OpenRaiser/Intern-Atlas huggingface dataset:
Impacts of Anthropomorphizing Large Language Models in Learning Environments
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) are increasingly being used in learning environments to support teaching-be it as learning companions or as tutors. With our contribution, we aim to discuss the implications of the anthropomorphization of LLMs in learning environments on educational theory to build a foundation for more effective learning outcomes and understand their emotional impact on learners. According to the media equation, people tend to respond to media in the same way as they would respond to another person. A study conducted by the Georgia Institute of Technology showed that chatbots can be successfully implemented in learning environments. In this study, learners in selected online courses were unable to distinguish the chatbot from a "real" teacher. As LLM-based chatbots such as OpenAI's GPT series are increasingly used in educational tools, it is important to understand how the attribution processes to LLM-based chatbots in terms of anthropomorphization affect learners' emotions.
Histopathology image embedding based on foundation models features aggregation for patient treatment response prediction
Venue: arXiv cs.CV 2024
Year: 2024

Predicting the response of a patient to a cancer treatment is of high interest. Nonetheless, this task is still challenging from a medical point of view due to the complexity of the interaction between the patient organism and the considered treatment. Recent works on foundation models pre-trained with self-supervised learning on large-scale unlabeled histopathology datasets have opened a new direction towards the development of new methods for cancer diagnosis related tasks. In this article, we propose a novel methodology for predicting Diffuse Large B-Cell Lymphoma patients treatment response from Whole Slide Images. Our method exploits several foundation models as feature extractors to obtain a local representation of the image corresponding to a small region of the tissue, then, a global representation of the image is obtained by aggregating these local representations using attention-based Multiple Instance Learning. Our experimental study conducted on a dataset of 152 patients, shows the promising results of our methodology, notably by highlighting the advantage of using foundation models compared to conventional ImageNet pre-training. Moreover, the obtained results clearly demonstrates the potential of foundation models for characterizing histopathology images and generating more suited semantic representation for this task.
Microservice Vulnerability Analysis: A Literature Review with Empirical Insights
Venue: arXiv cs.CR 2024
Year: 2024

Microservice architectures are revolutionizing both small businesses and large corporations, igniting a new era of innovation with their exceptional advantages in maintainability, reusability, and scalability. However, these benefits come with significant security challenges, as the increased complexity of service interactions, expanded attack surfaces, and intricate dependency management introduce a new array of cybersecurity vulnerabilities. While security concerns are mounting, there is a lack of comprehensive research that integrates a review of existing knowledge with empirical analysis of microservice vulnerabilities. This study aims to fill this gap by gathering, analyzing, and synthesizing existing literature on security vulnerabilities associated with microservice architectures. Through a thorough examination of 62 studies, we identify, analyze, and report 126 security vulnerabilities inherent in microservice architectures. This comprehensive analysis enables us to (i) propose a taxonomy that categorizes microservice vulnerabilities based on the distinctive features of microservice architectures; (ii) conduct an empirical analysis by performing vulnerability scans on four diverse microservice benchmark applications using three different scanning tools to validate our taxonomy; and (iii) map our taxonomy vulnerabilities with empirically identified vulnerabilities, providing an in-depth vulnerability analysis at microservice, application, and scanning tool levels. Our study offers crucial guidelines for practitioners and researchers to advance both the state-of-the-practice and the state-of-the-art in securing microservice architectures.
Large-Scale Graphs Community Detection using Spark GraphFrames
Venue: arXiv cs.SI 2024
Year: 2024

With the emergence of social networks, online platforms dedicated to different use cases, and sensor networks, the emergence of large-scale graph community detection has become a steady field of research with real-world applications. Community detection algorithms have numerous practical applications, particularly due to their scalability with data size. Nonetheless, a notable drawback of community detection algorithms is their computational intensity~\cite{Apostol2014}, resulting in decreasing performance as data size increases. For this purpose, new frameworks that employ distributed systems such as Apache Hadoop and Apache Spark which can seamlessly handle large-scale graphs must be developed. In this paper, we propose a novel framework for community detection algorithms, i.e., K-Cliques, Louvain, and Fast Greedy, developed using Apache Spark GraphFrames. We test their performance and scalability on two real-world datasets. The experimental results prove the feasibility of developing graph mining algorithms using Apache Spark GraphFrames.
Speaker Adaptation for Quantised End-to-End ASR Models
Venue: arXiv cs.SD 2024
Year: 2024

