OpenRaiser/Intern-Atlas huggingface dataset:
The Digital Foundation Platform -- A Multi-layered SOA Architecture for Intelligent Connected Vehicle Operating System
Venue: arXiv cs.OS 2022
Year: 2022

Legacy AD/ADAS development from OEMs centers around developing functions on ECUs using services provided by AUTOSAR Classic Platform (CP) to meet automotive-grade and mass-production requirements. The AUTOSAR CP couples hardware and software components statically and encounters challenges to provide sufficient capacities for the processing of high-level intelligent driving functions, whereas the new platform, AUTOSAR Adaptive Platform (AP) is designed to support dynamically communication and provide richer services and function abstractions for those resource-intensive (memory, CPU) applications. Yet for both platforms, application development and the supporting system software are still closely coupled together, and this makes application development and the enhancement less scalable and flexible, resulting in longer development cycles and slower time-to-market. This paper presents a multi-layered, service-oriented intelligent driving operating system foundation (we named it as Digital Foundation Platform) that provides abstractions for easier adoption of heterogeneous computing hardware. It features a multi-layer SOA software architecture with each layer providing adaptive service API at north-bound for application developers. The proposed Digital Foundation Platform (DFP) has significant advantages of decoupling hardware, operating system core, middle-ware, functional software and application software development. It provides SOA at multiple layers and enables application developers from OEMs, to customize and develop new applications or enhance existing applications with new features, either in autonomous domain or intelligent cockpit domain, with great agility, and less code through re-usability, and thus reduce the time-to-market.
Correlation between Alignment-Uniformity and Performance of Dense Contrastive Representations
Venue: arXiv cs.CV 2022
Year: 2022

Recently, dense contrastive learning has shown superior performance on dense prediction tasks compared to instance-level contrastive learning. Despite its supremacy, the properties of dense contrastive representations have not yet been carefully studied. Therefore, we analyze the theoretical ideas of dense contrastive learning using a standard CNN and straightforward feature matching scheme rather than propose a new complex method. Inspired by the analysis of the properties of instance-level contrastive representations through the lens of alignment and uniformity on the hypersphere, we employ and extend the same lens for the dense contrastive representations to analyze their underexplored properties. We discover the core principle in constructing a positive pair of dense features and empirically proved its validity. Also, we introduces a new scalar metric that summarizes the correlation between alignment-and-uniformity and downstream performance. Using this metric, we study various facets of densely learned contrastive representations such as how the correlation changes over single- and multi-object datasets or linear evaluation and dense prediction tasks. The source code is publicly available at: https://github.com/SuperSupermoon/DenseCL-analysis
A Unitary Transform Based Generalized Approximate Message Passing
Venue: arXiv cs.IT 2022
Year: 2022

We consider the problem of recovering an unknown signal ${\mathbf x}\in {\mathbb R}^n$ from general nonlinear measurements obtained through a generalized linear model (GLM), i.e., ${\mathbf y}= f\left({\mathbf A}{\mathbf x}+{\mathbf w}\right)$, where $f(\cdot)$ is a componentwise nonlinear function. Based on the unitary transform approximate message passing (UAMP) and expectation propagation, a unitary transform based generalized approximate message passing (GUAMP) algorithm is proposed for general measurement matrices $\bf{A}$, in particular highly correlated matrices. Experimental results on quantized compressed sensing demonstrate that the proposed GUAMP significantly outperforms state-of-the-art GAMP and GVAMP under correlated matrices $\bf{A}$.
Performance Analysis of Cell-Free Massive MIMO Systems with Asynchronous Reception
Venue: arXiv cs.IT 2022
Year: 2022

Cell-free (CF) massive multiple-input multiple-output (MIMO) is considered as a promising technology for achieving the ultimate performance limit. However, due to its distributed architecture and low-cost access points (APs), the signals received at user equipments (UEs) are most likely asynchronous. In this paper, we investigate the performance of CF massive MIMO systems with asynchronous reception, including both effects of delay and oscillator phases. Taking into account the imperfect channel state information caused by phase asynchronization and pilot contamination, we obtain novel and closed-form downlink spectral efficiency (SE) expressions with coherent and non-coherent data transmission schemes, respectively. Simulation results show that asynchronous reception destroys the orthogonality of pilots and coherent transmission of data, and thus results in poor system performance. In addition, getting a highly accurate delay phase is substantial for CF massive MIMO systems to achieve coherent transmission gain. Moreover, the oscillator phase of UEs has a larger effect on SE than that of the APs, because the latter can be significantly reduced by increasing the number of antennas.
RPoA: Redefined Proof of Activity
Venue: arXiv cs.CR 2022
Year: 2022

