OpenRaiser/Intern-Atlas huggingface dataset:
CamoFormer: Masked Separable Attention for Camouflaged Object Detection
Venue: arXiv cs.CV 2022
Year: 2022

How to identify and segment camouflaged objects from the background is challenging. Inspired by the multi-head self-attention in Transformers, we present a simple masked separable attention (MSA) for camouflaged object detection. We first separate the multi-head self-attention into three parts, which are responsible for distinguishing the camouflaged objects from the background using different mask strategies. Furthermore, we propose to capture high-resolution semantic representations progressively based on a simple top-down decoder with the proposed MSA to attain precise segmentation results. These structures plus a backbone encoder form a new model, dubbed CamoFormer. Extensive experiments show that CamoFormer surpasses all existing state-of-the-art methods on three widely-used camouflaged object detection benchmarks. There are on average around 5% relative improvements over previous methods in terms of S-measure and weighted F-measure.
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification
Venue: arXiv cs.LG 2022
Year: 2022

The automated machine learning (AutoML) field has become increasingly relevant in recent years. These algorithms can develop models without the need for expert knowledge, facilitating the application of machine learning techniques in the industry. Neural Architecture Search (NAS) exploits deep learning techniques to autonomously produce neural network architectures whose results rival the state-of-the-art models hand-crafted by AI experts. However, this approach requires significant computational resources and hardware investments, making it less appealing for real-usage applications. This article presents the third version of Pareto-Optimal Progressive Neural Architecture Search (POPNASv3), a new sequential model-based optimization NAS algorithm targeting different hardware environments and multiple classification tasks. Our method is able to find competitive architectures within large search spaces, while keeping a flexible structure and data processing pipeline to adapt to different tasks. The algorithm employs Pareto optimality to reduce the number of architectures sampled during the search, drastically improving the time efficiency without loss in accuracy. The experiments performed on images and time series classification datasets provide evidence that POPNASv3 can explore a large set of assorted operators and converge to optimal architectures suited for the type of data provided under different scenarios.
Mobile Localization Techniques Oriented to Tangible Web
Venue: arXiv cs.HC 2022
Year: 2022

We implemented a system able to locate people indoor, with the purpose of providing assistive services. Such approach is particularly important for the Art, for providing information on exhibitions, art galleries and museums, and to allow the access to the cultural heritage patrimony to people with disabilities. The system may provide also very important information and input to elderly people, helping them to perceive more deeply the reality and the beauty of art. The system is based on Beacons, very small and low power consumption devices, and Human Body Communication protocols. The Beacons, Bluetooth Low Energy devices, allow to obtain a position information related to predetermined reference points, and through proximity algorithms, locate a person or an object of interest. The position obtained has an error that depends from the interferences present in the area. The union of Beacons with Human Body Communication, a recent wireless technology that exploits the human body as a transmission channel, makes it possible to increase the accuracy of localization. The basic idea is to exploit the localization derived from Beacons to start a search for an electrical signal transmitted by the human body and to distinguish the position according to the information contained in the signal. The signal is transmitted by capacitance to the human body and revealed by a special resonant circuit (antenna) adapted to the microphone input of the mobile device.
AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models
Venue: arXiv cs.LG 2022
Year: 2022

Accurate PhotoVoltaic (PV) power generation forecasting is vital for the efficient operation of Smart Grids. The automated design of such accurate forecasting models for individual PV plants includes two challenges: First, information about the PV mounting configuration (i.e. inclination and azimuth angles) is often missing. Second, for new PV plants, the amount of historical data available to train a forecasting model is limited (cold-start problem). We address these two challenges by proposing a new method for day-ahead PV power generation forecasts called AutoPV. AutoPV is a weighted ensemble of forecasting models that represent different PV mounting configurations. This representation is achieved by pre-training each forecasting model on a separate PV plant and by scaling the model's output with the peak power rating of the corresponding PV plant. To tackle the cold-start problem, we initially weight each forecasting model in the ensemble equally. To tackle the problem of missing information about the PV mounting configuration, we use new data that become available during operation to adapt the ensemble weights to minimize the forecasting error. AutoPV is advantageous as the unknown PV mounting configuration is implicitly reflected in the ensemble weights, and only the PV plant's peak power rating is required to re-scale the ensemble's output. AutoPV also allows to represent PV plants with panels distributed on different roofs with varying alignments, as these mounting configurations can be reflected proportionally in the weighting. Additionally, the required computing memory is decoupled when scaling AutoPV to hundreds of PV plants, which is beneficial in Smart Grids with limited computing capabilities. For a real-world data set with 11 PV plants, the accuracy of AutoPV is comparable to a model trained on two years of data and outperforms an incrementally trained model.
An Exploratory Study of AI System Risk Assessment from the Lens of Data Distribution and Uncertainty
Venue: arXiv cs.LG 2022
Year: 2022

