OpenRaiser/Intern-Atlas huggingface dataset:
Physical and Software Based Fault Injection Attacks Against TEEs in Mobile Devices: A Systemisation of Knowledge
Venue: arXiv cs.CR 2024
Year: 2024

Trusted Execution Environments (TEEs) are critical components of modern secure computing, providing isolated zones in processors to safeguard sensitive data and execute secure operations. Despite their importance, TEEs are increasingly vulnerable to fault injection (FI) attacks, including both physical methods, such as Electromagnetic Fault Injection (EMFI), and software-based techniques. This survey examines these FI methodologies, exploring their ability to disrupt TEE operations and expose vulnerabilities in devices ranging from smartphones and IoT systems to cloud platforms. The study highlights the evolution and effectiveness of non-invasive techniques, such as EMFI, which induce faults through electromagnetic disturbances without physical modifications to hardware, making them harder to detect and mitigate. Real-world case studies illustrate the significant risks posed by these attacks, including unauthorised access, privilege escalation, and data corruption. In addition, the survey identifies gaps in existing TEE security architectures and emphasises the need for enhanced countermeasures, such as dynamic anomaly detection and updated threat models. The findings underline the importance of interdisciplinary collaboration to address these vulnerabilities, involving researchers, manufacturers, and policymakers. This survey provides actionable insights and recommendations to guide the development of more robust TEE architectures in mobile devices, fortify FI resilience, and shape global security standards. By advancing TEE security, this research aims to protect critical digital infrastructure and maintain trust in secure computing systems worldwide.
DAIRHuM: A Platform for Directly Aligning AI Representations with Human Musical Judgments applied to Carnatic Music
Venue: arXiv cs.SD 2024
Year: 2024

Quantifying and aligning music AI model representations with human behavior is an important challenge in the field of MIR. This paper presents a platform for exploring the Direct alignment between AI music model Representations and Human Musical judgments (DAIRHuM). It is designed to enable musicians and experimentalists to label similarities in a dataset of music recordings, and examine a pre-trained model's alignment with their labels using quantitative scores and visual plots. DAIRHuM is applied to analyze alignment between NSynth representations, and a rhythmic duet between two percussionists in a Carnatic quartet ensemble, an example of a genre where annotated data is scarce and assessing alignment is non-trivial. The results demonstrate significant findings on model alignment with human judgments of rhythmic harmony, while highlighting key differences in rhythm perception and music similarity judgments specific to Carnatic music. This work is among the first efforts to enable users to explore human-AI model alignment in Carnatic music and advance MIR research in Indian music while dealing with data scarcity and cultural specificity. The development of this platform provides greater accessibility to music AI tools for under-represented genres.
Predictive Modeling For Real-Time Personalized Health Monitoring in Muscular Dystrophy Management
Venue: arXiv cs.LG 2024
Year: 2024

Muscular Dystrophy is a group of genetic disorders that progressively affect the strength and functioning of muscles, thereby affecting millions of people worldwide. The lifetime nature of MD requires continuous follow-up care due to its progressive nature. This conceptual paper proposes an Internet of Things-based system to support the management of MD through remote, multi-dimensional monitoring of patients in order to provide real-time health status updates. Traditional methods have failed to give actionable data in real time, hence denying healthcare providers the opportunity to make evidence-based decisions. Technology-driven approaches are urgently needed to provide deep insights into disease progression and patient health. It aims to enhance treatment strategies, enabling patients to better manage their condition and giving healthcare professionals more confidence in their management decisions.
Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing
Venue: arXiv cs.CV 2024
Year: 2024

One of the most promising use-cases for machine learning in industrial manufacturing is the early detection of defective products using a quality control system. Such a system can save costs and reduces human errors due to the monotonous nature of visual inspections. Today, a rich body of research exists which employs machine learning methods to identify rare defective products in unbalanced visual quality control datasets. These methods typically rely on two components: A visual backbone to capture the features of the input image and an anomaly detection algorithm that decides if these features are within an expected distribution. With the rise of transformer architecture as visual backbones of choice, there exists now a great variety of different combinations of these two components, ranging all along the trade-off between detection quality and inference time. Facing this variety, practitioners in the field often have to spend a considerable amount of time on researching the right combination for their use-case at hand. Our contribution is to help practitioners with this choice by reviewing and evaluating current vision transformer models together with anomaly detection methods. For this, we chose SotA models of both disciplines, combined them and evaluated them towards the goal of having small, fast and efficient anomaly detection models suitable for industrial manufacturing. We evaluated the results of our experiments on the well-known MVTecAD and BTAD datasets. Moreover, we give guidelines for choosing a suitable model architecture for a quality control system in practice, considering given use-case and hardware constraints.
Generative AI may backfire for counterspeech
Venue: arXiv cs.SI 2024
Year: 2024

