OpenRaiser/Intern-Atlas huggingface dataset:
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
Venue: arXiv cs.AI 2024
Year: 2024

The training, testing, and deployment, of autonomous vehicles requires realistic and efficient simulators. Moreover, because of the high variability between different problems presented in different autonomous systems, these simulators need to be easy to use, and easy to modify. To address these problems we introduce TorchDriveSim and its benchmark extension TorchDriveEnv. TorchDriveEnv is a lightweight reinforcement learning benchmark programmed entirely in Python, which can be modified to test a number of different factors in learned vehicle behavior, including the effect of varying kinematic models, agent types, and traffic control patterns. Most importantly unlike many replay based simulation approaches, TorchDriveEnv is fully integrated with a state of the art behavioral simulation API. This allows users to train and evaluate driving models alongside data driven Non-Playable Characters (NPC) whose initializations and driving behavior are reactive, realistic, and diverse. We illustrate the efficiency and simplicity of TorchDriveEnv by evaluating common reinforcement learning baselines in both training and validation environments. Our experiments show that TorchDriveEnv is easy to use, but difficult to solve.
Inferring Discussion Topics about Exploitation of Vulnerabilities from Underground Hacking Forums
Venue: arXiv cs.CR 2024
Year: 2024

The increasing sophistication of cyber threats necessitates proactive measures to identify vulnerabilities and potential exploits. Underground hacking forums serve as breeding grounds for the exchange of hacking techniques and discussions related to exploitation. In this research, we propose an innovative approach using topic modeling to analyze and uncover key themes in vulnerabilities discussed within these forums. The objective of our study is to develop a machine learning-based model that can automatically detect and classify vulnerability-related discussions in underground hacking forums. By monitoring and analyzing the content of these forums, we aim to identify emerging vulnerabilities, exploit techniques, and potential threat actors. To achieve this, we collect a large-scale dataset consisting of posts and threads from multiple underground forums. We preprocess and clean the data to ensure accuracy and reliability. Leveraging topic modeling techniques, specifically Latent Dirichlet Allocation (LDA), we uncover latent topics and their associated keywords within the dataset. This enables us to identify recurring themes and prevalent discussions related to vulnerabilities, exploits, and potential targets.
NACSOS-nexus: NLP Assisted Classification, Synthesis and Online Screening with New and EXtended Usage Scenarios
Venue: arXiv cs.DL 2024
Year: 2024

NACSOS is a web-based platform for curating data used in systematic maps. It contains several (experimental) features that aid the evidence synthesis process from finding and ingesting primary data (mainly scientific publications), basic search and exploration thereof, but mainly the handling of managing the manual and automated annotations. The platform supports prioritised screening algorithms and is the first to fully implement statistical stopping criteria. Annotations by multiple coders can be resolved and customisable quality metrics are computed on-the-fly. In its current state, the annotations are performed on document level. The ecosystem around NACSOS offers packages for accessing the underlying database and practical utility functions that have proven useful in a multitude of projects. Further, it provides the backbone of living maps, review ecosystems, and our public literature hub for sharing high-quality curated corpora.
Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Study
Venue: arXiv cs.HC 2024
Year: 2024

Generative AI technologies demand new practical and critical competencies, which call on design to respond to and foster these. We present an exploratory study guided by Research-through-Design, in which we partnered with a primary school to develop a constructionist curriculum centered on students interacting with a generative AI technology. We provide a detailed account of the design of and outputs from the curriculum and learning materials, finding centrally that the reflexive and prolonged `hands-on' approach led to a co-development of students' practical and critical competencies. From the study, we contribute guidance for designing constructionist approaches to generative AI technology education; further arguing to do so with `critical responsivity.' We then discuss how HCI researchers may leverage constructionist strategies in designing interactions with generative AI technologies; and suggest that Research-through-Design can play an important role as a `rapid response methodology' capable of reacting to fast-evolving, disruptive technologies such as generative AI.
Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) are becoming crucial across various fields, emphasizing the urgency for high-quality models in underrepresented languages. This study explores the unique challenges faced by low-resource languages, such as data scarcity, model selection, evaluation, and computational limitations, with a special focus on Turkish. We conduct an in-depth analysis to evaluate the impact of training strategies, model choices, and data availability on the performance of LLMs designed for underrepresented languages. Our approach includes two methodologies: (i) adapting existing LLMs originally pretrained in English to understand Turkish, and (ii) developing a model from the ground up using Turkish pretraining data, both supplemented with supervised fine-tuning on a novel Turkish instruction-tuning dataset aimed at enhancing reasoning capabilities. The relative performance of these methods is evaluated through the creation of a new leaderboard for Turkish LLMs, featuring benchmarks that assess different reasoning and knowledge skills. Furthermore, we conducted experiments on data and model scaling, both during pretraining and fine-tuning, simultaneously emphasizing the capacity for knowledge transfer across languages and addressing the challenges of catastrophic forgetting encountered during fine-tuning on a different language. Our goal is to offer a detailed guide for advancing the LLM framework in low-resource linguistic contexts, thereby making natural language processing (NLP) benefits more globally accessible.
Community detection in multi-layer bipartite networks
Venue: arXiv cs.SI 2024
Year: 2024