End-to-end models have shown superior performance for automatic speech recognition (ASR). However, such models are often very large in size and thus challenging to deploy on resource-constrained edge devices. While quantisation can reduce model sizes, it can lead to increased word error rates (WERs). Although improved quantisation methods were proposed to address the issue of performance degradation, the fact that quantised models deployed on edge devices often target only on a small group of users is under-explored. To this end, we propose personalisation for quantised models (P4Q), a novel strategy that uses speaker adaptation (SA) to improve quantised end-to-end ASR models by fitting them to the characteristics of the target speakers. In this paper, we study the P4Q strategy based on Whisper and Conformer attention-based encoder-decoder (AED) end-to-end ASR models, which leverages a 4-bit block-wise NormalFloat4 (NF4) approach for quantisation and the low-rank adaptation (LoRA) approach for SA. Experimental results on the LibriSpeech and the TED-LIUM 3 corpora show that, with a 7-time reduction in model size and 1% extra speaker-specific parameters, 15.1% and 23.3% relative WER reductions were achieved on quantised Whisper and Conformer AED models respectively, comparing to the full precision models.
Ownership in low-level intermediate representation
Venue: arXiv cs.PL 2024
Year: 2024

The concept of ownership in high level languages can aid both the programmer and the compiler to reason about the validity of memory operations. Previously, ownership semantics has been used successfully in high level automatic program verification to model a reference to data by a first order logic (FOL) representation of data instead of maintaining an address map. However, ownership semantics is not used in low level program verification. We have identified two challenges. First, ownership information is lost when a program is compiled to a low level intermediate representation (e.g., in LLVM IR). Second, pointers in low level programs point to bytes using an address map (e.g., in unsafe Rust) and thus the verification condition (VC) cannot always replace a pointer by its FOL abstraction. To remedy the situation, we develop ownership semantics for an LLVM like low level intermediate representation. Using these semantics, the VC can opportunistically model some memory accesses by a direct access of a pointer cache that stores byte representation of data. This scheme reduces instances where an address map must be maintained, especially for mostly safe programs that follow ownership semantics. For unsafe functionality, memory accesses are modelled by operations on an address map and we provide mechanisms to keep the address map and pointer cache in sync. We implement these semantics in SEABMC, a bit precise bounded model checker for LLVM. For evaluation, the source programs are assumed to be written in C. Since C does not have ownership built in, suitable macros are added that introduce and preserve ownership during translation to LLVM like IR for verification. This approach is evaluated on mature open source C code. For both handcrafted benchmarks and practical programs, we observe a speedup of $1.3x-5x$ during SMT solving.
Deep Generative Models for Subgraph Prediction
Venue: arXiv cs.LG 2024
Year: 2024

Graph Neural Networks (GNNs) are important across different domains, such as social network analysis and recommendation systems, due to their ability to model complex relational data. This paper introduces subgraph queries as a new task for deep graph learning. Unlike traditional graph prediction tasks that focus on individual components like link prediction or node classification, subgraph queries jointly predict the components of a target subgraph based on evidence that is represented by an observed subgraph. For instance, a subgraph query can predict a set of target links and/or node labels. To answer subgraph queries, we utilize a probabilistic deep Graph Generative Model. Specifically, we inductively train a Variational Graph Auto-Encoder (VGAE) model, augmented to represent a joint distribution over links, node features and labels. Bayesian optimization is used to tune a weighting for the relative importance of links, node features and labels in a specific domain. We describe a deterministic and a sampling-based inference method for estimating subgraph probabilities from the VGAE generative graph distribution, without retraining, in zero-shot fashion. For evaluation, we apply the inference methods on a range of subgraph queries on six benchmark datasets. We find that inference from a model achieves superior predictive performance, surpassing independent prediction baselines with improvements in AUC scores ranging from 0.06 to 0.2 points, depending on the dataset.
Toward the Automated Localization of Buggy Mobile App UIs from Bug Descriptions
Venue: arXiv cs.SE 2024
Year: 2024

Bug report management is a costly software maintenance process comprised of several challenging tasks. Given the UI-driven nature of mobile apps, bugs typically manifest through the UI, hence the identification of buggy UI screens and UI components (Buggy UI Localization) is important to localizing the buggy behavior and eventually fixing it. However, this task is challenging as developers must reason about bug descriptions (which are often low-quality), and the visual or code-based representations of UI screens. This paper is the first to investigate the feasibility of automating the task of Buggy UI Localization through a comprehensive study that evaluates the capabilities of one textual and two multi-modal deep learning (DL) techniques and one textual unsupervised technique. We evaluate such techniques at two levels of granularity, Buggy UI Screen and UI Component localization. Our results illustrate the individual strengths of models that make use of different representations, wherein models that incorporate visual information perform better for UI screen localization, and models that operate on textual screen information perform better for UI component localization -- highlighting the need for a localization approach that blends the benefits of both types of techniques. Furthermore, we study whether Buggy UI Localization can improve traditional buggy code localization, and find that incorporating localized buggy UIs leads to improvements of 9%-12% in Hits@10.
Programmable Dataflows: Abstraction and Programming Model for Data Sharing
Venue: arXiv cs.DB 2024
Year: 2024