Online hate speech poses a serious threat to individual well-being and societal cohesion. A promising solution to curb online hate speech is counterspeech. Counterspeech is aimed at encouraging users to reconsider hateful posts by direct replies. However, current methods lack scalability due to the need for human intervention or fail to adapt to the specific context of the post. A potential remedy is the use of generative AI, specifically large language models (LLMs), to write tailored counterspeech messages. In this paper, we analyze whether contextualized counterspeech generated by state-of-the-art LLMs is effective in curbing online hate speech. To do so, we conducted a large-scale, pre-registered field experiment (N=2,664) on the social media platform Twitter/X. Our experiment followed a 2x2 between-subjects design and, additionally, a control condition with no counterspeech. On the one hand, users posting hateful content on Twitter/X were randomly assigned to receive either (a) contextualized counterspeech or (b) non-contextualized counterspeech. Here, the former is generated through LLMs, while the latter relies on predefined, generic messages. On the other hand, we tested two counterspeech strategies: (a) promoting empathy and (b) warning about the consequences of online misbehavior. We then measured whether users deleted their initial hateful posts and whether their behavior changed after the counterspeech intervention (e.g., whether users adopted a less toxic language). We find that non-contextualized counterspeech employing a warning-of-consequence strategy significantly reduces online hate speech. However, contextualized counterspeech generated by LLMs proves ineffective and may even backfire.
Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
Venue: arXiv cs.LG 2024
Year: 2024

Multi-agent reinforcement learning (MARL) has achieved notable success in cooperative tasks, demonstrating impressive performance and scalability. However, deploying MARL agents in real-world applications presents critical safety challenges. Current safe MARL algorithms are largely based on the constrained Markov decision process (CMDP) framework, which enforces constraints only on discounted cumulative costs and lacks an all-time safety assurance. Moreover, these methods often overlook the feasibility issue (the system will inevitably violate state constraints within certain regions of the constraint set), resulting in either suboptimal performance or increased constraint violations. To address these challenges, we propose a novel theoretical framework for safe MARL with $\textit{state-wise}$ constraints, where safety requirements are enforced at every state the agents visit. To resolve the feasibility issue, we leverage a control-theoretic notion of the feasible region, the controlled invariant set (CIS), characterized by the safety value function. We develop a multi-agent method for identifying CISs, ensuring convergence to a Nash equilibrium on the safety value function. By incorporating CIS identification into the learning process, we introduce a multi-agent dual policy iteration algorithm that guarantees convergence to a generalized Nash equilibrium in state-wise constrained cooperative Markov games, achieving an optimal balance between feasibility and performance. Furthermore, for practical deployment in complex high-dimensional systems, we propose $\textit{Multi-Agent Dual Actor-Critic}$ (MADAC), a safe MARL algorithm that approximates the proposed iteration scheme within the deep RL paradigm. Empirical evaluations on safe MARL benchmarks demonstrate that MADAC consistently outperforms existing methods, delivering much higher rewards while reducing constraint violations.
mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA
Venue: arXiv cs.AI 2024
Year: 2024

Advanced Multimodal Large Language Models (MLLMs) struggle with recent Knowledge-based VQA tasks, such as INFOSEEK and Encyclopedic-VQA, due to their limited and frozen knowledge scope, often leading to ambiguous and inaccurate responses. Thus, multimodal Retrieval-Augmented Generation (mRAG) is naturally introduced to provide MLLMs with comprehensive and up-to-date knowledge, effectively expanding the knowledge scope. However, current mRAG methods have inherent drawbacks, including: 1) Performing retrieval even when external knowledge is not needed. 2) Lacking of identification of evidence that supports the query. 3) Increasing model complexity due to additional information filtering modules or rules. To address these shortcomings, we propose a novel generalized framework called \textbf{m}ultimodal \textbf{R}etrieval-\textbf{R}eflection-\textbf{A}ugmented \textbf{G}eneration (mR$^2$AG), which achieves adaptive retrieval and useful information localization to enable answers through two easy-to-implement reflection operations, preventing high model complexity. In mR$^2$AG, Retrieval-Reflection is designed to distinguish different user queries and avoids redundant retrieval calls, and Relevance-Reflection is introduced to guide the MLLM in locating beneficial evidence of the retrieved content and generating answers accordingly. In addition, mR$^2$AG can be integrated into any well-trained MLLM with efficient fine-tuning on the proposed mR$^2$AG Instruction-Tuning dataset (mR$^2$AG-IT). mR$^2$AG significantly outperforms state-of-the-art MLLMs (e.g., GPT-4v/o) and RAG-based MLLMs on INFOSEEK and Encyclopedic-VQA, while maintaining the exceptional capabilities of base MLLMs across a wide range of Visual-dependent tasks.
Who is Funding Indian Research? A look at major funding sources acknowledged in Indian research papers
Venue: arXiv cs.DL 2024
Year: 2024

