OpenRaiser/Intern-Atlas huggingface dataset:
PSENet: Progressive Self-Enhancement Network for Unsupervised Extreme-Light Image Enhancement
Venue: arXiv cs.CV 2022
Year: 2022

The extremes of lighting (e.g. too much or too little light) usually cause many troubles for machine and human vision. Many recent works have mainly focused on under-exposure cases where images are often captured in low-light conditions (e.g. nighttime) and achieved promising results for enhancing the quality of images. However, they are inferior to handling images under over-exposure. To mitigate this limitation, we propose a novel unsupervised enhancement framework which is robust against various lighting conditions while does not require any well-exposed images to serve as the ground-truths. Our main concept is to construct pseudo-ground-truth images synthesized from multiple source images that simulate all potential exposure scenarios to train the enhancement network. Our extensive experiments show that the proposed approach consistently outperforms the current state-of-the-art unsupervised counterparts in several public datasets in terms of both quantitative metrics and qualitative results. Our code is available at https://github.com/VinAIResearch/PSENet-Image-Enhancement.
Inattentive in social, active in mind: VR-based design intervention for imagining desirable possibilities in the public space
Venue: arXiv cs.HC 2022
Year: 2022

The metro as a form of public transportation is an important urban infrastructure that takes a large population from place A to B every day. To achieve that, it is primarily designed for extreme functionality and efficiency. However, in terms of experiential aesthetics, the metro is seldom people s favourite place. When this modern infrastructure succeeds in serving urban mobility with high performance and efficiency, passengers seem to want more than the guaranteed functional performance. Recently, with the emergence of VR technologies, increasing efforts from design and HCI communities look at the value of VR technology in enhancing commuting experiences, bringing new possibilities of interaction and activities, and potentially transforming social public spaces. This study investigates how and why VR technology could be integrated with a metro ride. We experimented with ten passengers by showing them three 360 videos during their metro ride. The results show the narrative driven scene is most desirable. Despite wearing a VR headset might cause anxiety, our findings indicate a high level of acceptance towards VR experiences based on the finding that it does not challenge the normative behaviours of being a passenger inattentive in social, active in mind and further can enhance the experience. As the takeaway, we propose three strategies of VR content tailored for the metro context in which passengers would find a role participating in the virtual scene and turn the scene to one s own story, and at the same time, maintain physically constrained.
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection
Venue: arXiv cs.SD 2022
Year: 2022

Audio-visual active speaker detection (AVASD) is well-developed, and now is an indispensable front-end for several multi-modal applications. However, to the best of our knowledge, the adversarial robustness of AVASD models hasn't been investigated, not to mention the effective defense against such attacks. In this paper, we are the first to reveal the vulnerability of AVASD models under audio-only, visual-only, and audio-visual adversarial attacks through extensive experiments. What's more, we also propose a novel audio-visual interaction loss (AVIL) for making attackers difficult to find feasible adversarial examples under an allocated attack budget. The loss aims at pushing the inter-class embeddings to be dispersed, namely non-speech and speech clusters, sufficiently disentangled, and pulling the intra-class embeddings as close as possible to keep them compact. Experimental results show the AVIL outperforms the adversarial training by 33.14 mAP (%) under multi-modal attacks.
Fully Transformer Network for Change Detection of Remote Sensing Images
Venue: arXiv cs.CV 2022
Year: 2022

Recently, change detection (CD) of remote sensing images have achieved great progress with the advances of deep learning. However, current methods generally deliver incomplete CD regions and irregular CD boundaries due to the limited representation ability of the extracted visual features. To relieve these issues, in this work we propose a novel learning framework named Fully Transformer Network (FTN) for remote sensing image CD, which improves the feature extraction from a global view and combines multi-level visual features in a pyramid manner. More specifically, the proposed framework first utilizes the advantages of Transformers in long-range dependency modeling. It can help to learn more discriminative global-level features and obtain complete CD regions. Then, we introduce a pyramid structure to aggregate multi-level visual features from Transformers for feature enhancement. The pyramid structure grafted with a Progressive Attention Module (PAM) can improve the feature representation ability with additional interdependencies through channel attentions. Finally, to better train the framework, we utilize the deeply-supervised learning with multiple boundaryaware loss functions. Extensive experiments demonstrate that our proposed method achieves a new state-of-the-art performance on four public CD benchmarks. For model reproduction, the source code is released at https://github.com/AI-Zhpp/FTN.
Requirements Engineering for Machine Learning: A Review and Reflection
Venue: arXiv cs.SE 2022
Year: 2022

