OpenRaiser/Intern-Atlas huggingface dataset:
Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentation
Venue: arXiv cs.CV 2024
Year: 2024

Diabetic foot ulcers pose health risks, including higher morbidity, mortality, and amputation rates. Monitoring wound areas is crucial for proper care, but manual segmentation is subjective due to complex wound features and background variation. Expert annotations are costly and time-intensive, thus hampering large dataset creation. Existing segmentation models relying on extensive annotations are impractical in real-world scenarios with limited annotated data. In this paper, we propose a cross-domain augmentation method named TransMix that combines Augmented Global Pre-training AGP and Localized CutMix Fine-tuning LCF to enrich wound segmentation data for model learning. TransMix can effectively improve the foot ulcer segmentation model training by leveraging other dermatology datasets not on ulcer skins or wounds. AGP effectively increases the overall image variability, while LCF increases the diversity of wound regions. Experimental results show that TransMix increases the variability of wound regions and substantially improves the Dice score for models trained with only 40 annotated images under various proportions.
CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Cognitive dynamics are pivotal to advance human understanding of the world. Recent advancements in large language models (LLMs) reveal their potential for cognitive simulation. However, these LLM-based cognitive studies primarily focus on static modeling, overlooking the dynamic nature of cognition. To bridge this gap, we propose the concept of the cognitive dynamics of LLMs and present a corresponding task with the inspiration of longitudinal studies. Towards the task, we develop CogBench, a novel benchmark to assess the cognitive dynamics of LLMs and validate it through participant surveys. We also design two evaluation metrics for CogBench, including Authenticity and Rationality. Recognizing the inherent static nature of LLMs, we introduce CogGPT for the task, which features an innovative iterative cognitive mechanism aimed at enhancing lifelong cognitive dynamics. Empirical results demonstrate the superiority of CogGPT over existing methods, particularly in its ability to facilitate role-specific cognitive dynamics under continuous information flows.
TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras
Venue: arXiv cs.CV 2024
Year: 2024

Event-based cameras are predestined for Intelligent Transportation Systems (ITS). They provide very high temporal resolution and dynamic range, which can eliminate motion blur and improve detection performance at night. However, event-based images lack color and texture compared to images from a conventional RGB camera. Considering that, data fusion between event-based and conventional cameras can combine the strengths of both modalities. For this purpose, extrinsic calibration is necessary. To the best of our knowledge, no targetless calibration between event-based and RGB cameras can handle multiple moving objects, nor does data fusion optimized for the domain of roadside ITS exist. Furthermore, synchronized event-based and RGB camera datasets considering roadside perspective are not yet published. To fill these research gaps, based on our previous work, we extended our targetless calibration approach with clustering methods to handle multiple moving objects. Furthermore, we developed an early fusion, simple late fusion, and a novel spatiotemporal late fusion method. Lastly, we published the TUMTraf Event Dataset, which contains more than 4,111 synchronized event-based and RGB images with 50,496 labeled 2D boxes. During our extensive experiments, we verified the effectiveness of our calibration method with multiple moving objects. Furthermore, compared to a single RGB camera, we increased the detection performance of up to +9 % mAP in the day and up to +13 % mAP during the challenging night with our presented event-based sensor fusion methods. The TUMTraf Event Dataset is available at https://innovation-mobility.com/tumtraf-dataset.
FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning
Venue: arXiv cs.RO 2024
Year: 2024

In this paper, we propose a real-world benchmark for studying robotic learning in the context of functional manipulation: a robot needs to accomplish complex long-horizon behaviors by composing individual manipulation skills in functionally relevant ways. The core design principles of our Functional Manipulation Benchmark (FMB) emphasize a harmonious balance between complexity and accessibility. Tasks are deliberately scoped to be narrow, ensuring that models and datasets of manageable scale can be utilized effectively to track progress. Simultaneously, they are diverse enough to pose a significant generalization challenge. Furthermore, the benchmark is designed to be easily replicable, encompassing all essential hardware and software components. To achieve this goal, FMB consists of a variety of 3D-printed objects designed for easy and accurate replication by other researchers. The objects are procedurally generated, providing a principled framework to study generalization in a controlled fashion. We focus on fundamental manipulation skills, including grasping, repositioning, and a range of assembly behaviors. The FMB can be used to evaluate methods for acquiring individual skills, as well as methods for combining and ordering such skills to solve complex, multi-stage manipulation tasks. We also offer an imitation learning framework that includes a suite of policies trained to solve the proposed tasks. This enables researchers to utilize our tasks as a versatile toolkit for examining various parts of the pipeline. For example, researchers could propose a better design for a grasping controller and evaluate it in combination with our baseline reorientation and assembly policies as part of a pipeline for solving multi-stage tasks. Our dataset, object CAD files, code, and evaluation videos can be found on our project website: https://functional-manipulation-benchmark.github.io
PlayMyData: a curated dataset of multi-platform video games
Venue: arXiv cs.SE 2024
Year: 2024