The problem of community detection in multi-layer undirected networks has received considerable attention in recent years. However, practical scenarios often involve multi-layer bipartite networks, where each layer consists of two distinct types of nodes. Existing community detection algorithms tailored for multi-layer undirected networks are not directly applicable to multi-layer bipartite networks. To address this challenge, this paper introduces a novel multi-layer degree-corrected stochastic co-block model specifically designed to capture the underlying community structure within multi-layer bipartite networks. Within this framework, we propose an efficient debiased spectral co-clustering algorithm for detecting nodes' communities. We establish the consistent estimation property of our proposed algorithm and demonstrate that an increased number of layers in bipartite networks improves the accuracy of community detection. Through extensive numerical experiments, we showcase the superior performance of our algorithm compared to existing methods. Additionally, we validate our algorithm by applying it to real-world multi-layer network datasets, yielding meaningful and insightful results.
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have demonstrated astonishing capabilities in natural language processing (NLP) tasks, sparking interest in their application to professional domains with higher specialized requirements. However, restricted access to closed-source LLMs via APIs and the difficulty in collecting massive high-quality datasets pose obstacles to the development of large language models in education fields of various courses. Given these challenges, we propose CourseGPT-zh, a course-oriented education LLM that supports customization and low-cost deployment. To address the comprehensiveness and diversity requirements of course-specific corpora, we design a high-quality question-answering corpus distillation framework incorporating prompt optimization, which effectively mines textbook knowledge and enhances its diversity. Moreover, considering the alignment of LLM responses with user needs, a novel method for discrete prompt optimization based on LLM-as-Judge is introduced. During optimization, this framework leverages the LLM's ability to reflect on and exploit error feedback and patterns, allowing for prompts that meet user needs and preferences while saving response length. Lastly, we obtain CourseGPT-zh based on the open-source LLM using parameter-efficient fine-tuning. Experimental results show that our discrete prompt optimization framework effectively improves the response quality of ChatGPT, and CourseGPT-zh exhibits strong professional capabilities in specialized knowledge question-answering, significantly outperforming comparable open-source models.
Blockchains for Internet of Things: Fundamentals, Applications, and Challenges
Venue: arXiv cs.CR 2024
Year: 2024

Internet of Things (IoT) services necessitate the storage, transmission, and analysis of diverse data for inference, autonomy, and control. Blockchains, with their inherent properties of decentralization and security, offer efficient database solutions for these devices through consensus-based data sharing. However, it's essential to recognize that not every blockchain system is suitable for specific IoT applications, and some might be more beneficial when excluded with privacy concerns. For example, public blockchains are not suitable for storing sensitive data. This paper presents a detailed review of three distinct blockchains tailored for enhancing IoT applications. We initially delve into the foundational aspects of three blockchain systems, highlighting their strengths, limitations, and implementation needs. Additionally, we discuss the security issues in different blockchains. Subsequently, we explore the blockchain's application in three pivotal IoT areas: edge AI, communications, and healthcare. We underscore potential challenges and the future directions for integrating different blockchains in IoT. Ultimately, this paper aims to offer a comprehensive perspective on the synergies between blockchains and the IoT ecosystem, highlighting the opportunities and complexities involved.
Transformer Architecture for NetsDB
Venue: arXiv cs.CV 2024
Year: 2024

Transformers models have become the backbone of the current state-of-the-art models in language, vision, and multimodal domains. These models, at their core, utilize multi-head self-attention to selectively aggregate context, generating dynamic contextual embeddings and modeling long-range dependencies for a clear contextual understanding. Lixi et al. \cite{zhou2022serving} proposed a method to use relational databases for deploying large-scale deep learning models and created an open-source implementation called NetsDB for the same. We build upon the previous work of these authors by creating an end-to-end implementation of the Encoder part of the transformer for model serving in NetsDB. Specifically, we construct a two-block encoder that includes Multi-Head Attention and its accompanying self-attention mechanism, Layer-Norm, Dropout, FeedForward Layers, and the necessary residual connections. We load out weights from our model for distributed processing, deployment, and efficient inferencing. To prove the efficacy of our implementation, we conduct a comprehensive performance analysis by comparing it with existing implementations in PyTorch, Tensorflow, Flax, and MxNet across key metrics such as inference time and model size.
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attribution
Venue: arXiv cs.CR 2024
Year: 2024

