OpenRaiser/Intern-Atlas huggingface dataset:
Ground-VIO: Monocular Visual-Inertial Odometry with Online Calibration of Camera-Ground Geometric Parameters
Venue: arXiv cs.RO 2023
Year: 2023

Monocular visual-inertial odometry (VIO) is a low-cost solution to provide high-accuracy, low-drifting pose estimation. However, it has been meeting challenges in vehicular scenarios due to limited dynamics and lack of stable features. In this paper, we propose Ground-VIO, which utilizes ground features and the specific camera-ground geometry to enhance monocular VIO performance in realistic road environments. In the method, the camera-ground geometry is modeled with vehicle-centered parameters and integrated into an optimization-based VIO framework. These parameters could be calibrated online and simultaneously improve the odometry accuracy by providing stable scale-awareness. Besides, a specially designed visual front-end is developed to stably extract and track ground features via the inverse perspective mapping (IPM) technique. Both simulation tests and real-world experiments are conducted to verify the effectiveness of the proposed method. The results show that our implementation could dramatically improve monocular VIO accuracy in vehicular scenarios, achieving comparable or even better performance than state-of-art stereo VIO solutions. The system could also be used for the auto-calibration of IPM which is widely used in vehicle perception. A toolkit for ground feature processing, together with the experimental datasets, would be made open-source (https://github.com/GREAT-WHU/gv_tools).
UIERL: Internal-External Representation Learning Network for Underwater Image Enhancement
Venue: arXiv cs.CV 2023
Year: 2023

Underwater image enhancement (UIE) is a meaningful but challenging task, and many learning-based UIE methods have been proposed in recent years. Although much progress has been made, these methods still exist two issues: (1) There exists a significant region-wise quality difference in a single underwater image due to the underwater imaging process, especially in regions with different scene depths. However, existing methods neglect this internal characteristic of underwater images, resulting in inferior performance; (2) Due to the uniqueness of the acquisition approach, underwater image acquisition tools usually capture multiple images in the same or similar scenes. Thus, the underwater images to be enhanced in practical usage are highly correlated. However, when processing a single image, existing methods do not consider the rich external information provided by the related images. There is still room for improvement in their performance. Motivated by these two aspects, we propose a novel internal-external representation learning (UIERL) network to better perform UIE tasks with internal and external information, simultaneously. In the internal representation learning stage, a new depth-based region feature guidance network is designed, including a region segmentation based on scene depth to sense regions with different quality levels, followed by a region-wise space encoder module. With performing region-wise feature learning for regions with different quality separately, the network provides an effective guidance for global features and thus guides intra-image differentiated enhancement. In the external representation learning stage, we first propose an external information extraction network to mine the rich external information in the related images. Then, internal and external features interact with each other via the proposed external-assist-internal module and internal-assist-e
DistSim: A performance model of large-scale hybrid distributed DNN training
Venue: arXiv cs.DC 2023
Year: 2023

With the ever-increasing computational demand of DNN training workloads, distributed training has been widely adopted. A combination of data, model and pipeline parallelism strategy, called hybrid parallelism distributed training, is imported to tackle the problem of deploying large-scale models. However, how to evaluate the hybrid strategy and the utilization of each device remains a challenge since existing works either profile on a real large-scale cluster with high time and money costs or only analyze a specific type of parallelism without considering the hybrid parallelism. In this work, we proposed DistSim, an event-based performance model to accurately analyze each device's computation and communication activities with low profiling costs. DistDim breaks down the model into events according to the given distributed strategy, which can be profiled on two nodes. Then DistSim leverages the hierarchy of different parallel strategies to generate the computation and communication event-flow from layer level to model level and finally the activity timeline of each device participating in training. Experiment shows that DistSim can reach \revise{<4\%} errors when predicting distributing training batch time and \revise{<5\%} errors when predicting a single device's activity time in various hybrid strategy settings. We also provide a use-case of DistSim, automatically evaluate and search the best distributed training strategy, and find a hybrid strategy with at most $7.37\times$ throughput improvement.
Achievable Rate Analysis of the STAR-RIS Aided NOMA Uplink in the Face of Imperfect CSI and Hardware Impairments
Venue: arXiv cs.IT 2023
Year: 2023

