Reviewerly/exHarmony huggingface dataset:
Learned Hyperspectral Compression Using a Student’s T Hyperprior

Hyperspectral compression is one of the most common techniques in hyperspectral image processing. Most recent learned image compression methods have exhibited excellent rate-distortion performance for natural images, but they have not been fully explored for hyperspectral compression tasks. In this paper, we propose a trainable network architecture for hyperspectral compression tasks, which not only considers the anisotropic characteristic of hyperspectral images but also embeds an accurate entropy model using the non-Gaussian prior knowledge of hyperspectral images and nonlinear transform. Specifically, we first design a spatial-spectral block, involving a spatial net and a spectral net as the base components of the core autoencoder, which is more consistent with the anisotropic hyperspectral cubes than the existing compression methods based on deep learning. Then, we design a Student’s T hyperprior that merges the statistics of the latents and the side information concepts into a unified neural network to provide an accurate entropy model used for entropy coding. This not only remarkably enhances the flexibility of the entropy model by adjusting various values of the degree of freedom, but also leads to a superior rate-distortion performance. The results illustrate that the proposed compression scheme supersedes the Gaussian hyperprior universally for virtually all learned natural image codecs and the optimal linear transform coding methods for hyperspectral compression. Specifically, the proposed method provides a 1.51% to 59.95% average increase in peak signal-to-noise ratio, a 0.17% to 18.17% average increase in the structural similarity index metric and a 6.15% to 64.60% average reduction in spectral angle mapping over three public hyperspectral datasets compared to the Gaussian hyperprior and the optimal linear transform coding methods.
EasyTransfer

The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose. To bridge this gap, the EasyTransfer platform is designed to develop deep TL algorithms for NLP applications. EasyTransfer is backended with a high-performance and scalable engine for efficient training and inference, and also integrates comprehensive deep TL algorithms, to make the development of industrial-scale TL applications easier. In EasyTransfer, the built-in data and model parallelism strategies, combined with AI compiler optimization, show to be 4.0x faster than the community version of distributed training. EasyTransfer supports various NLP models in the ModelZoo, including mainstream PLMs and multi-modality models. It also features various in-house developed TL algorithms, together with the AppZoo for NLP applications. The toolkit is convenient for users to quickly start model training, evaluation, and online deployment. EasyTransfer is currently deployed at Alibaba to support a variety of business scenarios, including item recommendation, personalized search, conversational question answering, etc. Extensive experiments on real-world datasets and online applications show that EasyTransfer is suitable for online production with cutting-edge performance for various applications. The source code of EasyTransfer is released at Github1.
AS-level BGP community usage classification

BGP communities are a popular mechanism used by network operators for traffic engineering, blackholing, and to realize network policies and business strategies. In recent years, many research works have contributed to our understanding of how BGP communities are utilized, as well as how they can reveal secondary insights into real-world events such as outages and security attacks. However, one fundamental question remains unanswered: "Which ASes tag announcements with BGP communities and which remove communities in the announcements they receive?" A grounded understanding of where BGP communities are added or removed can help better model and predict BGP-based actions in the Internet and characterize the strategies of network operators.
Comparative Analysis of Machine Learning Algorithm for Classification of different Osteosarcoma types

Histopathological osteosarcoma data analysis gives a lucrative way to study the pathological texture of Osteosarcoma. Osteosarcoma is classifying into Viable, Necrotic, and Non-Tumor. A Viable cell indicates a cancerous cell element and a necrotic cell means the cancerous cell nuclei kill by chemotherapy or radiation therapy. Necrosis is the process where cell injury results in the death of a cell. Classification of Osteosarcoma is a very time-consuming and complicated task due to its inter-class similarities and variations. Improvement in Machine Learning(ML) algorithm and Graphical Processing Unit(GPU) gives more accurate results for the classification and prediction of Osteosarcoma. In this paper, we use four ML algorithms for the classification of OST: Decision Tree(DT), Support Vector Machine(SVM), K-Nearest Neighbors(KNN) and AdaBoost(Adaptive Boosting). The ML model performance evaluation uses performance metrics like Accuracy, Sensitivity, specificity, F1 -score, Kappa, and AUC (area under the curve). All four classifiers successfully classified Osteosarcoma into three types. The overall accuracy of DT, SVM, KNN and Adboost is 81.22%, 83.80%, 86.90% and 91.70% respectively. Adaboost algorithm outperforms the other three algorithms with overall accuracy 91.70 %, sensitivity 91.60 %, specificity 96.00%, F1 score 90.60%, Kappa 0.87 and AUC 0.99.
Decoupled self-supervised label augmentation for fully-supervised image classification