Being predominant in digital entertainment for decades, video games have been recognized as valuable software artifacts by the software engineering (SE) community just recently. Such an acknowledgment has unveiled several research opportunities, spanning from empirical studies to the application of AI techniques for classification tasks. In this respect, several curated game datasets have been disclosed for research purposes even though the collected data are insufficient to support the application of advanced models or to enable interdisciplinary studies. Moreover, the majority of those are limited to PC games, thus excluding notorious gaming platforms, e.g., PlayStation, Xbox, and Nintendo. In this paper, we propose PlayMyData, a curated dataset composed of 99,864 multi-platform games gathered by IGDB website. By exploiting a dedicated API, we collect relevant metadata for each game, e.g., description, genre, rating, gameplay video URLs, and screenshots. Furthermore, we enrich PlayMyData with the timing needed to complete each game by mining the HLTB website. To the best of our knowledge, this is the most comprehensive dataset in the domain that can be used to support different automated tasks in SE. More importantly, PlayMyData can be used to foster cross-domain investigations built on top of the provided multimedia data.
Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges
Venue: arXiv cs.AI 2024
Year: 2024

Online education platforms, leveraging the internet to distribute education resources, seek to provide convenient education but often fall short in real-time communication with students. They often struggle to address the diverse obstacles students encounter throughout their learning journey. Solving the problems encountered by students poses a significant challenge for traditional deep learning models, as it requires not only a broad spectrum of subject knowledge but also the ability to understand what constitutes a student's individual difficulties. It's challenging for traditional machine learning models, as they lack the capacity to comprehend students' personalized needs. Recently, the emergence of large language models (LLMs) offers the possibility for resolving this issue by comprehending individual requests. Although LLMs have been successful in various fields, creating an LLM-based education system is still challenging for the wide range of educational skills required. This paper reviews the recently emerged LLM research related to educational capabilities, including mathematics, writing, programming, reasoning, and knowledge-based question answering, with the aim to explore their potential in constructing the next-generation intelligent education system. Specifically, for each capability, we focus on investigating two aspects. Firstly, we examine the current state of LLMs regarding this capability: how advanced they have become, whether they surpass human abilities, and what deficiencies might exist. Secondly, we evaluate whether the development methods for LLMs in this area are generalizable, that is, whether these methods can be applied to construct a comprehensive educational supermodel with strengths across various capabilities, rather than being effective in only a singular aspect.
Visualizing the genealogy of video game specifications of video game genres -- through a case study of the raising up game genre
Venue: arXiv cs.HC 2024
Year: 2024

Although several methodologies for identifying the genealogy of video game genres and showing their relationships have been proposed in existing research, there have been few attempts to visualize the genealogy of a genre in a quantitative and qualitative manner. In this study, we propose a methodology to identify the scope of a specific game genre and to show how game specifications specific to that genre are related to each other.
Zero-Shot RTL Code Generation with Attention Sink Augmented Large Language Models
Venue: arXiv cs.AR 2024
Year: 2024

The design and optimization of hardware have traditionally been resource-intensive, demanding considerable expertise and dependence on established design automation tools. This paper discusses the possibility of exploiting large language models to streamline the code generation process in hardware design. In contrast to earlier studies, this paper aims to use large language models that accepts high-level design specifications through a single prompt to generate corresponding Register-Transfer Level (RTL) code. The ability to use large language models on RTL code generation not only expedites design iteration cycles but also facilitates the exploration of design spaces that have computational challenges for conventional techniques. Through our evaluation, we demonstrate the shortcoming of existing attention mechanisms, and present the abilities of language models to produce functional, optimized, and industry-standard compliant RTL code when a novel attention mechanism is used. These findings underscore the expanding role of large language models in shaping the future landscape of architectural exploration and automation in hardware design.
Apple Vision Pro: Comments in Healthcare
Venue: arXiv cs.HC 2024
Year: 2024

This paper objectively analyzes the emerging discourse surrounding Apple Vision Pro's application in healthcare and medical education. Released in June 2023, Apple Vision Pro represents a significant advancement in spatial computing, combining augmented and virtual reality to create new possibilities in digital interaction. We aim to compile and present recent articles. We used PubMed, IEEE Xplore, Google Scholar, and JSTOR. Non-academic publications were excluded. The results were six commentaries, one a pre-print. All were majorly optimistic, with one mentioning VR/AR sickness. For future research directions, we stress the need for continued exploration of Apple Vision Pro's capabilities and limitations and expect expert opinions to englobe this discussion.
Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study
Venue: arXiv cs.CV 2024
Year: 2024

