OpenRaiser/Intern-Atlas huggingface dataset:
Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates
Venue: arXiv cs.LG 2024
Year: 2024

Due to the latest environmental concerns in keeping at bay contaminants emissions in urban areas, air pollution forecasting has been rising the forefront of all researchers around the world. When predicting pollutant concentrations, it is common to include the effects of environmental factors that influence these concentrations within an extended period, like traffic, meteorological conditions and geographical information. Most of the existing approaches exploit this information as past covariates, i.e., past exogenous variables that affected the pollutant but were not affected by it. In this paper, we present a novel forecasting methodology to predict NO$_2$ concentration via both past and future covariates. Future covariates are represented by weather forecasts and future calendar events, which are already known at prediction time. In particular, we deal with air quality observations in a city-wide network of ground monitoring stations, modeling the data structure and estimating the predictions with a Spatiotemporal Graph Neural Network (STGNN). We propose a conditioning block that embeds past and future covariates into the current observations. After extracting meaningful spatiotemporal representations, these are fused together and projected into the forecasting horizon to generate the final prediction. To the best of our knowledge, it is the first time that future covariates are included in time series predictions in a structured way. Remarkably, we find that conditioning on future weather information has a greater impact than considering past traffic conditions. We release our code implementation at https://github.com/polimi-ispl/MAGCRN.
The role of non-scientific factors vis-a-vis the quality of publications in determining their scholarly impact
Venue: arXiv cs.DL 2024
Year: 2024

In the evaluation of scientific publications' impact, the interplay between intrinsic quality and non-scientific factors remains a subject of debate. While peer review traditionally assesses quality, bibliometric techniques gauge scholarly impact. This study investigates the role of non-scientific attributes alongside quality scores from peer review in determining scholarly impact. Leveraging data from the first Italian Research Assessment Exercise (VTR 2001-2003) and Web of Science citations, we analyse the relationship between quality scores, non-scientific factors, and publication short- and long-term impact. Our findings shed light on the significance of non-scientific elements overlooked in peer review, offering policymakers and research management insights in choosing evaluation methodologies. Sections delve into the debate, identify non-scientific influences, detail methodologies, present results, and discuss implications.
Iterative Refinement Strategy for Automated Data Labeling: Facial Landmark Diagnosis in Medical Imaging
Venue: arXiv cs.CV 2024
Year: 2024

Automated data labeling techniques are crucial for accelerating the development of deep learning models, particularly in complex medical imaging applications. However, ensuring accuracy and efficiency remains challenging. This paper presents iterative refinement strategies for automated data labeling in facial landmark diagnosis to enhance accuracy and efficiency for deep learning models in medical applications, including dermatology, plastic surgery, and ophthalmology. Leveraging feedback mechanisms and advanced algorithms, our approach iteratively refines initial labels, reducing reliance on manual intervention while improving label quality. Through empirical evaluation and case studies, we demonstrate the effectiveness of our proposed strategies in deep learning tasks across medical imaging domains. Our results highlight the importance of iterative refinement in automated data labeling to enhance the capabilities of deep learning systems in medical imaging applications.
CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery
Venue: arXiv cs.CV 2024
Year: 2024

In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes. A salient challenge arises due to domain shifts between these datasets. To address this, we present a novel setting: Across Domain Generalized Category Discovery (AD-GCD) and bring forth CDAD-NET (Class Discoverer Across Domains) as a remedy. CDAD-NET is architected to synchronize potential known class samples across both the labeled (source) and unlabeled (target) datasets, while emphasizing the distinct categorization of the target data. To facilitate this, we propose an entropy-driven adversarial learning strategy that accounts for the distance distributions of target samples relative to source-domain class prototypes. Parallelly, the discriminative nature of the shared space is upheld through a fusion of three metric learning objectives. In the source domain, our focus is on refining the proximity between samples and their affiliated class prototypes, while in the target domain, we integrate a neighborhood-centric contrastive learning mechanism, enriched with an adept neighborsmining approach. To further accentuate the nuanced feature interrelation among semantically aligned images, we champion the concept of conditional image inpainting, underscoring the premise that semantically analogous images prove more efficacious to the task than their disjointed counterparts. Experimentally, CDAD-NET eclipses existing literature with a performance increment of 8-15% on three AD-GCD benchmarks we present.
T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in Sports Videos
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we introduce T-DEED, a Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in sports videos. T-DEED addresses multiple challenges in the task, including the need for discriminability among frame representations, high output temporal resolution to maintain prediction precision, and the necessity to capture information at different temporal scales to handle events with varying dynamics. It tackles these challenges through its specifically designed architecture, featuring an encoder-decoder for leveraging multiple temporal scales and achieving high output temporal resolution, along with temporal modules designed to increase token discriminability. Leveraging these characteristics, T-DEED achieves SOTA performance on the FigureSkating and FineDiving datasets. Code is available at https://github.com/arturxe2/T-DEED.
Two Hands Are Better Than One: Resolving Hand to Hand Intersections via Occupancy Networks
Venue: arXiv cs.CV 2024
Year: 2024