Science and scientific research activities, in addition to the involvement of the researchers, require resources like research infrastructure, materials and reagents, databases and computational tools, journal subscriptions and publication charges etc. In order to meet these requirements, researchers try to attract research funding from different funding sources, both intramural and extramural. Though some recent reports provide details of the amount of funding provided by different funding agencies in India, it is not known what quantum of research output resulted from such funding. This paper, therefore, attempts to quantify the research output produced with the funding provided by different funding agencies to Indian researchers. The major funding agencies that supported Indian research publications are identified and are further characterized in terms of being national or international, and public or private. The analytical results not only provide a quantitative estimate of funded research from India and the major funding agencies supporting the research, but also discusses the overall context of research funding in India, particularly in the context of upcoming operationalization of Anusandhan National Research Foundation (ANRF).
Learning to Stabilize Faces
Venue: arXiv cs.CV 2024
Year: 2024

Nowadays, it is possible to scan faces and automatically register them with high quality. However, the resulting face meshes often need further processing: we need to stabilize them to remove unwanted head movement. Stabilization is important for tasks like game development or movie making which require facial expressions to be cleanly separated from rigid head motion. Since manual stabilization is labor-intensive, there have been attempts to automate it. However, previous methods remain impractical: they either still require some manual input, produce imprecise alignments, rely on dubious heuristics and slow optimization, or assume a temporally ordered input. Instead, we present a new learning-based approach that is simple and fully automatic. We treat stabilization as a regression problem: given two face meshes, our network directly predicts the rigid transform between them that brings their skulls into alignment. We generate synthetic training data using a 3D Morphable Model (3DMM), exploiting the fact that 3DMM parameters separate skull motion from facial skin motion. Through extensive experiments we show that our approach outperforms the state-of-the-art both quantitatively and qualitatively on the tasks of stabilizing discrete sets of facial expressions as well as dynamic facial performances. Furthermore, we provide an ablation study detailing the design choices and best practices to help others adopt our approach for their own uses. Supplementary videos can be found on the project webpage syntec-research.github.io/FaceStab.
Towards Speaker Identification with Minimal Dataset and Constrained Resources using 1D-Convolution Neural Network
Venue: arXiv cs.SD 2024
Year: 2024

Voice recognition and speaker identification are vital for applications in security and personal assistants. This paper presents a lightweight 1D-Convolutional Neural Network (1D-CNN) designed to perform speaker identification on minimal datasets. Our approach achieves a validation accuracy of 97.87%, leveraging data augmentation techniques to handle background noise and limited training samples. Future improvements include testing on larger datasets and integrating transfer learning methods to enhance generalizability. We provide all code, the custom dataset, and the trained models to facilitate reproducibility. These resources are available on our GitHub repository: https://github.com/IrfanNafiz/RecMe.
Learning-based Trajectory Tracking for Bird-inspired Flapping-Wing Robots
Venue: arXiv cs.RO 2024
Year: 2024

Bird-sized flapping-wing robots offer significant potential for agile flight in complex environments, but achieving agile and robust trajectory tracking remains a challenge due to the complex aerodynamics and highly nonlinear dynamics inherent in flapping-wing flight. In this work, a learning-based control approach is introduced to unlock the versatility and adaptiveness of flapping-wing flight. We propose a model-free reinforcement learning (RL)-based framework for a high degree-of-freedom (DoF) bird-inspired flapping-wing robot that allows for multimodal flight and agile trajectory tracking. Stability analysis was performed on the closed-loop system comprising of the flapping-wing system and the RL policy. Additionally, simulation results demonstrate that the RL-based controller can successfully learn complex wing trajectory patterns, achieve stable flight, switch between flight modes spontaneously, and track different trajectories under various aerodynamic conditions.
On Approximability of Satisfiable $k$-CSPs: VI
Venue: arXiv cs.CC 2024
Year: 2024