Today, many industrial processes are undergoing digital transformation, which often requires the integration of well-understood domain models and state-of-the-art machine learning technology in business processes. However, requirements elicitation and design decision making about when, where and how to embed various domain models and end-to-end machine learning techniques properly into a given business workflow requires further exploration. This paper aims to provide an overview of the requirements engineering process for machine learning applications in terms of cross domain collaborations. We first review the literature on requirements engineering for machine learning, and then go through the collaborative requirements analysis process step-by-step. An example case of industrial data-driven intelligence applications is also discussed in relation to the aforementioned steps.
Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
Venue: arXiv cs.AI 2022
Year: 2022

A tool that could suggest new personalized research directions and ideas by taking insights from the scientific literature could significantly accelerate the progress of science. A field that might benefit from such an approach is artificial intelligence (AI) research, where the number of scientific publications has been growing exponentially over the last years, making it challenging for human researchers to keep track of the progress. Here, we use AI techniques to predict the future research directions of AI itself. We develop a new graph-based benchmark based on real-world data -- the Science4Cast benchmark, which aims to predict the future state of an evolving semantic network of AI. For that, we use more than 100,000 research papers and build up a knowledge network with more than 64,000 concept nodes. We then present ten diverse methods to tackle this task, ranging from pure statistical to pure learning methods. Surprisingly, the most powerful methods use a carefully curated set of network features, rather than an end-to-end AI approach. It indicates a great potential that can be unleashed for purely ML approaches without human knowledge. Ultimately, better predictions of new future research directions will be a crucial component of more advanced research suggestion tools.
MotorBeat: Acoustic Communication for Home Appliances via Variable Pulse Width Modulation
Venue: arXiv cs.NI 2022
Year: 2022

More and more home appliances are now connected to the Internet, thus enabling various smart home applications. However, a critical problem that may impede the further development of smart home is overlooked: Small appliances account for the majority of home appliances, but they receive little attention and most of them are cut off from the Internet. To fill this gap, we propose MotorBeat, an acoustic communication approach that connects small appliances to a smart speaker. Our key idea is to exploit direct current (DC) motors, which are common components of small appliances, to transmit acoustic messages. We design a novel scheme named Variable Pulse Width Modulation (V-PWM) to drive DC motors. MotorBeat achieves the following 3C goals: (1) Comfortable to hear, (2) Compatible with multiple motor modes, and (3) Concurrent transmission. We implement MotorBeat with commercial devices and evaluate its performance on three small appliances and ten DC motors. The results show that the communication range can be up to 10 m
An efficient topology optimization based on multigrid assisted reanalysis for heat transfer problem
Venue: arXiv cs.CE 2022
Year: 2022

To improve the computational efficiency of heat transfer topology optimization, a Multigrid Assisted Reanalysis (MGAR) method is proposed in this study. The MGAR not only significantly improves the computational efficiency, but also relieves the hardware burden, and thus can efficiently solve large-scale heat transfer topology optimization problems. In addition, a projection-based post-processing strategy is also proposed and integrated with a continuous density filtering strategy to successfully obtain smooth boundary while eliminating some small-sized features. Several 2D and 3D numerical examples demonstrate that the computational efficiency of the MGAR is close to or even higher than that of the MGCG with almost identical optimization results, moreover, the efficiency improvement in the 3D scenario is superior than that of the 2D scenario, which reveals the excellent potential of the MGAR to save computational cost for large-scale problems.
Minimizing Age of Processed Information in Wireless Networks
Venue: arXiv cs.NI 2022
Year: 2022

The freshness of real-time status processing of time-sensitive information is crucial for several applications, including healthcare monitoring and autonomous vehicles. This freshness is considered in this paper for the system where unprocessed information is sent from sensors to a base station over a shared wireless network. The base station has a dedicated non-preemptive processor with a constant processing time to process information from each sensor. The age of processed information is the time elapsed since the generation of the packet that was most recently processed by a processor. Our objective is to minimize the average age of processed information over an infinite time-horizon. We first show that a drop-free policy simplifies the system without sacrificing optimality. From this simplification, we propose three transmission-scheduling policies with 2-optimal guarantees for different requirements. A distributed Power-2 policy can be implemented without a central scheduler. With a central scheduler, both Back-Off and Max-Weight policies are near optimal with different advantages. The Back-Off policy guarantees a bound on the maximum age of processed information, while the Max-Weight policy achieves the lowest average age in simulation without the guarantee of bound. Simulation results confirm our theoretical findings.
Green Learning: Introduction, Examples and Outlook
Venue: arXiv cs.LG 2022
Year: 2022

