OpenRaiser/Intern-Atlas huggingface dataset:
Meta-Gradients in Non-Stationary Environments
Venue: arXiv cs.LG 2022
Year: 2022

Meta-gradient methods (Xu et al., 2018; Zahavy et al., 2020) offer a promising solution to the problem of hyperparameter selection and adaptation in non-stationary reinforcement learning problems. However, the properties of meta-gradients in such environments have not been systematically studied. In this work, we bring new clarity to meta-gradients in non-stationary environments. Concretely, we ask: (i) how much information should be given to the learned optimizers, so as to enable faster adaptation and generalization over a lifetime, (ii) what meta-optimizer functions are learned in this process, and (iii) whether meta-gradient methods provide a bigger advantage in highly non-stationary environments. To study the effect of information provided to the meta-optimizer, as in recent works (Flennerhag et al., 2021; Almeida et al., 2021), we replace the tuned meta-parameters of fixed update rules with learned meta-parameter functions of selected context features. The context features carry information about agent performance and changes in the environment and hence can inform learned meta-parameter schedules. We find that adding more contextual information is generally beneficial, leading to faster adaptation of meta-parameter values and increased performance over a lifetime. We support these results with a qualitative analysis of resulting meta-parameter schedules and learned functions of context features. Lastly, we find that without context, meta-gradients do not provide a consistent advantage over the baseline in highly non-stationary environments. Our findings suggest that contextualizing meta-gradients can play a pivotal role in extracting high performance from meta-gradients in non-stationary settings.
Scheduling Algorithms for Federated Learning with Minimal Energy Consumption
Venue: arXiv cs.LG 2022
Year: 2022

Federated Learning (FL) has opened the opportunity for collaboratively training machine learning models on heterogeneous mobile or Edge devices while keeping local data private.With an increase in its adoption, a growing concern is related to its economic and environmental cost (as is also the case for other machine learning techniques).Unfortunately, little work has been done to optimize its energy consumption or emissions of carbon dioxide or equivalents, as energy minimization is usually left as a secondary objective.In this paper, we investigate the problem of minimizing the energy consumption of FL training on heterogeneous devices by controlling the workload distribution.We model this as the Minimal Cost FL Schedule problem, a total cost minimization problem with identical, independent, and atomic tasks that have to be assigned to heterogeneous resources with arbitrary cost functions.We propose a pseudo-polynomial optimal solution to the problem based on the previously unexplored Multiple-Choice Minimum-Cost Maximal Knapsack Packing Problem.We also provide four algorithms for scenarios where cost functions are monotonically increasing and follow the same behavior.These solutions are likewise applicable on the minimization of other kinds of costs, and in other one-dimensional data partition problems.
High-resolution semantically-consistent image-to-image translation
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning has become one of remote sensing scientists' most efficient computer vision tools in recent years. However, the lack of training labels for the remote sensing datasets means that scientists need to solve the domain adaptation problem to narrow the discrepancy between satellite image datasets. As a result, image segmentation models that are then trained, could better generalize and use an existing set of labels instead of requiring new ones. This work proposes an unsupervised domain adaptation model that preserves semantic consistency and per-pixel quality for the images during the style-transferring phase. This paper's major contribution is proposing the improved architecture of the SemI2I model, which significantly boosts the proposed model's performance and makes it competitive with the state-of-the-art CyCADA model. A second contribution is testing the CyCADA model on the remote sensing multi-band datasets such as WorldView-2 and SPOT-6. The proposed model preserves semantic consistency and per-pixel quality for the images during the style-transferring phase. Thus, the semantic segmentation model, trained on the adapted images, shows substantial performance gain compared to the SemI2I model and reaches similar results as the state-of-the-art CyCADA model. The future development of the proposed method could include ecological domain transfer, {\em a priori} evaluation of dataset quality in terms of data distribution, or exploration of the inner architecture of the domain adaptation model.
Deterministic and Stochastic Analysis of Deep Reinforcement Learning for Low Dimensional Sensing-based Navigation of Mobile Robots
Venue: arXiv cs.RO 2022
Year: 2022

