OpenRaiser/Intern-Atlas huggingface dataset:
Rethinking the Multi-view Stereo from the Perspective of Rendering-based Augmentation
Venue: arXiv cs.CV 2023
Year: 2023

GigaMVS presents several challenges to existing Multi-View Stereo (MVS) algorithms for its large scale, complex occlusions, and gigapixel images. To address these problems, we first apply one of the state-of-the-art learning-based MVS methods, --MVSFormer, to overcome intractable scenarios such as textureless and reflections regions suffered by traditional PatchMatch methods, but it fails in a few large scenes' reconstructions. Moreover, traditional PatchMatch algorithms such as ACMMP, OpenMVS, and RealityCapture are leveraged to further improve the completeness in large scenes. Furthermore, to unify both advantages of deep learning methods and the traditional PatchMatch, we propose to render depth and color images to further fine-tune the MVSFormer model. Notably, we find that the MVS method could produce much better predictions through rendered images due to the coincident illumination, which we believe is significant for the MVS community. Thus, MVSFormer is capable of generalizing to large-scale scenes and complementarily solves the textureless reconstruction problem. Finally, we have assembled all point clouds mentioned above \textit{except ones from RealityCapture} and ranked Top-1 on the competitive GigaReconstruction.
Design and Evaluation of a Rack-Scale Disaggregated Memory Architecture For Data Centers
Venue: arXiv cs.DC 2023
Year: 2023

Memory disaggregation is being considered as a strong alternative to traditional architecture to deal with the memory under-utilization in data centers. Disaggregated memory can adapt to dynamically changing memory requirements for the data center applications like data analytics, big data, etc., that require in-memory processing. However, such systems can face high remote memory access latency due to the interconnect speeds. In this paper, we explore a rack-scale disaggregated memory architecture and discuss the various design aspects. We design a trace-driven simulator that combines an event-based interconnect and a cycle-accurate memory simulator to evaluate the performance of disaggregated memory system at the rack scale. Our study shows that not only the interconnect but the contention in the remote memory queues also adds significantly to remote memory access latency. We introduces a memory allocation policy to reduce the latency compared to the conventional policies. We conduct experiments using various benchmarks with diverse memory access patterns. Our study shows encouraging results towards the rack-scale memory disaggregation and acceptable average memory access latency.
Improving the Robustness of Deep Convolutional Neural Networks Through Feature Learning
Venue: arXiv cs.CV 2023
Year: 2023

Deep convolutional neural network (DCNN for short) models are vulnerable to examples with small perturbations. Adversarial training (AT for short) is a widely used approach to enhance the robustness of DCNN models by data augmentation. In AT, the DCNN models are trained with clean examples and adversarial examples (AE for short) which are generated using a specific attack method, aiming to gain ability to defend themselves when facing the unseen AEs. However, in practice, the trained DCNN models are often fooled by the AEs generated by the novel attack methods. This naturally raises a question: can a DCNN model learn certain features which are insensitive to small perturbations, and further defend itself no matter what attack methods are presented. To answer this question, this paper makes a beginning effort by proposing a shallow binary feature module (SBFM for short), which can be integrated into any popular backbone. The SBFM includes two types of layers, i.e., Sobel layer and threshold layer. In Sobel layer, there are four parallel feature maps which represent horizontal, vertical, and diagonal edge features, respectively. And in threshold layer, it turns the edge features learnt by Sobel layer to the binary features, which then are feeded into the fully connected layers for classification with the features learnt by the backbone. We integrate SBFM into VGG16 and ResNet34, respectively, and conduct experiments on multiple datasets. Experimental results demonstrate, under FGSM attack with $ε=8/255$, the SBFM integrated models can achieve averagely 35\% higher accuracy than the original ones, and in CIFAR-10 and TinyImageNet datasets, the SBFM integrated models can achieve averagely 75\% classification accuracy. The work in this paper shows it is promising to enhance the robustness of DCNN models through feature learning.
Fine-grained Visual Classification with High-temperature Refinement and Background Suppression
Venue: arXiv cs.CV 2023
Year: 2023