Self-supervised label augmentation has emerged as an effective means to overcome the data scarcity problem for supervised vision tasks. Existing rotation-based self-supervised label augmentation methods either impose or relax the rotation invariance constraint on the primary classifier, which omit necessary supervisory information and may degrade the classification performance depending on the given working data. To overcome this problem, we propose a decoupled self-supervised label augmentation method to enhance the feature representation for fully-supervised image classification. Experimental results on diverse datasets demonstrate that the proposed method consistently outperforms state-of-the-art data augmentation methods. The proposed method is also complementary to conventional data augmentation methods, such as AutoAugment and Fast AutoAugment. • We decouple the image classification task into three tasks with different objectives. • We introduce a nonlinear projection head to address the semantic variation. • We introduce a gating mechanism for self-supervised feature representation learning.
A Semantic Data Marketplace for Easy Data Sharing within a Smart City

Today, smart city applications are largely based on data collected from different stakeholders. This presupposes that the required data sources are publicly available. While open data platforms already provide a number of urban data sources, enterprises and citizens have few opportunities to make their data available. To complicate things further, if the data is published, the processing of this data is already extremely time-consuming today, as the data sources are heterogeneous and the corresponding homogenization has to be carried out by the data consumers themselves. In this paper, we present a data marketplace that enables different stakeholders (public institutions, enterprises, citizens) to easily provide data that can especially contribute to the further realization of smart cities. This marketplace is based on the principles of semantic data management, i.e., data providers annotate their added data with semantic models. With the help of these models, the data sources can be found and understood by data consumers and finally homogenized in a way that is suitable for their application.
Shanghai memory as a digital humanities platform to rebuild the history of the city

Abstract With the development of digital technology, Memory Projects, such as the American Memory and the Venice Time Machine, have been explored continually as typical digital humanity projects. In these projects, humanists are able to look into the thousand-year-history with a macro view by a ‘data telescope’, as well as numerous micro views by ‘data microscopes’. This article investigates representative historical memory and city memory projects from over the past 20 years and analyses the purpose, investigation, cooperation, methods, implementation, and technical routes adopted. The following problems are apparent: resource decentralization and fragmentation, duplication of database construction, single-user service mode and low utilization rate of cultural heritage resources, etc. This study provides an overall technical route to solve these problems by examining ongoing practice. The Shanghai Memory Project, undertaken by Shanghai Library, uses knowledge reorganization and data reconciling technologies such as Big Data, Semantic Web, and Knowledge Graph to construct an overarching Humanities Research Environment, which is co-constructing, sharing, and interconnecting to avoid problems of resource decentralization and database duplication. The Project provides a user service model, which combines the library creating knowledge with user-generated content. Implementing data visualization technologies, such as AR, VR, etc., the project improves the collection display and promotes the utilization of the resources. The Urban Memory Project is a comprehensive program that relies on cooperation and interdisciplinary collaboration, co-construction, and the sharing of data. Meanwhile, an increasing number of Urban Memory Projects play a significant role in helping to develop the Digital Humanities’ infrastructure.
The Potential of Using Vision Videos for CrowdRE: Video Comments as a Source of Feedback