Ownership verification is currently the most critical and widely adopted post-hoc method to safeguard model copyright. In general, model owners exploit it to identify whether a given suspicious third-party model is stolen from them by examining whether it has particular properties `inherited' from their released models. Currently, backdoor-based model watermarks are the primary and cutting-edge methods to implant such properties in the released models. However, backdoor-based methods have two fatal drawbacks, including harmfulness and ambiguity. The former indicates that they introduce maliciously controllable misclassification behaviors ($i.e.$, backdoor) to the watermarked released models. The latter denotes that malicious users can easily pass the verification by finding other misclassified samples, leading to ownership ambiguity. In this paper, we argue that both limitations stem from the `zero-bit' nature of existing watermarking schemes, where they exploit the status ($i.e.$, misclassified) of predictions for verification. Motivated by this understanding, we design a new watermarking paradigm, $i.e.$, Explanation as a Watermark (EaaW), that implants verification behaviors into the explanation of feature attribution instead of model predictions. Specifically, EaaW embeds a `multi-bit' watermark into the feature attribution explanation of specific trigger samples without changing the original prediction. We correspondingly design the watermark embedding and extraction algorithms inspired by explainable artificial intelligence. In particular, our approach can be used for different tasks ($e.g.$, image classification and text generation). Extensive experiments verify the effectiveness and harmlessness of our EaaW and its resistance to potential attacks.
ChuXin: 1.6B Technical Report
Venue: arXiv cs.CL 2024
Year: 2024

In this report, we present ChuXin, an entirely open-source language model with a size of 1.6 billion parameters. Unlike the majority of works that only open-sourced the model weights and architecture, we have made everything needed to train a model available, including the training data, the training process, and the evaluation code. Our goal is to empower and strengthen the open research community, fostering transparency and enabling a new wave of innovation in the field of language modeling. Furthermore, we extend the context length to 1M tokens through lightweight continual pretraining and demonstrate strong needle-in-a-haystack retrieval performance. The weights for both models are available at Hugging Face to download and use.
Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages
Venue: arXiv cs.CL 2024
Year: 2024

Named Entity Recognition (NER) is a useful component in Natural Language Processing (NLP) applications. It is used in various tasks such as Machine Translation, Summarization, Information Retrieval, and Question-Answering systems. The research on NER is centered around English and some other major languages, whereas limited attention has been given to Indian languages. We analyze the challenges and propose techniques that can be tailored for Multilingual Named Entity Recognition for Indian Languages. We present a human annotated named entity corpora of 40K sentences for 4 Indian languages from two of the major Indian language families. Additionally,we present a multilingual model fine-tuned on our dataset, which achieves an F1 score of 0.80 on our dataset on average. We achieve comparable performance on completely unseen benchmark datasets for Indian languages which affirms the usability of our model.
Effective alpha theory certification using interval arithmetic: alpha theory over regions
Venue: arXiv cs.SC 2024
Year: 2024

We reexamine Smale's alpha theory as a way to certify a numerical solution to an analytic system. For a given point and a system, Smale's alpha theory determines whether Newton's method applied to this point shows the quadratic convergence to an exact solution. We introduce the alpha theory computation using interval arithmetic to avoid costly exact arithmetic. As a straightforward variation of the alpha theory, our work improves computational efficiency compared to software employing the traditional alpha theory.
Practice-informed Patterns for Organising Large Groups in Distributed Mixed Reality Collaboration
Venue: arXiv cs.HC 2024
Year: 2024

Collaborating across dissimilar, distributed spaces presents numerous challenges for computer-aided spatial communication. Mixed reality (MR) can blend selected surfaces, allowing collaborators to work in blended f-formations (facing formations), even when their workstations are physically misaligned. Since collaboration often involves more than just participant pairs, this research examines how we might scale MR experiences for large-group collaboration. To do so, this study recruited collaboration designers (CDs) to evaluate and reimagine MR for large-scale collaboration. These CDs were engaged in a four-part user study that involved a technology probe, a semi-structured interview, a speculative low-fidelity prototyping activity and a validation session. The outcomes of this paper contribute (1) a set of collaboration design principles to inspire future computer-supported collaborative work, (2) eight collaboration patterns for blended f-formations and collaboration at scale and (3) theoretical implications for f-formations and space-place relationships. As a result, this work creates a blueprint for scaling collaboration across distributed spaces.
The Need Of Trustworthy Announcements To Achieve Driving Comfort
Venue: arXiv cs.CR 2024
Year: 2024

