OpenRaiser/Intern-Atlas huggingface dataset:
Mapping Cardinality-based Feature Models to Weighted Automata over Featured Multiset Semirings (Extended Version)
Venue: arXiv cs.SE 2024
Year: 2024

Cardinality-based feature models permit to select multiple copies of the same feature, thus generalizing the notion of product configurations from subsets of Boolean features to multisets of feature instances. This increased expressiveness shapes a-priori infinite and non-convex configuration spaces, which renders established solution-space mappings based on Boolean presence conditions insufficient for cardinality-based feature models. To address this issue, we propose weighted automata over featured multiset semirings as a novel behavioral variability modeling formalism for cardinality-based feature models. The formalism uses multisets over features as a predefined semantic domain for transition weights. It permits to use any algebraic structure forming a proper semiring on multisets to aggregate the weights traversed along paths to map accepted words to multiset configurations. In particular, tropical semirings constitute a promising sub-class with a reasonable trade-off between expressiveness and computational tractability of canonical analysis problems. The formalism is strictly more expressive than featured transition systems, as it enables upper-bound multiplicity constraints depending on the length of words. We provide a tool implementation of the behavioral variability model and present preliminary experimental results showing applicability and computational feasibility of the proposed approach.
Remembering Everything Makes You Vulnerable: A Limelight on Machine Unlearning for Personalized Healthcare Sector
Venue: arXiv cs.LG 2024
Year: 2024

As the prevalence of data-driven technologies in healthcare continues to rise, concerns regarding data privacy and security become increasingly paramount. This thesis aims to address the vulnerability of personalized healthcare models, particularly in the context of ECG monitoring, to adversarial attacks that compromise patient privacy. We propose an approach termed "Machine Unlearning" to mitigate the impact of exposed data points on machine learning models, thereby enhancing model robustness against adversarial attacks while preserving individual privacy. Specifically, we investigate the efficacy of Machine Unlearning in the context of personalized ECG monitoring, utilizing a dataset of clinical ECG recordings. Our methodology involves training a deep neural classifier on ECG data and fine-tuning the model for individual patients. We demonstrate the susceptibility of fine-tuned models to adversarial attacks, such as the Fast Gradient Sign Method (FGSM), which can exploit additional data points in personalized models. To address this vulnerability, we propose a Machine Unlearning algorithm that selectively removes sensitive data points from fine-tuned models, effectively enhancing model resilience against adversarial manipulation. Experimental results demonstrate the effectiveness of our approach in mitigating the impact of adversarial attacks while maintaining the pre-trained model accuracy.
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection
Venue: arXiv cs.CV 2024
Year: 2024

In unsupervised anomaly detection (UAD) research, while state-of-the-art models have reached a saturation point with extensive studies on public benchmark datasets, they adopt large-scale tailor-made neural networks (NN) for detection performance or pursued unified models for various tasks. Towards edge computing, it is necessary to develop a computationally efficient and scalable solution that avoids large-scale complex NNs. Motivated by this, we aim to optimize the UAD performance with minimal changes to NN settings. Thus, we revisit the reconstruction-by-inpainting approach and rethink to improve it by analyzing strengths and weaknesses. The strength of the SOTA methods is a single deterministic masking approach that addresses the challenges of random multiple masking that is inference latency and output inconsistency. Nevertheless, the issue of failure to provide a mask to completely cover anomalous regions is a remaining weakness. To mitigate this issue, we propose Feature Attenuation of Defective Representation (FADeR) that only employs two MLP layers which attenuates feature information of anomaly reconstruction during decoding. By leveraging FADeR, features of unseen anomaly patterns are reconstructed into seen normal patterns, reducing false alarms. Experimental results demonstrate that FADeR achieves enhanced performance compared to similar-scale NNs. Furthermore, our approach exhibits scalability in performance enhancement when integrated with other single deterministic masking methods in a plug-and-play manner.
Gamification of Motor Imagery Brain-Computer Interface Training Protocols: a systematic review
Venue: arXiv cs.HC 2024
Year: 2024