3D hand pose estimation from images has seen considerable interest from the literature, with new methods improving overall 3D accuracy. One current challenge is to address hand-to-hand interaction where self-occlusions and finger articulation pose a significant problem to estimation. Little work has applied physical constraints that minimize the hand intersections that occur as a result of noisy estimation. This work addresses the intersection of hands by exploiting an occupancy network that represents the hand's volume as a continuous manifold. This allows us to model the probability distribution of points being inside a hand. We designed an intersection loss function to minimize the likelihood of hand-to-point intersections. Moreover, we propose a new hand mesh parameterization that is superior to the commonly used MANO model in many respects including lower mesh complexity, underlying 3D skeleton extraction, watertightness, etc. On the benchmark InterHand2.6M dataset, the models trained using our intersection loss achieve better results than the state-of-the-art by significantly decreasing the number of hand intersections while lowering the mean per-joint positional error. Additionally, we demonstrate superior performance for 3D hand uplift on Re:InterHand and SMILE datasets and show reduced hand-to-hand intersections for complex domains such as sign-language pose estimation.
Requirements Elicitation in Government Projects: A Preliminary Empirical Study
Venue: arXiv cs.SE 2024
Year: 2024

Government development projects vary significantly from private sector initiatives in scope, stakeholder complexity, and regulatory requirements. There is a lack of empirical studies focusing on requirements engineering (RE) activities specifically for government projects. We addressed this gap by conducting a series of semi-structured interviews with 12 professional software practitioners working on government projects. These interviewees are employed by two types of companies, each serving different government departments. Our findings uncover differences in the requirements elicitation phase between government projects, particularly for data visualization aspects, and other software projects, such as stakeholders and policy requirements. Additionally, we explore the coverage of human and social aspects in requirements elicitation, finding that culture, team dynamics, and policy implications are critical considerations. Our findings also pinpoint the main challenges encountered during the requirements elicitation phase for government projects. Our findings highlight future research work that is important to bridge the gap in RE activities for government software projects.
Unlocking Adaptive User Experience with Generative AI
Venue: arXiv cs.HC 2024
Year: 2024

Developing user-centred applications that address diverse user needs requires rigorous user research. This is time, effort and cost-consuming. With the recent rise of generative AI techniques based on Large Language Models (LLMs), there is a possibility that these powerful tools can be used to develop adaptive interfaces. This paper presents a novel approach to develop user personas and adaptive interface candidates for a specific domain using ChatGPT. We develop user personas and adaptive interfaces using both ChatGPT and a traditional manual process and compare these outcomes. To obtain data for the personas we collected data from 37 survey participants and 4 interviews in collaboration with a not-for-profit organisation. The comparison of ChatGPT generated content and manual content indicates promising results that encourage using LLMs in the adaptive interfaces design process.
Enhancing Lip Reading with Multi-Scale Video and Multi-Encoder
Venue: arXiv cs.CV 2024
Year: 2024

Automatic lip-reading (ALR) aims to automatically transcribe spoken content from a speaker's silent lip motion captured in video. Current mainstream lip-reading approaches only use a single visual encoder to model input videos of a single scale. In this paper, we propose to enhance lip-reading by incorporating multi-scale video data and multi-encoder. Specifically, we first propose a novel multi-scale lip motion extraction algorithm based on the size of the speaker's face and an Enhanced ResNet3D visual front-end (VFE) to extract lip features at different scales. For the multi-encoder, in addition to the mainstream Transformer and Conformer, we also incorporate the recently proposed Branchformer and E-Branchformer as visual encoders. In the experiments, we explore the influence of different video data scales and encoders on ALR system performance and fuse the texts transcribed by all ALR systems using recognizer output voting error reduction (ROVER). Finally, our proposed approach placed second in the ICME 2024 ChatCLR Challenge Task 2, with a 21.52% reduction in character error rate (CER) compared to the official baseline on the evaluation set.
The steady-states of splitter networks
Venue: arXiv cs.DM 2024
Year: 2024

