OpenRaiser/Intern-Atlas huggingface dataset:
CED: Catalog Extraction from Documents
Venue: arXiv cs.CL 2023
Year: 2023

Sentence-by-sentence information extraction from long documents is an exhausting and error-prone task. As the indicator of document skeleton, catalogs naturally chunk documents into segments and provide informative cascade semantics, which can help to reduce the search space. Despite their usefulness, catalogs are hard to be extracted without the assist from external knowledge. For documents that adhere to a specific template, regular expressions are practical to extract catalogs. However, handcrafted heuristics are not applicable when processing documents from different sources with diverse formats. To address this problem, we build a large manually annotated corpus, which is the first dataset for the Catalog Extraction from Documents (CED) task. Based on this corpus, we propose a transition-based framework for parsing documents into catalog trees. The experimental results demonstrate that our proposed method outperforms baseline systems and shows a good ability to transfer. We believe the CED task could fill the gap between raw text segments and information extraction tasks on extremely long documents. Data and code are available at \url{https://github.com/Spico197/CatalogExtraction}
Client Recruitment for Federated Learning in ICU Length of Stay Prediction
Venue: arXiv cs.LG 2023
Year: 2023

Machine and deep learning methods for medical and healthcare applications have shown significant progress and performance improvement in recent years. These methods require vast amounts of training data which are available in the medical sector, albeit decentralized. Medical institutions generate vast amounts of data for which sharing and centralizing remains a challenge as the result of data and privacy regulations. The federated learning technique is well-suited to tackle these challenges. However, federated learning comes with a new set of open problems related to communication overhead, efficient parameter aggregation, client selection strategies and more. In this work, we address the step prior to the initiation of a federated network for model training, client recruitment. By intelligently recruiting clients, communication overhead and overall cost of training can be reduced without sacrificing predictive performance. Client recruitment aims at pre-excluding potential clients from partaking in the federation based on a set of criteria indicative of their eventual contributions to the federation. In this work, we propose a client recruitment approach using only the output distribution and sample size at the client site. We show how a subset of clients can be recruited without sacrificing model performance whilst, at the same time, significantly improving computation time. By applying the recruitment approach to the training of federated models for accurate patient Length of Stay prediction using data from 189 Intensive Care Units, we show how the models trained in federations made up from recruited clients significantly outperform federated models trained with the standard procedure in terms of predictive power and training time.
X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation
Venue: arXiv cs.LG 2023
Year: 2023

Tensor graph superoptimisation systems perform a sequence of subgraph substitution to neural networks, to find the optimal computation graph structure. Such a graph transformation process naturally falls into the framework of sequential decision-making, and existing systems typically employ a greedy search approach, which cannot explore the whole search space as it cannot tolerate a temporary loss of performance. In this paper, we address the tensor graph superoptimisation problem by exploring an alternative search approach, reinforcement learning (RL). Our proposed approach, X-RLflow, can learn to perform neural network dataflow graph rewriting, which substitutes a subgraph one at a time. X-RLflow is based on a model-free RL agent that uses a graph neural network (GNN) to encode the target computation graph and outputs a transformed computation graph iteratively. We show that our approach can outperform state-of-the-art superoptimisation systems over a range of deep learning models and achieve by up to 40% on those that are based on transformer-style architectures.
Benchmarking Automated Machine Learning Methods for Price Forecasting Applications
Venue: arXiv cs.LG 2023
Year: 2023

Price forecasting for used construction equipment is a challenging task due to spatial and temporal price fluctuations. It is thus of high interest to automate the forecasting process based on current market data. Even though applying machine learning (ML) to these data represents a promising approach to predict the residual value of certain tools, it is hard to implement for small and medium-sized enterprises due to their insufficient ML expertise. To this end, we demonstrate the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions, which automatically generate the underlying pipelines. We combine AutoML methods with the domain knowledge of the companies. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. To take all complex industrial requirements into account and to demonstrate the applicability of our new approach, we designed a novel metric named method evaluation score, which incorporates the most important technical and non-technical metrics for quality and usability. Based on this metric, we show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts for innovative small and medium-sized enterprises which are interested in conducting such solutions.
Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters
Venue: arXiv cs.CV 2023
Year: 2023