Fine-grained visual classification is a challenging task due to the high similarity between categories and distinct differences among data within one single category. To address the challenges, previous strategies have focused on localizing subtle discrepancies between categories and enhencing the discriminative features in them. However, the background also provides important information that can tell the model which features are unnecessary or even harmful for classification, and models that rely too heavily on subtle features may overlook global features and contextual information. In this paper, we propose a novel network called ``High-temperaturE Refinement and Background Suppression'' (HERBS), which consists of two modules, namely, the high-temperature refinement module and the background suppression module, for extracting discriminative features and suppressing background noise, respectively. The high-temperature refinement module allows the model to learn the appropriate feature scales by refining the features map at different scales and improving the learning of diverse features. And, the background suppression module first splits the features map into foreground and background using classification confidence scores and suppresses feature values in low-confidence areas while enhancing discriminative features. The experimental results show that the proposed HERBS effectively fuses features of varying scales, suppresses background noise, discriminative features at appropriate scales for fine-grained visual classification.The proposed method achieves state-of-the-art performance on the CUB-200-2011 and NABirds benchmarks, surpassing 93% accuracy on both datasets. Thus, HERBS presents a promising solution for improving the performance of fine-grained visual classification tasks. code: https://github.com/chou141253/FGVC-HERBS
Hallucinated Heartbeats: Anomaly-Aware Remote Pulse Estimation
Venue: arXiv cs.CV 2023
Year: 2023

Camera-based physiological monitoring, especially remote photoplethysmography (rPPG), is a promising tool for health diagnostics, and state-of-the-art pulse estimators have shown impressive performance on benchmark datasets. We argue that evaluations of modern solutions may be incomplete, as we uncover failure cases for videos without a live person, or in the presence of severe noise. We demonstrate that spatiotemporal deep learning models trained only with live samples "hallucinate" a genuine-shaped pulse on anomalous and noisy videos, which may have negative consequences when rPPG models are used by medical personnel. To address this, we offer: (a) An anomaly detection model, built on top of the predicted waveforms. We compare models trained in open-set (unknown abnormal predictions) and closed-set (abnormal predictions known when training) settings; (b) An anomaly-aware training regime that penalizes the model for predicting periodic signals from anomalous videos. Extensive experimentation with eight research datasets (rPPG-specific: DDPM, CDDPM, PURE, UBFC, ARPM; deep fakes: DFDC; face presentation attack detection: HKBU-MARs; rPPG outlier: KITTI) show better accuracy of anomaly detection for deep learning models incorporating the proposed training (75.8%), compared to models trained regularly (73.7%) and to hand-crafted rPPG methods (52-62%).
Graph Neural Network contextual embedding for Deep Learning on Tabular Data
Venue: arXiv cs.LG 2023
Year: 2023

All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record is composed of a number of heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has constituted a major breakthrough for AI in fields related to human skills like natural language processing, but its applicability to tabular data has been more challenging. More classical Machine Learning (ML) models like tree-based ensemble ones usually perform better. This paper presents a novel DL model using Graph Neural Network (GNN) more specifically Interaction Network (IN), for contextual embedding and modelling interactions among tabular features. Its results outperform those of a recently published survey with DL benchmark based on five public datasets, also achieving competitive results when compared to boosted-tree solutions.
GeoCamera: Telling Stories in Geographic Visualizations with Camera Movements
Venue: arXiv cs.HC 2023
Year: 2023

In geographic data videos, camera movements are frequently used and combined to present information from multiple perspectives. However, creating and editing camera movements requires significant time and professional skills. This work aims to lower the barrier of crafting diverse camera movements for geographic data videos. First, we analyze a corpus of 66 geographic data videos and derive a design space of camera movements with a dimension for geospatial targets and one for narrative purposes. Based on the design space, we propose a set of adaptive camera shots and further develop an interactive tool called GeoCamera. This interactive tool allows users to flexibly design camera movements for geographic visualizations. We verify the expressiveness of our tool through case studies and evaluate its usability with a user study. The participants find that the tool facilitates the design of camera movements.
Reproduction Report for SV-COMP 2023
Venue: arXiv cs.LO 2023
Year: 2023

The Competition on Software Verification (SV-COMP) is a large computational experiment benchmarking many different software verification tools on a vast collection of C and Java benchmarks. Such experimental research should be reproducible by researchers independent from the team that performed the original experiments. In this reproduction report, we present our recent attempt at reproducing SV-COMP 2023: We chose a meaningful subset of the competition and re-ran it on the competition organiser's infrastructure, using the scripts and tools provided in the competition's archived artifacts. We see minor differences in tool scores that appear explainable by the interaction of small runtime fluctuations with the competition's scoring rules, and successfully reproduce the overall ranking within our chosen subset. Overall, we consider SV-COMP 2023 to be reproducible.
Knowledge Distillation for Efficient Sequences of Training Runs
Venue: arXiv cs.LG 2023
Year: 2023

In many practical scenarios -- like hyperparameter search or continual retraining with new data -- related training runs are performed many times in sequence. Current practice is to train each of these models independently from scratch. We study the problem of exploiting the computation invested in previous runs to reduce the cost of future runs using knowledge distillation (KD). We find that augmenting future runs with KD from previous runs dramatically reduces the time necessary to train these models, even taking into account the overhead of KD. We improve on these results with two strategies that reduce the overhead of KD by 80-90% with minimal effect on accuracy and vast pareto-improvements in overall cost. We conclude that KD is a promising avenue for reducing the cost of the expensive preparatory work that precedes training final models in practice.
SHIELD: An Adaptive and Lightweight Defense against the Remote Power Side-Channel Attacks on Multi-tenant FPGAs
Venue: arXiv cs.CR 2023
Year: 2023

