OpenRaiser/Intern-Atlas huggingface dataset:
Blockchain-enabled supply chain: An experimental study
Venue: arXiv cs.CR 2022
Year: 2022

Despite Information and Communication Technologies (ICT) have reduced the information asymmetry and increased the degree of interorganizational collaboration, the companies participating a supply chain are less inclined to share data when information is sensible and partners cannot be fully trusted. In such a context, Blockchain is a decentralized certificate authority that may provide economic and operational benefits but companies operating in a supply chain claim to have little knowledge about Blockchain due to its novelty and to the lack of use cases and application studies. In this work, a software connector has been designed and developed to connect an Ethereum-like blockchain with the enterprises' information systems to allow companies to share information with their partners with different levels of visibility and to check data authenticity, integrity and invariability over time through the blockchain, thus building trust. In order to explore the potential of deploying the blockchain in a supply chain, a simulation model has been developed to recreate the supply chain operations and integrated with the blockchain through the same software connector to carry out a scenario statistical analysis. Application results shows how blockchain technology is a convenient instrument to overcome collaboration and trust issues in a supply chain, to increase the supply chain overall performance, to minimize the negative consequences of information asymmetry over the echelons of a supply chain but also to discourage companies from any misconduct (e.g. counterfeiting data or low data accuracy).
Towards Schema Inference for Data Lakes
Venue: arXiv cs.DB 2022
Year: 2022

A data lake is a repository of data with potential for future analysis. However, both discovering what data is in a data lake and exploring related data sets can take significant effort, as a data lake can contain an intimidating amount of heterogeneous data. In this paper, we propose the use of schema inference to support the interpretation of the data in the data lake. If a data lake is to support a schema-on-read paradigm, understanding the existing schema of relevant portions of the data lake seems like a prerequisite. In this paper, we make use of approximate indexes that can be used for data discovery to inform the inference of a schema for a data lake, consisting of entity types and the relationships between them. The specific approach identifies candidate entity types by clustering similar data sets from the data lake, and then relationships between data sets in different clusters are used to inform the identification of relationships between the entity types. The approach is evaluated using real-world data repositories, to identify where the proposal is effective, and to inform the identification of areas for further work.
ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain. Contrastive learning (CL) in the context of UDA can help to better separate classes in feature space. However, in image segmentation, the large memory footprint due to the computation of the pixel-wise contrastive loss makes it prohibitive to use. Furthermore, labeled target data is not easily available in medical imaging, and obtaining new samples is not economical. As a result, in this work, we tackle a more challenging UDA task when there are only a few (fewshot) or a single (oneshot) image available from the target domain. We apply a style transfer module to mitigate the scarcity of target samples. Then, to align the source and target features and tackle the memory issue of the traditional contrastive loss, we propose the centroid-based contrastive learning (CCL) and a centroid norm regularizer (CNR) to optimize the contrastive pairs in both direction and magnitude. In addition, we propose multi-partition centroid contrastive learning (MPCCL) to further reduce the variance in the target features. Fewshot evaluation on MS-CMRSeg dataset demonstrates that ConFUDA improves the segmentation performance by 0.34 of the Dice score on the target domain compared with the baseline, and 0.31 Dice score improvement in a more rigorous oneshot setting.
To remove or not remove Mobile Apps? A data-driven predictive model approach
Venue: arXiv cs.CR 2022
Year: 2022

Mobile app stores are the key distributors of mobile applications. They regularly apply vetting processes to the deployed apps. Yet, some of these vetting processes might be inadequate or applied late. The late removal of applications might have unpleasant consequences for developers and users alike. Thus, in this work we propose a data-driven predictive approach that determines whether the respective app will be removed or accepted. It also indicates the features' relevance that help the stakeholders in the interpretation. In turn, our approach can support developers in improving their apps and users in downloading the ones that are less likely to be removed. We focus on the Google App store and we compile a new data set of 870,515 applications, 56% of which have actually been removed from the market. Our proposed approach is a bootstrap aggregating of multiple XGBoost machine learning classifiers. We propose two models: user-centered using 47 features, and developer-centered using 37 features, the ones only available before deployment. We achieve the following Areas Under the ROC Curves (AUCs) on the test set: user-centered = 0.792, developer-centered = 0.762.
Direct Triangulation with Spherical Projection for Omnidirectional Cameras
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, it is proposed to solve the problem of triangulation for calibrated omnidirectional cameras through the optimisation of ray-pairs on the projective sphere. The proposed solution boils down to finding the roots of a quadratic function, and as such is closed form, completely non-iterative and computationally inexpensive when compared to previous methods. In addition, even thought the motivation is clearly to solve the triangulation problem for omnidirectional cameras, it is demonstrated that the proposed methods can be applied to non-omnidirectional, narrow field-of-view cameras.
Construction of a spike-based memory using neural-like logic gates based on Spiking Neural Networks on SpiNNaker
Venue: arXiv cs.NE 2022
Year: 2022