Reconfigurable intelligent surfaces (RIS) are capable of beneficially ameliorating the propagation environment by appropriately controlling the passive reflecting elements. To extend the coverage area, the concept of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) has been proposed, yielding supporting 360^circ coverage user equipment (UE) located on both sides of the RIS. In this paper, we theoretically formulate the ergodic sum-rate of the STAR-RIS assisted non-orthogonal multiple access (NOMA) uplink in the face of channel estimation errors and hardware impairments (HWI). Specifically, the STAR-RIS phase shift is configured based on the statistical channel state information (CSI), followed by linear minimum mean square error (LMMSE) channel estimation of the equivalent channel spanning from the UEs to the access point (AP). Afterwards, successive interference cancellation (SIC) is employed at the AP using the estimated instantaneous CSI, and we derive the theoretical ergodic sum-rate upper bound for both perfect and imperfect SIC decoding algorithm. The theoretical analysis and the simulation results show that both the channel estimation and the ergodic sum-rate have performance floor at high transmit power region caused by transceiver hardware impairments.
It Is Rude to Ask a Sensor Its Age-of-Information: Status Updates Against an Eavesdropping Node
Venue: arXiv cs.CR 2023
Year: 2023

We consider periodical status updates between a transmitter and a legitimate receiver, in the presence of an eavesdropper that is sometimes able to capture pieces of information. We assume that, in the absence of such a threat, the connection between the transmitter and the receiver is controlled by the transmitter with the aim to minimize the age of information at the receiver's side. However, if the presence of an eavesdropper is known, the transmitter may further tune the generation rate of status updates to trade off the age of information values acquired by the eavesdropper and the receiver, respectively. To analyze this problem, we first propose a metric that combines both objectives according to a Bergson social welfare framework, and then we solve the problem of finding the optimal generation rate as a function of the probability of data capture by the eavesdropper. This enables us to derive notable and sometimes counter-intuitive conclusions, and possibly establish an extension of the age of information framework to security aspects from a performance evaluation perspective.
From Driver to Supervisor: Comparing Cognitive Load and EEG-based Attentional Resource Allocation across Automation Levels
Venue: arXiv cs.HC 2023
Year: 2023

With increasing automation, drivers' roles transition from active operators to passive system supervisors, affecting their behaviour and cognitive processes. This study addresses the attentional resource allocation and subjective cognitive load during manual, SAE Level 2, and SAE Level 3 driving in a realistic environment. An experiment was conducted on a test track with 30 participants using a prototype automated vehicle. While driving, participants were subjected to a passive auditory oddball task and their electroencephalogram was recorded. The study analysed the amplitude of the P3a event-related potential component elicited by novel environmental stimuli, an objective measure of attentional resource allocation. The subjective cognitive load was assessed using the NASA Task Load Index. Results showed no significant difference in subjective cognitive load between manual and Level 2 driving, but a decrease in subjective cognitive load in Level 3 driving. The P3a amplitude was highest during manual driving, indicating increased attentional resource allocation to environmental sounds compared to Level 2 and Level 3 driving. This may suggest that during automated driving, drivers allocate fewer attentional resources to processing environmental information. It remains unclear whether the decreased processing of environmental stimuli in automated driving is due to top-down attention control (leading to attention withdrawal) or bottom-up competition for resources induced by cognitive load. This study provides novel empirical evidence on resource allocation and subjective cognitive load in automated driving. The findings highlight the importance of managing drivers' attention and cognitive load with implications for enhancing automation safety and the design of user interfaces.
DiffuDetox: A Mixed Diffusion Model for Text Detoxification
Venue: arXiv cs.CL 2023
Year: 2023

Text detoxification is a conditional text generation task aiming to remove offensive content from toxic text. It is highly useful for online forums and social media, where offensive content is frequently encountered. Intuitively, there are diverse ways to detoxify sentences while preserving their meanings, and we can select from detoxified sentences before displaying text to users. Conditional diffusion models are particularly suitable for this task given their demonstrated higher generative diversity than existing conditional text generation models based on language models. Nonetheless, text fluency declines when they are trained with insufficient data, which is the case for this task. In this work, we propose DiffuDetox, a mixed conditional and unconditional diffusion model for text detoxification. The conditional model takes toxic text as the condition and reduces its toxicity, yielding a diverse set of detoxified sentences. The unconditional model is trained to recover the input text, which allows the introduction of additional fluent text for training and thus ensures text fluency. Extensive experimental results and in-depth analysis demonstrate the effectiveness of our proposed DiffuDetox.
Early-Stopped Technique for BCH Decoding Algorithm Under Tolerant Fault Probability
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, a technique for the Berlekamp-Massey(BM) algorithm is provided to reduce the latency of decoding and save decoding power by early termination or early-stopped checking. We investigate the consecutive zero discrepancies during the decoding iteration and decide to early stop the decoding process. This technique is subject to decoding failure in exchange for the decoding latency. We analyze our proposed technique by considering the weight distribution of BCH code and estimating the bounds of undetected error probability as the event of enormous stop checking. The proposed method is effective in numerical results and the probability of decoding failure is lower than $10^{-119}$ for decoding 16383 code length of BCH codes. Furthermore, the complexity compared the conventional early termination method with the proposed approach for decoding the long BCH code. The proposed approach reduces the complexity of the conventional approach by up to 80\%. As a result, the FPGA testing on a USB device validates the reliability of the proposed method.
Tagged End-to-End Simultaneous Speech Translation Training using Simultaneous Interpretation Data
Venue: arXiv cs.CL 2023
Year: 2023

