OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Streaming Algorithms for Multi-Armed Bandits
Venue: arXiv cs.LG 2024
Year: 2024

This paper studies two variants of the best arm identification (BAI) problem under the streaming model, where we have a stream of $n$ arms with reward distributions supported on $[0,1]$ with unknown means. The arms in the stream are arriving one by one, and the algorithm cannot access an arm unless it is stored in a limited size memory. We first study the streaming \eps-$top$-$k$ arms identification problem, which asks for $k$ arms whose reward means are lower than that of the $k$-th best arm by at most $\eps$ with probability at least $1-δ$. For general $\eps \in (0,1)$, the existing solution for this problem assumes $k = 1$ and achieves the optimal sample complexity $O(\frac{n}{\eps^2} \log \frac{1}δ)$ using $O(\log^*(n))$ ($\log^*(n)$ equals the number of times that we need to apply the logarithm function on $n$ before the results is no more than 1.) memory and a single pass of the stream. We propose an algorithm that works for any $k$ and achieves the optimal sample complexity $O(\frac{n}{\eps^2} \log\frac{k}δ)$ using a single-arm memory and a single pass of the stream. Second, we study the streaming BAI problem, where the objective is to identify the arm with the maximum reward mean with at least $1-δ$ probability, using a single-arm memory and as few passes of the input stream as possible. We present a single-arm-memory algorithm that achieves a near instance-dependent optimal sample complexity within $O(\log Δ_2^{-1})$ passes, where $Δ_2$ is the gap between the mean of the best arm and that of the second best arm.
A utility-based spatial analysis of residential street-level conditions; A case study of Rotterdam
Venue: arXiv cs.CV 2024
Year: 2024

Residential location choices are traditionally modelled using factors related to accessibility and socioeconomic environments, neglecting the importance of local street-level conditions. Arguably, this neglect is due to data practices. Today, however, street-level images -- which are highly effective at encoding street-level conditions -- are widely available. Additionally, recent advances in discrete choice models incorporating computer vision capabilities offer opportunities to integrate street-level conditions into residential location choice analysis. This study leverages these developments to investigate the spatial distribution of utility derived from street-level conditions in residential location choices on a city-wide scale. In our case study of Rotterdam, the Netherlands, we find that the utility derived from street-level conditions varies significantly on a highly localised scale, with conditions rapidly changing even within neighbourhoods. Our results also reveal that the high real-estate prices in the city centre cannot be attributed to attractive street-level conditions. Furthermore, whereas the city centre is characterised by relatively unattractive residential street-level conditions, neighbourhoods in the southern part of the city -- often perceived as problematic -- exhibit surprisingly appealing street-level environments. The methodological contribution of this paper is that it advances the discrete choice models incorporating computer vision capabilities by introducing a semantic regularisation layer to the model. Thereby, it adds explainability and eliminates the need for a separate pipeline to extract information from images, streamlining the analysis. As such, this paper's findings and methodological advancements pave the way for further studies to explore integrating street-level conditions in urban planning.
ARAS: An Adaptive Low-Cost ReRAM-Based Accelerator for DNNs
Venue: arXiv cs.AR 2024
Year: 2024

Processing Using Memory (PUM) accelerators have the potential to perform Deep Neural Network (DNN) inference by using arrays of memory cells as computation engines. Among various memory technologies, ReRAM crossbars show promising performance in computing dot-product operations in the analog domain. Nevertheless, the expensive writing procedure of ReRAM cells has led researchers to design accelerators whose crossbars have enough capacity to store the full DNN. Given the tremendous and continuous increase in DNN model sizes, this approach is unfeasible for some networks, or inefficient due to the huge hardware requirements. Those accelerators lack the flexibility to adapt to any given DNN model, facing an challenge. To address this issue we introduce ARAS, a cost-effective ReRAM-based accelerator that employs a smart scheduler to adapt different DNNs to the resource-limited hardware. ARAS also overlaps the computation of a layer with the weight writing of several layers to mitigate the high writing latency of ReRAM. Furthermore, ARAS introduces three optimizations aimed at reducing the energy overheads of writing in ReRAM. Our key optimization capitalizes on the observation that DNN weights can be re-encoded to augment their similarity between layers, increasing the amount of bitwise values that are equal or similar when overwriting ReRAM cells and, hence, reducing the amount of energy required to update the cells. Overall, ARAS greatly reduces the ReRAM writing activity. We evaluate ARAS on a popular set of DNNs. ARAS provides up to 2.2x speedup and 45% energy savings over a baseline PUM accelerator without any optimization. Compared to a TPU-like accelerator, ARAS provides up to 1.5x speedup and 61% energy savings.
AI driven health recommender
Venue: arXiv cs.AI 2024
Year: 2024

