OpenRaiser/Intern-Atlas huggingface dataset:
Regression of Dense Distortion Field from a Single Fingerprint Image
Venue: arXiv cs.CV 2024
Year: 2024

Skin distortion is a long standing challenge in fingerprint matching, which causes false non-matches. Previous studies have shown that the recognition rate can be improved by estimating the distortion field from a distorted fingerprint and then rectifying it into a normal fingerprint. However, existing rectification methods are based on principal component representation of distortion fields, which is not accurate and are very sensitive to finger pose. In this paper, we propose a rectification method where a self-reference based network is utilized to directly estimate the dense distortion field of distorted fingerprint instead of its low dimensional representation. This method can output accurate distortion fields of distorted fingerprints with various finger poses and distortion patterns. We conducted experiments on FVC2004 DB1\_A, expanded Tsinghua Distorted Fingerprint database (with additional distorted fingerprints in diverse finger poses and distortion patterns) and a latent fingerprint database. Experimental results demonstrate that our proposed method achieves the state-of-the-art rectification performance in terms of distortion field estimation and rectified fingerprint matching.
VisAnywhere: Developing Multi-platform Scientific Visualization Applications
Venue: arXiv cs.HC 2024
Year: 2024

Scientists often explore and analyze large-scale scientific simulation data by leveraging two- and three-dimensional visualizations. The data and tasks can be complex and therefore best supported using myriad display technologies, from mobile devices to large high-resolution display walls to virtual reality headsets. Using a simulation of neuron connections in the human brain, we present our work leveraging various web technologies to create a multi-platform scientific visualization application. Users can spread visualization and interaction across multiple devices to support flexible user interfaces and both co-located and remote collaboration. Drawing inspiration from responsive web design principles, this work demonstrates that a single codebase can be adapted to develop scientific visualization applications that operate everywhere.
An analysis of the suitability of OpenAlex for bibliometric analyses
Venue: arXiv cs.DL 2024
Year: 2024

Scopus and the Web of Science have been the foundation for research in the science of science even though these traditional databases systematically underrepresent certain disciplines and world regions. In response, new inclusive databases, notably OpenAlex, have emerged. While many studies have begun using OpenAlex as a data source, few critically assess its limitations. This study, conducted in collaboration with the OpenAlex team, addresses this gap by comparing OpenAlex to Scopus across a number of dimensions. The analysis concludes that OpenAlex is a superset of Scopus and can be a reliable alternative for some analyses, particularly at the country level. Despite this, issues of metadata accuracy and completeness show that additional research is needed to fully comprehend and address OpenAlex's limitations. Doing so will be necessary to confidently use OpenAlex across a wider set of analyses, including those that are not at all possible with more constrained databases.
SIM2VR: Towards Automated Biomechanical Testing in VR
Venue: arXiv cs.HC 2024
Year: 2024

Automated biomechanical testing has great potential for the development of VR applications, as initial insights into user behaviour can be gained in silico early in the design process. In particular, it allows prediction of user movements and ergonomic variables, such as fatigue, prior to conducting user studies. However, there is a fundamental disconnect between simulators hosting state-of-the-art biomechanical user models and simulators used to develop and run VR applications. Existing user simulators often struggle to capture the intricacies of real-world VR applications, reducing ecological validity of user predictions. In this paper, we introduce SIM2VR, a system that aligns user simulation with a given VR application by establishing a continuous closed loop between the two processes. This, for the first time, enables training simulated users directly in the same VR application that real users interact with. We demonstrate that SIM2VR can predict differences in user performance, ergonomics and strategies in a fast-paced, dynamic arcade game. In order to expand the scope of automated biomechanical testing beyond simple visuomotor tasks, advances in cognitive models and reward function design will be needed.
Lower Bounds for Private Estimation of Gaussian Covariance Matrices under All Reasonable Parameter Regimes
Venue: arXiv cs.DS 2024
Year: 2024

We prove lower bounds on the number of samples needed to privately estimate the covariance matrix of a Gaussian distribution. Our bounds match existing upper bounds in the widest known setting of parameters. Our analysis relies on the Stein-Haff identity, an extension of the classical Stein's identity used in previous fingerprinting lemma arguments.
Lessons from Deploying CropFollow++: Under-Canopy Agricultural Navigation with Keypoints
Venue: arXiv cs.RO 2024
Year: 2024

