OpenRaiser/Intern-Atlas huggingface dataset:
Kawaii Computing: Scoping Out the Japanese Notion of Cute in User Experiences with Interactive Systems
Venue: arXiv cs.HC 2024
Year: 2024

Kawaii computing is a new term for a steadily growing body of work on the Japanese notion of "cute" in human-computer interaction (HCI) research and practice. Kawaii is distinguished from general notions of cute by its experiential and culturally-sensitive nature. While it can be designed into the appearance and behaviour of interactive agents, interfaces, and systems, kawaii also refers to certain affective and cultural dimensions experienced by culturally Japanese users, i.e., kawaii user experiences (UX) and mental models of kawaii elicited by the socio-cultural context of Japan. In this scoping review, we map out the ways in which kawaii has been explored within HCI research and related fields as a factor of design and experience. We illuminate theoretical and methodological gaps and opportunities for future work on kawaii computing.
Detecting Fallacies in Climate Misinformation: A Technocognitive Approach to Identifying Misleading Argumentation
Venue: arXiv cs.CL 2024
Year: 2024

Misinformation about climate change is a complex societal issue requiring holistic, interdisciplinary solutions at the intersection between technology and psychology. One proposed solution is a "technocognitive" approach, involving the synthesis of psychological and computer science research. Psychological research has identified that interventions in response to misinformation require both fact-based (e.g., factual explanations) and technique-based (e.g., explanations of misleading techniques) content. However, little progress has been made on documenting and detecting fallacies in climate misinformation. In this study, we apply a previously developed critical thinking methodology for deconstructing climate misinformation, in order to develop a dataset mapping different types of climate misinformation to reasoning fallacies. This dataset is used to train a model to detect fallacies in climate misinformation. Our study shows F1 scores that are 2.5 to 3.5 better than previous works. The fallacies that are easiest to detect include fake experts and anecdotal arguments, while fallacies that require background knowledge, such as oversimplification, misrepresentation, and slothful induction, are relatively more difficult to detect. This research lays the groundwork for development of solutions where automatically detected climate misinformation can be countered with generative technique-based corrections.
Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Deep learning-based medical image segmentation models often face performance degradation when deployed across various medical centers, largely due to the discrepancies in data distribution. Test Time Adaptation (TTA) methods, which adapt pre-trained models to test data, have been employed to mitigate such discrepancies. However, existing TTA methods primarily focus on manipulating Batch Normalization (BN) layers or employing prompt and adversarial learning, which may not effectively rectify the inconsistencies arising from divergent data distributions. In this paper, we propose a novel Human-in-the-loop TTA (HiTTA) framework that stands out in two significant ways. First, it capitalizes on the largely overlooked potential of clinician-corrected predictions, integrating these corrections into the TTA process to steer the model towards predictions that coincide more closely with clinical annotation preferences. Second, our framework conceives a divergence loss, designed specifically to diminish the prediction divergence instigated by domain disparities, through the careful calibration of BN parameters. Our HiTTA is distinguished by its dual-faceted capability to acclimatize to the distribution of test data whilst ensuring the model's predictions align with clinical expectations, thereby enhancing its relevance in a medical context. Extensive experiments on a public dataset underscore the superiority of our HiTTA over existing TTA methods, emphasizing the advantages of integrating human feedback and our divergence loss in enhancing the model's performance and adaptability across diverse medical centers.
VS-Assistant: Versatile Surgery Assistant on the Demand of Surgeons
Venue: arXiv cs.CV 2024
Year: 2024