As AI emerged as highest valued technology, We used that to create a web application that makes a patient work easier .It detects the disease name based on the symptoms given by the patient and recommends medication for respective disease, precautions to take, diet to follow and workouts to do, so the disease can be minimized. The web application is made with clean and Realtime data by using Machine learning as root. We used flask to create a user-friendly platform.
A Comparative Assessment of Technology Acceptance and Learning Outcomes in Computer-based versus VR-based Pedagogical Agents
Venue: arXiv cs.HC 2024
Year: 2024

As educational technology evolves, the potential of Pedagogical Agents (PAs) in supporting education is extensively explored. Typically, research on PAs has primarily focused on computer-based learning environments, but their use in VR-based environments and integration into education is still in its infancy. To address this gap, this paper presents a mixed method comparative study that has been conducted to evaluate and examine how these computer-based PAs and VR-based PAs compare, towards their learning efficacy and technology acceptance. 92 Computing and Engineering undergraduate students were recruited and participated in an educational experience focusing on computing machinery education. The findings of this study revealed that both approaches can effectively facilitate learning acquisition, and both technologies have been positively perceived by participants toward acceptance, without any significant differences. The findings of this study shed light on the potential of utilizing intelligent PAs to support education, contributing towards the advancement of our understanding of how to integrate such technologies to develop learning interventions, and establishing the foundation for future investigations that aim to successfully integrate and use PAs in education.
Real time anomalies detection on video
Venue: arXiv cs.CV 2024
Year: 2024

Nowadays, many places use security cameras. Unfortunately, when an incident occurs, these technologies are used to show past events. So it can be considered as a deterrence tool than a detection tool. In this article, we will propose a deep learning approach trying to solve this problematic. This approach uses convolutional models (CNN) to extract relevant characteristics linked to the video images, theses characteristics will form times series to be analyzed by LSTM / GRU models.
FreeVS: Generative View Synthesis on Free Driving Trajectory
Venue: arXiv cs.CV 2024
Year: 2024

Existing reconstruction-based novel view synthesis methods for driving scenes focus on synthesizing camera views along the recorded trajectory of the ego vehicle. Their image rendering performance will severely degrade on viewpoints falling out of the recorded trajectory, where camera rays are untrained. We propose FreeVS, a novel fully generative approach that can synthesize camera views on free new trajectories in real driving scenes. To control the generation results to be 3D consistent with the real scenes and accurate in viewpoint pose, we propose the pseudo-image representation of view priors to control the generation process. Viewpoint transformation simulation is applied on pseudo-images to simulate camera movement in each direction. Once trained, FreeVS can be applied to any validation sequences without reconstruction process and synthesis views on novel trajectories. Moreover, we propose two new challenging benchmarks tailored to driving scenes, which are novel camera synthesis and novel trajectory synthesis, emphasizing the freedom of viewpoints. Given that no ground truth images are available on novel trajectories, we also propose to evaluate the consistency of images synthesized on novel trajectories with 3D perception models. Experiments on the Waymo Open Dataset show that FreeVS has a strong image synthesis performance on both the recorded trajectories and novel trajectories. Project Page: https://freevs24.github.io/
TextureMeDefect: LLM-based Defect Texture Generation for Railway Components on Mobile Devices
Venue: arXiv cs.CV 2024
Year: 2024

