OpenRaiser/Intern-Atlas huggingface dataset:
Speech Recognition Transformers: Topological-lingualism Perspective
Venue: arXiv cs.CL 2024
Year: 2024

Transformers have evolved with great success in various artificial intelligence tasks. Thanks to our recent prevalence of self-attention mechanisms, which capture long-term dependency, phenomenal outcomes in speech processing and recognition tasks have been produced. The paper presents a comprehensive survey of transformer techniques oriented in speech modality. The main contents of this survey include (1) background of traditional ASR, end-to-end transformer ecosystem, and speech transformers (2) foundational models in a speech via lingualism paradigm, i.e., monolingual, bilingual, multilingual, and cross-lingual (3) dataset and languages, acoustic features, architecture, decoding, and evaluation metric from a specific topological lingualism perspective (4) popular speech transformer toolkit for building end-to-end ASR systems. Finally, highlight the discussion of open challenges and potential research directions for the community to conduct further research in this domain.
Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover
Venue: arXiv cs.RO 2024
Year: 2024

Transparent objects are common in daily life, while their optical properties pose challenges for RGB-D cameras to capture accurate depth information. This issue is further amplified when these objects are hand-held, as hand occlusions further complicate depth estimation. For assistant robots, however, accurately perceiving hand-held transparent objects is critical to effective human-robot interaction. This paper presents a Hand-Aware Depth Restoration (HADR) method based on creating an implicit neural representation function from a single RGB-D image. The proposed method utilizes hand posture as an important guidance to leverage semantic and geometric information of hand-object interaction. To train and evaluate the proposed method, we create a high-fidelity synthetic dataset named TransHand-14K with a real-to-sim data generation scheme. Experiments show that our method has better performance and generalization ability compared with existing methods. We further develop a real-world human-to-robot handover system based on HADR, demonstrating its potential in human-robot interaction applications.
Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation
Venue: arXiv cs.IR 2024
Year: 2024

Optical Music Recognition (OMR) automates the transcription of musical notation from images into machine-readable formats like MusicXML, MEI, or MIDI, significantly reducing the costs and time of manual transcription. This study explores knowledge discovery in OMR by applying instance segmentation using Mask R-CNN to enhance the detection and delineation of musical symbols in sheet music. Unlike Optical Character Recognition (OCR), OMR must handle the intricate semantics of Common Western Music Notation (CWMN), where symbol meanings depend on shape, position, and context. Our approach leverages instance segmentation to manage the density and overlap of musical symbols, facilitating more precise information retrieval from music scores. Evaluations on the DoReMi and MUSCIMA++ datasets demonstrate substantial improvements, with our method achieving a mean Average Precision (mAP) of up to 59.70\% in dense symbol environments, achieving comparable results to object detection. Furthermore, using traditional computer vision techniques, we add a parallel step for staff detection to infer the pitch for the recognised symbols. This study emphasises the role of pixel-wise segmentation in advancing accurate music symbol recognition, contributing to knowledge discovery in OMR. Our findings indicate that instance segmentation provides more precise representations of musical symbols, particularly in densely populated scores, advancing OMR technology. We make our implementation, pre-processing scripts, trained models, and evaluation results publicly available to support further research and development.
SLA Conceptual Model for IoT Applications
Venue: arXiv cs.DC 2024
Year: 2024

Since SLAs specify the contractual terms that are formally used between consumers and providers, there is a need to aggregate QoS requirements from the perspectives of Clouds, networks, and devices to deliver the promised IoT functionalities. Therefore, the main objective of this chapter is to provide a conceptual model of SLA for the IoT as well as rich vocabularies to describe the QoS and domain-specific configuration parameters of the IoT on an end-to-end basis. We first propose a conceptual model that identifies the main concepts that play a role in specifying end-to-end SLAs. Then, we identify some of the most common QoS metrics and configuration parameters related to each concept. We evaluated the proposed conceptual model using a goal-oriented approach, and the participants in the study reported a high level of satisfaction regarding the proposed conceptual model and its ability to capture main concepts in a general way.
Enabling Efficient and Scalable DRAM Read Disturbance Mitigation via New Experimental Insights into Modern DRAM Chips
Venue: arXiv cs.CR 2024
Year: 2024

