OpenRaiser/Intern-Atlas huggingface dataset:
ReCon1M:A Large-scale Benchmark Dataset for Relation Comprehension in Remote Sensing Imagery
Venue: arXiv cs.CV 2024
Year: 2024

Scene Graph Generation (SGG) is a high-level visual understanding and reasoning task aimed at extracting entities (such as objects) and their interrelationships from images. Significant progress has been made in the study of SGG in natural images in recent years, but its exploration in the domain of remote sensing images remains very limited. The complex characteristics of remote sensing images necessitate higher time and manual interpretation costs for annotation compared to natural images. The lack of a large-scale public SGG benchmark is a major impediment to the advancement of SGG-related research in aerial imagery. In this paper, we introduce the first publicly available large-scale, million-level relation dataset in the field of remote sensing images which is named as ReCon1M. Specifically, our dataset is built upon Fair1M and comprises 21,392 images. It includes annotations for 859,751 object bounding boxes across 60 different categories, and 1,149,342 relation triplets across 64 categories based on these bounding boxes. We provide a detailed description of the dataset's characteristics and statistical information. We conducted two object detection tasks and three sub-tasks within SGG on this dataset, assessing the performance of mainstream methods on these tasks.
Greedy SLIM: A SLIM-Based Approach For Preference Elicitation
Venue: arXiv cs.IR 2024
Year: 2024

Preference elicitation is an active learning approach to tackle the cold-start problem of recommender systems. Roughly speaking, new users are asked to rate some carefully selected items in order to compute appropriate recommendations for them. To the best of our knowledge, we are the first to propose a method for preference elicitation that is based on SLIM , a state-of-the-art technique for top-N recommendation. Our approach mainly consists of a new training technique for SLIM, which we call Greedy SLIM. This technique iteratively selects items for the training in order to minimize the SLIM loss greedily. We conduct offline experiments as well as a user study to assess the performance of this new method. The results are remarkable, especially with respect to the user study. We conclude that Greedy SLIM seems to be more suitable for preference elicitation than widely used methods based on latent factor models.
ProcessPainter: Learn Painting Process from Sequence Data
Venue: arXiv cs.CV 2024
Year: 2024

The painting process of artists is inherently stepwise and varies significantly among different painters and styles. Generating detailed, step-by-step painting processes is essential for art education and research, yet remains largely underexplored. Traditional stroke-based rendering methods break down images into sequences of brushstrokes, yet they fall short of replicating the authentic processes of artists, with limitations confined to basic brushstroke modifications. Text-to-image models utilizing diffusion processes generate images through iterative denoising, also diverge substantially from artists' painting process. To address these challenges, we introduce ProcessPainter, a text-to-video model that is initially pre-trained on synthetic data and subsequently fine-tuned with a select set of artists' painting sequences using the LoRA model. This approach successfully generates painting processes from text prompts for the first time. Furthermore, we introduce an Artwork Replication Network capable of accepting arbitrary-frame input, which facilitates the controlled generation of painting processes, decomposing images into painting sequences, and completing semi-finished artworks. This paper offers new perspectives and tools for advancing art education and image generation technology.
Text Analysis of ETDs in ProQuest Dissertations and Theses (PQDT) Global (2016-2018)
Venue: arXiv cs.DL 2024
Year: 2024

The information explosion in the form of ETDs poses the challenge of management and extraction of appropriate knowledge for decision-making. Thus, the present study forwards a solution to the above problem by applying topic mining and prediction modeling tools to 263 ETDs submitted to the PQDT Global database during 2016-18 in the field of library science. This study was divided into two phases. The first phase determined the core topics from the ETDs using Topic-Modeling-Tool (TMT), which was based on latent dirichlet allocation (LDA), whereas the second phase employed prediction analysis using RapidMinerplatform to annotate the future research articles on the basis of the modeled topics. The core topics (tags) for the studied period were found to be book history, school librarian, public library, communicative ecology, and informatics followed by text network and trend analysis on the high probability cooccurred words. Lastly, a prediction model using Support Vector Machine (SVM) classifier was created in order to accurately predict the placement of future ETDs going to be submitted to PQDT Global under the five modeled topics (a to e). The tested dataset against the trained data set for the predictive performed perfectly.
Recurrent Context Compression: Efficiently Expanding the Context Window of LLM
Venue: arXiv cs.CL 2024
Year: 2024