The surgical intervention is crucial to patient healthcare, and many studies have developed advanced algorithms to provide understanding and decision-making assistance for surgeons. Despite great progress, these algorithms are developed for a single specific task and scenario, and in practice require the manual combination of different functions, thus limiting the applicability. Thus, an intelligent and versatile surgical assistant is expected to accurately understand the surgeon's intentions and accordingly conduct the specific tasks to support the surgical process. In this work, by leveraging advanced multimodal large language models (MLLMs), we propose a Versatile Surgery Assistant (VS-Assistant) that can accurately understand the surgeon's intention and complete a series of surgical understanding tasks, e.g., surgical scene analysis, surgical instrument detection, and segmentation on demand. Specifically, to achieve superior surgical multimodal understanding, we devise a mixture of projectors (MOP) module to align the surgical MLLM in VS-Assistant to balance the natural and surgical knowledge. Moreover, we devise a surgical Function-Calling Tuning strategy to enable the VS-Assistant to understand surgical intentions, and thus make a series of surgical function calls on demand to meet the needs of the surgeons. Extensive experiments on neurosurgery data confirm that our VS-Assistant can understand the surgeon's intention more accurately than the existing MLLM, resulting in overwhelming performance in textual analysis and visual tasks. Source code and models will be made public.
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments
Venue: arXiv cs.LG 2024
Year: 2024

This paper explores the optimization of Ground Delay Programs (GDP), a prevalent Traffic Management Initiative used in Air Traffic Management (ATM) to reconcile capacity and demand discrepancies at airports. Employing Reinforcement Learning (RL) to manage the inherent uncertainties in the national airspace system-such as weather variability, fluctuating flight demands, and airport arrival rates-we developed two RL models: Behavioral Cloning (BC) and Conservative Q-Learning (CQL). These models are designed to enhance GDP efficiency by utilizing a sophisticated reward function that integrates ground and airborne delays and terminal area congestion. We constructed a simulated single-airport environment, SAGDP_ENV, which incorporates real operational data along with predicted uncertainties to facilitate realistic decision-making scenarios. Utilizing the whole year 2019 data from Newark Liberty International Airport (EWR), our models aimed to preemptively set airport program rates. Despite thorough modeling and simulation, initial outcomes indicated that the models struggled to learn effectively, attributed potentially to oversimplified environmental assumptions. This paper discusses the challenges encountered, evaluates the models' performance against actual operational data, and outlines future directions to refine RL applications in ATM.
Are Generics and Negativity about Social Groups Common on Social Media? A Comparative Analysis of Twitter (X) Data
Venue: arXiv cs.SI 2024
Year: 2024

Generics (unquantified generalizations) are thought to be pervasive in communication and when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) techniques, we therefore developed an automatic classifier for social generics, applied it to more than a million tweets about people, and analyzed the tweets. We found that most tweets (78%) about people contained no generics. However, tweets with social generics received more 'likes' and retweets. Furthermore, while recent psychological research may lead to the prediction that tweets with generics about political groups are more common than tweets with generics about ethnic groups, we found the opposite. However, consistent with recent claims that political animosity is less constrained by social norms than animosity against gender and ethnic groups, negative tweets with generics about political groups were significantly more prevalent and retweeted than negative tweets about ethnic groups. Our study provides the first ML-based insights into the use and impact of social generics on Twitter.
Could Chemical LLMs benefit from Message Passing
Venue: arXiv cs.LG 2024
Year: 2024

Pretrained language models (LMs) showcase significant capabilities in processing molecular text, while concurrently, message passing neural networks (MPNNs) demonstrate resilience and versatility in the domain of molecular science. Despite these advancements, we find there are limited studies investigating the bidirectional interactions between molecular structures and their corresponding textual representations. Therefore, in this paper, we propose two strategies to evaluate whether an information integration can enhance the performance: contrast learning, which involves utilizing an MPNN to supervise the training of the LM, and fusion, which exploits information from both models. Our empirical analysis reveals that the integration approaches exhibit superior performance compared to baselines when applied to smaller molecular graphs, while these integration approaches do not yield performance enhancements on large scale graphs.
No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding
Venue: arXiv cs.CV 2024
Year: 2024