Increasing storage density exacerbates DRAM read disturbance, a circuit-level vulnerability exploited by system-level attacks. Unfortunately, existing defenses are either ineffective or prohibitively expensive. Efficient mitigation is critical to ensure robust (reliable, secure, and safe) execution in future DRAM-based systems. This dissertation tackles two problems: 1) protecting DRAM-based systems becomes more expensive as technology scaling increases read disturbance vulnerability, and 2) many existing solutions depend on proprietary knowledge of DRAM internals. First, we build a detailed understanding of DRAM read disturbance by rigorously characterizing off-the-shelf modern DRAM chips under varying 1) temperatures, 2) memory access patterns, 3) in-chip locations, and 4) voltage. Our novel observations demystify the implications of large DRAM read disturbance variation on future DRAM read disturbance attacks and solutions. Second, we propose new mechanisms that mitigate read disturbance bitflips efficiently and scalably by leveraging insights into DRAM chip design: 1) subarray-level parallelism and 2) variation in read disturbance across DRAM rows in off-the-shelf DRAM chips. Third, we propose a novel solution that mitigates DRAM read disturbance by selectively throttling unsafe memory accesses that might otherwise cause read disturbance bitflips without proprietary knowledge of DRAM chip internals. We demonstrate that it is possible to mitigate DRAM read disturbance efficiently and scalably with worsening DRAM read disturbance by 1) building a detailed understanding of DRAM read disturbance, 2) leveraging insights into DRAM chips, and 3) devising novel solutions that do not require proprietary knowledge of DRAM chip internals. Our experimental insights and solutions enable future works targeting robust memory systems.
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
Venue: arXiv cs.CL 2024
Year: 2024

The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several institutions. To mitigate this issue, we open-source the details of a universally applicable data processing pipeline and validate its effectiveness and potential by introducing a competitive LLM baseline. Specifically, the data processing pipeline consists of broad collection to scale up and reweighting to improve quality. We then pretrain a 7B model BaichuanSEED with 3T tokens processed by our pipeline without any deliberate downstream task-related optimization, followed by an easy but effective supervised fine-tuning stage. BaichuanSEED demonstrates consistency and predictability throughout training and achieves comparable performance on comprehensive benchmarks with several commercial advanced large language models, such as Qwen1.5 and Llama3. We also conduct several heuristic experiments to discuss the potential for further optimization of downstream tasks, such as mathematics and coding.
The Illusion of Randomness: An Empirical Analysis of Address Space Layout Randomization Implementations
Venue: arXiv cs.CR 2024
Year: 2024

Address Space Layout Randomization (ASLR) is a crucial defense mechanism employed by modern operating systems to mitigate exploitation by randomizing processes' memory layouts. However, the stark reality is that real-world implementations of ASLR are imperfect and subject to weaknesses that attackers can exploit. This work evaluates the effectiveness of ASLR on major desktop platforms, including Linux, MacOS, and Windows, by examining the variability in the placement of memory objects across various processes, threads, and system restarts. In particular, we collect samples of memory object locations, conduct statistical analyses to measure the randomness of these placements and examine the memory layout to find any patterns among objects that could decrease this randomness. The results show that while some systems, like Linux distributions, provide robust randomization, others, like Windows and MacOS, often fail to adequately randomize key areas like executable code and libraries. Moreover, we find a significant entropy reduction in the entropy of libraries after the Linux 5.18 version and identify correlation paths that an attacker could leverage to reduce exploitation complexity significantly. Ultimately, we rank the identified weaknesses based on severity and validate our entropy estimates with a proof-of-concept attack. In brief, this paper provides the first comprehensive evaluation of ASLR effectiveness across different operating systems and highlights opportunities for Operating System (OS) vendors to strengthen ASLR implementations.
Comments or Issues: Where to Document Technical Debt?
Venue: arXiv cs.SE 2024
Year: 2024

Self-Admitted Technical Debt (SATD) is a form of Technical Debt where developers document the debt using source code comments (SATD-C) or issues (SATD-I). However, it is still unclear the circumstances that drive developers to choose one or another. In this paper, we survey authors of both types of debts using a large-scale dataset containing 74K SATD-C and 20K SATD-I instances, extracted from 190 GitHub projects. As a result, we provide 13 guidelines to support developers to decide when to use comments or issues to report Technical Debt.
Evaluation of Local Planner-Based Stanley Control in Autonomous RC Car Racing Series
Venue: arXiv cs.RO 2024
Year: 2024

This paper proposes a control technique for autonomous RC car racing. The presented method does not require any map-building phase beforehand since it operates only local path planning on the actual LiDAR point cloud. Racing control algorithms must have the capability to be optimized to the actual track layout for minimization of lap time. In the examined one, it is guaranteed with the improvement of the Stanley controller with additive control components to stabilize the movement in both low and high-speed ranges, and with the integration of an adaptive lookahead point to induce sharp and dynamic cornering for traveled distance reduction. The developed method is tested on a 1/10-sized RC car, and the tuning procedure from a base solution to the optimal setting in a real F1Tenth race is presented. Furthermore, the proposed method is evaluated with a comparison to a more simple reactive method, and in parallel to a more complex optimization-based technique that involves offline map building the global optimal trajectory calculation. The performance of the proposed method compared to the latter, referring to the lap time, is that the proposed one has only 8% lower average speed. This demonstrates that with appropriate tuning, a local planning-based method can be comparable with a more complex optimization-based one. Thus, the performance gap is lower than 10% from the state-of-the-art method. Moreover, the proposed technique has significantly higher similarity to real scenarios, therefore the results can be interesting in the context of automotive industry.
Latent Ewald summation for machine learning of long-range interactions
Venue: arXiv cs.LG 2024
Year: 2024