The consensus protocol is the core of a blockchain system which guarantees its secure and stable operation. Proof of Activity (PoA) is a consensus protocol that tries to address some of the issues pertinent to the most widely used protocols, such as Proof of Stake (PoS) and Proof of Work (PoW). However, it still needs to solve the issues regarding high energy consumption, significant resources required, high mining latency, and the need for private blockchains. In this paper, we propose Redefined Proof of Activity (RPoA), a new consensus protocol that builds on top of some of the best features of the existing protocols, such as PoW, PoS, and PoA, and values active service provided by users on the network. Our approach tries to address the issues above and falls in the service-based protocols category that gives mining credit to users as they serve on the network.
RIO: Order-Preserving and CPU-Efficient Remote Storage Access
Venue: arXiv cs.OS 2022
Year: 2022

Modern NVMe SSDs and RDMA networks provide dramatically higher bandwidth and concurrency. Existing networked storage systems (e.g., NVMe over Fabrics) fail to fully exploit these new devices due to inefficient storage ordering guarantees. Severe synchronous execution for storage order in these systems stalls the CPU and I/O devices and lowers the CPU and I/O performance efficiency of the storage system. We present Rio, a new approach to the storage order of remote storage access. The key insight in Rio is that the layered design of the software stack, along with the concurrent and asynchronous network and storage devices, makes the storage stack conceptually similar to the CPU pipeline. Inspired by the CPU pipeline that executes out-of-order and commits in-order, Rio introduces the I/O pipeline that allows internal out-of-order and asynchronous execution for ordered write requests while offering intact external storage order to applications. Together with merging consecutive ordered requests, these design decisions make for write throughput and CPU efficiency close to that of orderless requests. We implement Rio in Linux NVMe over RDMA stack, and further build a file system named RioFS atop Rio. Evaluations show that Rio outperforms Linux NVMe over RDMA and a state-of-the-art storage stack named Horae by two orders of magnitude and 4.9 times on average in terms of throughput of ordered write requests, respectively. RioFS increases the throughput of RocksDB by 1.9 times and 1.5 times on average, against Ext4 and HoraeFS, respectively.
Approximating Continuous Convolutions for Deep Network Compression
Venue: arXiv cs.CV 2022
Year: 2022

We present ApproxConv, a novel method for compressing the layers of a convolutional neural network. Reframing conventional discrete convolution as continuous convolution of parametrised functions over space, we use functional approximations to capture the essential structures of CNN filters with fewer parameters than conventional operations. Our method is able to reduce the size of trained CNN layers requiring only a small amount of fine-tuning. We show that our method is able to compress existing deep network models by half whilst losing only 1.86% accuracy. Further, we demonstrate that our method is compatible with other compression methods like quantisation allowing for further reductions in model size.
APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing
Venue: arXiv cs.CY 2022
Year: 2022

Knowledge tracing (KT) is a fundamental task in educational data mining that mainly focuses on students' dynamic cognitive states of skills. The question-answering process of students can be regarded as a thinking process that considers the following two problems. One problem is which skills are needed to answer the question, and the other is how to use these skills in order. If a student wants to answer a question correctly, the student should not only master the set of skills involved in the question but also think and obtain the associative path on the skills graph. The nodes in the associative path refer to the skills needed and the path shows the order of using them. The associative path is referred to as the skill mode. Thus, obtaining the skill modes is the key to answering questions successfully. However, most existing KT models only focus on a set of skills, without considering the skill modes. We propose a KT model, called APGKT, that exploits skill modes. Specifically, we extract the subgraph topology of the skills involved in the question and combine the difficulty level of the skills to obtain the skill modes via encoding; then, through multi-layer recurrent neural networks, we obtain a student's higher-order cognitive states of skills, which is used to predict the student's future answering performance. Experiments on five benchmark datasets validate the effectiveness of the proposed model.
Prioritizing emergency evacuations under compounding levels of uncertainty
Venue: arXiv cs.CY 2022
Year: 2022