An Intelligent Transport System (ITS) is more demanding nowadays and it can be achieved through deploying Vehicular Ad Hoc Networks (VANETs). Vehicles and Roadside Units (RSUs) exchange traffic events. Malicious drivers generate false events. Thus, they need to be identified to maintain trustworthy communication. When an authorised user acts maliciously, the security scheme typically fails. However, a trust model can isolate false messages. In this paper, the significance of trustworthy announcements for VANETs is analysed. To this end, a series of experiments is conducted in Veins to illustrate how the trustworthiness of announcements affects travel time. A traffic scenario is created where vehicles detour to an alternate route with an announcement from the leading vehicle. Both true and false announcements are considered. Results confirm that false announcements and refraining from announcements increase travel time. However, the travel time is reduced with trustworthy announcements. From this analysis, it can be concluded that trustworthy announcements facilitate driver comfort.
Fast LiDAR Upsampling using Conditional Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

The search for refining 3D LiDAR data has attracted growing interest motivated by recent techniques such as supervised learning or generative model-based methods. Existing approaches have shown the possibilities for using diffusion models to generate refined LiDAR data with high fidelity, although the performance and speed of such methods have been limited. These limitations make it difficult to execute in real-time, causing the approaches to struggle in real-world tasks such as autonomous navigation and human-robot interaction. In this work, we introduce a novel approach based on conditional diffusion models for fast and high-quality sparse-to-dense upsampling of 3D scene point clouds through an image representation. Our method employs denoising diffusion probabilistic models trained with conditional inpainting masks, which have been shown to give high performance on image completion tasks. We introduce a series of experiments, including multiple datasets, sampling steps, and conditional masks. This paper illustrates that our method outperforms the baselines in sampling speed and quality on upsampling tasks using the KITTI-360 dataset. Furthermore, we illustrate the generalization ability of our approach by simultaneously training on real-world and synthetic datasets, introducing variance in quality and environments.
Empowering Wireless Networks with Artificial Intelligence Generated Graph
Venue: arXiv cs.NI 2024
Year: 2024

In wireless communications, transforming network into graphs and processing them using deep learning models, such as Graph Neural Networks (GNNs), is one of the mainstream network optimization approaches. While effective, the generative AI (GAI) shows stronger capabilities in graph analysis, processing, and generation, than conventional methods such as GNN, offering a broader exploration space for graph-based network optimization. Therefore, this article proposes to use GAI-based graph generation to support wireless networks. Specifically, we first explore applications of graphs in wireless networks. Then, we introduce and analyze common GAI models from the perspective of graph generation. On this basis, we propose a framework that incorporates the conditional diffusion model and an evaluation network, which can be trained with reward functions and conditions customized by network designers and users. Once trained, the proposed framework can create graphs based on new conditions, helping to tackle problems specified by the user in wireless networks. Finally, using the link selection in integrated sensing and communication (ISAC) as an example, the effectiveness of the proposed framework is validated.
Unsupervised Skin Feature Tracking with Deep Neural Networks
Venue: arXiv cs.CV 2024
Year: 2024

Facial feature tracking is essential in imaging ballistocardiography for accurate heart rate estimation and enables motor degradation quantification in Parkinson's disease through skin feature tracking. While deep convolutional neural networks have shown remarkable accuracy in tracking tasks, they typically require extensive labeled data for supervised training. Our proposed pipeline employs a convolutional stacked autoencoder to match image crops with a reference crop containing the target feature, learning deep feature encodings specific to the object category in an unsupervised manner, thus reducing data requirements. To overcome edge effects making the performance dependent on crop size, we introduced a Gaussian weight on the residual errors of the pixels when calculating the loss function. Training the autoencoder on facial images and validating its performance on manually labeled face and hand videos, our Deep Feature Encodings (DFE) method demonstrated superior tracking accuracy with a mean error ranging from 0.6 to 3.3 pixels, outperforming traditional methods like SIFT, SURF, Lucas Kanade, and the latest transformers like PIPs++ and CoTracker. Overall, our unsupervised learning approach excels in tracking various skin features under significant motion conditions, providing superior feature descriptors for tracking, matching, and image registration compared to both traditional and state-of-the-art supervised learning methods.