Deterministic and Stochastic techniques in Deep Reinforcement Learning (Deep-RL) have become a promising solution to improve motion control and the decision-making tasks for a wide variety of robots. Previous works showed that these Deep-RL algorithms can be applied to perform mapless navigation of mobile robots in general. However, they tend to use simple sensing strategies since it has been shown that they perform poorly with a high dimensional state spaces, such as the ones yielded from image-based sensing. This paper presents a comparative analysis of two Deep-RL techniques - Deep Deterministic Policy Gradients (DDPG) and Soft Actor-Critic (SAC) - when performing tasks of mapless navigation for mobile robots. We aim to contribute by showing how the neural network architecture influences the learning itself, presenting quantitative results based on the time and distance of navigation of aerial mobile robots for each approach. Overall, our analysis of six distinct architectures highlights that the stochastic approach (SAC) better suits with deeper architectures, while the opposite happens with the deterministic approach (DDPG).
CNN-Trans-Enc: A CNN-Enhanced Transformer-Encoder On Top Of Static BERT representations for Document Classification
Venue: arXiv cs.CL 2022
Year: 2022

BERT achieves remarkable results in text classification tasks, it is yet not fully exploited, since only the last layer is used as a representation output for downstream classifiers. The most recent studies on the nature of linguistic features learned by BERT, suggest that different layers focus on different kinds of linguistic features. We propose a CNN-Enhanced Transformer-Encoder model which is trained on top of fixed BERT $[CLS]$ representations from all layers, employing Convolutional Neural Networks to generate QKV feature maps inside the Transformer-Encoder, instead of linear projections of the input into the embedding space. CNN-Trans-Enc is relatively small as a downstream classifier and doesn't require any fine-tuning of BERT, as it ensures an optimal use of the $[CLS]$ representations from all layers, leveraging different linguistic features with more meaningful, and generalizable QKV representations of the input. Using BERT with CNN-Trans-Enc keeps $98.9\%$ and $94.8\%$ of current state-of-the-art performance on the IMDB and SST-5 datasets respectably, while obtaining new state-of-the-art on YELP-5 with $82.23$ ($8.9\%$ improvement), and on Amazon-Polarity with $0.98\%$ ($0.2\%$ improvement) (K-fold Cross Validation on a 1M sample subset from both datasets). On the AG news dataset CNN-Trans-Enc achieves $99.94\%$ of the current state-of-the-art, and achieves a new top performance with an average accuracy of $99.51\%$ on DBPedia-14. Index terms: Text Classification, Natural Language Processing, Convolutional Neural Networks, Transformers, BERT
DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation
Venue: arXiv cs.HC 2022
Year: 2022

Image classification models often learn to predict a class based on irrelevant co-occurrences between input features and an output class in training data. We call the unwanted correlations "data biases," and the visual features causing data biases "bias factors." It is challenging to identify and mitigate biases automatically without human intervention. Therefore, we conducted a design study to find a human-in-the-loop solution. First, we identified user tasks that capture the bias mitigation process for image classification models with three experts. Then, to support the tasks, we developed a visual analytics system called DASH that allows users to visually identify bias factors, to iteratively generate synthetic images using a state-of-the-art image-to-image translation model, and to supervise the model training process for improving the classification accuracy. Our quantitative evaluation and qualitative study with ten participants demonstrate the usefulness of DASH and provide lessons for future work.
Data Quality, Mismatched Expectations, and Moving Requirements: The Challenges of User-Centred Dashboard Design
Venue: arXiv cs.HC 2022
Year: 2022

Interactive information dashboards can help both specialists and the general public understand complex datasets; but interacting with these dashboards often presents users with challenges such as understanding and verifying the presented information. To overcome these challenges, developers first need to acquire a thorough understanding of user perspectives, including strategies that users take when presented with problematic dashboards. We interviewed seventeen dashboard developers to establish (i) their understanding of user problems, (ii) the adaptations introduced as a result, and (iii) whether user-tailored dashboards can cater for users' individual differences. We find that users' literacy does not typically align with that required to use dashboards, while dashboard developers struggle with keeping up with changing requirements. We also find that developers are able to propose solutions to most users' problems but not all. Encouragingly, our findings also highlight that tailoring dashboards to individual user needs is not only desirable, but also feasible. These findings inform future dashboard design recommendations that can mitigate the identified challenges including recommendations for data presentation and visual literacy.
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Venue: arXiv cs.NE 2022
Year: 2022