To extend the context length of Transformer-based large language models (LLMs) and improve comprehension capabilities, we often face limitations due to computational resources and bounded memory storage capacity. This work introduces a method called Recurrent Context Compression (RCC), designed to efficiently expand the context window length of LLMs within constrained storage space. We also investigate the issue of poor model responses when both instructions and context are compressed in downstream tasks, and propose an instruction reconstruction method to mitigate this problem. We validated the effectiveness of our approach on multiple tasks, achieving a compression rate of up to 32x on text reconstruction tasks with a BLEU4 score close to 0.95, and nearly 100\% accuracy on a passkey retrieval task with a sequence length of 1M. Finally, our method demonstrated competitive performance in long-text question-answering tasks compared to non-compressed methods, while significantly saving storage resources in long-text inference tasks. Our code, models, and demo are available at https://github.com/WUHU-G/RCC_Transformer
W-Net: One-Shot Arbitrary-Style Chinese Character Generation with Deep Neural Networks
Venue: arXiv cs.CV 2024
Year: 2024

Due to the huge category number, the sophisticated combinations of various strokes and radicals, and the free writing or printing styles, generating Chinese characters with diverse styles is always considered as a difficult task. In this paper, an efficient and generalized deep framework, namely, the W-Net, is introduced for the one-shot arbitrary-style Chinese character generation task. Specifically, given a single character (one-shot) with a specific style (e.g., a printed font or hand-writing style), the proposed W-Net model is capable of learning and generating any arbitrary characters sharing the style similar to the given single character. Such appealing property was rarely seen in the literature. We have compared the proposed W-Net framework to many other competitive methods. Experimental results showed the proposed method is significantly superior in the one-shot setting.
DiffInject: Revisiting Debias via Synthetic Data Generation using Diffusion-based Style Injection
Venue: arXiv cs.CV 2024
Year: 2024

Dataset bias is a significant challenge in machine learning, where specific attributes, such as texture or color of the images are unintentionally learned resulting in detrimental performance. To address this, previous efforts have focused on debiasing models either by developing novel debiasing algorithms or by generating synthetic data to mitigate the prevalent dataset biases. However, generative approaches to date have largely relied on using bias-specific samples from the dataset, which are typically too scarce. In this work, we propose, DiffInject, a straightforward yet powerful method to augment synthetic bias-conflict samples using a pretrained diffusion model. This approach significantly advances the use of diffusion models for debiasing purposes by manipulating the latent space. Our framework does not require any explicit knowledge of the bias types or labelling, making it a fully unsupervised setting for debiasing. Our methodology demonstrates substantial result in effectively reducing dataset bias.
Attributed Tree Transducers for Partial Functions
Venue: arXiv cs.FL 2024
Year: 2024

Attributed tree transducers (atts) have been equipped with regular look-around (i.e., a preprocessing via an attributed relabeling) in order to obtain a more robust class of translations. Here we give further evidence of this robustness: we show that if the class of translations realized by nondeterministic atts with regular look-around is restricted to partial functions, then we obtain exactly the class of translations realized by deterministic atts with regular look-around.
Gameful Introduction to Cryptography for Dyslexic Students
Venue: arXiv cs.SE 2024
Year: 2024

Cryptography has a pivotal role in securing our digital world. Nonetheless, it is a challenging topic to learn. In this paper, we show that despite its complex nature, dyslexia$-$a learning disorder that influences reading and writing skills$-$does not hinder one's ability to comprehend cryptography. In particular, we conducted a gameful workshop with 14 high-school dyslexic students and taught them fundamental encryption methods. The students engaged well, learned the techniques, and enjoyed the training. We conclude that with a proper approach, dyslexia cannot hinder learning a complex subject such as cryptography.
Towards a real-time distributed feedback system for the transportation assistance of PwD
Venue: arXiv cs.CY 2024
Year: 2024

In this work we propose the design principles of an integrated distributed system for the augment of the transportation for people with disabilities inside the road network of a city area utilizing the IT technologies. We propose the basis of our system upon the utilization of a distributed sensor network that will be incorporated by a real-time integrated feedback system. The main components of the proposed architecture include the Inaccessible City Point System, the Live Data Analysis and Response System, and the Obstruction Detection and Prevention System. The incorporation of these subsystems will provide real-time feedback assisting the transportation of individuals with mobility problems informing them on real-time about blocked ramps across the path defined to their destination, being also responsible for the information of the authorities about incidents regarding the collision of accessibility in place where the sensors detect an inaccessible point. The proposed design allows the addition of further extensions regarding the assistance of individuals with mobility problems providing a basis for its further implementation and improvement. In this work we provide the fundamental parts regarding the interconnection of the proposed architecture's components as also its potential deployment regarding the proposed architecture and its application in the area of a city.
Stronger, Cheaper and Demonstration-Free Log Parsing with LLMs
Venue: arXiv cs.SE 2024
Year: 2024