Neuromorphic engineering concentrates the efforts of a large number of researchers due to its great potential as a field of research, in a search for the exploitation of the advantages of the biological nervous system and the brain as a whole for the design of more efficient and real-time capable applications. For the development of applications as close to biology as possible, Spiking Neural Networks (SNNs) are used, considered biologically-plausible and that form the third generation of Artificial Neural Networks (ANNs). Since some SNN-based applications may need to store data in order to use it later, something that is present both in digital circuits and, in some form, in biology, a spiking memory is needed. This work presents a spiking implementation of a memory, which is one of the most important components in the computer architecture, and which could be essential in the design of a fully spiking computer. In the process of designing this spiking memory, different intermediate components were also implemented and tested. The tests were carried out on the SpiNNaker neuromorphic platform and allow to validate the approach used for the construction of the presented blocks. In addition, this work studies in depth how to build spiking blocks using this approach and includes a comparison between it and those used in other similar works focused on the design of spiking components, which include both spiking logic gates and spiking memory. All implemented blocks and developed tests are available in a public repository.
How to structure citations data and bibliographic metadata in the OpenCitations accepted format
Venue: arXiv cs.DL 2022
Year: 2022

The OpenCitations organization is working on ingesting citation data and bibliographic metadata directly provided by the community (e.g., scholars and publishers). The aim is to improve the general coverage of open citations, which is still far from being complete, and use the provided metadata to enrich the characterization of the citing and cited entities. This paper illustrates how the citation data and bibliographic metadata should be structured to comply with the OpenCitations accepted format.
Unsupervised Knowledge Adaptation for Passenger Demand Forecasting
Venue: arXiv cs.LG 2022
Year: 2022

Considering the multimodal nature of transport systems and potential cross-modal correlations, there is a growing trend of enhancing demand forecasting accuracy by learning from multimodal data. These multimodal forecasting models can improve accuracy but be less practical when different parts of multimodal datasets are owned by different institutions who cannot directly share data among them. While various institutions may can not share their data with each other directly, they may share forecasting models trained by their data, where such models cannot be used to identify the exact information from their datasets. This study proposes an Unsupervised Knowledge Adaptation Demand Forecasting framework to forecast the demand of the target mode by utilizing a pre-trained model based on data of another mode, which does not require direct data sharing of the source mode. The proposed framework utilizes the potential shared patterns among multiple transport modes to improve forecasting performance while avoiding the direct sharing of data among different institutions. Specifically, a pre-trained forecasting model is first learned based on the data of a source mode, which can capture and memorize the source travel patterns. Then, the demand data of the target dataset is encoded into an individual knowledge part and a sharing knowledge part which will extract travel patterns by individual extraction network and sharing extraction network, respectively. The unsupervised knowledge adaptation strategy is utilized to form the sharing features for further forecasting by making the pre-trained network and the sharing extraction network analogous. Our findings illustrate that unsupervised knowledge adaptation by sharing the pre-trained model to the target mode can improve the forecasting performance without the dependence on direct data sharing.
Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions
Venue: arXiv cs.RO 2022
Year: 2022

A key challenge for autonomous vehicles is to navigate in unseen dynamic environments. Separating moving objects from static ones is essential for navigation, pose estimation, and understanding how other traffic participants are likely to move in the near future. In this work, we tackle the problem of distinguishing 3D LiDAR points that belong to currently moving objects, like walking pedestrians or driving cars, from points that are obtained from non-moving objects, like walls but also parked cars. Our approach takes a sequence of observed LiDAR scans and turns them into a voxelized sparse 4D point cloud. We apply computationally efficient sparse 4D convolutions to jointly extract spatial and temporal features and predict moving object confidence scores for all points in the sequence. We develop a receding horizon strategy that allows us to predict moving objects online and to refine predictions on the go based on new observations. We use a binary Bayes filter to recursively integrate new predictions of a scan resulting in more robust estimation. We evaluate our approach on the SemanticKITTI moving object segmentation challenge and show more accurate predictions than existing methods. Since our approach only operates on the geometric information of point clouds over time, it generalizes well to new, unseen environments, which we evaluate on the Apollo dataset.
Texture Extraction Methods Based Ensembling Framework for Improved Classification
Venue: arXiv cs.CV 2022
Year: 2022