Current architectures for video understanding mainly build upon 3D convolutional blocks or 2D convolutions with additional operations for temporal modeling. However, these methods all regard the temporal axis as a separate dimension of the video sequence, which requires large computation and memory budgets and thus limits their usage on mobile devices. In this paper, we propose to squeeze the time axis of a video sequence into the channel dimension and present a lightweight video recognition network, term as \textit{SqueezeTime}, for mobile video understanding. To enhance the temporal modeling capability of the proposed network, we design a Channel-Time Learning (CTL) Block to capture temporal dynamics of the sequence. This module has two complementary branches, in which one branch is for temporal importance learning and another branch with temporal position restoring capability is to enhance inter-temporal object modeling ability. The proposed SqueezeTime is much lightweight and fast with high accuracies for mobile video understanding. Extensive experiments on various video recognition and action detection benchmarks, i.e., Kinetics400, Kinetics600, HMDB51, AVA2.1 and THUMOS14, demonstrate the superiority of our model. For example, our SqueezeTime achieves $+1.2\%$ accuracy and $+80\%$ GPU throughput gain on Kinetics400 than prior methods. Codes are publicly available at https://github.com/xinghaochen/SqueezeTime and https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/SqueezeTime.
ASP-Completeness of Hamiltonicity in Grid Graphs, with Applications to Loop Puzzles
Venue: arXiv cs.CC 2024
Year: 2024

We prove that Hamiltonicity in maximum-degree-3 grid graphs (directed or undirected) is ASP-complete, i.e., it has a parsimonious reduction from every NP search problem (including a polynomial-time bijection between solutions). As a consequence, given k Hamiltonian cycles, it is NP-complete to find another; and counting Hamiltonian cycles is #P-complete. If we require the grid graph's vertices to form a full $m \times n$ rectangle, then we show that Hamiltonicity remains ASP-complete if the edges are directed or if we allow removing some edges (whereas including all undirected edges is known to be easy). These results enable us to develop a stronger "T-metacell" framework for proving ASP-completeness of rectangular puzzles, which requires building just a single gadget representing a degree-3 grid-graph vertex. We apply this general theory to prove ASP-completeness of 38 pencil-and-paper puzzles where the goal is to draw a loop subject to given constraints: Slalom, Onsen-meguri, Mejilink, Detour, Tapa-Like Loop, Kouchoku, Icelom; Masyu, Yajilin, Nagareru, Castle Wall, Moon or Sun, Country Road, Geradeweg, Maxi Loop, Mid-loop, Balance Loop, Simple Loop, Haisu, Reflect Link, Linesweeper; Vertex/Touch Slitherlink, Dotchi-Loop, Ovotovata, Building Walk, Rail Pool, Disorderly Loop, Ant Mill, Koburin, Mukkonn Enn, Rassi Silai, (Crossing) Ichimaga, Tapa, Canal View, Aqre, and Paintarea. The last 14 of these puzzles were not even known to be NP-hard. Along the way, we prove ASP-completeness of some simple forms of Tree-Residue Vertex-Breaking (TRVB), including planar multigraphs with degree-6 breakable vertices, or with degree-4 breakable and degree-1 unbreakable vertices.
DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting
Venue: arXiv cs.LG 2024
Year: 2024

Multivariate time series forecasting tasks are usually conducted in a channel-dependent (CD) way since it can incorporate more variable-relevant information. However, it may also involve a lot of irrelevant variables, and this even leads to worse performance than the channel-independent (CI) strategy. This paper combines the strengths of both strategies and proposes the Deep Graph Clustering Transformer (DGCformer) for multivariate time series forecasting. Specifically, it first groups these relevant variables by a graph convolutional network integrated with an autoencoder, and a former-latter masked self-attention mechanism is then considered with the CD strategy being applied to each group of variables while the CI one for different groups. Extensive experimental results on eight datasets demonstrate the superiority of our method against state-of-the-art models, and our code will be publicly available upon acceptance.
RDPN6D: Residual-based Dense Point-wise Network for 6Dof Object Pose Estimation Based on RGB-D Images
Venue: arXiv cs.CV 2024
Year: 2024