We introduce splitter networks, which abstract the behavior of conveyor belts found in the video game Factorio. Based on this definition, we show how to compute the steady-state of a splitter network. Then, leveraging insights from the players community, we provide multiple designs of splitter networks capable of load-balancing among several conveyor belts, and prove that any load-balancing network on $n$ belts must have $Ω(n \log n)$ nodes. Incidentally, we establish connections between splitter networks and various concepts including flow algorithms, flows with equality constraints, Markov chains and the Knuth-Yao theorem about sampling over rational distributions using a fair coin.
Session Types for the Transport Layer: Towards an Implementation of TCP
Venue: arXiv cs.PL 2024
Year: 2024

Session types are a typing discipline used to formally describe communication-driven applications with the aim of fewer errors and easier debugging later into the life cycle of the software. Protocols at the transport layer such as TCP, UDP, and QUIC underpin most of the communication on the modern Internet and affect billions of end-users. The transport layer has different requirements and constraints compared to the application layer resulting in different requirements for verification. Despite this, to our best knowledge, no work shows the application of session types at the transport layer. In this work, we discuss how multiparty session types (MPST) can be applied to implement the TCP protocol. We develop an MPST-based implementation of a subset of a TCP server in Rust and test its interoperability against the Linux TCP stack. Our results highlight the differences in assumptions between session type theory and the way transport layer protocols are usually implemented. This work is the first step towards bringing session types into the transport layer.
Behavioural Types for Heterogeneous Systems (Position Paper)
Venue: arXiv cs.PL 2024
Year: 2024

Behavioural types provide a promising way to achieve lightweight, language-integrated verification for communication-centric software. However, a large barrier to the adoption of behavioural types is that the current state of the art expects software to be written using the same tools and typing discipline throughout a system, and has little support for components over which a developer has no control. This position paper describes the outcomes of a working group discussion at Dagstuhl Seminar 24051 (Next-Generation Protocols for Heterogeneous Systems). We propose a methodology for integrating multiple behaviourally-typed components, written in different languages. Our proposed approach involves an extensible protocol description language, a session IR that can describe data transformations and boundary monitoring and which can be compiled into program-specific session proxies, and finally a session middleware to aid session establishment. We hope that this position paper will stimulate discussion on one of the most pressing challenges facing the widespread adoption of behavioural typing.
Three Subtyping Algorithms for Binary Session Types and their Complexity Analyses
Venue: arXiv cs.PL 2024
Year: 2024

Session types are a type discipline for describing and specifying communication behaviours of concurrent processes. Session subtyping, firstly introduced by Gay and Hole, is widely used for enlarging typability of session programs. This paper gives the complexity analysis of three algorithms for subtyping of synchronous binary session types. First, we analyse the complexity of the algorithm from the original paper, which is based on an inductive tree search. We then introduce its optimised version, which improves the complexity, but is still exponential against the size of the two types. Finally, we propose a new quadratic algorithm based on a graph search using the concept of XYZW-simulation, recently introduced by Silva et al.
Impact of LiDAR visualisations on semantic segmentation of archaeological objects
Venue: arXiv cs.CV 2024
Year: 2024

Deep learning methods in LiDAR-based archaeological research often leverage visualisation techniques derived from Digital Elevation Models to enhance characteristics of archaeological objects present in the images. This paper investigates the impact of visualisations on deep learning performance through a comprehensive testing framework. The study involves the use of eight semantic segmentation models to evaluate seven diverse visualisations across two study areas, encompassing five archaeological classes. Experimental results reveal that the choice of appropriate visualisations can influence performance by up to 8%. Yet, pinpointing one visualisation that outperforms the others in segmenting all archaeological classes proves challenging. The observed performance variation, reaching up to 25% across different model configurations, underscores the importance of thoughtfully selecting model configurations and LiDAR visualisations for successfully segmenting archaeological objects.
Best-of-Venom: Attacking RLHF by Injecting Poisoned Preference Data
Venue: arXiv cs.CL 2024
Year: 2024