Simultaneous speech translation (SimulST) translates partial speech inputs incrementally. Although the monotonic correspondence between input and output is preferable for smaller latency, it is not the case for distant language pairs such as English and Japanese. A prospective approach to this problem is to mimic simultaneous interpretation (SI) using SI data to train a SimulST model. However, the size of such SI data is limited, so the SI data should be used together with ordinary bilingual data whose translations are given in offline. In this paper, we propose an effective way to train a SimulST model using mixed data of SI and offline. The proposed method trains a single model using the mixed data with style tags that tell the model to generate SI- or offline-style outputs. Experiment results show improvements of BLEURT in different latency ranges, and our analyses revealed the proposed model generates SI-style outputs more than the baseline.
Self-Supervised Polyp Re-Identification in Colonoscopy
Venue: arXiv cs.CV 2023
Year: 2023

Computer-aided polyp detection (CADe) is becoming a standard, integral part of any modern colonoscopy system. A typical colonoscopy CADe detects a polyp in a single frame and does not track it through the video sequence. Yet, many downstream tasks including polyp characterization (CADx), quality metrics, automatic reporting, require aggregating polyp data from multiple frames. In this work we propose a robust long term polyp tracking method based on re-identification by visual appearance. Our solution uses an attention-based self-supervised ML model, specifically designed to leverage the temporal nature of video input. We quantitatively evaluate method's performance and demonstrate its value for the CADx task.
Heterogeneous Continual Learning
Venue: arXiv cs.CV 2023
Year: 2023

We propose a novel framework and a solution to tackle the continual learning (CL) problem with changing network architectures. Most CL methods focus on adapting a single architecture to a new task/class by modifying its weights. However, with rapid progress in architecture design, the problem of adapting existing solutions to novel architectures becomes relevant. To address this limitation, we propose Heterogeneous Continual Learning (HCL), where a wide range of evolving network architectures emerge continually together with novel data/tasks. As a solution, we build on top of the distillation family of techniques and modify it to a new setting where a weaker model takes the role of a teacher; meanwhile, a new stronger architecture acts as a student. Furthermore, we consider a setup of limited access to previous data and propose Quick Deep Inversion (QDI) to recover prior task visual features to support knowledge transfer. QDI significantly reduces computational costs compared to previous solutions and improves overall performance. In summary, we propose a new setup for CL with a modified knowledge distillation paradigm and design a quick data inversion method to enhance distillation. Our evaluation of various benchmarks shows a significant improvement on accuracy in comparison to state-of-the-art methods over various networks architectures.
Does mBERT understand Romansh? Evaluating word embeddings using word alignment
Venue: arXiv cs.CL 2023
Year: 2023

We test similarity-based word alignment models (SimAlign and awesome-align) in combination with word embeddings from mBERT and XLM-R on parallel sentences in German and Romansh. Since Romansh is an unseen language, we are dealing with a zero-shot setting. Using embeddings from mBERT, both models reach an alignment error rate of 0.22, which outperforms fast_align, a statistical model, and is on par with similarity-based word alignment for seen languages. We interpret these results as evidence that mBERT contains information that can be meaningful and applicable to Romansh. To evaluate performance, we also present a new trilingual corpus, which we call the DERMIT (DE-RM-IT) corpus, containing press releases made by the Canton of Grisons in German, Romansh and Italian in the past 25 years. The corpus contains 4 547 parallel documents and approximately 100 000 sentence pairs in each language combination. We additionally present a gold standard for German-Romansh word alignment. The data is available at https://github.com/eyldlv/DERMIT-Corpus.
RAID Organizations for Improved Reliability and Performance: A Not Entirely Unbiased Tutorial
Venue: arXiv cs.DC 2023
Year: 2023