We present a vision-based navigation system for under-canopy agricultural robots using semantic keypoints. Autonomous under-canopy navigation is challenging due to the tight spacing between the crop rows ($\sim 0.75$ m), degradation in RTK-GPS accuracy due to multipath error, and noise in LiDAR measurements from the excessive clutter. Our system, CropFollow++, introduces modular and interpretable perception architecture with a learned semantic keypoint representation. We deployed CropFollow++ in multiple under-canopy cover crop planting robots on a large scale (25 km in total) in various field conditions and we discuss the key lessons learned from this.
An RFP dataset for Real, Fake, and Partially fake audio detection
Venue: arXiv cs.SD 2024
Year: 2024

Recent advances in deep learning have enabled the creation of natural-sounding synthesised speech. However, attackers have also utilised these tech-nologies to conduct attacks such as phishing. Numerous public datasets have been created to facilitate the development of effective detection models. How-ever, available datasets contain only entirely fake audio; therefore, detection models may miss attacks that replace a short section of the real audio with fake audio. In recognition of this problem, the current paper presents the RFP da-taset, which comprises five distinct audio types: partial fake (PF), audio with noise, voice conversion (VC), text-to-speech (TTS), and real. The data are then used to evaluate several detection models, revealing that the available detec-tion models incur a markedly higher equal error rate (EER) when detecting PF audio instead of entirely fake audio. The lowest EER recorded was 25.42%. Therefore, we believe that creators of detection models must seriously consid-er using datasets like RFP that include PF and other types of fake audio.
Boltzmann State-Dependent Rationality
Venue: arXiv cs.AI 2024
Year: 2024

This paper expands on existing learned models of human behavior via a measured step in structured irrationality. Specifically, by replacing the suboptimality constant $β$ in a Boltzmann rationality model with a function over states $β(s)$, we gain natural expressivity in a computationally tractable manner. This paper discusses relevant mathematical theory, sets up several experimental designs, presents limited preliminary results, and proposes future investigations.
Generative Dataset Distillation: Balancing Global Structure and Local Details
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we propose a new dataset distillation method that considers balancing global structure and local details when distilling the information from a large dataset into a generative model. Dataset distillation has been proposed to reduce the size of the required dataset when training models. The conventional dataset distillation methods face the problem of long redeployment time and poor cross-architecture performance. Moreover, previous methods focused too much on the high-level semantic attributes between the synthetic dataset and the original dataset while ignoring the local features such as texture and shape. Based on the above understanding, we propose a new method for distilling the original image dataset into a generative model. Our method involves using a conditional generative adversarial network to generate the distilled dataset. Subsequently, we ensure balancing global structure and local details in the distillation process, continuously optimizing the generator for more information-dense dataset generation.
Building a Large Japanese Web Corpus for Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Open Japanese large language models (LLMs) have been trained on the Japanese portions of corpora such as CC-100, mC4, and OSCAR. However, these corpora were not created for the quality of Japanese texts. This study builds a large Japanese web corpus by extracting and refining text from the Common Crawl archive (21 snapshots of approximately 63.4 billion pages crawled between 2020 and 2023). This corpus consists of approximately 312.1 billion characters (approximately 173 million pages), which is the largest of all available training corpora for Japanese LLMs, surpassing CC-100 (approximately 25.8 billion characters), mC4 (approximately 239.7 billion characters) and OSCAR 23.10 (approximately 74 billion characters). To confirm the quality of the corpus, we performed continual pre-training on Llama 2 7B, 13B, 70B, Mistral 7B v0.1, and Mixtral 8x7B Instruct as base LLMs and gained consistent (6.6-8.1 points) improvements on Japanese benchmark datasets. We also demonstrate that the improvement on Llama 2 13B brought from the presented corpus was the largest among those from other existing corpora.
Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
Venue: arXiv cs.LG 2024
Year: 2024

Diffusion probabilistic models (DPMs) have become the state-of-the-art in high-quality image generation. However, DPMs have an arbitrary noisy latent space with no interpretable or controllable semantics. Although there has been significant research effort to improve image sample quality, there is little work on representation-controlled generation using diffusion models. Specifically, causal modeling and controllable counterfactual generation using DPMs is an underexplored area. In this work, we propose CausalDiffAE, a diffusion-based causal representation learning framework to enable counterfactual generation according to a specified causal model. Our key idea is to use an encoder to extract high-level semantically meaningful causal variables from high-dimensional data and model stochastic variation using reverse diffusion. We propose a causal encoding mechanism that maps high-dimensional data to causally related latent factors and parameterize the causal mechanisms among latent factors using neural networks. To enforce the disentanglement of causal variables, we formulate a variational objective and leverage auxiliary label information in a prior to regularize the latent space. We propose a DDIM-based counterfactual generation procedure subject to do-interventions. Finally, to address the limited label supervision scenario, we also study the application of CausalDiffAE when a part of the training data is unlabeled, which also enables granular control over the strength of interventions in generating counterfactuals during inference. We empirically show that CausalDiffAE learns a disentangled latent space and is capable of generating high-quality counterfactual images.
Un análisis bibliométrico de la producción científica acerca del agrupamiento de trayectorias GPS
Venue: arXiv cs.IR 2024
Year: 2024

