OpenRaiser/Intern-Atlas huggingface dataset:
SketchDreamer: Interactive Text-Augmented Creative Sketch Ideation
Venue: arXiv cs.CV 2023
Year: 2023

Artificial Intelligence Generated Content (AIGC) has shown remarkable progress in generating realistic images. However, in this paper, we take a step "backward" and address AIGC for the most rudimentary visual modality of human sketches. Our objective is on the creative nature of sketches, and that creative sketching should take the form of an interactive process. We further enable text to drive the sketch ideation process, allowing creativity to be freely defined, while simultaneously tackling the challenge of "I can't sketch". We present a method to generate controlled sketches using a text-conditioned diffusion model trained on pixel representations of images. Our proposed approach, referred to as SketchDreamer, integrates a differentiable rasteriser of Bezier curves that optimises an initial input to distil abstract semantic knowledge from a pretrained diffusion model. We utilise Score Distillation Sampling to learn a sketch that aligns with a given caption, which importantly enable both text and sketch to interact with the ideation process. Our objective is to empower non-professional users to create sketches and, through a series of optimisation processes, transform a narrative into a storyboard by expanding the text prompt while making minor adjustments to the sketch input. Through this work, we hope to aspire the way we create visual content, democratise the creative process, and inspire further research in enhancing human creativity in AIGC. The code is available at \url{https://github.com/WinKawaks/SketchDreamer}.
Machine Learning for Administrative Health Records: A Systematic Review of Techniques and Applications
Venue: arXiv cs.LG 2023
Year: 2023

Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learning on AHRs is a growing subfield of EHR analytics. Existing reviews of EHR analytics emphasise that the data-modality of the EHR limits the breadth of suitable machine learning techniques, and pursuable healthcare applications. Despite emphasising the importance of data modality, the literature fails to analyse which techniques and applications are relevant to AHRs. AHRs contain uniquely well-structured, categorically encoded records which are distinct from other data-modalities captured by EHRs, and they can provide valuable information pertaining to how patients interact with the healthcare system. This paper systematically reviews AHR-based research, analysing 70 relevant studies and spanning multiple databases. We identify and analyse which machine learning techniques are applied to AHRs and which health informatics applications are pursued in AHR-based research. We also analyse how these techniques are applied in pursuit of each application, and identify the limitations of these approaches. We find that while AHR-based studies are disconnected from each other, the use of AHRs in health informatics research is substantial and accelerating. Our synthesis of these studies highlights the utility of AHRs for pursuing increasingly complex and diverse research objectives despite a number of pervading data- and technique-based limitations. Finally, through our findings, we propose a set of future research directions that can enhance the utility of AHR data and machine learning techniques for health informatics research.
Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact
Venue: arXiv cs.DB 2023
Year: 2023

Knowledge Graphs (KGs) have been used to support a wide range of applications, from web search to personal assistant. In this paper, we describe three generations of knowledge graphs: entity-based KGs, which have been supporting general search and question answering (e.g., at Google and Bing); text-rich KGs, which have been supporting search and recommendations for products, bio-informatics, etc. (e.g., at Amazon and Alibaba); and the emerging integration of KGs and LLMs, which we call dual neural KGs. We describe the characteristics of each generation of KGs, the crazy ideas behind the scenes in constructing such KGs, and the techniques developed over time to enable industry impact. In addition, we use KGs as examples to demonstrate a recipe to evolve research ideas from innovations to production practice, and then to the next level of innovations, to advance both science and business.
The Cultural Psychology of Large Language Models: Is ChatGPT a Holistic or Analytic Thinker?
Venue: arXiv cs.AI 2023
Year: 2023