Dynamic partial reconfiguration enables multi-tenancy in cloud-based FPGAs, which presents security challenges for tenants, IPs, and data. Malicious users can exploit FPGAs for remote side-channel attacks (SCAs), and shared on-chip resources can be used for attacks. Logical separation can ensure design integrity, but on-chip resources can still be exploited. Conventional SCA mitigation can help, but it requires significant effort, and bitstream checking techniques are not highly accurate. An active on-chip defense mechanism is needed for tenant confidentiality. Toward this, we propose a lightweight shielding technique utilizing ring oscillators (ROs) to protect applications against remote power SCA. Unlike existing RO-based approaches, in our methodology, an offline pre-processing stage is proposed to carefully configure power monitors and an obfuscating circuit concerning the resource constraints of the board. Detection of power fluctuations due to application execution enables the obfuscating circuit to flatten the power consumption trace. To evaluate the effectiveness of the proposed SHIELD, we implemented it on a Xilinx Zynq-7000 FPGA board executing an RSA encryption algorithm. Due to the SHIELD, the number of traces required to extract the encryption key is increased by 166x, making an attack extremely hard at run-time. Note that the proposed SHIELD does not require any modification in the target application. Our methodology also shows up to 54% less power consumption and up to 26% less area overhead than the state-of-the-art random noise-addition-based defense.
E2CoPre: Energy Efficient and Cooperative Collision Avoidance for UAV Swarms with Trajectory Prediction
Venue: arXiv cs.RO 2023
Year: 2023

This paper presents a novel solution to address the challenges in achieving energy efficiency and cooperation for collision avoidance in UAV swarms. The proposed method combines Artificial Potential Field (APF) and Particle Swarm Optimization (PSO) techniques. APF provides environmental awareness and implicit coordination to UAVs, while PSO searches for collision-free and energy-efficient trajectories for each UAV in a decentralized manner under the implicit coordination. This decentralized approach is achieved by minimizing a novel cost function that leverages the advantages of the active contour model from image processing. Additionally, future trajectories are predicted by approximating the minima of the novel cost function using calculus of variation, which enables proactive actions and defines the initial conditions for PSO. We propose a two-branch trajectory planning framework that ensures UAVs only change altitudes when necessary for energy considerations. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method in various situations.
Data Dependent Regret Guarantees Against General Comparators for Full or Bandit Feedback
Venue: arXiv cs.LG 2023
Year: 2023

We study the adversarial online learning problem and create a completely online algorithmic framework that has data dependent regret guarantees in both full expert feedback and bandit feedback settings. We study the expected performance of our algorithm against general comparators, which makes it applicable for a wide variety of problem scenarios. Our algorithm works from a universal prediction perspective and the performance measure used is the expected regret against arbitrary comparator sequences, which is the difference between our losses and a competing loss sequence. The competition class can be designed to include fixed arm selections, switching bandits, contextual bandits, periodic bandits or any other competition of interest. The sequences in the competition class are generally determined by the specific application at hand and should be designed accordingly. Our algorithm neither uses nor needs any preliminary information about the loss sequences and is completely online. Its performance bounds are data dependent, where any affine transform of the losses has no effect on the normalized regret.
Perceptual Pat: A Virtual Human System for Iterative Visualization Design
Venue: arXiv cs.HC 2023
Year: 2023

Designing a visualization is often a process of iterative refinement where the designer improves a chart over time by adding features, improving encodings, and fixing mistakes. However, effective design requires external critique and evaluation. Unfortunately, such critique is not always available on short notice and evaluation can be costly. To address this need, we present Perceptual Pat, an extensible suite of AI and computer vision techniques that forms a virtual human visual system for supporting iterative visualization design. The system analyzes snapshots of a visualization using an extensible set of filters - including gaze maps, text recognition, color analysis, etc - and generates a report summarizing the findings. The web-based Pat Design Lab provides a version tracking system that enables the designer to track improvements over time. We validate Perceptual Pat using a longitudinal qualitative study involving 4 professional visualization designers that used the tool over a few days to design a new visualization.
Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges
Venue: arXiv cs.CR 2023
Year: 2023