Texture image generation has been studied for various applications, including gaming and entertainment. However, context-specific realistic texture generation for industrial applications, such as generating defect textures on railway components, remains unexplored. A mobile-friendly, LLM-based tool that generates fine-grained defect characteristics offers a solution to the challenge of understanding the impact of defects from actual occurrences. We introduce TextureMeDefect, an innovative tool leveraging an LLM-based AI-Inferencing engine. The tool allows users to create realistic defect textures interactively on images of railway components taken with smartphones or tablets. We conducted a multifaceted evaluation to assess the relevance of the generated texture, time, and cost in using this tool on iOS and Android platforms. We also analyzed the software usability score (SUS) across three scenarios. TextureMeDefect outperformed traditional image generation tools by generating meaningful textures faster, showcasing the potential of AI-driven mobile applications on consumer-grade devices.
Empowering Cognitive Digital Twins with Generative Foundation Models: Developing a Low-Carbon Integrated Freight Transportation System
Venue: arXiv cs.CY 2024
Year: 2024

Effective monitoring of freight transportation is essential for advancing sustainable, low-carbon economies. Traditional methods relying on single-modal data and discrete simulations fall short in optimizing intermodal systems holistically. These systems involve interconnected processes that affect shipping time, costs, emissions, and socio-economic factors. Developing digital twins for real-time awareness, predictive analytics, and urban logistics optimization requires extensive efforts in knowledge discovery, data integration, and multi-domain simulation. Recent advancements in generative AI offer new opportunities to streamline digital twin development by automating knowledge discovery and data integration, generating innovative simulation and optimization solutions. These models extend digital twins' capabilities by promoting autonomous workflows for data engineering, analytics, and software development. This paper proposes an innovative paradigm that leverages generative AI to enhance digital twins for urban research and operations. Using freight decarbonization as a case study, we propose a conceptual framework employing transformer-based language models to enhance an urban digital twin through foundation models. We share preliminary results and our vision for more intelligent, autonomous, and general-purpose digital twins for optimizing integrated freight systems from multimodal to synchromodal paradigms.
Liver Cancer Knowledge Graph Construction based on dynamic entity replacement and masking strategies RoBERTa-BiLSTM-CRF model
Venue: arXiv cs.IR 2024
Year: 2024

Background: Liver cancer ranks as the fifth most common malignant tumor and the second most fatal in our country. Early diagnosis is crucial, necessitating that physicians identify liver cancer in patients at the earliest possible stage. However, the diagnostic process is complex and demanding. Physicians must analyze a broad spectrum of patient data, encompassing physical condition, symptoms, medical history, and results from various examinations and tests, recorded in both structured and unstructured medical formats. This results in a significant workload for healthcare professionals. In response, integrating knowledge graph technology to develop a liver cancer knowledge graph-assisted diagnosis and treatment system aligns with national efforts toward smart healthcare. Such a system promises to mitigate the challenges faced by physicians in diagnosing and treating liver cancer. Methods: This paper addresses the major challenges in building a knowledge graph for hepatocellular carcinoma diagnosis, such as the discrepancy between public data sources and real electronic medical records, the effective integration of which remains a key issue. The knowledge graph construction process consists of six steps: conceptual layer design, data preprocessing, entity identification, entity normalization, knowledge fusion, and graph visualization. A novel Dynamic Entity Replacement and Masking Strategy (DERM) for named entity recognition is proposed. Results: A knowledge graph for liver cancer was established, including 7 entity types such as disease, symptom, and constitution, containing 1495 entities. The recognition accuracy of the model was 93.23%, the recall was 94.69%, and the F1 score was 93.96%.
NaVIP: An Image-Centric Indoor Navigation Solution for Visually Impaired People
Venue: arXiv cs.CV 2024
Year: 2024