Rapid advances in artificial intelligence (AI) in the last decade have largely been built upon the wide applications of deep learning (DL). However, the high carbon footprint yielded by larger and larger DL networks becomes a concern for sustainability. Furthermore, DL decision mechanism is somewhat obsecure and can only be verified by test data. Green learning (GL) has been proposed as an alternative paradigm to address these concerns. GL is characterized by low carbon footprints, small model sizes, low computational complexity, and logical transparency. It offers energy-effective solutions in cloud centers as well as mobile/edge devices. GL also provides a clear and logical decision-making process to gain people's trust. Several statistical tools have been developed to achieve this goal in recent years. They include subspace approximation, unsupervised and supervised representation learning, supervised discriminant feature selection, and feature space partitioning. We have seen a few successful GL examples with performance comparable with state-of-the-art DL solutions. This paper offers an introduction to GL, its demonstrated applications, and future outlook.
Nonstationary data stream classification with online active learning and siamese neural networks
Venue: arXiv cs.LG 2022
Year: 2022

We have witnessed in recent years an ever-growing volume of information becoming available in a streaming manner in various application areas. As a result, there is an emerging need for online learning methods that train predictive models on-the-fly. A series of open challenges, however, hinder their deployment in practice. These are, learning as data arrive in real-time one-by-one, learning from data with limited ground truth information, learning from nonstationary data, and learning from severely imbalanced data, while occupying a limited amount of memory for data storage. We propose the ActiSiamese algorithm, which addresses these challenges by combining online active learning, siamese networks, and a multi-queue memory. It develops a new density-based active learning strategy which considers similarity in the latent (rather than the input) space. We conduct an extensive study that compares the role of different active learning budgets and strategies, the performance with/without memory, the performance with/without ensembling, in both synthetic and real-world datasets, under different data nonstationarity characteristics and class imbalance levels. ActiSiamese outperforms baseline and state-of-the-art algorithms, and is effective under severe imbalance, even only when a fraction of the arriving instances' labels is available. We publicly release our code to the community.
Local Computation of Maximal Independent Set
Venue: arXiv cs.DS 2022
Year: 2022

We present a randomized Local Computation Algorithm (LCA) with query complexity $poly(Δ) \cdot \log n$ for the Maximal Independent Set (MIS) problem. That is, the algorithm determines whether each node is in the computed MIS or not using $poly(Δ) \cdot \log n$ queries to the adjacency lists of the graph, with high probability, and this can be done for different nodes simultaneously and independently. Here $Δ$ and $n$ denote the maximum degree and the number of nodes. This algorithm resolves a key open problem in the study of local computations and sublinear algorithms (attributed to Rubinfeld).
Aggregator Reuse and Extension for Richer Web Archive Interaction
Venue: arXiv cs.DL 2022
Year: 2022

Memento aggregators enable users to query multiple web archives for captures of a URI in time through a single HTTP endpoint. While this one-to-many access point is useful for researchers and end-users, aggregators are in a position to provide additional functionality to end-users beyond black box style aggregation. This paper identifies the state-of-the-art of Memento aggregation, abstracts its processes, highlights shortcomings, and offers systematic enhancements.
Estimating productivity gains in digital automation
Venue: arXiv cs.AI 2022
Year: 2022

This paper proposes a novel productivity estimation model to evaluate the effects of adopting Artificial Intelligence (AI) components in a production chain. Our model provides evidence to address the "AI's" Solow's Paradox. We provide (i) theoretical and empirical evidence to explain Solow's dichotomy; (ii) a data-driven model to estimate and asses productivity variations; (iii) a methodology underpinned on process mining datasets to determine the business process, BP, and productivity; (iv) a set of computer simulation parameters; (v) and empirical analysis on labour-distribution. These provide data on why we consider AI Solow's paradox a consequence of metric mismeasurement.
Interpretable Deep Tracking
Venue: arXiv cs.CV 2022
Year: 2022

Imagine experiencing a crash as the passenger of an autonomous vehicle. Wouldn't you want to know why it happened? Current end-to-end optimizable deep neural networks (DNNs) in 3D detection, multi-object tracking, and motion forecasting provide little to no explanations about how they make their decisions. To help bridge this gap, we design an end-to-end optimizable multi-object tracking architecture and training protocol inspired by the recently proposed method of interchange intervention training (IIT). By enumerating different tracking decisions and associated reasoning procedures, we can train individual networks to reason about the possible decisions via IIT. Each network's decisions can be explained by the high-level structural causal model (SCM) it is trained in alignment with. Moreover, our proposed model learns to rank these outcomes, leveraging the promise of deep learning in end-to-end training, while being inherently interpretable.
Random Weight Factorization Improves the Training of Continuous Neural Representations
Venue: arXiv cs.LG 2022
Year: 2022