The prevalent use of Large Language Models (LLMs) has necessitated studying their mental models, yielding noteworthy theoretical and practical implications. Current research has demonstrated that state-of-the-art LLMs, such as ChatGPT, exhibit certain theory of mind capabilities and possess relatively stable Big Five and/or MBTI personality traits. In addition, cognitive process features form an essential component of these mental models. Research in cultural psychology indicated significant differences in the cognitive processes of Eastern and Western people when processing information and making judgments. While Westerners predominantly exhibit analytical thinking that isolates things from their environment to analyze their nature independently, Easterners often showcase holistic thinking, emphasizing relationships and adopting a global viewpoint. In our research, we probed the cultural cognitive traits of ChatGPT. We employed two scales that directly measure the cognitive process: the Analysis-Holism Scale (AHS) and the Triadic Categorization Task (TCT). Additionally, we used two scales that investigate the value differences shaped by cultural thinking: the Dialectical Self Scale (DSS) and the Self-construal Scale (SCS). In cognitive process tests (AHS/TCT), ChatGPT consistently tends towards Eastern holistic thinking, but regarding value judgments (DSS/SCS), ChatGPT does not significantly lean towards the East or the West. We suggest that the result could be attributed to both the training paradigm and the training data in LLM development. We discuss the potential value of this finding for AI research and directions for future research.
Draco 2: An Extensible Platform to Model Visualization Design
Venue: arXiv cs.HC 2023
Year: 2023

Draco introduced a constraint-based framework to model visualization design in an extensible and testable form. It provides a way to abstract design guidelines from theoretical and empirical studies and applies the knowledge in automated design tools. However, Draco is challenging to use because there is limited tooling and documentation. In response, we present Draco 2, the successor with (1) a more flexible visualization specification format, (2) a comprehensive test suite and documentation, and (3) flexible and convenient APIs. We designed Draco 2 to be more extensible and easier to integrate into visualization systems. We demonstrate these advantages and believe that they make Draco 2 a platform for future research.
SalesBot 2.0: A Human-Like Intent-Guided Chit-Chat Dataset
Venue: arXiv cs.CL 2023
Year: 2023

In recent research on dialogue systems and corpora, there has been a significant focus on two distinct categories: task-oriented (TOD) and open-domain (chit-chat) dialogues. TOD systems aim to satisfy specific user goals, such as finding a movie to watch, whereas open-domain systems primarily focus on generating engaging conversations. A recent study by Chiu et al. (2022) introduced SalesBot, which provides simulators and a dataset with one-turn transition from chit-chat to task-oriented dialogues. However, the previously generated data solely relied on BlenderBot, which raised concerns about its long-turn naturalness and consistency during a conversation. To address this issue, this paper aims to build SalesBot 2.0, a revised version of the published data, by leveraging the commonsense knowledge of large language models (LLMs) through proper prompting. The objective is to gradually bridge the gap between chit-chat and TOD towards better naturalness and consistency. The newly released large-scale dataset with detailed annotations exhibits smoother transitions between topics and is more human-like in terms of naturalness and consistency. It can serve as a valuable resource for both academic research and commercial applications. Furthermore, our proposed framework can be applied to generate numerous dialogues with various target intents.
Alleviating Video-Length Effect for Micro-video Recommendation
Venue: arXiv cs.IR 2023
Year: 2023

Micro-videos platforms such as TikTok are extremely popular nowadays. One important feature is that users no longer select interested videos from a set, instead they either watch the recommended video or skip to the next one. As a result, the time length of users' watching behavior becomes the most important signal for identifying preferences. However, our empirical data analysis has shown a video-length effect that long videos are easier to receive a higher value of average view time, thus adopting such view-time labels for measuring user preferences can easily induce a biased model that favors the longer videos. In this paper, we propose a Video Length Debiasing Recommendation (VLDRec) method to alleviate such an effect for micro-video recommendation. VLDRec designs the data labeling approach and the sample generation module that better capture user preferences in a view-time oriented manner. It further leverages the multi-task learning technique to jointly optimize the above samples with original biased ones. Extensive experiments show that VLDRec can improve the users' view time by 1.81% and 11.32% on two real-world datasets, given a recommendation list of a fixed overall video length, compared with the best baseline method. Moreover, VLDRec is also more effective in matching users' interests in terms of the video content.
Spread Control Method on Unknown Networks Based on Hierarchical Reinforcement Learning
Venue: arXiv cs.AI 2023
Year: 2023

Epidemics such as COVID-19 pose serious threats to public health and our society, and it is critical to investigate effective methods to control the spread of epidemics over networks. Prior works on epidemic control often assume complete knowledge of network structures, a presumption seldom valid in real-world situations. In this paper, we study epidemic control on networks with unknown structures, and propose a hierarchical reinforcement learning framework for joint network structure exploration and epidemic control. To reduce the action space and achieve computation tractability, our proposed framework contains three modules: the Policy Selection Module, which determines whether to explore the structure or remove nodes to control the epidemic; the Explore Module, responsible for selecting nodes to explore; and the Remove Module, which decides which nodes to remove to stop the epidemic spread. Simulation results show that our proposed method outperforms baseline methods.
InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models
Venue: arXiv cs.SD 2023
Year: 2023