Indoor navigation is challenging due to the absence of satellite positioning. This challenge is manifold greater for Visually Impaired People (VIPs) who lack the ability to get information from wayfinding signage. Other sensor signals (e.g., Bluetooth and LiDAR) can be used to create turn-by-turn navigation solutions with position updates for users. Unfortunately, these solutions require tags to be installed all around the environment or the use of fairly expensive hardware. Moreover, these solutions require a high degree of manual involvement that raises costs, thus hampering scalability. We propose an image dataset and associated image-centric solution called NaVIP towards visual intelligence that is infrastructure-free and task-scalable, and can assist VIPs in understanding their surroundings. Specifically, we start by curating large-scale phone camera data in a four-floor research building, with 300K images, to lay the foundation for creating an image-centric indoor navigation and exploration solution for inclusiveness. Every image is labelled with precise 6DoF camera poses, details of indoor PoIs, and descriptive captions to assist VIPs. We benchmark on two main aspects: 1) positioning system and 2) exploration support, prioritizing training scalability and real-time inference, to validate the prospect of image-based solution towards indoor navigation. The dataset, code, and model checkpoints are made publicly available at https://github.com/junfish/VIP_Navi.
Movement Control of Smart Mosque's Domes using CSRNet and Fuzzy Logic Techniques
Venue: arXiv cs.AI 2024
Year: 2024

Mosques are worship places of Allah and must be preserved clean, immaculate, provide all the comforts of the worshippers in them. The prophet's mosque in Medina/ Saudi Arabia is one of the most important mosques for Muslims. It occupies second place after the sacred mosque in Mecca/ Saudi Arabia, which is in constant overcrowding by all Muslims to visit the prophet Mohammad's tomb. This paper aims to propose a smart dome model to preserve the fresh air and allow the sunlight to enter the mosque using artificial intelligence techniques. The proposed model controls domes movements based on the weather conditions and the overcrowding rates in the mosque. The data have been collected from two different resources, the first one from the database of Saudi Arabia weather's history, and the other from Shanghai Technology Database. Congested Scene Recognition Network (CSRNet) and Fuzzy techniques have applied using Python programming language to control the domes to be opened and closed for a specific time to renew the air inside the mosque. Also, this model consists of several parts that are connected for controlling the mechanism of opening/closing domes according to weather data and the situation of crowding in the mosque. Finally, the main goal of this paper has been achieved, and the proposed model has worked efficiently and specifies the exact duration time to keep the domes open automatically for a few minutes for each hour head.
Cost Analysis for Import and Export Using an Abstract Machine
Venue: arXiv cs.PL 2024
Year: 2024

This paper presents the syntax and reduction rules for an abstract machine based on the JavaScript XML language. We incorporate the notion of cost into our reduction rules, and create a type system that over-approximate this cost. This over-approximation results in an equation that may contain unknowns originating from while loops. We conclude with a formal proof of soundness of the type system for our abstract machine, demonstrating that it over-approximates the cost of any terminating program. An implementation of the type system, constraint gathering, and the abstract machine is also presented
PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control
Venue: arXiv cs.MA 2024
Year: 2024

Multi-Agent Reinforcement Learning (MARL) presents a promising approach for addressing the complexity of Traffic Signal Control (TSC) in urban environments. However, existing platforms for MARL-based TSC research face challenges such as slow simulation speeds and convoluted, difficult-to-maintain codebases. To address these limitations, we introduce PyTSC, a robust and flexible simulation environment that facilitates the training and evaluation of MARL algorithms for TSC. PyTSC integrates multiple simulators, such as SUMO and CityFlow, and offers a streamlined API, empowering researchers to explore a broad spectrum of MARL approaches efficiently. PyTSC accelerates experimentation and provides new opportunities for advancing intelligent traffic management systems in real-world applications.
DMTG: A Human-Like Mouse Trajectory Generation Bot Based on Entropy-Controlled Diffusion Networks
Venue: arXiv cs.CR 2024
Year: 2024