Well-executed emergency evacuations can save lives and reduce suffering. However, decision makers struggle to determine optimal evacuation policies given the chaos, uncertainty, and value judgments inherent in emergency evacuations. We propose and analyze a decision support tool for pre-crisis training exercises for teams preparing for civilian evacuations and explore the tool in the case of the 2021 U.S.-led evacuation from Afghanistan. We use different classes of Markov decision processes (MDPs) to capture compounding levels of uncertainty in (1) the priority category of who appears next at the gate for evacuation, (2) the distribution of priority categories at the population level, and (3) individuals' claimed priority category. We compare the number of people evacuated by priority status under eight heuristic policies. The optimized MDP policy achieves the best performance compared to all heuristic baselines. We also show that accounting for the compounding levels of model uncertainty incurs added complexity without improvement in policy performance. Useful heuristics can be extracted from the optimized policies to inform human decision makers. We open-source all tools to encourage robust dialogue about the trade-offs, limitations, and potential of integrating algorithms into high-stakes humanitarian decision-making.
AI Governance and Ethics Framework for Sustainable AI and Sustainability
Venue: arXiv cs.CY 2022
Year: 2022

AI is transforming the existing technology landscape at a rapid phase enabling data-informed decision making and autonomous decision making. Unlike any other technology, because of the decision-making ability of AI, ethics and governance became a key concern. There are many emerging AI risks for humanity, such as autonomous weapons, automation-spurred job loss, socio-economic inequality, bias caused by data and algorithms, privacy violations and deepfakes. Social diversity, equity and inclusion are considered key success factors of AI to mitigate risks, create values and drive social justice. Sustainability became a broad and complex topic entangled with AI. Many organizations (government, corporate, not-for-profits, charities and NGOs) have diversified strategies driving AI for business optimization and social-and-environmental justice. Partnerships and collaborations become important more than ever for equity and inclusion of diversified and distributed people, data and capabilities. Therefore, in our journey towards an AI-enabled sustainable future, we need to address AI ethics and governance as a priority. These AI ethics and governance should be underpinned by human ethics.
AIM 2022 Challenge on Instagram Filter Removal: Methods and Results
Venue: arXiv cs.CV 2022
Year: 2022

This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal. Social media filters transform the images by consecutive non-linear operations, and the feature maps of the original content may be interpolated into a different domain. This reduces the overall performance of the recent deep learning strategies. The main goal of this challenge is to produce realistic and visually plausible images where the impact of the filters applied is mitigated while preserving the content. The proposed solutions are ranked in terms of the PSNR value with respect to the original images. There are two prior studies on this task as the baseline, and a total of 9 teams have competed in the final phase of the challenge. The comparison of qualitative results of the proposed solutions and the benchmark for the challenge are presented in this report.
Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization
Venue: arXiv cs.CY 2022
Year: 2022

Knowledge Tracing (KT), which aims to model student knowledge level and predict their performance, is one of the most important applications of user modeling. Modern KT approaches model and maintain an up-to-date state of student knowledge over a set of course concepts according to students' historical performance in attempting the problems. However, KT approaches were designed to model knowledge by observing relatively small problem-solving steps in Intelligent Tutoring Systems. While these approaches were applied successfully to model student knowledge by observing student solutions for simple problems, they do not perform well for modeling complex problem solving in students.M ost importantly, current models assume that all problem attempts are equally valuable in quantifying current student knowledge.However, for complex problems that involve many concepts at the same time, this assumption is deficient. In this paper, we argue that not all attempts are equivalently important in discovering students' knowledge state, and some attempts can be summarized together to better represent student performance. We propose a novel student knowledge tracing approach, Granular RAnk based TEnsor factorization (GRATE), that dynamically selects student attempts that can be aggregated while predicting students' performance in problems and discovering the concepts presented in them. Our experiments on three real-world datasets demonstrate the improved performance of GRATE, compared to the state-of-the-art baselines, in the task of student performance prediction. Our further analysis shows that attempt aggregation eliminates the unnecessary fluctuations from students' discovered knowledge states and helps in discovering complex latent concepts in the problems.
Research Data Management and Services in South Asian Academic Libraries
Venue: arXiv cs.CY 2022
Year: 2022

The study examined the research data management and related services offered by South Asian countries' academic libraries. Research applied quantitative approach and survey research design method were used for this study. The survey questionnaire was distributed randomly to academic library professionals in five countries: Afghanistan, Bangladesh, India, Pakistan, and Sri Lanka. The sample population comprised 67 library professionals from various institutes of five countries. The study recommends that institutes or funding organizations support staff to attend conferences and workshops on research data management, library professionals have to join MOOC to take courses related to research data services, Institute or professionals conduct in-house staff workshops and presentations. The study also found that 64.2 per cent agreed compliance with funder requirements and preservation are major issues.
Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention
Venue: arXiv cs.CV 2022
Year: 2022