Log parsing, the process of converting raw log messages into structured formats, is an important initial step for automated analysis of logs of large-scale software systems. Traditional log parsers often rely on heuristics or handcrafted features, which may not generalize well across diverse log sources or require extensive model tuning. Recently, some log parsers have utilized powerful generative capabilities of large language models (LLMs). However, they heavily rely on demonstration examples, resulting in substantial overhead in LLM invocations. To address these issues, we propose LogBatcher, a cost-effective LLM-based log parser that requires no training process or labeled data. To leverage latent characteristics of log data and reduce the overhead, we divide logs into several partitions through clustering. Then we perform a cache matching process to match logs with previously parsed log templates. Finally, we provide LLMs with better prompt context specialized for log parsing by batching a group of logs from each partition. We have conducted experiments on 16 public log datasets and the results show that LogBatcher is effective and efficient for log parsing.
Time to Separate from StackOverflow and Match with ChatGPT for Encryption
Venue: arXiv cs.CR 2024
Year: 2024

Cryptography is known as a challenging topic for developers. We studied StackOverflow posts to identify the problems that developers encounter when using Java Cryptography Architecture (JCA) for symmetric encryption. We investigated security risks that are disseminated in these posts, and we examined whether ChatGPT helps avoid cryptography issues. We found that developers frequently struggle with key and IV generations, as well as padding. Security is a top concern among developers, but security issues are pervasive in code snippets. ChatGPT can effectively aid developers when they engage with it properly. Nevertheless, it does not substitute human expertise, and developers should remain alert.
Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios
Venue: arXiv cs.CV 2024
Year: 2024

Rate-distortion optimization through neural networks has accomplished competitive results in compression efficiency and image quality. This learning-based approach seeks to minimize the compromise between compression rate and reconstructed image quality by automatically extracting and retaining crucial information, while discarding less critical details. A successful technique consists in introducing a deep hyperprior that operates within a 2-level nested latent variable model, enhancing compression by capturing complex data dependencies. This paper extends this concept by designing a generalized L-level nested generative model with a Markov chain structure. We demonstrate as L increases that a trainable prior is detrimental and explore a common dimensionality along the distinct latent variables to boost compression performance. As this structured framework can represent autoregressive coders, we outperform the hyperprior model and achieve state-of-the-art performance while reducing substantially the computational cost. Our experimental evaluation is performed on wind turbine scenarios to study its application on visual inspections
AI Cat Narrator: Designing an AI Tool for Exploring the Shared World and Social Connection with a Cat
Venue: arXiv cs.HC 2024
Year: 2024

As technology continues to advance, the interaction between humans and cats is becoming more diverse. Our research introduces a new tool called the AI Cat Narrator, which offers a unique perspective on the shared lives of humans and cats. We combined the method of ethnography with fictional storytelling, using a defamiliarization strategy to merge real-world data seen through the eyes of cats with excerpts from cat literature. This combination serves as the foundation for a database to instruct the AI Cat Narrator in crafting alternative narrative. Our findings indicate that using defamiliarized data for training purposes significantly contributes to the development of characters that are both more empathetic and individualized. The contributions of our study are twofold: 1) proposing an innovative approach to prompting a reevaluation of living alongside cats; 2) establishing a collaborative, exploratory tool developed by humans, cats, and AI together.
Implications for Governance in Public Perceptions of Societal-scale AI Risks
Venue: arXiv cs.CY 2024
Year: 2024

Amid growing concerns over AI's societal risks--ranging from civilizational collapse to misinformation and systemic bias--this study explores the perceptions of AI experts and the general US registered voters on the likelihood and impact of 18 specific AI risks, alongside their policy preferences for managing these risks. While both groups favor international oversight over national or corporate governance, our survey reveals a discrepancy: voters perceive AI risks as both more likely and more impactful than experts, and also advocate for slower AI development. Specifically, our findings indicate that policy interventions may best assuage collective concerns if they attempt to more carefully balance mitigation efforts across all classes of societal-scale risks, effectively nullifying the near-vs-long-term debate over AI risks. More broadly, our results will serve not only to enable more substantive policy discussions for preventing and mitigating AI risks, but also to underscore the challenge of consensus building for effective policy implementation.
I-MPN: Inductive Message Passing Network for Efficient Human-in-the-Loop Annotation of Mobile Eye Tracking Data
Venue: arXiv cs.CV 2024
Year: 2024

Comprehending how humans process visual information in dynamic settings is crucial for psychology and designing user-centered interactions. While mobile eye-tracking systems combining egocentric video and gaze signals can offer valuable insights, manual analysis of these recordings is time-intensive. In this work, we present a novel human-centered learning algorithm designed for automated object recognition within mobile eye-tracking settings. Our approach seamlessly integrates an object detector with a spatial relation-aware inductive message-passing network (I-MPN), harnessing node profile information and capturing object correlations. Such mechanisms enable us to learn embedding functions capable of generalizing to new object angle views, facilitating rapid adaptation and efficient reasoning in dynamic contexts as users navigate their environment. Through experiments conducted on three distinct video sequences, our interactive-based method showcases significant performance improvements over fixed training/testing algorithms, even when trained on considerably smaller annotated samples collected through user feedback. Furthermore, we demonstrate exceptional efficiency in data annotation processes and surpass prior interactive methods that use complete object detectors, combine detectors with convolutional networks, or employ interactive video segmentation.
Understanding Students' Acceptance of ChatGPT as a Translation Tool: A UTAUT Model Analysis
Venue: arXiv cs.HC 2024
Year: 2024