Reinforcement Learning from Human Feedback (RLHF) is a popular method for aligning Language Models (LM) with human values and preferences. RLHF requires a large number of preference pairs as training data, which are often used in both the Supervised Fine-Tuning and Reward Model training and therefore publicly available datasets are commonly used. In this work, we study to what extent a malicious actor can manipulate the LMs generations by poisoning the preferences, i.e., injecting poisonous preference pairs into these datasets and the RLHF training process. We propose strategies to build poisonous preference pairs and test their performance by poisoning two widely used preference datasets. Our results show that preference poisoning is highly effective: injecting a small amount of poisonous data (1-5\% of the original dataset), we can effectively manipulate the LM to generate a target entity in a target sentiment (positive or negative). The findings from our experiments also shed light on strategies to defend against the preference poisoning attack.
Cell-Free Multi-User MIMO Equalization via In-Context Learning
Venue: arXiv cs.IT 2024
Year: 2024

Large pre-trained sequence models, such as transformers, excel as few-shot learners capable of in-context learning (ICL). In ICL, a model is trained to adapt its operation to a new task based on limited contextual information, typically in the form of a few training examples for the given task. Previous work has explored the use of ICL for channel equalization in single-user multi-input and multiple-output (MIMO) systems. In this work, we demonstrate that ICL can be also used to tackle the problem of multi-user equalization in cell-free MIMO systems with limited fronthaul capacity. In this scenario, a task is defined by channel statistics, signal-to-noise ratio, and modulation schemes. The context encompasses the users' pilot sequences, the corresponding quantized received signals, and the current received data signal. Different prompt design strategies are proposed and evaluated that encompass also large-scale fading and modulation information. Experiments demonstrate that ICL-based equalization provides estimates with lower mean squared error as compared to the linear minimum mean squared error equalizer, especially in the presence of limited fronthaul capacity and pilot contamination.
Responsible Visual Editing
Venue: arXiv cs.CV 2024
Year: 2024

With recent advancements in visual synthesis, there is a growing risk of encountering images with detrimental effects, such as hate, discrimination, or privacy violations. The research on transforming harmful images into responsible ones remains unexplored. In this paper, we formulate a new task, responsible visual editing, which entails modifying specific concepts within an image to render it more responsible while minimizing changes. However, the concept that needs to be edited is often abstract, making it challenging to locate what needs to be modified and plan how to modify it. To tackle these challenges, we propose a Cognitive Editor (CoEditor) that harnesses the large multimodal model through a two-stage cognitive process: (1) a perceptual cognitive process to focus on what needs to be modified and (2) a behavioral cognitive process to strategize how to modify. To mitigate the negative implications of harmful images on research, we create a transparent and public dataset, AltBear, which expresses harmful information using teddy bears instead of humans. Experiments demonstrate that CoEditor can effectively comprehend abstract concepts within complex scenes and significantly surpass the performance of baseline models for responsible visual editing. We find that the AltBear dataset corresponds well to the harmful content found in real images, offering a consistent experimental evaluation, thereby providing a safer benchmark for future research. Moreover, CoEditor also shows great results in general editing. We release our code and dataset at https://github.com/kodenii/Responsible-Visual-Editing.
Learning Prehensile Dexterity by Imitating and Emulating State-only Observations
Venue: arXiv cs.RO 2024
Year: 2024

When human acquire physical skills (e.g., tennis) from experts, we tend to first learn from merely observing the expert. But this is often insufficient. We then engage in practice, where we try to emulate the expert and ensure that our actions produce similar effects on our environment. Inspired by this observation, we introduce Combining IMitation and Emulation for Motion Refinement (CIMER) -- a two-stage framework to learn dexterous prehensile manipulation skills from state-only observations. CIMER's first stage involves imitation: simultaneously encode the complex interdependent motions of the robot hand and the object in a structured dynamical system. This results in a reactive motion generation policy that provides a reasonable motion prior, but lacks the ability to reason about contact effects due to the lack of action labels. The second stage involves emulation: learn a motion refinement policy via reinforcement that adjusts the robot hand's motion prior such that the desired object motion is reenacted. CIMER is both task-agnostic (no task-specific reward design or shaping) and intervention-free (no additional teleoperated or labeled demonstrations). Detailed experiments with prehensile dexterity reveal that i) imitation alone is insufficient, but adding emulation drastically improves performance, ii) CIMER outperforms existing methods in terms of sample efficiency and the ability to generate realistic and stable motions, iii) CIMER can either zero-shot generalize or learn to adapt to novel objects from the YCB dataset, even outperforming expert policies trained with action labels in most cases. Source code and videos are available at https://sites.google.com/view/cimer-2024/.