Deep learning (DL) has become a driving force and has been widely adopted in many domains and applications with competitive performance. In practice, to solve the nontrivial and complicated tasks in real-world applications, DL is often not used standalone, but instead contributes as a piece of gadget of a larger complex AI system. Although there comes a fast increasing trend to study the quality issues of deep neural networks (DNNs) at the model level, few studies have been performed to investigate the quality of DNNs at both the unit level and the potential impacts on the system level. More importantly, it also lacks systematic investigation on how to perform the risk assessment for AI systems from unit level to system level. To bridge this gap, this paper initiates an early exploratory study of AI system risk assessment from both the data distribution and uncertainty angles to address these issues. We propose a general framework with an exploratory study for analyzing AI systems. After large-scale (700+ experimental configurations and 5000+ GPU hours) experiments and in-depth investigations, we reached a few key interesting findings that highlight the practical need and opportunities for more in-depth investigations into AI systems.
3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding
Venue: arXiv cs.CV 2022
Year: 2022

Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community. Recent research efforts have quickly led to the design of novel algorithms able to reduce the impact of the catastrophic forgetting phenomenon in deep neural networks. Due to this surge of interest in the field, many competitions have been held in recent years, as they are an excellent opportunity to stimulate research in promising directions. This paper summarizes the ideas, design choices, rules, and results of the challenge held at the 3rd Continual Learning in Computer Vision (CLVision) Workshop at CVPR 2022. The focus of this competition is the complex continual object detection task, which is still underexplored in literature compared to classification tasks. The challenge is based on the challenge version of the novel EgoObjects dataset, a large-scale egocentric object dataset explicitly designed to benchmark continual learning algorithms for egocentric category-/instance-level object understanding, which covers more than 1k unique main objects and 250+ categories in around 100k video frames.
Towards Efficient and Domain-Agnostic Evasion Attack with High-dimensional Categorical Inputs
Venue: arXiv cs.LG 2022
Year: 2022

Our work targets at searching feasible adversarial perturbation to attack a classifier with high-dimensional categorical inputs in a domain-agnostic setting. This is intrinsically an NP-hard knapsack problem where the exploration space becomes explosively larger as the feature dimension increases. Without the help of domain knowledge, solving this problem via heuristic method, such as Branch-and-Bound, suffers from exponential complexity, yet can bring arbitrarily bad attack results. We address the challenge via the lens of multi-armed bandit based combinatorial search. Our proposed method, namely FEAT, treats modifying each categorical feature as pulling an arm in multi-armed bandit programming. Our objective is to achieve highly efficient and effective attack using an Orthogonal Matching Pursuit (OMP)-enhanced Upper Confidence Bound (UCB) exploration strategy. Our theoretical analysis bounding the regret gap of FEAT guarantees its practical attack performance. In empirical analysis, we compare FEAT with other state-of-the-art domain-agnostic attack methods over various real-world categorical data sets of different applications. Substantial experimental observations confirm the expected efficiency and attack effectiveness of FEAT applied in different application scenarios. Our work further hints the applicability of FEAT for assessing the adversarial vulnerability of classification systems with high-dimensional categorical inputs.
IIVA: A Simulation Based Generalized Framework for Interdependent Infrastructure Vulnerability Assessment
Venue: arXiv cs.CY 2022
Year: 2022

Accurate vulnerability assessment of critical infrastructure systems is cardinal to enhance infrastructure resilience. Unlike traditional approaches, this paper proposes a novel infrastructure vulnerability assessment framework that accounts for: various types of infrastructure interdependencies including physical, logical and geographical from a holistic perspective; lack of/incomplete information on supply-demand flow characteristics of interdependent infrastructure; and, unavailability/inadequate data on infrastructure network topology and/or interdependencies. Specifically, this paper models multi-infrastructure vulnerabilities leveraging simulation-based hybrid approach coupled with time-dependent Bayesian network analysis while considering cascading failures within and across CIS networks, under incomplete information. Existing synthetic data on electricity, water and supply chain networks are used to implement/validate the framework. Infrastructure vulnerabilities are depicted on a geo-map using Voronoi polygons. Our results indicate that infrastructure vulnerability is inversely proportional to the number of redundancies inbuilt in the infrastructure system, indicating that allocating resources to add redundancies in an existing infrastructure system is essential to reduce its risk of failure. It is observed that higher the initial failure rate of the components, higher is the vulnerability of the infrastructure, highlighting the importance of modernizing and upgrading the infrastructure system aiming to reduce the initial failure probabilities. Our results also underline the importance of collaborative working and sharing the necessary information among multiple infrastructure systems, aiming towards minimizing the overall failure risk of interdependent infrastructure systems.
Verifying term graph optimizations using Isabelle/HOL
Venue: arXiv cs.PL 2022
Year: 2022