Within (semi-)automated visual inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery. The emergence of these often rarely occurring defect patterns explains the general need for labeled data corpora. To not only alleviate this issue but to furthermore advance the current state of the art in unsupervised visual inspection, this contribution proposes a DifferNet-based solution enhanced with attention modules utilizing SENet and CBAM as backbone - AttentDifferNet - to improve the detection and classification capabilities on three different visual inspection and anomaly detection datasets: MVTec AD, InsPLAD-fault, and Semiconductor Wafer. In comparison to the current state of the art, it is shown that AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quantitative as well as qualitative evaluation, indicated by a general improvement in AUC of 94.34 vs. 92.46, 96.67 vs. 94.69, and 90.20 vs. 88.74%. As our variants to AttentDifferNet show great prospects in the context of currently investigated approaches, a baseline is formulated, emphasizing the importance of attention for anomaly detection.
Contrastive Learning with Negative Sampling Correction
Venue: arXiv cs.LG 2024
Year: 2024

As one of the most effective self-supervised representation learning methods, contrastive learning (CL) relies on multiple negative pairs to contrast against each positive pair. In the standard practice of contrastive learning, data augmentation methods are utilized to generate both positive and negative pairs. While existing works have been focusing on improving the positive sampling, the negative sampling process is often overlooked. In fact, the generated negative samples are often polluted by positive samples, which leads to a biased loss and performance degradation. To correct the negative sampling bias, we propose a novel contrastive learning method named Positive-Unlabeled Contrastive Learning (PUCL). PUCL treats the generated negative samples as unlabeled samples and uses information from positive samples to correct bias in contrastive loss. We prove that the corrected loss used in PUCL only incurs a negligible bias compared to the unbiased contrastive loss. PUCL can be applied to general contrastive learning problems and outperforms state-of-the-art methods on various image and graph classification tasks. The code of PUCL is in the supplementary file.
Assistant, Parrot, or Colonizing Loudspeaker? ChatGPT Metaphors for Developing Critical AI Literacies
Venue: arXiv cs.HC 2024
Year: 2024

This study explores how discussing metaphors for AI can help build awareness of the frames that shape our understanding of AI systems, particularly large language models (LLMs) like ChatGPT. Given the pressing need to teach "critical AI literacy", discussion of metaphor provides an opportunity for inquiry and dialogue with space for nuance, playfulness, and critique. Using a collaborative autoethnographic methodology, we analyzed metaphors from a range of sources, and reflected on them individually according to seven questions, then met and discussed our interpretations. We then analyzed how our reflections contributed to the three kinds of literacies delineated in Selber's multiliteracies framework: functional, critical, and rhetorical. These allowed us to analyze questions of ethics, equity, and accessibility in relation to AI. We explored each metaphor along the dimension of whether or not it was promoting anthropomorphizing, and to what extent such metaphors imply that AI is sentient. Our findings highlight the role of metaphor reflection in fostering a nuanced understanding of AI, suggesting that our collaborative autoethnographic approach as well as the heuristic model of plotting AI metaphors on dimensions of anthropomorphism and multiliteracies, might be useful for educators and researchers in the pursuit of advancing critical AI literacy.
The Impact of Differential Feature Under-reporting on Algorithmic Fairness
Venue: arXiv cs.LG 2024
Year: 2024

Predictive risk models in the public sector are commonly developed using administrative data that is more complete for subpopulations that more greatly rely on public services. In the United States, for instance, information on health care utilization is routinely available to government agencies for individuals supported by Medicaid and Medicare, but not for the privately insured. Critiques of public sector algorithms have identified such differential feature under-reporting as a driver of disparities in algorithmic decision-making. Yet this form of data bias remains understudied from a technical viewpoint. While prior work has examined the fairness impacts of additive feature noise and features that are clearly marked as missing, the setting of data missingness absent indicators (i.e. differential feature under-reporting) has been lacking in research attention. In this work, we present an analytically tractable model of differential feature under-reporting which we then use to characterize the impact of this kind of data bias on algorithmic fairness. We demonstrate how standard missing data methods typically fail to mitigate bias in this setting, and propose a new set of methods specifically tailored to differential feature under-reporting. Our results show that, in real world data settings, under-reporting typically leads to increasing disparities. The proposed solution methods show success in mitigating increases in unfairness.
Energy-adaptive Buffering for Efficient, Responsive, and Persistent Batteryless Systems
Venue: arXiv cs.AR 2024
Year: 2024