Current Motor Imagery Brain-Computer Interfaces (MI-BCI) require a lengthy and monotonous training procedure to train both the system and the user. Considering many users struggle with effective control of MI-BCI systems, a more user-centered approach to training might help motivate users and facilitate learning, alleviating inefficiency of the BCI system. With the increase of BCI-controlled games, researchers have suggested using game principles for BCI training, as games are naturally centered on the player. This review identifies and evaluates the application of game design elements to MI-BCI training, a process known as gamification. Through a systematic literature search, we examined how MI-BCI training protocols have been gamified and how specific game elements impacted the training outcomes. We identified 86 studies that employed gamified MI-BCI protocols in the past decade. The prevalence and reported effects of individual game elements on user experience and performance were extracted and synthesized. Results reveal that MI-BCI training protocols are most often gamified by having users move an avatar in a virtual environment that provides visual feedback. Furthermore, in these virtual environments, users were provided with goals that guided their actions. Using gamification, the reviewed protocols allowed users to reach effective MI-BCI control, with studies reporting positive effects of four individual elements on user performance and experience, namely: feedback, avatars, assistance, and social interaction. Based on these elements, this review makes current and future recommendations for effective gamification, such as the use of virtual reality and adaptation of game difficulty to user skill level.
Encoding of algebraic geometry codes with quasi-linear complexity $O(N\log N)$
Venue: arXiv cs.CC 2024
Year: 2024

Fast encoding and decoding of codes have been always an important topic in code theory as well as complexity theory. Although encoding is easier than decoding in general, designing an encoding algorithm of codes of length $N$ with quasi-linear complexity $O(N\log N)$ is not an easy task. Despite the fact that algebraic geometry codes were discovered in the early of 1980s, encoding algorithms of algebraic geometry codes with quasi-linear complexity $O(N\log N)$ have not been found except for the simplest algebraic geometry codes--Reed-Solomon codes. The best-known encoding algorithm of algebraic geometry codes based on a class of plane curves has quasi-linear complexity at least $O(N\log^2 N)$. In this paper, we design an encoding algorithm of algebraic geometry codes with quasi-linear complexity $O(N\log N)$. Our algorithm works well for a large class of algebraic geometry codes based on both plane and non-plane curves. The main idea of this paper is to generalize the divide-and-conquer method from the fast Fourier Transform over finite fields to algebraic curves. Suppose we consider encoding of algebraic geometry codes based on an algebraic curve ${\mathcal X}$ over $\mathbb{F}_q$. We first consider a tower of Galois coverings ${\mathcal X}={\mathcal X}_0\rightarrow{\mathcal X}_1\rightarrow\cdots\rightarrow{\mathcal X}_r$ over a finite field $\mathbb{F}_q$, i.e., their function field tower $\mathbb{F}_q({\mathcal X}_0)\supsetneq\mathbb{F}_q({\mathcal X}_{1})\supsetneq\cdots \supsetneq\mathbb{F}_q({\mathcal X}_r)$ satisfies that each of extension $\mathbb{F}_q({\mathcal X}_{i-1})/\mathbb{F}_q({\mathcal X}_i)$ is a Galois extension and the extension degree $[\mathbb{F}_q({\mathcal X}_{i-1}):\mathbb{F}_q({\mathcal X}_i)]$ {is a constant}. Then encoding of an algebraic geometry code based on ${\mathcal X}$ is reduced to the encoding of an algebraic geometry code based on ${\mathcal X}_r$.
Rapid Mixing via Coupling Independence for Spin Systems with Unbounded Degree
Venue: arXiv cs.DS 2024
Year: 2024

We develop a new framework to prove the mixing or relaxation time for the Glauber dynamics on spin systems with unbounded degree. It works for general spin systems including both $2$-spin and multi-spin systems. As applications for this approach: $\bullet$ We prove the optimal $O(n)$ relaxation time for the Glauber dynamics of random $q$-list-coloring on an $n$-vertices triangle-tree graph with maximum degree $Δ$ such that $q/Δ> α^\star$, where $α^\star \approx 1.763$ is the unique positive solution of the equation $α= \exp(1/α)$. This improves the $n^{1+o(1)}$ relaxation time for Glauber dynamics obtained by the previous work of Jain, Pham, and Vuong (2022). Besides, our framework can also give a near-linear time sampling algorithm under the same condition. $\bullet$ We prove the optimal $O(n)$ relaxation time and near-optimal $\widetilde{O}(n)$ mixing time for the Glauber dynamics on hardcore models with parameter $λ$ in $\textit{balanced}$ bipartite graphs such that $λ< λ_c(Δ_L)$ for the max degree $Δ_L$ in left part and the max degree $Δ_R$ of right part satisfies $Δ_R = O(Δ_L)$. This improves the previous result by Chen, Liu, and Yin (2023). At the heart of our proof is the notion of $\textit{coupling independence}$ which allows us to consider multiple vertices as a huge single vertex with exponentially large domain and do a "coarse-grained" local-to-global argument on spin systems. The technique works for general (multi) spin systems and helps us obtain some new comparison results for Glauber dynamics.
RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation
Venue: arXiv cs.RO 2024
Year: 2024