The success of deep learning is frequently described as the ability to train all parameters of a network on a specific application in an end-to-end fashion. Yet, several design choices on the camera level, including the pixel layout of the sensor, are considered as pre-defined and fixed, and high resolution, regular pixel layouts are considered to be the most generic ones in computer vision and graphics, treating all regions of an image as equally important. While several works have considered non-uniform, \eg, hexagonal or foveated, pixel layouts in hardware and image processing, the layout has not been integrated into the end-to-end learning paradigm so far. In this work, we present the first truly end-to-end trained imaging pipeline that optimizes the size and distribution of pixels on the imaging sensor jointly with the parameters of a given neural network on a specific task. We derive an analytic, differentiable approach for the sensor layout parameterization that allows for task-specific, local varying pixel resolutions. We present two pixel layout parameterization functions: rectangular and curvilinear grid shapes that retain a regular topology. We provide a drop-in module that approximates sensor simulation given existing high-resolution images to directly connect our method with existing deep learning models. We show that network predictions benefit from learnable pixel layouts for two different downstream tasks, classification and semantic segmentation.
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics
Venue: arXiv cs.LG 2023
Year: 2023

Self-training based semi-supervised learning algorithms have enabled the learning of highly accurate deep neural networks, using only a fraction of labeled data. However, the majority of work on self-training has focused on the objective of improving accuracy, whereas practical machine learning systems can have complex goals (e.g. maximizing the minimum of recall across classes, etc.) that are non-decomposable in nature. In this work, we introduce the Cost-Sensitive Self-Training (CSST) framework which generalizes the self-training-based methods for optimizing non-decomposable metrics. We prove that our framework can better optimize the desired non-decomposable metric utilizing unlabeled data, under similar data distribution assumptions made for the analysis of self-training. Using the proposed CSST framework, we obtain practical self-training methods (for both vision and NLP tasks) for optimizing different non-decomposable metrics using deep neural networks. Our results demonstrate that CSST achieves an improvement over the state-of-the-art in majority of the cases across datasets and objectives.
The Logical Essence of Compiling With Continuations
Venue: arXiv cs.LO 2023
Year: 2023

The essence of compiling with continuations is that conversion to continuation-passing style (CPS) is equivalent to a source language transformation converting to administrative normal form (ANF). Taking as source language Moggi's computational lambda-calculus (lbc), we define an alternative to the CPS-translation with target in the sequent calculus LJQ, named value-filling style (VFS) translation, and making use of the ability of the sequent calculus to represent contexts formally. The VFS-translation requires no type translation: indeed, double negations are introduced only when encoding the VFS target language in the CPS target language. This optional encoding, when composed with the VFS-translation reconstructs the original CPS-translation. Going back to direct style, the "essence" of the VFS-translation is that it reveals a new sublanguage of ANF, the value-enclosed style (VES), next to another one, the continuation-enclosing style (CES): such an alternative is due to a dilemma in the syntax of lbc, concerning how to expand the application constructor. In the typed scenario, VES and CES correspond to an alternative between two proof systems for call-by-value, LJQ and natural deduction with generalized applications, confirming proof theory as a foundation for intermediate representations.
A Network Perspective on the Influence of Code Review Bots on the Structure of Developer Collaborations
Venue: arXiv cs.SE 2023
Year: 2023

Background: Despite a growing body of literature on the impact of software bots on open source software development teams, their effects on team communication, coordination, and collaboration practices are not well understood. Bots can have negative consequences, such as producing information overload or reducing interactions between developers. Objective: The objective of this study is to investigate the effect of specific GitHub Actions on the collaboration networks of Open Source Software teams, using a network-analytic approach. The study will focus on Code Review bots, one of the most frequently implemented types of bots. Method: Fine-grained, time-stamped data of co-editing networks, developer-file contribution networks, as well as workflow runs and git commit logs will be obtained from a large sample of GitHub repositories. This will allow us to study how bots affect the collaboration networks of developers over time. By using a more representative sample of GitHub repositories than previous studies, which includes projects whose sizes span the whole range of Open Source communities, this study will provide generalizable results and updated findings on the general usage and distribution of GitHub Actions. With this study, we aim to contribute to advancing our knowledge of human-bot interaction and the effects of support tools on software engineering teams.
A Benchmarking Proposal for DevOps Practices on Open Source Software Projects
Venue: arXiv cs.SE 2023
Year: 2023