Data sharing is central to a wide variety of applications such as fraud detection, ad matching, and research. The lack of data sharing abstractions makes the solution to each data sharing problem bespoke and cost-intensive, hampering value generation. In this paper, we first introduce a data sharing model to represent every data sharing problem with a sequence of dataflows. From the model, we distill an abstraction, the contract, which agents use to communicate the intent of a dataflow and evaluate its consequences, before the dataflow takes place. This helps agents move towards a common sharing goal without violating any regulatory and privacy constraints. Then, we design and implement the contract programming model (CPM), which allows agents to program data sharing applications catered to each problem's needs. Contracts permit data sharing, but their interactive nature may introduce inefficiencies. To mitigate those inefficiencies, we extend the CPM so that it can save intermediate outputs of dataflows, and skip computation if a dataflow tries to access data that it does not have access to. In our evaluation, we show that 1) the contract abstraction is general enough to represent a wide range of sharing problems, 2) we can write programs for complex data sharing problems and exhibit qualitative improvements over other alternate technologies, and 3) quantitatively, our optimizations make sharing programs written with the CPM efficient.
Force-Motion Control For A Six Degree-Of-Freedom Robotic Manipulator
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents a unified algorithm for motion and force control for a six degree-of-freedom spatial manipulator. The motion-force controller performs trajectory tracking, maneuvering the manipulator's end-effector through desired position, orientations and rates. When contacting an obstacle or target object, the force module of the controller restricts the manipulator movements with a novel force exertion method, which prevents damage to the manipulator, the end-effector, and the objects during the contact or collision. The core strategy presented in this paper is to design the linear acceleration for the end-effector which ensures both trajectory tracking and restriction of any contact force at the end-effector. The design of the controller is validated through numerical simulations and digital twin validation.
Decorrelating Structure via Adapters Makes Ensemble Learning Practical for Semi-supervised Learning
Venue: arXiv cs.CV 2024
Year: 2024

In computer vision, traditional ensemble learning methods exhibit either a low training efficiency or the limited performance to enhance the reliability of deep neural networks. In this paper, we propose a lightweight, loss-function-free, and architecture-agnostic ensemble learning by the Decorrelating Structure via Adapters (DSA) for various visual tasks. Concretely, the proposed DSA leverages the structure-diverse adapters to decorrelate multiple prediction heads without any tailed regularization or loss. This allows DSA to be easily extensible to architecture-agnostic networks for a range of computer vision tasks. Importantly, the theoretically analysis shows that the proposed DSA has a lower bias and variance than that of the single head based method (which is adopted by most of the state of art approaches). Consequently, the DSA makes deep networks reliable and robust for the various real-world challenges, \textit{e.g.}, data corruption, and label noises. Extensive experiments combining the proposed method with FreeMatch achieved the accuracy improvements of 5.35% on CIFAR-10 dataset with 40 labeled data and 0.71% on CIFAR-100 dataset with 400 labeled data. Besides, combining the proposed method with DualPose achieved the improvements in the Percentage of Correct Keypoints (PCK) by 2.08% on the Sniffing dataset with 100 data (30 labeled data), 5.2% on the FLIC dataset with 100 data (including 50 labeled data), and 2.35% on the LSP dataset with 200 data (100 labeled data).
Semantics or spelling? Probing contextual word embeddings with orthographic noise
Venue: arXiv cs.CL 2024
Year: 2024

Pretrained language model (PLM) hidden states are frequently employed as contextual word embeddings (CWE): high-dimensional representations that encode semantic information given linguistic context. Across many areas of computational linguistics research, similarity between CWEs is interpreted as semantic similarity. However, it remains unclear exactly what information is encoded in PLM hidden states. We investigate this practice by probing PLM representations using minimal orthographic noise. We expect that if CWEs primarily encode semantic information, a single character swap in the input word will not drastically affect the resulting representation,given sufficient linguistic context. Surprisingly, we find that CWEs generated by popular PLMs are highly sensitive to noise in input data, and that this sensitivity is related to subword tokenization: the fewer tokens used to represent a word at input, the more sensitive its corresponding CWE. This suggests that CWEs capture information unrelated to word-level meaning and can be manipulated through trivial modifications of input data. We conclude that these PLM-derived CWEs may not be reliable semantic proxies, and that caution is warranted when interpreting representational similarity
Rotation center identification based on geometric relationships for rotary motion deblurring
Venue: arXiv cs.CV 2024
Year: 2024