Our objective is to formally verify the correctness of the hundreds of expression optimization rules used within the GraalVM compiler. When defining the semantics of a programming language, expressions naturally form abstract syntax trees, or, terms. However, in order to facilitate sharing of common subexpressions, modern compilers represent expressions as term graphs. Defining the semantics of term graphs is more complicated than defining the semantics of their equivalent term representations. More significantly, defining optimizations directly on term graphs and proving semantics preservation is considerably more complicated than on the equivalent term representations. On terms, optimizations can be expressed as conditional term rewriting rules, and proofs that the rewrites are semantics preserving are relatively straightforward. In this paper, we explore an approach to using term rewrites to verify term graph transformations of optimizations within the GraalVM compiler. This approach significantly reduces the overall verification effort and allows for simpler encoding of optimization rules.
A Survey on Privacy of Personal and Non-Personal Data in B5G/6G Networks
Venue: arXiv cs.CR 2022
Year: 2022

The upcoming Beyond 5G (B5G) and 6G networks are expected to provide enhanced capabilities such as ultra-high data rates, dense connectivity, and high scalability. It opens many possibilities for a new generation of services driven by Artificial Intelligence (AI) and billions of interconnected smart devices. However, with this expected massive upgrade, the privacy of people, organizations, and states is becoming a rising concern. The recent introduction of privacy laws and regulations for personal and non-personal data signals that global awareness is emerging in the current privacy landscape. Yet, many gaps need to be identified in the case of two data types. If not detected, they can lead to significant privacy leakages and attacks that will affect billions of people and organizations who utilize B5G/6G. This survey is a comprehensive study of personal and non-personal data privacy in B5G/6G to identify the current progress and future directions to ensure data privacy. We provide a detailed comparison of the two data types and a set of related privacy goals for B5G/6G. Next, we bring data privacy issues with possible solutions. This paper also provides future directions to preserve personal and non-personal data privacy in future networks.
Object Delineation in Satellite Images
Venue: arXiv cs.CV 2022
Year: 2022

Machine learning is being widely applied to analyze satellite data with problems such as classification and feature detection. Unlike traditional image processing algorithms, geospatial applications need to convert the detected objects from a raster form to a geospatial vector form to further analyze it. This gem delivers a simple and light-weight algorithm for delineating the pixels that are marked by ML algorithms to extract geospatial objects from satellite images. The proposed algorithm is exact and users can further apply simplification and approximation based on the application needs.
Improving group robustness under noisy labels using predictive uncertainty
Venue: arXiv cs.LG 2022
Year: 2022

The standard empirical risk minimization (ERM) can underperform on certain minority groups (i.e., waterbirds in lands or landbirds in water) due to the spurious correlation between the input and its label. Several studies have improved the worst-group accuracy by focusing on the high-loss samples. The hypothesis behind this is that such high-loss samples are \textit{spurious-cue-free} (SCF) samples. However, these approaches can be problematic since the high-loss samples may also be samples with noisy labels in the real-world scenarios. To resolve this issue, we utilize the predictive uncertainty of a model to improve the worst-group accuracy under noisy labels. To motivate this, we theoretically show that the high-uncertainty samples are the SCF samples in the binary classification problem. This theoretical result implies that the predictive uncertainty is an adequate indicator to identify SCF samples in a noisy label setting. Motivated from this, we propose a novel ENtropy based Debiasing (END) framework that prevents models from learning the spurious cues while being robust to the noisy labels. In the END framework, we first train the \textit{identification model} to obtain the SCF samples from a training set using its predictive uncertainty. Then, another model is trained on the dataset augmented with an oversampled SCF set. The experimental results show that our END framework outperforms other strong baselines on several real-world benchmarks that consider both the noisy labels and the spurious-cues.
Shared Coupling-bridge for Weakly Supervised Local Feature Learning
Venue: arXiv cs.CV 2022
Year: 2022