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, image-related tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research.
Efficient low-thrust trajectory data generation based on generative adversarial network
Venue: arXiv cs.LG 2022
Year: 2022

Deep learning-based techniques have been introduced into the field of trajectory optimization in recent years. Deep Neural Networks (DNNs) are trained and used as the surrogates of conventional optimization process. They can provide low thrust (LT) transfer cost estimation and enable more complex preliminary mission designs. However, it is a challenge to efficiently obtain the required amount of trajectory data for training. A Generative Adversarial Network (GAN) is adapted to generate the feasible LT trajectory data efficiently. The GAN consists of a generator and a discriminator, both of which are deep networks. The generator generates fake LT transfer features using random noise as input, while the discriminator distinguishes the generator's fake LT transfer features from real LT transfer features. The GAN is trained until the generator generates fake LT transfers that the discriminator cannot identify. This indicates the generator generates low thrust transfer features that have the same distribution as the real transfer features. The generated low thrust transfer data have a high convergence rate, and they can be used to efficiently produce training data for deep learning models. The proposed approach is validated by generating feasible LT transfers in a Near-Earth Asteroid (NEA) mission scenario. The convergence rate of GAN-generated samples is 84.3%.
Inputs, Outputs, and Composition in the Logic of Information Flows
Venue: arXiv cs.LO 2022
Year: 2022

The logic of information flows (LIF) is a general framework in which tasks of a procedural nature can be modeled in a declarative, logic-based fashion. The first contribution of this paper is to propose semantic and syntactic definitions of inputs and outputs of LIF expressions. We study how the two relate and show that our syntactic definition is optimal in a sense that is made precise. The second contribution is a systematic study of the expressive power of sequential composition in LIF. Our results on composition tie in the results on inputs and outputs, and relate LIF to first-order logic (FO) and bounded-variable LIF to bounded-variable FO.
Evaluation of Text Selection Techniques in Virtual Reality Head-Mounted Displays
Venue: arXiv cs.HC 2022
Year: 2022

Text selection is an essential activity in interactive systems, including virtual reality (VR) head-mounted displays (HMDs). It is useful for: sharing information across apps or platforms, highlighting and making notes while reading articles, and text editing tasks. Despite its usefulness, the space of text selection interaction is underexplored in VR HMDs. In this research, we performed a user study with 24 participants to investigate the performance and user preference of six text selection techniques (Controller+Dwell, Controller+Click, Head+Dwell, Head+Click, Hand+Dwell, Hand+Pinch). Results reveal that Head+Click is ranked first since it has excellent speed-accuracy performance (2nd fastest task completion speed with 3rd lowest total error rate), provides the best user experience, and produces a very low workload -- followed by Controller+Click, which has the fastest speed and comparable experience with Head+Click, but much higher total error rate. Other methods can also be useful depending on the goals of the system or the users. As a first systematic evaluation of pointing*selection techniques for text selection in VR, the results of this work provide a strong foundation for further research in this area of growing importance to the future of VR to help it become a more ubiquitous and pervasive platform.
A patch-based architecture for multi-label classification from single label annotations
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we propose a patch-based architecture for multi-label classification problems where only a single positive label is observed in images of the dataset. Our contributions are twofold. First, we introduce a light patch architecture based on the attention mechanism. Next, leveraging on patch embedding self-similarities, we provide a novel strategy for estimating negative examples and deal with positive and unlabeled learning problems. Experiments demonstrate that our architecture can be trained from scratch, whereas pre-training on similar databases is required for related methods from the literature.
Stability and bifurcations in transportation networks with heterogeneous users
Venue: arXiv cs.GT 2022
Year: 2022

A critical aspect in strategic modeling of transportation systems is user heterogeneity. In many real-world scenarios, e.g., when tolls are charged and drivers have different trade-offs between time and money, or when they get informed about current congestion by different routing apps, modeling users as rational decision makers with homogeneous utility functions becomes too restrictive. While global asymptotic stability of user equilibria in homogeneous routing games is known to hold for a broad class of evolutionary dynamics, the stability analysis of user equilibria in heterogeneous routing games is a largely open problem. In this work we study the logit dynamics in heterogeneous routing games on arbitrary network topologies. We show that the dynamics may exhibit bifurcations as the noise level of the dynamics varies, and provide sufficient conditions for asymptotic stability of user equilibria.
The Role of Executable Abstract Programs in Software Development and Documentation
Venue: arXiv cs.SE 2022
Year: 2022