This work proposes a retrieve-and-transfer framework for zero-shot robotic manipulation, dubbed RAM, featuring generalizability across various objects, environments, and embodiments. Unlike existing approaches that learn manipulation from expensive in-domain demonstrations, RAM capitalizes on a retrieval-based affordance transfer paradigm to acquire versatile manipulation capabilities from abundant out-of-domain data. First, RAM extracts unified affordance at scale from diverse sources of demonstrations including robotic data, human-object interaction (HOI) data, and custom data to construct a comprehensive affordance memory. Then given a language instruction, RAM hierarchically retrieves the most similar demonstration from the affordance memory and transfers such out-of-domain 2D affordance to in-domain 3D executable affordance in a zero-shot and embodiment-agnostic manner. Extensive simulation and real-world evaluations demonstrate that our RAM consistently outperforms existing works in diverse daily tasks. Additionally, RAM shows significant potential for downstream applications such as automatic and efficient data collection, one-shot visual imitation, and LLM/VLM-integrated long-horizon manipulation. For more details, please check our website at https://yxkryptonite.github.io/RAM/.
MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models
Venue: arXiv cs.CV 2024
Year: 2024

Fruit harvesting poses a significant labor and financial burden for the industry, highlighting the critical need for advancements in robotic harvesting solutions. Machine vision-based fruit detection has been recognized as a crucial component for robust identification of fruits to guide robotic manipulation. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated using several metrics, which outperforms the existing state-of-the-art algorithms in both our MetaFruit dataset and other open-sourced fruit datasets, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset and detection framework are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector.
A GPT-based Code Review System for Programming Language Learning
Venue: arXiv cs.SE 2024
Year: 2024

The increasing demand for programming language education and growing class sizes require immediate and personalized feedback. However, traditional code review methods have limitations in providing this level of feedback. As the capabilities of Large Language Models (LLMs) like GPT for generating accurate solutions and timely code reviews are verified, this research proposes a system that employs GPT-4 to offer learner-friendly code reviews and minimize the risk of AI-assist cheating. To provide learner-friendly code reviews, a dataset was collected from an online judge system, and this dataset was utilized to develop and enhance the system's prompts. In addition, to minimize AI-assist cheating, the system flow was designed to provide code reviews only for code submitted by a learner, and a feature that highlights code lines to fix was added. After the initial system was deployed on the web, software education experts conducted usability test. Based on the results, improvement strategies were developed to improve code review and code correctness check module, thereby enhancing the system. The improved system underwent evaluation by software education experts based on four criteria: strict code correctness checks, response time, lower API call costs, and the quality of code reviews. The results demonstrated a performance to accurately identify error types, shorten response times, lower API call costs, and maintain high-quality code reviews without major issues. Feedback from participants affirmed the tool's suitability for teaching programming to primary and secondary school students. Given these benefits, the system is anticipated to be a efficient learning tool in programming language learning for educational settings.
Secure Rewind and Discard on ARM Morello
Venue: arXiv cs.CR 2024
Year: 2024

Memory-unsafe programming languages such as C and C++ are the preferred languages for systems programming, embedded systems, and performance-critical applications. The widespread use of these languages makes the risk of memory-related attacks very high. There are well-known detection mechanisms, but they do not address software resilience. An earlier approach proposes the Secure Domain Rewind and Discard (SDRaD) of isolated domains as a method to enhance the resilience of software targeted by runtime attacks on x86 architecture, based on hardware-enforced Memory Protection Key (MPK). In this work, SDRaD has been adapted to work with the Capability Hardware Enhanced RISC Instructions (CHERI) architecture to be more lightweight and performant. The results obtained in this thesis show that CHERI-SDRaD, the prototype adaption that leverages the memory-safety properties inherent to the CHERI architecture, results in a solution with less performance degradation (2.2% in Nginx benchmarks) compared to earlier results obtained with the original SDRaD prototype on an Intel-based architecture. The adaption to CHERI additionally allowed limitations inherent to the MPK-based approach to be resolved.
SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection
Venue: arXiv cs.LG 2024
Year: 2024