Monocular Depth Estimation (MDE) aims to predict pixel-wise depth given a single RGB image. For both, the convolutional as well as the recent attention-based models, encoder-decoder-based architectures have been found to be useful due to the simultaneous requirement of global context and pixel-level resolution. Typically, a skip connection module is used to fuse the encoder and decoder features, which comprises of feature map concatenation followed by a convolution operation. Inspired by the demonstrated benefits of attention in a multitude of computer vision problems, we propose an attention-based fusion of encoder and decoder features. We pose MDE as a pixel query refinement problem, where coarsest-level encoder features are used to initialize pixel-level queries, which are then refined to higher resolutions by the proposed Skip Attention Module (SAM). We formulate the prediction problem as ordinal regression over the bin centers that discretize the continuous depth range and introduce a Bin Center Predictor (BCP) module that predicts bins at the coarsest level using pixel queries. Apart from the benefit of image adaptive depth binning, the proposed design helps learn improved depth embedding in initial pixel queries via direct supervision from the ground truth. Extensive experiments on the two canonical datasets, NYUV2 and KITTI, show that our architecture outperforms the state-of-the-art by 5.3% and 3.9%, respectively, along with an improved generalization performance by 9.4% on the SUNRGBD dataset. Code is available at https://github.com/ashutosh1807/PixelFormer.git.
Reversing Image Signal Processors by Reverse Style Transferring
Venue: arXiv cs.CV 2022
Year: 2022

RAW image datasets are more suitable than the standard RGB image datasets for the ill-posed inverse problems in low-level vision, but not common in the literature. There are also a few studies to focus on mapping sRGB images to RAW format. Mapping from sRGB to RAW format could be a relevant domain for reverse style transferring since the task is an ill-posed reversing problem. In this study, we seek an answer to the question: Can the ISP operations be modeled as the style factor in an end-to-end learning pipeline? To investigate this idea, we propose a novel architecture, namely RST-ISP-Net, for learning to reverse the ISP operations with the help of adaptive feature normalization. We formulate this problem as a reverse style transferring and mostly follow the practice used in the prior work. We have participated in the AIM Reversed ISP challenge with our proposed architecture. Results indicate that the idea of modeling disruptive or modifying factors as style is still valid, but further improvements are required to be competitive in such a challenge.
An Assessment of Safety-Based Driver Behavior Modeling in Microscopic Simulation Utilizing Real-Time Vehicle Trajectories
Venue: arXiv cs.RO 2022
Year: 2022

Accurate representation of observed driving behavior is critical for effectively evaluating safety and performance interventions in simulation modeling. In this study, we implement and evaluate a safety-based Optimal Velocity Model (OVM) to provide a high-fidelity replication of safety-critical behavior in microscopic simulation and showcase its implications for safety-focused assessments of traffic control strategies. A comprehensive simulation model is created for the site of study in PTV VISSIM utilizing detailed vehicle trajectory information extracted from real-time video inference, which are also used to calibrate the parameters of the safety-based OVM to replicate the observed driving behavior in the site of study. The calibrated model is then incorporated as an external driver model that overtakes VISSIM's default Wiedemann 74 model during simulated car-following episodes. The results of the preliminary analysis show the significant improvements achieved by using our model in replicating the existing safety conflicts observed at the site of the study. We then utilize this improved representation of the status quo to assess the potential impact of different scenarios of signal control and speed limit enforcement in reducing those existing conflicts by up to 23%. The results of this study showcase the considerable improvements that can be achieved by utilizing data-driven car-following behavior modeling, and the workflow presented provides an end-to-end, scalable, automated, and generalizable approach for replicating the existing driving behavior observed at a site of interest in microscopic simulation by utilizing vehicle trajectories efficiently extracted via roadside video inference.
Gated Recurrent Unit for Video Denoising
Venue: arXiv cs.CV 2022
Year: 2022

Current video denoising methods perform temporal fusion by designing convolutional neural networks (CNN) or combine spatial denoising with temporal fusion into basic recurrent neural networks (RNNs). However, there have not yet been works which adapt gated recurrent unit (GRU) mechanisms for video denoising. In this letter, we propose a new video denoising model based on GRU, namely GRU-VD. First, the reset gate is employed to mark the content related to the current frame in the previous frame output. Then the hidden activation works as an initial spatial-temporal denoising with the help from the marked relevant content. Finally, the update gate recursively fuses the initial denoised result with previous frame output to further increase accuracy. To handle various light conditions adaptively, the noise standard deviation of the current frame is also fed to these three modules. A weighted loss is adopted to regulate initial denoising and final fusion at the same time. The experimental results show that the GRU-VD network not only can achieve better quality than state of the arts objectively and subjectively, but also can obtain satisfied subjective quality on real video.