Batteryless energy harvesting systems enable a wide array of new sensing, computation, and communication platforms untethered by power delivery or battery maintenance demands. Energy harvesters charge a buffer capacitor from an unreliable environmental source until enough energy is stored to guarantee a burst of operation despite changes in power input. Current platforms use a fixed-size buffer chosen at design time to meet constraints on charge time or application longevity, but static energy buffers are a poor fit for the highly volatile power sources found in real-world deployments: fixed buffers waste energy both as heat when they reach capacity during a power surplus and as leakage when they fail to charge the system during a power deficit. To maximize batteryless system performance in the face of highly dynamic input power, we propose REACT: a responsive buffering circuit which varies total capacitance according to net input power. REACT uses a variable capacitor bank to expand capacitance to capture incoming energy during a power surplus and reconfigures internal capacitors to reclaim additional energy from each capacitor as power input falls. Compared to fixed-capacity systems, REACT captures more energy, maximizes usable energy, and efficiently decouples system voltage from stored charge -- enabling low-power and high-performance designs previously limited by ambient power. Our evaluation on real-world platforms shows that REACT eliminates the tradeoff between responsiveness, efficiency, and longevity, increasing the energy available for useful work by an average 25.6% over static buffers optimized for reactivity and capacity, improving event responsiveness by an average 7.7x without sacrificing capacity, and enabling programmer directed longevity guarantees.
MambaTab: A Plug-and-Play Model for Learning Tabular Data
Venue: arXiv cs.LG 2024
Year: 2024

Despite the prevalence of images and texts in machine learning, tabular data remains widely used across various domains. Existing deep learning models, such as convolutional neural networks and transformers, perform well however demand extensive preprocessing and tuning limiting accessibility and scalability. This work introduces an innovative approach based on a structured state-space model (SSM), MambaTab, for tabular data. SSMs have strong capabilities for efficiently extracting effective representations from data with long-range dependencies. MambaTab leverages Mamba, an emerging SSM variant, for end-to-end supervised learning on tables. Compared to state-of-the-art baselines, MambaTab delivers superior performance while requiring significantly fewer parameters, as empirically validated on diverse benchmark datasets. MambaTab's efficiency, scalability, generalizability, and predictive gains signify it as a lightweight, "plug-and-play" solution for diverse tabular data with promise for enabling wider practical applications.
DCRMTA: Unbiased Causal Representation for Multi-touch Attribution
Venue: arXiv cs.LG 2024
Year: 2024

Multi-touch attribution (MTA) currently plays a pivotal role in achieving a fair estimation of the contributions of each advertising touchpoint to-wards conversion behavior, deeply influencing budget allocation and advertising recommenda-tion. Previous works attempted to eliminate the bias caused by user preferences to achieve the unbiased assumption of the conversion model. The multi-model collaboration method is not ef-ficient, and the complete elimination of user in-fluence also eliminates the causal effect of user features on conversion, resulting in limited per-formance of the conversion model. This paper re-defines the causal effect of user features on con-versions and proposes a novel end-to-end ap-proach, Deep Causal Representation for MTA (DCRMTA). Our model focuses on extracting causa features between conversions and users while eliminating confounding variables. Fur-thermore, extensive experiments demonstrate DCRMTA's superior performance in converting prediction across varying data distributions, while also effectively attributing value across dif-ferent advertising channels.
cedar: Optimized and Unified Machine Learning Input Data Pipelines
Venue: arXiv cs.LG 2024
Year: 2024

The input data pipeline is an essential component of each machine learning (ML) training job. It is responsible for reading massive amounts of training data, processing batches of samples using complex transformations, and loading them onto training nodes at low latency and high throughput. Performant input data systems are becoming increasingly critical, driven by skyrocketing data volumes and training throughput demands. Unfortunately, current input data systems cannot fully leverage key performance optimizations, resulting in hugely inefficient infrastructures that require significant resources - or worse - underutilize expensive accelerators. To address these demands, we present cedar, an optimized and unified programming framework for ML input data pipelines. cedar allows users to define input data pipelines using composable operators that support arbitrary ML frameworks and libraries. cedar introduces an extensible optimizer that systematically applies a complex combination of optimizations (e.g., offloading, caching, prefetching, fusion, and reordering). It orchestrates processing across a customizable set of local and distributed compute resources in order to improve processing performance and efficiency, all without user input. Across eight pipelines, cedar improves performance by up to 1.87x to 10.65x compared to state-of-the-art input data systems.