This paper presents a novel anomaly and outlier detection algorithm from the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This algorithm leverages the concept of similarity and higher-order interactions across multiple subspaces to identify outliers. A comprehensive set of experiments was conducted to evaluate the performance of SPINEX. This algorithm was examined against 21 commonly used anomaly detection algorithms, namely, namely, Angle-Based Outlier Detection (ABOD), Connectivity-Based Outlier Factor (COF), Copula-Based Outlier Detection (COPOD), ECOD, Elliptic Envelope (EE), Feature Bagging with KNN, Gaussian Mixture Models (GMM), Histogram-based Outlier Score (HBOS), Isolation Forest (IF), Isolation Neural Network Ensemble (INNE), Kernel Density Estimation (KDE), K-Nearest Neighbors (KNN), Lightweight Online Detector of Anomalies (LODA), Linear Model Deviation-based Detector (LMDD), Local Outlier Factor (LOF), Minimum Covariance Determinant (MCD), One-Class SVM (OCSVM), Quadratic MCD (QMCD), Robust Covariance (RC), Stochastic Outlier Selection (SOS), and Subspace Outlier Detection (SOD), and across 39 synthetic and real datasets from various domains and of a variety of dimensions and complexities. Furthermore, a complexity analysis was carried out to examine the complexity of the proposed algorithm. Our results demonstrate that SPINEX achieves superior performance, outperforms commonly used anomaly detection algorithms, and has moderate complexity (e.g., O(n log n d)). More specifically, SPINEX was found to rank at the top of algorithms on the synthetic datasets and the 7th on the real datasets. Finally, a demonstration of the explainability capabilities of SPINEX, along with future research needs, is presented.
Segmentation-Free Guidance for Text-to-Image Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

We introduce segmentation-free guidance, a novel method designed for text-to-image diffusion models like Stable Diffusion. Our method does not require retraining of the diffusion model. At no additional compute cost, it uses the diffusion model itself as an implied segmentation network, hence named segmentation-free guidance, to dynamically adjust the negative prompt for each patch of the generated image, based on the patch's relevance to concepts in the prompt. We evaluate segmentation-free guidance both objectively, using FID, CLIP, IS, and PickScore, and subjectively, through human evaluators. For the subjective evaluation, we also propose a methodology for subsampling the prompts in a dataset like MS COCO-30K to keep the number of human evaluations manageable while ensuring that the selected subset is both representative in terms of content and fair in terms of model performance. The results demonstrate the superiority of our segmentation-free guidance to the widely used classifier-free method. Human evaluators preferred segmentation-free guidance over classifier-free 60% to 19%, with 18% of occasions showing a strong preference. Additionally, PickScore win-rate, a recently proposed metric mimicking human preference, also indicates a preference for our method over classifier-free.
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models
Venue: arXiv cs.LG 2024
Year: 2024

Deep reinforcement learning (DRL) has achieved remarkable success across various domains, such as video games, robotics, and, recently, large language models. However, the computational costs and memory requirements of DRL models often limit their deployment in resource-constrained environments. The challenge underscores the urgent need to explore neural network compression methods to make RDL models more practical and broadly applicable. Our study investigates the impact of two prominent compression methods, quantization and pruning on DRL models. We examine how these techniques influence four performance factors: average return, memory, inference time, and battery utilization across various DRL algorithms and environments. Despite the decrease in model size, we identify that these compression techniques generally do not improve the energy efficiency of DRL models, but the model size decreases. We provide insights into the trade-offs between model compression and DRL performance, offering guidelines for deploying efficient DRL models in resource-constrained settings.
K-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data
Venue: arXiv cs.CR 2024
Year: 2024

Data mining has various real-time applications in fields such as finance telecommunications, biology, and government. Classification is a primary task in data mining. With the rise of cloud computing, users can outsource and access their data from anywhere, offloading data and it is processing to the cloud. However, in public cloud environments while data is often encrypted, the cloud service provider typically controls the encryption keys, meaning they can potentially access the data at any time. This situation makes traditional privacy-preserving classification systems inadequate. The recommended protocol ensures data privacy, protects user queries, and conceals access patterns. Given that encrypted data on the cloud cannot be directly mined, we focus on a secure k nearest neighbor classification algorithm for encrypted, outsourced data. This approach maintains the privacy of user queries and data access patterns while allowing effective data mining operations to be conducted securely in the cloud. With cloud computing, particularly in public cloud environments, the encryption of data necessitates advanced methods like secure k nearest neighbor algorithms to ensure privacy and functionality in data mining. This innovation protects sensitive information and user privacy, addressing the challenges posed by traditional systems where cloud providers control encryption keys.
Diversity, Representation, and Accessibility Concerns in Game Development
Venue: arXiv cs.HC 2024
Year: 2024

This study delves into the key issues of representation and accessibility in game development. Despite their societal significance, video games face ongoing criticism for lacking diversity in both the workforce and content, excluding marginalized gamers. This study explores game-based learning (GBL) while emphasizing the importance of accurate representation, particularly in educational settings to enhance engagement and learning outcomes. Our research findings revolve around the perspectives of a professional in the gaming industry and the challenges associated with creating accessible games. By providing actionable insights, it aims to influence regulatory reforms, industry practices, and game creation itself, to foster diversity, representation, and accessibility in the video game industry. In doing so, we seek to promote a more inclusive and equitable future in the educational gaming world.