Non-blind rotary motion deblurring (RMD) aims to recover the latent clear image from a rotary motion blurred (RMB) image. The rotation center is a crucial input parameter in non-blind RMD methods. Existing methods directly estimate the rotation center from the RMB image. However they always suffer significant errors, and the performance of RMD is limited. For the assembled imaging systems, the position of the rotation center remains fixed. Leveraging this prior knowledge, we propose a geometric-based method for rotation center identification and analyze its error range. Furthermore, we construct a RMB imaging system. The experiment demonstrates that our method achieves less than 1-pixel error along a single axis (x-axis or y-axis). We utilize the constructed imaging system to capture real RMB images, and experimental results show that our method can help existing RMD approaches yield better RMD images.
FDI: Attack Neural Code Generation Systems through User Feedback Channel
Venue: arXiv cs.SE 2024
Year: 2024

Neural code generation systems have recently attracted increasing attention to improve developer productivity and speed up software development. Typically, these systems maintain a pre-trained neural model and make it available to general users as a service (e.g., through remote APIs) and incorporate a feedback mechanism to extensively collect and utilize the users' reaction to the generated code, i.e., user feedback. However, the security implications of such feedback have not yet been explored. With a systematic study of current feedback mechanisms, we find that feedback makes these systems vulnerable to feedback data injection (FDI) attacks. We discuss the methodology of FDI attacks and present a pre-attack profiling strategy to infer the attack constraints of a targeted system in the black-box setting. We demonstrate two proof-of-concept examples utilizing the FDI attack surface to implement prompt injection attacks and backdoor attacks on practical neural code generation systems. The attacker may stealthily manipulate a neural code generation system to generate code with vulnerabilities, attack payload, and malicious and spam messages. Our findings reveal the security implications of feedback mechanisms in neural code generation systems, paving the way for increasing their security.
Design and Implementation of Smart Infrastructures and Connected Vehicles in A Mini-city Platform
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents a 1/10th scale mini-city platform used as a testing bed for evaluating autonomous and connected vehicles. Using the mini-city platform, we can evaluate different driving scenarios including human-driven and autonomous driving. We provide a unique, visual feature-rich environment for evaluating computer vision methods. The conducted experiments utilize onboard sensors mounted on a robotic platform we built, allowing them to navigate in a controlled real-world urban environment. The designed city is occupied by cars, stop signs, a variety of residential and business buildings, and complex intersections mimicking an urban area. Furthermore, We have designed an intelligent infrastructure at one of the intersections in the city which helps safer and more efficient navigation in the presence of multiple cars and pedestrians. We have used the mini-city platform for the analysis of three different applications: city mapping, depth estimation in challenging occluded environments, and smart infrastructure for connected vehicles. Our smart infrastructure is among the first to develop and evaluate Vehicle-to-Infrastructure (V2I) communication at intersections. The intersection-related result shows how inaccuracy in perception, including mapping and localization, can affect safety. The proposed mini-city platform can be considered as a baseline environment for developing research and education in intelligent transportation systems.
F1tenth Autonomous Racing With Offline Reinforcement Learning Methods
Venue: arXiv cs.RO 2024
Year: 2024

Autonomous racing serves as a critical platform for evaluating automated driving systems and enhancing vehicle mobility intelligence. This work investigates offline reinforcement learning methods to train agents within the dynamic F1tenth racing environment. The study begins by exploring the challenges of online training in the Austria race track environment, where agents consistently fail to complete the laps. Consequently, this research pivots towards an offline strategy, leveraging `expert' demonstration dataset to facilitate agent training. A waypoint-based suboptimal controller is developed to gather data with successful lap episodes. This data is then employed to train offline learning-based algorithms, with a subsequent analysis of the agents' cross-track performance, evaluating their zero-shot transferability from seen to unseen scenarios and their capacity to adapt to changes in environment dynamics. Beyond mere algorithm benchmarking in autonomous racing scenarios, this study also introduces and describes the machinery of our return-conditioned decision tree-based policy, comparing its performance with methods that employ fully connected neural networks, Transformers, and Diffusion Policies and highlighting some insights into method selection for training autonomous agents in driving interactions.
InstantStyleGaussian: Efficient Art Style Transfer with 3D Gaussian Splatting
Venue: arXiv cs.CV 2024
Year: 2024

We present InstantStyleGaussian, an innovative 3D style transfer method based on the 3D Gaussian Splatting (3DGS) scene representation. By inputting a target-style image, it quickly generates new 3D GS scenes. Our method operates on pre-reconstructed GS scenes, combining diffusion models with an improved iterative dataset update strategy. It utilizes diffusion models to generate target style images, adds these new images to the training dataset, and uses this dataset to iteratively update and optimize the GS scenes, significantly accelerating the style editing process while ensuring the quality of the generated scenes. Extensive experimental results demonstrate that our method ensures high-quality stylized scenes while offering significant advantages in style transfer speed and consistency.