We prove local and global inverse theorems for general $3$-wise correlations over pairwise-connected distributions. Let $μ$ be a distribution over $Σ\times Γ\times Φ$ such that the supports of $μ_{xy}$, $μ_{xz}$, and $μ_{yz}$ are all connected, and let $f: Σ^n \to \mathbb{C}$, $g: Γ^n \to \mathbb{C}$, $h: Φ^n \to \mathbb{C}$ be $1$-bounded functions satisfying \[ \left|\mathbb{E}_{(x,y,z) \sim μ^{\otimes n}}[f(x)g(y)h(z)]\right| \geq \varepsilon. \] In this setting, our local inverse theorem asserts that there is $δ:=\textsf{exp}(-\varepsilon^{-O_μ(1)})$ such that with probability at least $δ$, a random restriction of $f$ down to $δn$ coordinates $δ$-correlates to a product function. To get a global inverse theorem, we prove a restriction inverse theorem for general product functions, stating that if a random restriction of $f$ down to $δn$ coordinates is $δ$-correlated with a product function with probability at least $δ$, then $f$ is $2^{-\textsf{poly}(\log(1/δ))}$-correlated with a function of the form $L\cdot P$, where $L$ is a function of degree $\textsf{poly}(1/δ)$, $\|L\|_2\leq 1$, and $P$ is a product function. We show applications to property testing and to additive combinatorics. In particular, we show the following result via a density increment argument. Let $Σ$ be a finite set and $S \subseteq Σ\times Σ\times Σ$ such that: (1) $(x, x, x) \in S$ for all $x \in S$, and (2) the supports of $S_{xy}$, $S_{xz}$, and $S_{yz}$ are all connected. Then, any set $A \subseteq Σ^n$ with $|Σ|^{-n}|A| \geq Ω((\log \log \log n)^{-c})$ contains $x, y, z \in A$, not all equal, such that $(x_i,y_i,z_i) \in S$ for all $i$. This gives the first reasonable bounds for the restricted 3-AP problem over finite fields.
Material Anything: Generating Materials for Any 3D Object via Diffusion
Venue: arXiv cs.CV 2024
Year: 2024

We present Material Anything, a fully-automated, unified diffusion framework designed to generate physically-based materials for 3D objects. Unlike existing methods that rely on complex pipelines or case-specific optimizations, Material Anything offers a robust, end-to-end solution adaptable to objects under diverse lighting conditions. Our approach leverages a pre-trained image diffusion model, enhanced with a triple-head architecture and rendering loss to improve stability and material quality. Additionally, we introduce confidence masks as a dynamic switcher within the diffusion model, enabling it to effectively handle both textured and texture-less objects across varying lighting conditions. By employing a progressive material generation strategy guided by these confidence masks, along with a UV-space material refiner, our method ensures consistent, UV-ready material outputs. Extensive experiments demonstrate our approach outperforms existing methods across a wide range of object categories and lighting conditions.
Role of Data Mining in Nigerian Tertiary Education Sector
Venue: arXiv cs.CY 2024
Year: 2024

Over a decade there has been a rapid growth in Nigerian educational system particularly higher education. Various institutions have come up both from public and private sector offering many of courses both under and post graduate students. Therefore, rates of students enroll for higher educational institutions in Nigeria have also increased. Hence it is very important to understand the roles play by data mining in analyzing the collected data of students and their academic progression. It is a concern for today's education system and this gap has to be identified and properly addressed to the learning community. Data Mining it helps in various ways to resolve issues face in predictions students and staff performances within Nigerian education system. This paperwork we discuss the roles of Data Mining tools and techniques which can be used effectively in resolving issues in some functional unit of Nigerian tertiary institutions.
Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset
Venue: arXiv cs.LG 2024
Year: 2024

Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be missing and neglecting implicit relationships across multiple layers of omics data integration. This paper introduces Multi-Layer Matrix Factorization (MLMF), a novel approach for cancer subtyping that employs multi-omics data clustering. MLMF initially processes multi-omics feature matrices by performing multi-layer linear or nonlinear factorization, decomposing the original data into latent feature representations unique to each omics type. These latent representations are subsequently fused into a consensus form, on which spectral clustering is performed to determine subtypes. Additionally, MLMF incorporates a class indicator matrix to handle missing omics data, creating a unified framework that can manage both complete and incomplete multi-omics data. Extensive experiments conducted on 10 multi-omics cancer datasets, both complete and with missing values, demonstrate that MLMF achieves results that are comparable to or surpass the performance of several state-of-the-art approaches.
Transforming Triple-Entry Accounting with Machine Learning: A Path to Enhanced Transparency Through Analytics
Venue: arXiv cs.CR 2024
Year: 2024

Triple Entry (TE) is an accounting method that utilizes three accounts or 'entries' to record each transaction, rather than the conventional double-entry bookkeeping system. Existing studies have found that TE accounting, with its additional layer of verification and disclosure of inter-organizational relationships, could help improve transparency in complex financial and supply chain transactions such as blockchain. Machine learning (ML) presents a promising avenue to augment the transparency advantages of TE accounting. By automating some of the data collection and analysis needed for TE bookkeeping, ML techniques have the potential to make this more transparent accounting method scalable for large organizations with complex international supply chains, further enhancing the visibility and trustworthiness of financial reporting. By leveraging ML algorithms, anomalies within distributed ledger data can be swiftly identified, flagging potential instances of fraud or errors. Furthermore, by delving into transaction relationships over time, ML can untangle intricate webs of transactions, shedding light on obscured dealings and adding an investigative dimension. This paper aims to demonstrate the interaction between TE and ML and how they can leverage transparency levels.