propose a self-iterative optimization mechanism, enabling CellAgent to autonomously evaluate and optimize solutions, thereby guaranteeing output quality. We evaluate CellAgent on a comprehensive benchmark dataset encompassing dozens of tissues and hundreds of distinct cell types. Evaluation results consistently show that CellAgent effectively identifies the most suitable tools and hyperparameters for single-cell analysis tasks, achieving optimal performance. This automated framework dramatically reduces the workload for science data analyses, bringing us into the "Agent for Science" era.