The popularity of open-source software (OSS) projects has grown significantly over the last few years with more organizations relying on them. As these projects become larger, the need for higher quality also increases. DevOps practices have been shown to improve quality and performance. The DORA benchmarking reports provide useful information to compare DevOps practices performance between organizations, but they focus on continuous deployment and delivery to production, while OSS projects focus on the continuous release of code and its impact on third parties. The DORA reports mention the increasing presence of OSS projects as they are widely used in the industry, but they have never been used to measure OSS projects performance levels. This study reveals that the DORA benchmark cannot be applied to OSS projects and proposes benchmarking metrics for OSS projects, being the first one that adapts the DORA metrics and applies them in OSS projects. The metrics proposed in this study for benchmarking OSS projects include Release Frequency and Lead Time For Released Changes to measure throughput, and Time To Repair Code and Bug Issues Rate to assess stability. In contrast to the DORA reports, where data is collected through manual surveys, in our proposal, data is collected automatically by a tool we developed that retrieves information from public GitHub repositories. This reduces the risk of survey-based data collection. Our study also shows the benchmark feasibility by applying it to four popular OSS projects: Angular, Kubernetes, Tensorflow, and VS Code. In addition, we proposed challenges that address the topics and future works to expand the knowledge and findings of this study. Overall, the findings of the study can help to improve future research on OSS projects and provide a better understanding and challenges of the role of DevOps practices in OSS projects.
Channel Orthogonalization with Reconfigurable Surfaces
Venue: arXiv cs.IT 2023
Year: 2023

Orthogonal multi-user multiple-input multiple-output (MU-MIMO) channels allow for optimum performance with simplified precoding/equalization, and they achieve maximum multiplexing gain which is shared fairly among users. Reconfigurable intelligent surface (RIS) constitutes a promising cost-efficient solution to improve the wireless channel, since they consist of passive reflecting elements able to adjust the phases of the incoming waves. However, it is still widely unclear how these surfaces can improve spatial-multiplexing. In fact, the common RIS model cannot achieve perfect orthogonalization of MU-MIMO channels with a reasonable number of elements. Furthermore, efficient channel estimation algorithms for RIS, which are key for taking advantage of its benefits, are still a matter of research. We study two types of reconfigurable surfaces (RSs), namely amplitude-reconfigurable intelligent surface (ARIS) and fully-reconfigurable intelligent surface (FRIS), with extended capabilities over RIS. We show how these RSs allow for perfect channel orthogonalization, and, by minimizing the applied power, we show that they can potentially be implemented without the need of amplification. We also present an efficient channel estimation method for each of them that allows the base station (BS) to select the desired propagation channel.
Adaptive Gravity Compensation Control of a Cable-Driven Upper-Arm Soft Exosuit
Venue: arXiv cs.RO 2023
Year: 2023

This paper proposes an adaptive gravity compensation (AGC) control strategy for a cable-driven upper-limb exosuit intended to assist the wearer with lifting tasks. Unlike most model-based control techniques used for this human-robot interaction task, the proposed control design does not assume knowledge of the anthropometric parameters of the wearer's arm and the payload. Instead, the uncertainties in human arm parameters, such as mass, length, and payload, are estimated online using an indirect adaptive control law that compensates for the gravity moment about the elbow joint. Additionally, the AGC controller is agnostic to the desired joint trajectory followed by the human arm. For the purpose of controller design, the human arm is modeled using a 1-DOF manipulator model. Further, a cable-driven actuator model is proposed that maps the assistive elbow torque to the actuator torque. The performance of the proposed method is verified through a co-simulation, wherein the control input realized in MATLAB is applied to the human bio-mechanical model in OpenSim under varying payload conditions. Significant reductions in human effort in terms of human muscle torque and metabolic cost are observed with the proposed control strategy. Further, simulation results show that the performance of the AGC controller converges to that of the gravity compensation (GC) controller, demonstrating the efficacy of AGC-based online parameter learning.
Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption
Venue: arXiv cs.LG 2023
Year: 2023

Training large-scale CNNs that during inference can be run under Homomorphic Encryption (HE) is challenging due to the need to use only polynomial operations. This limits HE-based solutions adoption. We address this challenge and pioneer in providing a novel training method for large polynomial CNNs such as ResNet-152 and ConvNeXt models, and achieve promising accuracy on encrypted samples on large-scale dataset such as ImageNet. Additionally, we provide optimization insights regarding activation functions and skip-connection latency impacts, enhancing HE-based evaluation efficiency. Finally, to demonstrate the robustness of our method, we provide a polynomial adaptation of the CLIP model for secure zero-shot prediction, unlocking unprecedented capabilities at the intersection of HE and transfer learning.
The ACM Multimedia 2023 Computational Paralinguistics Challenge: Emotion Share & Requests
Venue: arXiv cs.SD 2023
Year: 2023