Clustering algorithms or methods for GPS trajectories are in constant evolution due to the interest aroused in part of the scientific community. With the development of clustering algorithms considered traditional, improvements to these algorithms and even unique methods considered as "novelty" for science have emerged. This work aims to analyze the scientific production that exists around the topic "GPS trajectory clustering" by means of bibliometrics. Therefore, a total of 559 articles from the main collection of Scopus were analyzed, previously filtering the generated sample to discard any article that does not have a direct relationship with the topic to be analyzed. This analysis establishes an ideal environment for other disciplines and researchers, since it provides a current state of the trend of the subject of study in their field of research. -- Los algoritmos o métodos de agrupamiento para trayectorias GPS se encuentran en constante evolución debido al interés que despierta en parte de la comunidad científica. Con el desarrollo de los algoritmos de agrupamiento considerados tradicionales han surgido mejoras a estos algoritmos e incluso métodos únicos considerados como "novedad" para la ciencia. Este trabajo tiene como objetivo analizar la producción científica que existe alrededor del tema "agrupamiento de trayectorias GPS" mediante la bibliometría. Por lo tanto, fueron analizados un total de 559 artículos de la colección principal de Scopus, realizando previamente un filtrado de la muestra generada para descartar todo aquel artículo que no tenga una relación directa con el tema a analizar. Este análisis establece un ambiente ideal para otras disciplinas e investigadores, ya que entrega un estado actual de la tendencia que lleva la temática de estudio en su campo de investigación.
A Value-Oriented Investigation of Photoshop's Generative Fill
Venue: arXiv cs.HC 2024
Year: 2024

The creative industry is both concerned and enthusiastic about how generative AI will reshape creativity. How might these tools interact with the workflow values of creative artists? In this paper, we adopt a value-sensitive design framework to examine how generative AI, particularly Photoshop's Generative Fill (GF), helps or hinders creative professionals' values. We obtained 566 unique posts about GF from online forums for creative professionals who use Photoshop in their current work practices. We conducted reflexive thematic analysis focusing on usefulness, ease of use, and user values. Users found GF useful in doing touch-ups, expanding images, and generating composite images. GF helped users' values of productivity by making work efficient but created a value tension around creativity: it helped reduce barriers to creativity but hindered distinguishing 'human' from algorithmic art. Furthermore, GF hindered lived experiences shaping creativity and hindered the honed prideful skills of creative work.
KnapsackLB: Enabling Performance-Aware Layer-4 Load Balancing
Venue: arXiv cs.DC 2024
Year: 2024

Layer-4 load balancer (LB) is a key building block of online services. In this paper, we empower such LBs to adapt to different and dynamic performance of backend instances (DIPs). Our system, KNAPSACKLB, is generic (can work with variety of LBs), does not require agents on DIPs, LBs or clients, and scales to large numbers of DIPs. KNAPSACKLB uses judicious active probes to learn a mapping from LB weights to the response latency of each DIP, and then applies Integer Linear Programming (ILP) to calculate LB weights that optimize latency, using an iterative method to scale the computation to large numbers of DIPs. Using testbed experiments and simulations, we show that KNAPSACKLB load balances traffic as per the performance and cuts average latency by up to 45% compared to existing designs.
Quantum resistant multi-signature scheme with optimal communication round: A Blockchain-based approach
Venue: arXiv cs.CR 2024
Year: 2024

Blockchain is a decentralized network to increase trust, integrity, and transparency of transactions. With the exponential growth of transactions in the realm of Blockchain, especially in Bitcoin, Blockchain size increases as all transactions must be stored and verified. In Bitcoin, validating M of N transactions involves the necessity of M authentic signatures out of the total N transactions. This procedure is so time-consuming and needs a significant storage capacity. To address these issues, several multi signature schemes have been proposed, enabling users to interactively generate a common signature on a single message. Recently, some lattice based multi signature schemes have been presented to deal with the threats of quantum computers. However, none of them have met all desirable features of multi signature schemes like aggregate public key, low numbers of communication rounds, or resistant to quantum computers. Within this paper, we present a new multi signature scheme based on lattices, known as Razhims, that has aggregate public key, necessitates solely a single round of communication, and is resistant to quantum computers. In Razhims, the aggregate public key size and the final signature size are equal to the public key size and the final signature size of a standard signature respectively, and are independent of the number of signers.
Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputs
Venue: arXiv cs.AI 2024
Year: 2024