In this work, we introduce a novel method for calculating the 6DoF pose of an object using a single RGB-D image. Unlike existing methods that either directly predict objects' poses or rely on sparse keypoints for pose recovery, our approach addresses this challenging task using dense correspondence, i.e., we regress the object coordinates for each visible pixel. Our method leverages existing object detection methods. We incorporate a re-projection mechanism to adjust the camera's intrinsic matrix to accommodate cropping in RGB-D images. Moreover, we transform the 3D object coordinates into a residual representation, which can effectively reduce the output space and yield superior performance. We conducted extensive experiments to validate the efficacy of our approach for 6D pose estimation. Our approach outperforms most previous methods, especially in occlusion scenarios, and demonstrates notable improvements over the state-of-the-art methods. Our code is available on https://github.com/AI-Application-and-Integration-Lab/RDPN6D.
Archimedes-AUEB at SemEval-2024 Task 5: LLM explains Civil Procedure
Venue: arXiv cs.CL 2024
Year: 2024

The SemEval task on Argument Reasoning in Civil Procedure is challenging in that it requires understanding legal concepts and inferring complex arguments. Currently, most Large Language Models (LLM) excelling in the legal realm are principally purposed for classification tasks, hence their reasoning rationale is subject to contention. The approach we advocate involves using a powerful teacher-LLM (ChatGPT) to extend the training dataset with explanations and generate synthetic data. The resulting data are then leveraged to fine-tune a small student-LLM. Contrary to previous work, our explanations are not directly derived from the teacher's internal knowledge. Instead they are grounded in authentic human analyses, therefore delivering a superior reasoning signal. Additionally, a new `mutation' method generates artificial data instances inspired from existing ones. We are publicly releasing the explanations as an extension to the original dataset, along with the synthetic dataset and the prompts that were used to generate both. Our system ranked 15th in the SemEval competition. It outperforms its own teacher and can produce explanations aligned with the original human analyses, as verified by legal experts.
EEG-Features for Generalized Deepfake Detection
Venue: arXiv cs.LG 2024
Year: 2024

Since the advent of Deepfakes in digital media, the development of robust and reliable detection mechanism is urgently called for. In this study, we explore a novel approach to Deepfake detection by utilizing electroencephalography (EEG) measured from the neural processing of a human participant who viewed and categorized Deepfake stimuli from the FaceForensics++ datset. These measurements serve as input features to a binary support vector classifier, trained to discriminate between real and manipulated facial images. We examine whether EEG data can inform Deepfake detection and also if it can provide a generalized representation capable of identifying Deepfakes beyond the training domain. Our preliminary results indicate that human neural processing signals can be successfully integrated into Deepfake detection frameworks and hint at the potential for a generalized neural representation of artifacts in computer generated faces. Moreover, our study provides next steps towards the understanding of how digital realism is embedded in the human cognitive system, possibly enabling the development of more realistic digital avatars in the future.
Strict Self-Assembly of Discrete Self-Similar Fractal Shapes
Venue: arXiv cs.DM 2024
Year: 2024

This paper gives a (polynomial time) algorithm to decide whether a given Discrete Self-Similar Fractal Shape can be assembled in the aTAM model.In the positive case, the construction relies on a Self-Assembling System in the aTAM which strictly assembles a particular self-similar fractal shape, namely a variant $K^\infty$ of the Sierpinski Carpet. We prove that the aTAM we propose is correct through a novel device, \emph{self-describing circuits} which are generally useful for rigorous yet readable proofs of the behaviour of aTAMs.We then discuss which self-similar fractals can or cannot be strictly self-assembled in the aTAM. It turns out that the ability of iterates of the generator to pass information is crucial: either this \emph{bandwidth} is eventually sufficient in both cardinal directions and $K^\infty$ appears within the fractal pattern after some finite number of iterations, or that bandwidth remains ever insufficient in one direction and any aTAM trying to self-assemble the shape will end up either bounded with an ultimately periodic pattern covering arbitrarily large squares. This is established thanks to a new characterization of the productions of systems whose productions have a uniformly bounded treewidth.
Can 3GPP New Radio Non-Terrestrial Networks Meet the IMT-2020 Requirements for Satellite Radio Interface Technology?
Venue: arXiv cs.NI 2024
Year: 2024