Continuous neural representations have recently emerged as a powerful and flexible alternative to classical discretized representations of signals. However, training them to capture fine details in multi-scale signals is difficult and computationally expensive. Here we propose random weight factorization as a simple drop-in replacement for parameterizing and initializing conventional linear layers in coordinate-based multi-layer perceptrons (MLPs) that significantly accelerates and improves their training. We show how this factorization alters the underlying loss landscape and effectively enables each neuron in the network to learn using its own self-adaptive learning rate. This not only helps with mitigating spectral bias, but also allows networks to quickly recover from poor initializations and reach better local minima. We demonstrate how random weight factorization can be leveraged to improve the training of neural representations on a variety of tasks, including image regression, shape representation, computed tomography, inverse rendering, solving partial differential equations, and learning operators between function spaces.
Probabilistic Volumetric Fusion for Dense Monocular SLAM
Venue: arXiv cs.CV 2022
Year: 2022

We present a novel method to reconstruct 3D scenes from images by leveraging deep dense monocular SLAM and fast uncertainty propagation. The proposed approach is able to 3D reconstruct scenes densely, accurately, and in real-time while being robust to extremely noisy depth estimates coming from dense monocular SLAM. Differently from previous approaches, that either use ad-hoc depth filters, or that estimate the depth uncertainty from RGB-D cameras' sensor models, our probabilistic depth uncertainty derives directly from the information matrix of the underlying bundle adjustment problem in SLAM. We show that the resulting depth uncertainty provides an excellent signal to weight the depth-maps for volumetric fusion. Without our depth uncertainty, the resulting mesh is noisy and with artifacts, while our approach generates an accurate 3D mesh with significantly fewer artifacts. We provide results on the challenging Euroc dataset, and show that our approach achieves 92% better accuracy than directly fusing depths from monocular SLAM, and up to 90% improvements compared to the best competing approach.
Strength-Adaptive Adversarial Training
Venue: arXiv cs.LG 2022
Year: 2022

Adversarial training (AT) is proved to reliably improve network's robustness against adversarial data. However, current AT with a pre-specified perturbation budget has limitations in learning a robust network. Firstly, applying a pre-specified perturbation budget on networks of various model capacities will yield divergent degree of robustness disparity between natural and robust accuracies, which deviates from robust network's desideratum. Secondly, the attack strength of adversarial training data constrained by the pre-specified perturbation budget fails to upgrade as the growth of network robustness, which leads to robust overfitting and further degrades the adversarial robustness. To overcome these limitations, we propose \emph{Strength-Adaptive Adversarial Training} (SAAT). Specifically, the adversary employs an adversarial loss constraint to generate adversarial training data. Under this constraint, the perturbation budget will be adaptively adjusted according to the training state of adversarial data, which can effectively avoid robust overfitting. Besides, SAAT explicitly constrains the attack strength of training data through the adversarial loss, which manipulates model capacity scheduling during training, and thereby can flexibly control the degree of robustness disparity and adjust the tradeoff between natural accuracy and robustness. Extensive experiments show that our proposal boosts the robustness of adversarial training.
Distance Based Image Classification: A solution to generative classification's conundrum?
Venue: arXiv cs.CV 2022
Year: 2022

Most classifiers rely on discriminative boundaries that separate instances of each class from everything else. We argue that discriminative boundaries are counter-intuitive as they define semantics by what-they-are-not; and should be replaced by generative classifiers which define semantics by what-they-are. Unfortunately, generative classifiers are significantly less accurate. This may be caused by the tendency of generative models to focus on easy to model semantic generative factors and ignore non-semantic factors that are important but difficult to model. We propose a new generative model in which semantic factors are accommodated by shell theory's hierarchical generative process and non-semantic factors by an instance specific noise term. We use the model to develop a classification scheme which suppresses the impact of noise while preserving semantic cues. The result is a surprisingly accurate generative classifier, that takes the form of a modified nearest-neighbor algorithm; we term it distance classification. Unlike discriminative classifiers, a distance classifier: defines semantics by what-they-are; is amenable to incremental updates; and scales well with the number of classes.
Understanding Older Adults' Perceptions and Challenges in Using AI-enabled Everyday Technologies
Venue: arXiv cs.HC 2022
Year: 2022

Artificial intelligence (AI)-enabled everyday technologies could help address age-related challenges like physical impairments and cognitive decline. While recent research studied older adults' experiences with specific AI-enabled products (e.g., conversational agents and assistive robots), it remains unknown how older adults perceive and experience current AI-enabled everyday technologies in general, which could impact their adoption of future AI-enabled products. We conducted a survey study (N=41) and semi-structured interviews (N=15) with older adults to understand their experiences and perceptions of AI. We found that older adults were enthusiastic about learning and using AI-enabled products, but they lacked learning avenues. Additionally, they worried when AI-enabled products outwitted their expectations, intruded on their privacy, or impacted their decision-making skills. Therefore, they held mixed views towards AI-enabled products such as AI, an aid, or an adversary. We conclude with design recommendations that make older adults feel inclusive, secure, and in control of their interactions with AI-enabled products.