Music editing primarily entails the modification of instrument tracks or remixing in the whole, which offers a novel reinterpretation of the original piece through a series of operations. These music processing methods hold immense potential across various applications but demand substantial expertise. Prior methodologies, although effective for image and audio modifications, falter when directly applied to music. This is attributed to music's distinctive data nature, where such methods can inadvertently compromise the intrinsic harmony and coherence of music. In this paper, we develop InstructME, an Instruction guided Music Editing and remixing framework based on latent diffusion models. Our framework fortifies the U-Net with multi-scale aggregation in order to maintain consistency before and after editing. In addition, we introduce chord progression matrix as condition information and incorporate it in the semantic space to improve melodic harmony while editing. For accommodating extended musical pieces, InstructME employs a chunk transformer, enabling it to discern long-term temporal dependencies within music sequences. We tested InstructME in instrument-editing, remixing, and multi-round editing. Both subjective and objective evaluations indicate that our proposed method significantly surpasses preceding systems in music quality, text relevance and harmony. Demo samples are available at https://musicedit.github.io/
Ensemble of Anchor-Free Models for Robust Bangla Document Layout Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

In this research paper, we introduce a novel approach designed for the purpose of segmenting the layout of Bangla documents. Our methodology involves the utilization of a sophisticated ensemble of YOLOv8 models, which were trained for the DL Sprint 2.0 - BUET CSE Fest 2023 Competition focused on Bangla document layout segmentation. Our primary emphasis lies in enhancing various aspects of the task, including techniques such as image augmentation, model architecture, and the incorporation of model ensembles. We deliberately reduce the quality of a subset of document images to enhance the resilience of model training, thereby resulting in an improvement in our cross-validation score. By employing Bayesian optimization, we determine the optimal confidence and Intersection over Union (IoU) thresholds for our model ensemble. Through our approach, we successfully demonstrate the effectiveness of anchor-free models in achieving robust layout segmentation in Bangla documents.
Data-iterative Optimization Score Model for Stable Ultra-Sparse-View CT Reconstruction
Venue: arXiv cs.CV 2023
Year: 2023

Score-based generative models (SGMs) have gained prominence in sparse-view CT reconstruction for their precise sampling of complex distributions. In SGM-based reconstruction, data consistency in the score-based diffusion model ensures close adherence of generated samples to observed data distribution, crucial for improving image quality. Shortcomings in data consistency characterization manifest in three aspects. Firstly, data from the optimization process can lead to artifacts in reconstructed images. Secondly, it often neglects that the generation model and original data constraints are independently completed, fragmenting unity. Thirdly, it predominantly focuses on constraining intermediate results in the inverse sampling process, rather than ideal real images. Thus, we propose an iterative optimization data scoring model. This paper introduces the data-iterative optimization score-based model (DOSM), integrating innovative data consistency into the Stochastic Differential Equation, a valuable constraint for ultra-sparse-view CT reconstruction. The novelty of this data consistency element lies in its sole reliance on original measurement data to confine generation outcomes, effectively balancing measurement data and generative model constraints. Additionally, we pioneer an inference strategy that traces back from current iteration results to ideal truth, enhancing reconstruction stability. We leverage conventional iteration techniques to optimize DOSM updates. Quantitative and qualitative results from 23 views of numerical and clinical cardiac datasets demonstrate DOSM's superiority over other methods. Remarkably, even with 10 views, our method achieves excellent performance.
STEAM: Simulating the InTeractive BEhavior of ProgrAMmers for Automatic Bug Fixing
Venue: arXiv cs.SE 2023
Year: 2023

Bug fixing holds significant importance in software development and maintenance. Recent research has made notable progress in exploring the potential of large language models (LLMs) for automatic bug fixing. However, existing studies often overlook the collaborative nature of bug resolution, treating it as a single-stage process. To overcome this limitation, we introduce a novel stage-wise framework named STEAM in this paper. The objective of STEAM is to simulate the interactive behavior of multiple programmers involved in various stages across the bug's life cycle. Taking inspiration from bug management practices, we decompose the bug fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to imitate the collaborative abilities of programmers during the resolution of software bugs. By harnessing the collective contribution, STEAM effectively enhances the bug-fixing capabilities of LLMs. We implement STEAM by employing the powerful dialogue-based LLM -- ChatGPT. Our evaluation on the widely adopted bug-fixing benchmark demonstrates that STEAM has achieved a new state-of-the-art level of bug-fixing performance.
Improving the performance of object detection by preserving label distribution
Venue: arXiv cs.CV 2023
Year: 2023

Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class , is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on custom datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution.
Medical needle tip tracking based on Optical Imaging and AI
Venue: arXiv cs.CV 2023
Year: 2023

Deep needle insertion to a target often poses a huge challenge, requiring a combination of specialized skills, assistive technology, and extensive training. One of the frequently encountered medical scenarios demanding such expertise includes the needle insertion into a femoral vessel in the groin. After the access to the femoral vessel, various medical procedures, such as cardiac catheterization and extracorporeal membrane oxygenation (ECMO) can be performed. However, even with the aid of Ultrasound imaging, achieving successful insertion can necessitate multiple attempts due to the complexities of anatomy and tissue deformation. To address this challenge, this paper presents an innovative technology for needle tip real-time tracking, aiming for enhanced needle insertion guidance. Specifically, our approach revolves around the creation of scattering imaging using an optical fiber-equipped needle, and uses Convolutional Neural Network (CNN) based algorithms to enable real-time estimation of the needle tip's position and orientation during insertion procedures. The efficacy of the proposed technology was rigorously evaluated through three experiments. The first two experiments involved rubber and bacon phantoms to simulate groin anatomy. The positional errors averaging 2.3+1.5mm and 2.0+1.2mm, and the orientation errors averaging 0.2+0.11rad and 0.16+0.1rad. Furthermore, the system's capabilities were validated through experiments conducted on fresh porcine phantom mimicking more complex anatomical structures, yielding positional accuracy results of 3.2+3.1mm and orientational accuracy of 0.19+0.1rad. Given the average femoral arterial radius of 4 to 5mm, the proposed system is demonstrated with a great potential for precise needle guidance in femoral artery insertion procedures. In addition, the findings highlight the broader potential applications of the system in the medical field.
RESTORE: Graph Embedding Assessment Through Reconstruction
Venue: arXiv cs.LG 2023
Year: 2023

Following the success of Word2Vec embeddings, graph embeddings (GEs) have gained substantial traction. GEs are commonly generated and evaluated extrinsically on downstream applications, but intrinsic evaluations of the original graph properties in terms of topological structure and semantic information have been lacking. Understanding these will help identify the deficiency of the various families of GE methods when vectorizing graphs in terms of preserving the relevant knowledge or learning incorrect knowledge. To address this, we propose RESTORE, a framework for intrinsic GEs assessment through graph reconstruction. We show that reconstructing the original graph from the underlying GEs yields insights into the relative amount of information preserved in a given vector form. We first introduce the graph reconstruction task. We generate GEs from three GE families based on factorization methods, random walks, and deep learning (with representative algorithms from each family) on the CommonSense Knowledge Graph (CSKG). We analyze their effectiveness in preserving the (a) topological structure of node-level graph reconstruction with an increasing number of hops and (b) semantic information on various word semantic and analogy tests. Our evaluations show deep learning-based GE algorithm (SDNE) is overall better at preserving (a) with a mean average precision (mAP) of 0.54 and 0.35 for 2 and 3-hop reconstruction respectively, while the factorization-based algorithm (HOPE) is better at encapsulating (b) with an average Euclidean distance of 0.14, 0.17, and 0.11 for 1, 2, and 3-hop reconstruction respectively. The modest performance of these GEs leaves room for further research avenues on better graph representation learning.
Fast Feedforward Networks
Venue: arXiv cs.LG 2023
Year: 2023

We break the linear link between the layer size and its inference cost by introducing the fast feedforward (FFF) architecture, a log-time alternative to feedforward networks. We demonstrate that FFFs are up to 220x faster than feedforward networks, up to 6x faster than mixture-of-experts networks, and exhibit better training properties than mixtures of experts thanks to noiseless conditional execution. Pushing FFFs to the limit, we show that they can use as little as 1% of layer neurons for inference in vision transformers while preserving 94.2% of predictive performance.