Texture-based classification solutions have proven their significance in many domains, from industrial inspections to health-related applications. New methods have been developed based on texture feature learning and CNN-based architectures to address computer vision use cases for images with rich texture-based features. In recent years, architectures solving texture-based classification problems and demonstrating state-of-the-art results have emerged. Yet, one limitation of these approaches is that they cannot claim to be suitable for all types of image texture patterns. Each technique has an advantage for a specific texture type only. To address this shortcoming, we propose a framework that combines more than one texture-based techniques together, uniquely, with a CNN backbone to extract the most relevant texture features. This enables the model to be trained in a self-selective manner and produce improved results over current published benchmarks -- with almost same number of model parameters. Our proposed framework works well on most texture types simultaneously and allows flexibility for additional texture-based methods to be accommodated to achieve better results than existing architectures. In this work, firstly, we present an analysis on the relative importance of existing techniques when used alone and in combination with other TE methods on benchmark datasets. Secondly, we show that Global Average Pooling which represents the spatial information -- is of less significance in comparison to the TE method(s) applied in the network while training for texture-based classification tasks. Finally, we present state-of-the-art results for several texture-based benchmark datasets by combining three existing texture-based techniques using our proposed framework.
Improved two-stage hate speech classification for twitter based on Deep Neural Networks
Venue: arXiv cs.CL 2022
Year: 2022

Hate speech is a form of online harassment that involves the use of abusive language, and it is commonly seen in social media posts. This sort of harassment mainly focuses on specific group characteristics such as religion, gender, ethnicity, etc and it has both societal and economic consequences nowadays. The automatic detection of abusive language in text postings has always been a difficult task, but it is lately receiving much interest from the scientific community. This paper addresses the important problem of discerning hateful content in social media. The model we propose in this work is an extension of an existing approach based on LSTM neural network architectures, which we appropriately enhanced and fine-tuned to detect certain forms of hatred language, such as racism or sexism, in a short text. The most significant enhancement is the conversion to a two-stage scheme consisting of Recurrent Neural Network (RNN) classifiers. The output of all One-vs-Rest (OvR) classifiers from the first stage are combined and used to train the second stage classifier, which finally determines the type of harassment. Our study includes a performance comparison of several proposed alternative methods for the second stage evaluated on a public corpus of 16k tweets, followed by a generalization study on another dataset. The reported results show the superior classification quality of the proposed scheme in the task of hate speech detection as compared to the current state-of-the-art.
Incremental Recursive Ranking Grouping for Large Scale Global Optimization
Venue: arXiv cs.NE 2022
Year: 2022

Real-world optimization problems may have a different underlying structure. In black-box optimization, the dependencies between decision variables remain unknown. However, some techniques can discover such interactions accurately. In Large Scale Global Optimization (LSGO), problems are high-dimensional. It was shown effective to decompose LSGO problems into subproblems and optimize them separately. The effectiveness of such approaches may be highly dependent on the accuracy of problem decomposition. Many state-of-the-art decomposition strategies are derived from Differential Grouping (DG). However, if a given problem consists of non-additively separable subproblems, DG-based strategies may discover many non-existing interactions. On the other hand, monotonicity checking strategies proposed so far do not report non-existing interactions for any separable subproblems but may miss discovering many of the existing ones. Therefore, we propose Incremental Recursive Ranking Grouping (IRRG) that suffers from none of these flaws. IRRG consumes more fitness function evaluations than the recent DG-based propositions, e.g., Recursive DG 3 (RDG3). Nevertheless, the effectiveness of the considered Cooperative Co-evolution frameworks after embedding IRRG or RDG3 was similar for problems with additively separable subproblems that are suitable for RDG3. After replacing the additive separability with non-additive, embedding IRRG leads to results of significantly higher quality.
Abstraction not Memory: BERT and the English Article System
Venue: arXiv cs.CL 2022
Year: 2022