We present Executable Abstract Programs and analyse their role for software development and documentation. The intuitive understanding of these programs fits the computational mindset of software system engineers and is supported by a simple but precise behavioural definition. Therefore, they can be smoothly integrated in the practitioner's daily work to rigorously formulate every design and implementation decision taken on the path from the Executable Abstract Program for the requirements to the targeted and efficiently runnable code. The Executable Abstract Programs of the resulting system documentation represent definitions of implementation steps one can check and justify by testing (due to their executable character) or by reasoning (due to the mathematical definition of their behaviour). For complex systems the implementation involves multiple (orthogonal or successive) implementation steps which represent instances of a practical computational refinement concept. Such a system development process is driven by computational refinements and is strictly limited to explicitly formulate and justify -- besides the requirements -- only the necessary implementation steps. As a consequence, it produces as side-effect a corpus of documentation that facilitates the understandability of the final code and improves its reliability and resilience; it also enhances the maintenance process (including reuse and change of abstract programs and code) and reduces maintenance cost.
Cryptanalysis of a privacy-preserving behavior-oriented authentication scheme
Venue: arXiv cs.CR 2022
Year: 2022

Continuous authentication has been proposed as a complementary security mechanism to password-based authentication for computer devices that are handled directly by humans, such as smart phones. Continuous authentication has some privacy issues as certain user features and actions are revealed to the authentication server, which is not assumed to be trusted. Wei et al. proposed in 2021 a privacy-preserving protocol for behavioral authentication that utilizes homomorphic encryption. The encryption prevents the server from obtaining sampled user features. In this paper, we show that the Wei et al. scheme is insecure regarding both an honest-but-curious server and an active eavesdropper. We present two attacks: The first attack enables the authentication server to obtain the secret user key, plaintext behavior template and plaintext authentication behavior data from encrypted data. The second attack enables an active eavesdropper to restore the plaintext authentication behavior data from the transmitted encrypted data.
A Clustering Method Based on Information Entropy Payload
Venue: arXiv cs.LG 2022
Year: 2022

Existing clustering algorithms such as K-means often need to preset parameters such as the number of categories K, and such parameters may lead to the failure to output objective and consistent clustering results. This paper introduces a clustering method based on the information theory, by which clusters in the clustering result have maximum average information entropy (called entropy payload in this paper). This method can bring the following benefits: firstly, this method does not need to preset any super parameter such as category number or other similar thresholds, secondly, the clustering results have the maximum information expression efficiency. it can be used in image segmentation, object classification, etc., and could be the basis of unsupervised learning.
Ermittlung und Kommunikation von Anforderungen in etablierten UX-Prozessen
Venue: arXiv cs.SE 2022
Year: 2022

There is a strong overlap between requirements engineering (RE) and user experience (UX). Nevertheless, in practice both disciplines are often performed by separate roles and there are deficits in collaboration. In order to provide starting points for the further development of roles, activities and artifacts of the disciplines, the Requirements Engineering and User Experience Working Group (AK REUX) has been conducting a series of case studies since 2021, analyzing the UX processes of different companies from a RE perspective. We presented interim results of this investigation at the RE specialist group meeting in 2022 and compared them with the experiences of the participants.
Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation
Venue: arXiv cs.CL 2022
Year: 2022

Out-of-Domain (OOD) detection is a key component in a task-oriented dialog system, which aims to identify whether a query falls outside the predefined supported intent set. Previous softmax-based detection algorithms are proved to be overconfident for OOD samples. In this paper, we analyze overconfident OOD comes from distribution uncertainty due to the mismatch between the training and test distributions, which makes the model can't confidently make predictions thus probably causing abnormal softmax scores. We propose a Bayesian OOD detection framework to calibrate distribution uncertainty using Monte-Carlo Dropout. Our method is flexible and easily pluggable into existing softmax-based baselines and gains 33.33\% OOD F1 improvements with increasing only 0.41\% inference time compared to MSP. Further analyses show the effectiveness of Bayesian learning for OOD detection.