Machine learning interatomic potentials (MLIPs) often neglect long-range interactions, such as electrostatic and dispersion forces. In this work, we introduce a straightforward and efficient method to account for long-range interactions by learning a latent variable from local atomic descriptors and applying an Ewald summation to this variable. We demonstrate that in systems including charged and polar molecular dimers, bulk water, and water-vapor interface, standard short-ranged MLIPs can lead to unphysical predictions even when employing message passing. The long-range models effectively eliminate these artifacts, with only about twice the computational cost of short-range MLIPs.
Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning
Venue: arXiv cs.GT 2024
Year: 2024

Mean-field games (MFG) have become significant tools for solving large-scale multi-agent reinforcement learning problems under symmetry. However, the assumption of exact symmetry limits the applicability of MFGs, as real-world scenarios often feature inherent heterogeneity. Furthermore, most works on MFG assume access to a known MFG model, which might not be readily available for real-world finite-agent games. In this work, we broaden the applicability of MFGs by providing a methodology to extend any finite-player, possibly asymmetric, game to an "induced MFG". First, we prove that $N$-player dynamic games can be symmetrized and smoothly extended to the infinite-player continuum via explicit Kirszbraun extensions. Next, we propose the notion of $α,β$-symmetric games, a new class of dynamic population games that incorporate approximate permutation invariance. For $α,β$-symmetric games, we establish explicit approximation bounds, demonstrating that a Nash policy of the induced MFG is an approximate Nash of the $N$-player dynamic game. We show that TD learning converges up to a small bias using trajectories of the $N$-player game with finite-sample guarantees, permitting symmetrized learning without building an explicit MFG model. Finally, for certain games satisfying monotonicity, we prove a sample complexity of $\widetilde{\mathcal{O}}(\varepsilon^{-6})$ for the $N$-agent game to learn an $\varepsilon$-Nash up to symmetrization bias. Our theory is supported by evaluations on MARL benchmarks with thousands of agents.
A Review of Transformer-Based Models for Computer Vision Tasks: Capturing Global Context and Spatial Relationships
Venue: arXiv cs.CV 2024
Year: 2024

Transformer-based models have transformed the landscape of natural language processing (NLP) and are increasingly applied to computer vision tasks with remarkable success. These models, renowned for their ability to capture long-range dependencies and contextual information, offer a promising alternative to traditional convolutional neural networks (CNNs) in computer vision. In this review paper, we provide an extensive overview of various transformer architectures adapted for computer vision tasks. We delve into how these models capture global context and spatial relationships in images, empowering them to excel in tasks such as image classification, object detection, and segmentation. Analyzing the key components, training methodologies, and performance metrics of transformer-based models, we highlight their strengths, limitations, and recent advancements. Additionally, we discuss potential research directions and applications of transformer-based models in computer vision, offering insights into their implications for future advancements in the field.
Crossing Rays: Evaluation of Bimanual Mid-air Selection Techniques in an Immersive Environment
Venue: arXiv cs.HC 2024
Year: 2024

Mid-air navigation offers a method of aerial travel that mitigates the constraints associated with continuous navigation. A mid-air selection technique is essential to enable such navigation. In this paper, we consider four variations of intersection-based bimanual mid-air selection techniques with visual aids and supporting features: Simple-Ray, Simple-Stripe, Precision-Stripe, and Cursor-Sync. We evaluate their performance and user experience compared to an unimanual mid-air selection technique using two tasks that require selecting a mid-air position with or without a reference object. Our findings indicate that the bimanual techniques generally demonstrate faster selection times compared to the unimanual technique. With a supporting feature, the bimanual techniques can provide a more accurate selection than the unimanual technique. Based on our results, we discuss the effect of selection technique's visual aids and supporting features on performance and user experience for mid-air selection.
An Edge AI System Based on FPGA Platform for Railway Fault Detection
Venue: arXiv cs.CV 2024
Year: 2024