Agents based on large language models (LLMs) have demonstrated effectiveness in solving a wide range of tasks by integrating LLMs with key modules such as planning, memory, and tool usage. Increasingly, customers are adopting LLM agents across a variety of commercial applications critical to reliability, including support for mental well-being, chemical synthesis, and software development. Nevertheless, our observations and daily use of LLM agents indicate that they are prone to making erroneous plans, especially when the tasks are complex and require long-term planning. In this paper, we propose PDoctor, a novel and automated approach to testing LLM agents and understanding their erroneous planning. As the first work in this direction, we formulate the detection of erroneous planning as a constraint satisfiability problem: an LLM agent's plan is considered erroneous if its execution violates the constraints derived from the user inputs. To this end, PDoctor first defines a domain-specific language (DSL) for user queries and synthesizes varying inputs with the assistance of the Z3 constraint solver. These synthesized inputs are natural language paragraphs that specify the requirements for completing a series of tasks. Then, PDoctor derives constraints from these requirements to form a testing oracle. We evaluate PDoctor with three mainstream agent frameworks and two powerful LLMs (GPT-3.5 and GPT-4). The results show that PDoctor can effectively detect diverse errors in agent planning and provide insights and error characteristics that are valuable to both agent developers and users. We conclude by discussing potential alternative designs and directions to extend PDoctor.
Improving Smart Contract Security with Contrastive Learning-based Vulnerability Detection
Venue: arXiv cs.CR 2024
Year: 2024

Currently, smart contract vulnerabilities (SCVs) have emerged as a major factor threatening the transaction security of blockchain. Existing state-of-the-art methods rely on deep learning to mitigate this threat. They treat each input contract as an independent entity and feed it into a deep learning model to learn vulnerability patterns by fitting vulnerability labels. It is a pity that they disregard the correlation between contracts, failing to consider the commonalities between contracts of the same type and the differences among contracts of different types. As a result, the performance of these methods falls short of the desired level. To tackle this problem, we propose a novel Contrastive Learning Enhanced Automated Recognition Approach for Smart Contract Vulnerabilities, named Clear. In particular, Clear employs a contrastive learning (CL) model to capture the fine-grained correlation information among contracts and generates correlation labels based on the relationships between contracts to guide the training process of the CL model. Finally, it combines the correlation and the semantic information of the contract to detect SCVs. Through an empirical evaluation of a large-scale real-world dataset of over 40K smart contracts and compare 13 state-of-the-art baseline methods. We show that Clear achieves (1) optimal performance over all baseline methods; (2) 9.73%-39.99% higher F1-score than existing deep learning methods.
Toxicity Classification in Ukrainian
Venue: arXiv cs.CL 2024
Year: 2024

The task of toxicity detection is still a relevant task, especially in the context of safe and fair LMs development. Nevertheless, labeled binary toxicity classification corpora are not available for all languages, which is understandable given the resource-intensive nature of the annotation process. Ukrainian, in particular, is among the languages lacking such resources. To our knowledge, there has been no existing toxicity classification corpus in Ukrainian. In this study, we aim to fill this gap by investigating cross-lingual knowledge transfer techniques and creating labeled corpora by: (i)~translating from an English corpus, (ii)~filtering toxic samples using keywords, and (iii)~annotating with crowdsourcing. We compare LLMs prompting and other cross-lingual transfer approaches with and without fine-tuning offering insights into the most robust and efficient baselines.
Using LLMs in Software Requirements Specifications: An Empirical Evaluation
Venue: arXiv cs.SE 2024
Year: 2024

The creation of a Software Requirements Specification (SRS) document is important for any software development project. Given the recent prowess of Large Language Models (LLMs) in answering natural language queries and generating sophisticated textual outputs, our study explores their capability to produce accurate, coherent, and structured drafts of these documents to accelerate the software development lifecycle. We assess the performance of GPT-4 and CodeLlama in drafting an SRS for a university club management system and compare it against human benchmarks using eight distinct criteria. Our results suggest that LLMs can match the output quality of an entry-level software engineer to generate an SRS, delivering complete and consistent drafts. We also evaluate the capabilities of LLMs to identify and rectify problems in a given requirements document. Our experiments indicate that GPT-4 is capable of identifying issues and giving constructive feedback for rectifying them, while CodeLlama's results for validation were not as encouraging. We repeated the generation exercise for four distinct use cases to study the time saved by employing LLMs for SRS generation. The experiment demonstrates that LLMs may facilitate a significant reduction in development time for entry-level software engineers. Hence, we conclude that the LLMs can be gainfully used by software engineers to increase productivity by saving time and effort in generating, validating and rectifying software requirements.