The International Telecommunication Union defined the requirements for 5G in the International Mobile Telecommunications 2020 (IMT-2020) standard in 2017. Since then, advances in technology and standardization have made the ubiquitous deployment of 5G via satellite a practical possibility, for example, in locations where terrestrial networks (TNs) are not available. However, it may be difficult for satellite networks to achieve the same performance as TNs. To address this, the IMT-2020 requirements for satellite radio interface technology have recently been established. In this paper, these requirements are evaluated through system simulations for the 3rd Generation Partnership Project New Radio non-terrestrial networks with a low Earth orbit satellite. The focus is on the throughput, area traffic capacity, and spectral efficiency requirements. It is observed that the downlink (DL) requirements can be met for user equipment with 2 receive antenna elements. The results also reveal that frequency reuse factor 1 (FRF1) may outperform FRF3 in DL with a dual-antenna setup, which is a surprising finding since FRF3 is typically considered to outperform FRF1 due to better interference reduction. For uplink (UL), 1 transmit antenna is sufficient to meet the requirements by a relatively large margin - a promising result given that UL is generally more demanding.
Towards Robust Audio Deepfake Detection: A Evolving Benchmark for Continual Learning
Venue: arXiv cs.SD 2024
Year: 2024

The rise of advanced large language models such as GPT-4, GPT-4o, and the Claude family has made fake audio detection increasingly challenging. Traditional fine-tuning methods struggle to keep pace with the evolving landscape of synthetic speech, necessitating continual learning approaches that can adapt to new audio while retaining the ability to detect older types. Continual learning, which acts as an effective tool for detecting newly emerged deepfake audio while maintaining performance on older types, lacks a well-constructed and user-friendly evaluation framework. To address this gap, we introduce EVDA, a benchmark for evaluating continual learning methods in deepfake audio detection. EVDA includes classic datasets from the Anti-Spoofing Voice series, Chinese fake audio detection series, and newly generated deepfake audio from models like GPT-4 and GPT-4o. It supports various continual learning techniques, such as Elastic Weight Consolidation (EWC), Learning without Forgetting (LwF), and recent methods like Regularized Adaptive Weight Modification (RAWM) and Radian Weight Modification (RWM). Additionally, EVDA facilitates the development of robust algorithms by providing an open interface for integrating new continual learning methods
Dynamic NeRF: A Review
Venue: arXiv cs.CV 2024
Year: 2024

Neural Radiance Field(NeRF) is an novel implicit method to achieve the 3D reconstruction and representation with a high resolution. After the first research of NeRF is proposed, NeRF has gained a robust developing power and is booming in the 3D modeling, representation and reconstruction areas. However the first and most of the followed research projects based on NeRF is static, which are weak in the practical applications. Therefore, more researcher are interested and focused on the study of dynamic NeRF that is more feasible and useful in practical applications or situations. Compared with the static NeRF, implementing the Dynamic NeRF is more difficult and complex. But Dynamic is more potential in the future even is the basic of Editable NeRF. In this review, we made a detailed and abundant statement for the development and important implementation principles of Dynamci NeRF. The analysis of main principle and development of Dynamic NeRF is from 2021 to 2023, including the most of the Dynamic NeRF projects. What is more, with colorful and novel special designed figures and table, We also made a detailed comparison and analysis of different features of various of Dynamic. Besides, we analyzed and discussed the key methods to implement a Dynamic NeRF. The volume of the reference papers is large. The statements and comparisons are multidimensional. With a reading of this review, the whole development history and most of the main design method or principles of Dynamic NeRF can be easy understood and gained.