As the demands for railway transportation safety increase, traditional methods of rail track inspection no longer meet the needs of modern railway systems. To address the issues of automation and efficiency in rail fault detection, this study introduces a railway inspection system based on Field Programmable Gate Array (FPGA). This edge AI system collects track images via cameras and uses Convolutional Neural Networks (CNN) to perform real-time detection of track defects and automatically reports fault information. The innovation of this system lies in its high level of automation and detection efficiency. The neural network approach employed by this system achieves a detection accuracy of 88.9%, significantly enhancing the reliability and efficiency of detection. Experimental results demonstrate that this FPGA-based system is 1.39* and 4.67* better in energy efficiency than peer implementation on the GPU and CPU platform, respectively.
vFusedSeg3D: 3rd Place Solution for 2024 Waymo Open Dataset Challenge in Semantic Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

In this technical study, we introduce VFusedSeg3D, an innovative multi-modal fusion system created by the VisionRD team that combines camera and LiDAR data to significantly enhance the accuracy of 3D perception. VFusedSeg3D uses the rich semantic content of the camera pictures and the accurate depth sensing of LiDAR to generate a strong and comprehensive environmental understanding, addressing the constraints inherent in each modality. Through a carefully thought-out network architecture that aligns and merges these information at different stages, our novel feature fusion technique combines geometric features from LiDAR point clouds with semantic features from camera images. With the use of multi-modality techniques, performance has significantly improved, yielding a state-of-the-art mIoU of 72.46% on the validation set as opposed to the prior 70.51%.VFusedSeg3D sets a new benchmark in 3D segmentation accuracy. making it an ideal solution for applications requiring precise environmental perception.
Learning Granularity Representation for Temporal Knowledge Graph Completion
Venue: arXiv cs.LG 2024
Year: 2024

Temporal Knowledge Graphs (TKGs) incorporate temporal information to reflect the dynamic structural knowledge and evolutionary patterns of real-world facts. Nevertheless, TKGs are still limited in downstream applications due to the problem of incompleteness. Consequently, TKG completion (also known as link prediction) has been widely studied, with recent research focusing on incorporating independent embeddings of time or combining them with entities and relations to form temporal representations. However, most existing methods overlook the impact of history from a multi-granularity aspect. The inherent semantics of human-defined temporal granularities, such as ordinal dates, reveal general patterns to which facts typically adhere. To counter this limitation, this paper proposes \textbf{L}earning \textbf{G}ranularity \textbf{Re}presentation (termed $\mathsf{LGRe}$) for TKG completion. It comprises two main components: Granularity Representation Learning (GRL) and Adaptive Granularity Balancing (AGB). Specifically, GRL employs time-specific multi-layer convolutional neural networks to capture interactions between entities and relations at different granularities. After that, AGB generates adaptive weights for these embeddings according to temporal semantics, resulting in expressive representations of predictions. Moreover, to reflect similar semantics of adjacent timestamps, a temporal loss function is introduced. Extensive experimental results on four event benchmarks demonstrate the effectiveness of $\mathsf{LGRe}$ in learning time-related representations. To ensure reproducibility, our code is available at https://github.com/KcAcoZhang/LGRe.
Corrigendum to: A Systematic Study of DDR4 DRAM Faults in the Field
Venue: arXiv cs.AR 2024
Year: 2024

This paper is a corrigendum to the paper by Beigi et al. published at HPCA 2023 https://doi.org/10.1109/HPCA56546.2023.10071066. The HPCA paper presented a detailed field data analysis of faults observed at scale in DDR4 DRAM from two different memory vendors. This analysis included a breakdown of fault patterns or modes. Upon further study of the data, we found a bug in how we decoded errors based on the logged row-bank-column address. Specifically, we found that some errors that occurred in one column were mis-interpreted as occurring in two non-adjacent columns. As a result of this, some single-bit faults were misclassified as partial-row faults (i.e., two-bit faults). Similarly, some single-column faults were misclassified as two-column faults. The result of these misclassification errors is that the proportion of single-bit faults is higher than reported in the paper, with a commensurate reduction in the fraction of certain types of multi-bit faults. These misclassifications also slightly change the Failure In Time (FIT) per DRAM device values presented in the original paper. In this corrigendum, we provide an updated version of the relevant tables and figures and point out the corresponding page numbers and references in the original paper that they replace.
Multi-domain Network Slice Partitioning: A Graph Neural Network Algorithm
Venue: arXiv cs.NI 2024
Year: 2024

In the context of multi-domain network slices, multiple domains need to work together to provide a service. The problem of determining which part of the service fits within which domain is referred to as slice partitioning. The partitioning of multi-domain network slices poses a challenging problem, particularly when striving to strike the right balance between inter-domain and intra-domain costs, as well as ensuring optimal load distribution within each domain. To approach the optimal partition solution while maintaining load balance between domains, a framework has been proposed. This framework not only generates partition plans with various characteristics but also employs a Graph Neural Network solver, which significantly reduces the plan generation time. The proposed approach is promising in generating partition plans for multi-domain network slices and is expected to improve the overall performance of the network.