The ACM Multimedia 2023 Computational Paralinguistics Challenge addresses two different problems for the first time in a research competition under well-defined conditions: In the Emotion Share Sub-Challenge, a regression on speech has to be made; and in the Requests Sub-Challenges, requests and complaints need to be detected. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the usual ComPaRE features, the auDeep toolkit, and deep feature extraction from pre-trained CNNs using the DeepSpectRum toolkit; in addition, wav2vec2 models are used.
Uncertainty Aware Neural Network from Similarity and Sensitivity
Venue: arXiv cs.LG 2023
Year: 2023

Researchers have proposed several approaches for neural network (NN) based uncertainty quantification (UQ). However, most of the approaches are developed considering strong assumptions. Uncertainty quantification algorithms often perform poorly in an input domain and the reason for poor performance remains unknown. Therefore, we present a neural network training method that considers similar samples with sensitivity awareness in this paper. In the proposed NN training method for UQ, first, we train a shallow NN for the point prediction. Then, we compute the absolute differences between prediction and targets and train another NN for predicting those absolute differences or absolute errors. Domains with high average absolute errors represent a high uncertainty. In the next step, we select each sample in the training set one by one and compute both prediction and error sensitivities. Then we select similar samples with sensitivity consideration and save indexes of similar samples. The ranges of an input parameter become narrower when the output is highly sensitive to that parameter. After that, we construct initial uncertainty bounds (UB) by considering the distribution of sensitivity aware similar samples. Prediction intervals (PIs) from initial uncertainty bounds are larger and cover more samples than required. Therefore, we train bound correction NN. As following all the steps for finding UB for each sample requires a lot of computation and memory access, we train a UB computation NN. The UB computation NN takes an input sample and provides an uncertainty bound. The UB computation NN is the final product of the proposed approach. Scripts of the proposed method are available in the following GitHub repository: github.com/dipuk0506/UQ
Randomness Requirements for Three-Secret Sharing
Venue: arXiv cs.IT 2023
Year: 2023

We study a secret sharing problem with three secrets where the secrets are allowed to be related to each other, i.e., only certain combinations of the three secrets are permitted. The dealer produces three shares such that every pair of shares reveals a unique secret and reveals nothing about the other two secrets, other than what can be inferred from the revealed secret. For the case of binary secrets, we exactly determine the minimum amount of randomness required by the dealer, for each possible set of permitted combinations. Our characterization is based on new lower and upper bounds.
QR-SACP: Quantitative Risk-based Situational Awareness Calculation and Projection through Threat Information Sharing
Venue: arXiv cs.CR 2023
Year: 2023

When a threat is observed, one of the most important challenges is to choose the most appropriate and adequate timely decisions in response to the current and near future situation in order to have the least consequences and costs. Making the appropriate and sufficient decisions requires knowing what situations the threat has engendered or may engender. In this paper, we propose a quantitative risk-based method called QR-SACP to calculate and project situational awareness in a network based on threat information sharing. In this method, we investigate a threat from different aspects and evaluate the threat's effects through dependency weight among a network's services. We calculate the definite effect of a threat on a service and the cascading propagation of the threat's definite effect on other dependent services to that service. In addition, we project the probability of a threat propagation or recurrence of the threat in other network services in three ways: procedurally, network connections and similar infrastructure or services. Experimental results demonstrate that the QR-SACP method can calculate and project definite and probable threats' effects across the entire network and reveal more details about the threat's current and near future situations.
Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps
Venue: arXiv cs.CV 2023
Year: 2023

A road is the skeleton of a city and is a fundamental and important geographical component. Currently, many countries have built geo-information databases and gathered large amounts of geographic data. However, with the extensive construction of infrastructure and rapid expansion of cities, automatic updating of road data is imperative to maintain the high quality of current basic geographic information. However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases.To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road.Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results. Further, to fully utilize the limited amount of label information and to enhance the prediction accuracy on unlabeled images, we utilized the mean teacher framework as the basic semi-supervised learning framework and introduced Regional Contrast (ReCo) in our work to improve the model capacity for distinguishing between the characteristics of roads and background elements.We applied our method to two datasets. Our model can effectively improve the performance of a model with fewer labels. Overall, the proposed SRUNet can provide stable, up-to-date, and reliable prediction results for a wide range of road renewal tasks.