Various data-sharing platforms have emerged with the growing public demand for open data and legislation mandating certain data to remain open. Most of these platforms remain opaque, leading to many questions about data accuracy, provenance and lineage, privacy implications, consent management, and the lack of fair incentives for data providers. With their transparency, immutability, non-repudiation, and decentralization properties, blockchains could not be more apt to answer these questions and enhance trust in a data-sharing platform. However, blockchains are not good at handling the four Vs of big data (i.e., volume, variety, velocity, and veracity) due to their limited performance, scalability, and high cost. Given many related works proposes blockchain-based trustworthy data-sharing solutions, there is increasing confusion and difficulties in understanding and selecting these technologies and platforms in terms of their sharing mechanisms, sharing services, quality of services, and applications. In this paper, we conduct a comprehensive survey on blockchain-based data-sharing architectures and applications to fill the gap. First, we present the foundations of blockchains and discuss the challenges of current data-sharing techniques. Second, we focus on the convergence of blockchain and data sharing to give a clear picture of this landscape and propose a reference architecture for blockchain-based data sharing. Third, we discuss the industrial applications of blockchain-based data sharing, ranging from healthcare and smart grid to transportation and decarbonization. For each application, we provide lessons learned for the deployment of Blockchain-based data sharing. Finally, we discuss research challenges and open research directions.
Minimal Sleep Delay Driven Aggregation Tree Construction in IoT Sensor Networks
Venue: arXiv cs.NI 2023
Year: 2023

Data aggregation is a fundamental technique in wireless sensor networks (WSNs) in which sensory data collected by intermediate nodes is merged by in-network computation using maximum, average, or sum functions. Because sensors run on batteries, energy conservation is a critical issue. Duty cycle is a well-known energy-saving mechanism in WSNs, but it causes data aggregation latency to increase. As a result, the use of multichannel technology allows more sensor nodes to send data simultaneously, reducing data aggregation latency. We investigate the minimum latency aggregation scheduling problem in multi-channel duty-cycled IoT sensor networks in this paper. We propose a scheduling scheme that first constructs an aggregation tree based on sensor node sleep delay, then improves parallel transmissions by scheduling all eligible nodes in the constructed aggregation tree to enhance data aggregation. Based on extensive simulation experiments, our proposed approach lowers the aggregation delay by at most 61% compared to a novel approach.
Reconstructing Graphs from Connected Triples
Venue: arXiv cs.DM 2023
Year: 2023

We introduce a new model of indeterminacy in graphs: instead of specifying all the edges of the graph, the input contains all triples of vertices that form a connected subgraph. In general, different (labelled) graphs may have the same set of connected triples, making unique reconstruction of the original graph from the triples impossible. We identify some families of graphs (including triangle-free graphs) for which all graphs have a different set of connected triples. We also give algorithms that reconstruct a graph from a set of triples, and for testing if this reconstruction is unique. Finally, we study a possible extension of the model in which the subsets of size $k$ that induce a connected graph are given for larger (fixed) values of $k$.
Focus on Change: Mood Prediction by Learning Emotion Changes via Spatio-Temporal Attention
Venue: arXiv cs.HC 2023
Year: 2023

While emotion and mood interchangeably used, they differ in terms of duration, intensity and attributes. Even as multiple psychology studies examine the mood-emotion relationship, mood prediction has barely been studied. Recent machine learning advances such as the attention mechanism to focus on salient parts of the input data, have only been applied to infer emotions rather than mood. We perform mood prediction by incorporating both mood and emotion change information. We additionally explore spatial and temporal attention, and parallel/sequential arrangements of the spatial and temporal attention modules to improve mood prediction performance. To examine generalizability of the proposed method, we evaluate models trained on the AFEW dataset with EMMA. Experiments reveal that (a) emotion change information is inherently beneficial to mood prediction, and (b) prediction performance improves with the integration of sequential and parallel spatial-temporal attention modules.
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network
Venue: arXiv cs.CV 2023
Year: 2023

Although 3D point cloud classification neural network models have been widely used, the in-depth interpretation of the activation of the neurons and layers is still a challenge. We propose a novel approach, named Relevance Flow, to interpret the hidden semantics of 3D point cloud classification neural networks. It delivers the class Relevance to the activated neurons in the intermediate layers in a back-propagation manner, and associates the activation of neurons with the input points to visualize the hidden semantics of each layer. Specially, we reveal that the 3D point cloud classification neural network has learned the plane-level and part-level hidden semantics in the intermediate layers, and utilize the normal and IoU to evaluate the consistency of both levels' hidden semantics. Besides, by using the hidden semantics, we generate the adversarial attack samples to attack 3D point cloud classifiers. Experiments show that our proposed method reveals the hidden semantics of the 3D point cloud classification neural network on ModelNet40 and ShapeNet, which can be used for the unsupervised point cloud part segmentation without labels and attacking the 3D point cloud classifiers.