This is a followup to the 1994 tutorial by Berkeley RAID researchers whose 1988 RAID paper foresaw a revolutionary change in storage industry based on advances in magnetic disk technology, i.e., replacement of large capacity expensive disks with arrays of small capacity inexpensive disks. NAND flash SSDs which use less power, incur very low latency, provide high bandwidth, and are more reliable than HDDs are expected to replace HDDs as their prices drop. Replication in the form of mirrored disks and erasure coding via parity and Reed-Solomon codes are two methods to achieve higher reliability through redundancy in disk arrays. RAID(4+k), k=1,2,... arrays utilizing k check strips makes them k-disk-failure-tolerant with maximum distance separable coding with minimum redundancy. Clustered RAID, local recovery codes, partial MDS, and multilevel RAID are proposals to improve RAID reliability and performance. We discuss RAID5 performance and reliability analysis in conjunction with HDDs w/o and with latent sector errors - LSEs, which can be dealt with by intradisk redundancy and disk scrubbing, the latter enhanced with machine learning algorithms. Undetected disk errors causing silent data corruption are propagated by rebuild. We utilize the M/G/1 queueing model for RAID5 performance evaluation, present approximations for fork/join response time in degraded mode analysis, and the vacationing server model for rebuild analysis. Methods and tools for reliability evaluation with Markov chain modeling and simulation are discussed. Queueing and reliability analysis are based on probability theory and stochastic processes so that the two topics can be studied together. Their application is presented here in the context of RAID arrays in a tutorial manner.
A Copernican Revolution in Data
Venue: arXiv cs.DB 2023
Year: 2023

Half a century ago, Charles Bachman foresaw the significance and centrality of data in the digital world. In this short paper, we delve into the evolution of these ideas within the database community over the past decades. We believe that this historical analysis helps deepen our comprehension of the fundamental changes undergoing our discipline and provides insights into the future trajectory of our field.
A Self-Supervised Miniature One-Shot Texture Segmentation (MOSTS) Model for Real-Time Robot Navigation and Embedded Applications
Venue: arXiv cs.CV 2023
Year: 2023

Determining the drivable area, or free space segmentation, is critical for mobile robots to navigate indoor environments safely. However, the lack of coherent markings and structures (e.g., lanes, curbs, etc.) in indoor spaces places the burden of traversability estimation heavily on the mobile robot. This paper explores the use of a self-supervised one-shot texture segmentation framework and an RGB-D camera to achieve robust drivable area segmentation. With a fast inference speed and compact size, the developed model, MOSTS is ideal for real-time robot navigation and various embedded applications. A benchmark study was conducted to compare MOSTS's performance with existing one-shot texture segmentation models to evaluate its performance. Additionally, a validation dataset was built to assess MOSTS's ability to perform texture segmentation in the wild, where it effectively identified small low-lying objects that were previously undetectable by depth measurements. Further, the study also compared MOSTS's performance with two State-Of-The-Art (SOTA) indoor semantic segmentation models, both quantitatively and qualitatively. The results showed that MOSTS offers comparable accuracy with up to eight times faster inference speed in indoor drivable area segmentation.
Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank
Venue: arXiv cs.CL 2023
Year: 2023

Reading comprehension is a crucial skill in many aspects of education, including language learning, cognitive development, and fostering early literacy skills in children. Automated answer-aware reading comprehension question generation has significant potential to scale up learner support in educational activities. One key technical challenge in this setting is that there can be multiple questions, sometimes very different from each other, with the same answer; a trained question generation method may not necessarily know which question human educators would prefer. To address this challenge, we propose 1) a data augmentation method that enriches the training dataset with diverse questions given the same context and answer and 2) an overgenerate-and-rank method to select the best question from a pool of candidates. We evaluate our method on the FairytaleQA dataset, showing a 5% absolute improvement in ROUGE-L over the best existing method. We also demonstrate the effectiveness of our method in generating harder, "implicit" questions, where the answers are not contained in the context as text spans.
Convert Monolithic Application to Microservice Application
Venue: arXiv cs.SE 2023
Year: 2023

Microservice architecture is a trending topic in software design architecture and many enterprises adopted microservice design due its benefits and the rapid and wide deployment of cloud computing and as a result, many enterprises transformed their existing monolithic application to microservice to achieve business requirements as scaling up and agile development. In this paper we will guide software developers how to convert their existing monolithic application into microservice application without re-writing the whole microservice application from scratch, and we will also discuss the common issues that may face the software developer during the conversion processes. In addition to converting the business logic to microservice, we mention steps for converting the monolithic database into a database per service. Also, we summarize how Netflix and Airbnb converted their monolithic application to microservice application.