Article prediction is a task that has long defied accurate linguistic description. As such, this task is ideally suited to evaluate models on their ability to emulate native-speaker intuition. To this end, we compare the performance of native English speakers and pre-trained models on the task of article prediction set up as a three way choice (a/an, the, zero). Our experiments with BERT show that BERT outperforms humans on this task across all articles. In particular, BERT is far superior to humans at detecting the zero article, possibly because we insert them using rules that the deep neural model can easily pick up. More interestingly, we find that BERT tends to agree more with annotators than with the corpus when inter-annotator agreement is high but switches to agreeing more with the corpus as inter-annotator agreement drops. We contend that this alignment with annotators, despite being trained on the corpus, suggests that BERT is not memorising article use, but captures a high level generalisation of article use akin to human intuition.
OOD Augmentation May Be at Odds with Open-Set Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Despite advances in image classification methods, detecting the samples not belonging to the training classes is still a challenging problem. There has been a burst of interest in this subject recently, which is called Open-Set Recognition (OSR). In OSR, the goal is to achieve both the classification and detecting out-of-distribution (OOD) samples. Several ideas have been proposed to push the empirical result further through complicated techniques. We believe that such complication is indeed not necessary. To this end, we have shown that Maximum Softmax Probability (MSP), as the simplest baseline for OSR, applied on Vision Transformers (ViTs) as the base classifier that is trained with non-OOD augmentations can surprisingly outperform many recent methods. Non-OOD augmentations are the ones that do not alter the data distribution by much. Our results outperform state-of-the-art in CIFAR-10 datasets, and is also better than most of the current methods in SVHN and MNIST. We show that training augmentation has a significant effect on the performance of ViTs in the OSR tasks, and while they should produce significant diversity in the augmented samples, the generated sample OOD-ness must remain limited.
CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summaries
Venue: arXiv cs.CL 2022
Year: 2022

The abstractive methods lack of creative ability is particularly a problem in automatic text summarization. The summaries generated by models are mostly extracted from the source articles. One of the main causes for this problem is the lack of dataset with abstractiveness, especially for Chinese. In order to solve this problem, we paraphrase the reference summaries in CLTS, the Chinese Long Text Summarization dataset, correct errors of factual inconsistencies, and propose the first Chinese Long Text Summarization dataset with a high level of abstractiveness, CLTS+, which contains more than 180K article-summary pairs and is available online. Additionally, we introduce an intrinsic metric based on co-occurrence words to evaluate the dataset we constructed. We analyze the extraction strategies used in CLTS+ summaries against other datasets to quantify the abstractiveness and difficulty of our new data and train several baselines on CLTS+ to verify the utility of it for improving the creative ability of models.
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes
Venue: arXiv cs.LG 2022
Year: 2022

Interpretability is an essential building block for trustworthiness in reinforcement learning systems. However, interpretability might come at the cost of deteriorated performance, leading many researchers to build complex models. Our goal is to analyze the cost of interpretability. We show that in certain cases, one can achieve policy interpretability while maintaining its optimality. We focus on a classical problem from reinforcement learning: mazes with $k$ obstacles in $\mathbb{R}^d$. We prove the existence of a small decision tree with a linear function at each inner node and depth $O(\log k + 2^d)$ that represents an optimal policy. Note that for the interesting case of a constant $d$, we have $O(\log k)$ depth. Thus, in this setting, there is no accuracy-interpretability tradeoff. To prove this result, we use a new "compressing" technique that might be useful in additional settings.
Reconstruct Face from Features Using GAN Generator as a Distribution Constraint
Venue: arXiv cs.CV 2022
Year: 2022

Face recognition based on the deep convolutional neural networks (CNN) shows superior accuracy performance attributed to the high discriminative features extracted. Yet, the security and privacy of the extracted features from deep learning models (deep features) have been often overlooked. This paper proposes the reconstruction of face images from deep features without accessing the CNN network configurations as a constrained optimization problem. Such optimization minimizes the distance between the features extracted from the original face image and the reconstructed face image. Instead of directly solving the optimization problem in the image space, we innovatively reformulate the problem by looking for a latent vector of a GAN generator, then use it to generate the face image. The GAN generator serves as a dual role in this novel framework, i.e., face distribution constraint of the optimization goal and a face generator. On top of the novel optimization task, we also propose an attack pipeline to impersonate the target user based on the generated face image. Our results show that the generated face images can achieve a state-of-the-art successful attack rate of 98.0\% on LFW under type-I attack @ FAR of 0.1\%. Our work sheds light on the biometric deployment to meet the privacy-preserving and security policies.