CAPTCHAs protect against resource misuse and data theft by distinguishing human activity from automated bots. Advances in machine learning have made traditional image and text-based CAPTCHAs vulnerable to attacks, leading modern CAPTCHAs, such as GeeTest and Akamai, to incorporate behavioral analysis like mouse trajectory detection. Existing bypass techniques struggle to fully mimic human behavior, making it difficult to evaluate the effectiveness of anti-bot measures. To address this, we propose a diffusion model-based mouse trajectory generation framework (DMTG), which controls trajectory complexity and produces realistic human-like mouse movements. DMTG also provides white-box and black-box testing methods to assess its ability to bypass CAPTCHA systems. In experiments, DMTG reduces bot detection accuracy by 4.75%-9.73% compared to other models. Additionally, it mimics physical human behaviors, such as slow initiation and directional force differences, demonstrating improved performance in both simulation and real-world CAPTCHA scenarios.
Navigating the Landscape of Reproducible Research: A Predictive Modeling Approach
Venue: arXiv cs.DL 2024
Year: 2024

The reproducibility of scientific articles is central to the advancement of science. Despite this importance, evaluating reproducibility remains challenging due to the scarcity of ground truth data. Predictive models can address this limitation by streamlining the tedious evaluation process. Typically, a paper's reproducibility is inferred based on the availability of artifacts such as code, data, or supplemental information, often without extensive empirical investigation. To address these issues, we utilized artifacts of papers as fundamental units to develop a novel, dual-spectrum framework that focuses on author-centric and external-agent perspectives. We used the author-centric spectrum, followed by the external-agent spectrum, to guide a structured, model-based approach to quantify and assess reproducibility. We explored the interdependencies between different factors influencing reproducibility and found that linguistic features such as readability and lexical diversity are strongly correlated with papers achieving the highest statuses on both spectrums. Our work provides a model-driven pathway for evaluating the reproducibility of scientific research. The code, methods, and artifacts for our study are publicly available at: https://github.com/reproducibilityproject/NLRR/
KhmerST: A Low-Resource Khmer Scene Text Detection and Recognition Benchmark
Venue: arXiv cs.CV 2024
Year: 2024

Developing effective scene text detection and recognition models hinges on extensive training data, which can be both laborious and costly to obtain, especially for low-resourced languages. Conventional methods tailored for Latin characters often falter with non-Latin scripts due to challenges like character stacking, diacritics, and variable character widths without clear word boundaries. In this paper, we introduce the first Khmer scene-text dataset, featuring 1,544 expert-annotated images, including 997 indoor and 547 outdoor scenes. This diverse dataset includes flat text, raised text, poorly illuminated text, distant and partially obscured text. Annotations provide line-level text and polygonal bounding box coordinates for each scene. The benchmark includes baseline models for scene-text detection and recognition tasks, providing a robust starting point for future research endeavors. The KhmerST dataset is publicly accessible at https://gitlab.com/vannkinhnom123/khmerst.
Crux, a Precise Verifier for Rust and Other Languages
Venue: arXiv cs.PL 2024
Year: 2024

We present Crux, a cross-language verification tool for Rust and C/LLVM. Crux targets bounded, intricate pieces of code that are difficult for humans to get right: for example, cryptographic modules and serializer / deserializer pairs. Crux builds on the same framework as the mature SAW-Cryptol toolchain, but Crux provides an interface where proofs are phrased as symbolic unit tests. Crux is designed for use in production environments, and has already seen use in industry. In this paper, we focus on Crux-MIR, our verification tool for Rust. Crux-MIR provides a bit-precise model of safe and unsafe Rust which can be used to check both inline properties about Rust code, and extensional equality to executable specifications written in Cryptol or in the hacspec dialect of Rust. Notably, Crux-MIR supports compositional reasoning, which is necessary to scale to even moderately complex proofs. We demonstrate Crux-MIR by verifying the Ring library implementations of SHA1 and SHA2 against pre-existing functional specifications. Crux is available at https://crux.galois.com.