The potential of ChatGPT to transform the education landscape is drawing increasing attention. With its translation-related capabilities being tested and examined, ChatGPT presents both opportunities and challenges for translation training. The effective integration of ChatGPT into translation training necessitates an understanding of students' reactions to and acceptance of ChatGPT-assisted translation. Against this backdrop, this study draws on the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the potential determinants of students' adoption of ChatGPT for translation and investigates the moderating effects of use experience and translation training on those relationships. An online survey targeting university students in Hong Kong collected 308 valid responses, including 148 from translation students and 160 from non-translation students. Respondents were divided into two groups based on their ChatGPT use experience. Data were analyzed using structural equation modeling. A multigroup analysis revealed different structural relationships between the influencing factors of students' intention to use ChatGPT across groups. Notably, less-experienced users' behavioral intention to use ChatGPT for translation was more strongly correlated with social influence compared with experienced users. Non-translation students' use intention was more strongly driven by facilitating conditions compared to translation majors. These results are discussed with the different primary purposes of translation and non-translation students' translation practices. The findings of this study contribute to the growing body of research on AI-powered translation training and provide insights for the ongoing adaptation of translation training programs.
Zero-Shot Audio Captioning Using Soft and Hard Prompts
Venue: arXiv cs.SD 2024
Year: 2024

In traditional audio captioning methods, a model is usually trained in a fully supervised manner using a human-annotated dataset containing audio-text pairs and then evaluated on the test sets from the same dataset. Such methods have two limitations. First, these methods are often data-hungry and require time-consuming and expensive human annotations to obtain audio-text pairs. Second, these models often suffer from performance degradation in cross-domain scenarios, i.e., when the input audio comes from a different domain than the training set, which, however, has received little attention. We propose an effective audio captioning method based on the contrastive language-audio pre-training (CLAP) model to address these issues. Our proposed method requires only textual data for training, enabling the model to generate text from the textual feature in the cross-modal semantic space.In the inference stage, the model generates the descriptive text for the given audio from the audio feature by leveraging the audio-text alignment from CLAP.We devise two strategies to mitigate the discrepancy between text and audio embeddings: a mixed-augmentation-based soft prompt and a retrieval-based acoustic-aware hard prompt. These approaches are designed to enhance the generalization performance of our proposed model, facilitating the model to generate captions more robustly and accurately. Extensive experiments on AudioCaps and Clotho benchmarks show the effectiveness of our proposed method, which outperforms other zero-shot audio captioning approaches for in-domain scenarios and outperforms the compared methods for cross-domain scenarios, underscoring the generalization ability of our method.
Optimal Preprocessing for Answering On-Line Product Queries
Venue: arXiv cs.DS 2024
Year: 2024

We examine the amount of preprocessing needed for answering certain on-line queries as fast as possible. We start with the following basic problem. Suppose we are given a semigroup $(S,\circ )$. Let $s_1 ,\ldots, s_n$ be elements of $S$. We want to answer on-line queries of the form, ``What is the product $s_i \circ s_{i+1} \circ \cdots \circ s_{j-1} \circ s_j$?'' for any given $1\le i\le j\le n$. We show that a preprocessing of $Θ(n λ(k,n))$ time and space is both necessary and sufficient to answer each such query in at most $k$ steps, for any fixed $k$. The function $λ(k,\cdot)$ is the inverse of a certain function at the $\lfloor {k/2}\rfloor$-th level of the primitive recursive hierarchy. In case linear preprocessing is desired, we show that one can answer each such query in $O( α(n))$ steps and that this is best possible. The function $α(n)$ is the inverse Ackermann function. We also consider the following extended problem. Let $T$ be a tree with an element of $S$ associated with each of its vertices. We want to answer on-line queries of the form, ``What is the product of the elements associated with the vertices along the path from $u$ to $v$?'' for any pair of vertices $u$ and $v$ in $T$. We derive results that are similar to the above, for the preprocessing needed for answering such queries. All our sequential preprocessing algorithms can be parallelized efficiently to give optimal parallel algorithms which run in $O(\log n)$ time on a CREW PRAM. These parallel algorithms are optimal in both running time and total number of operations. Our algorithms, especially for the semigroup of the real numbers with the minimum or maximum operations, have various applications in certain graph algorithms, in the utilization of communication networks and in Database retrieval.