Sparse local feature extraction is usually believed to be of important significance in typical vision tasks such as simultaneous localization and mapping, image matching and 3D reconstruction. At present, it still has some deficiencies needing further improvement, mainly including the discrimination power of extracted local descriptors, the localization accuracy of detected keypoints, and the efficiency of local feature learning. This paper focuses on promoting the currently popular sparse local feature learning with camera pose supervision. Therefore, it pertinently proposes a Shared Coupling-bridge scheme with four light-weight yet effective improvements for weakly-supervised local feature (SCFeat) learning. It mainly contains: i) the \emph{Feature-Fusion-ResUNet Backbone} (F2R-Backbone) for local descriptors learning, ii) a shared coupling-bridge normalization to improve the decoupling training of description network and detection network, iii) an improved detection network with peakiness measurement to detect keypoints and iv) the fundamental matrix error as a reward factor to further optimize feature detection training. Extensive experiments prove that our SCFeat improvement is effective. It could often obtain a state-of-the-art performance on classic image matching and visual localization. In terms of 3D reconstruction, it could still achieve competitive results. For sharing and communication, our source codes are available at https://github.com/sunjiayuanro/SCFeat.git.
Reform and Practice of Computer Application Technology Major Construction and Development in Higher Vocational Colleges in China -- Taking Jiangxi Vocational College of Applied Technology as An Example
Venue: arXiv cs.CY 2022
Year: 2022

This study takes the development path of computer application technology specialty construction in Higher Vocational Colleges under the background of high-level higher vocational schools and specialty construction plan with Chinese characteristics (double high plan) as the main research object, and puts forward the core concept of computer application technology specialty construction and development in Higher Vocational Colleges in China through the practice of computer application technology specialty construction and development reform in recent years The main measures and construction objectives provide specific experience and solutions for deepening the reform of computer application technology specialty in higher vocational colleges.
Decoding Multi-class Motor-related Intentions with User-optimized and Robust BCI System Based on Multimodal Dataset
Venue: arXiv cs.HC 2022
Year: 2022

A brain-computer interface (BCI) based on electroencephalography (EEG) can be useful for rehabilitation and the control of external devices. Five grasping tasks were decoded for motor execution (ME) and motor imagery (MI). During this experiment, eight healthy subjects were asked to imagine and grasp five objects. Analysis of EEG signals was performed after detecting muscle signals on electromyograms (EMG) with a time interval selection technique on data taken from these ME and MI experiments. By refining only data corresponding to the exact time when the users performed the motor intention, the proposed method can train the decoding model using only the EEG data generated by various motor intentions with strong correlation with a specific class. There was an accuracy of 70.73% for ME and 47.95% for MI for the five offline tasks. This method may be applied to future applications, such as controlling robot hands with BCIs.
Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines
Venue: arXiv cs.SE 2022
Year: 2022

Deep Neural Networks (DNN) are increasingly used as components of larger software systems that need to process complex data, such as images, written texts, audio/video signals. DNN predictions cannot be assumed to be always correct for several reasons, among which the huge input space that is dealt with, the ambiguity of some inputs data, as well as the intrinsic properties of learning algorithms, which can provide only statistical warranties. Hence, developers have to cope with some residual error probability. An architectural pattern commonly adopted to manage failure-prone components is the supervisor, an additional component that can estimate the reliability of the predictions made by untrusted (e.g., DNN) components and can activate an automated healing procedure when these are likely to fail, ensuring that the Deep Learning based System (DLS) does not cause damages, despite its main functionality being suspended. In this paper, we consider DLS that implement a supervisor by means of uncertainty estimation. After overviewing the main approaches to uncertainty estimation and discussing their pros and cons, we motivate the need for a specific empirical assessment method that can deal with the experimental setting in which supervisors are used, where the accuracy of the DNN matters only as long as the supervisor lets the DLS continue to operate. Then we present a large empirical study conducted to compare the alternative approaches to uncertainty estimation. We distilled a set of guidelines for developers that are useful to incorporate a supervisor based on uncertainty monitoring into a DLS.
Towards mapping the contemporary art world with ArtLM: an art-specific NLP model
Venue: arXiv cs.CL 2022
Year: 2022

With an increasing amount of data in the art world, discovering artists and artworks suitable to collectors' tastes becomes a challenge. It is no longer enough to use visual information, as contextual information about the artist has become just as important in contemporary art. In this work, we present a generic Natural Language Processing framework (called ArtLM) to discover the connections among contemporary artists based on their biographies. In this approach, we first continue to pre-train the existing general English language models with a large amount of unlabelled art-related data. We then fine-tune this new pre-trained model with our biography pair dataset manually annotated by a team of professionals in the art industry. With extensive experiments, we demonstrate that our ArtLM achieves 85.6% accuracy and 84.0% F1 score and outperforms other baseline models. We also provide a visualisation and a qualitative analysis of the artist network built from ArtLM's outputs.