Vision videos are established for soliciting feedback and stimulating discussions in requirements engineering (RE) practices such as focus groups. Different researchers motivated the transfer of these benefits into crowd-based RE (CrowdRE) by using vision videos on social media platforms. So far, however, little research explored the potential of using vision videos for CrowdRE in detail. In this paper, we analyze and assess this potential, in particular, focusing on video comments as a source of feedback. In a case study, we analyzed 4505 comments on a vision video from YouTube. We found that the video solicited 2770 comments from 2660 viewers in four days. This is more than 50% of all comments the video received in four years. Even though only a certain fraction of these comments are relevant to RE, the relevant comments address typical intentions and topics of user feedback, such as feature request or problem report. Besides the typical user feedback categories, we found more than 300 comments that address the topic safety which has not appeared in previous analyses of user feedback. In an automated analysis, we compared the performance of three machine learning algorithms on classifying the video comments. Despite certain differences, the algorithms classified the video comments well. Based on these findings, we conclude that the use of vision videos for CrowdRE has a large potential. Despite the preliminary nature of the case study, we are optimistic that vision videos can motivate stakeholders to actively participate in a crowd and solicit numerous of video comments as a valuable source of feedback.
Equity and Privacy: More Than Just a Tradeoff

While the entire field of privacy preserving data analytics is focused on the privacy-utility tradeoff, recent work has shown that privacy preserving data publishing can introduce different levels of utility across different population groups. It is important to understand this new tradeoff between privacy and equity as privacy technology is being deployed in situations where the data products will be used for research and policy making. Will marginal populations see disproportionately less utility from privacy technology? If there is an inequity how can we address it?
Philosophy of Education in Times of Crises and Pandemics

In much of the philosophy of education today, diagnoses of socio-political pathologies underpin visions of a more desirable, democratic future. However, the very philosophical act of making an educational vision responsive to (and dependent on) crises of the times is rarely, if ever, critiqued. On the contrary, a pattern of standardised research steps is being consolidated, one that reflects medicalised politics of identifying a critically “ill” present, offering “cures” that promise a better future. In this article, it is argued that this pattern has major epistemic and political risks. It may jeopardise the quality of educational–philosophical research, and it may make philosophy of education overlook new, undemocratic politics. This article briefly discusses the pattern, and then the risks of the medical metaphors on which the pattern relies. One such risk concerns what counts as politically “ill” in “pandemic times”, and new polarisations, such as “the vaccinated versus the unvaccinated”, may thus be introduced. Finally, the article suggests that philosophy of education should consider some de-medicalisation of the notion of pandemics.
Inductive Matrix Completion Using Graph Autoencoder

Recently, the graph neural network (GNN) has shown great power in matrix completion by formulating a rating matrix as a bipartite graph and then predicting the link between the corresponding user and item nodes. The majority of GNN-based matrix completion methods are based on Graph Autoencoder (GAE), which considers the one-hot index as input, maps a user (or item) index to a learnable embedding, applies a GNN to learn the node-specific representations based on these learnable embeddings and finally aggregates the representations of the target users and its corresponding item nodes to predict missing links. However, without node content (i.e., side information) for training, the user (or item) specific representation can not be learned in the inductive setting, that is, a model trained on one group of users (or items) cannot adapt to new users (or items). To this end, we propose an inductive matrix completion method using GAE (IMC-GAE), which utilizes the GAE to learn both the user-specific (or item-specific) representation for personalized recommendation and local graph patterns for inductive matrix completion. Specifically, we design two informative node features and employ a layer-wise node dropout scheme in GAE to learn local graph patterns which can be generalized to unseen data. The main contribution of our paper is the capability to efficiently learn local graph patterns in GAE, with good scalability and superior expressiveness compared to previous GNN-based matrix completion methods. Furthermore, extensive experiments demonstrate that our model achieves state-of-the-art performance on several matrix completion benchmarks.
Graph Structure Aware Contrastive Knowledge Distillation for Incremental Learning in Recommender Systems

Personalized recommender systems are playing an increasingly important role for online services. Graph Neural Network (GNN) based recommender models have demonstrated a superior capability to model users' interests thanks to rich relational information encoded in graphs. However, with the ever-growing volume of online information and the high computational complexity of training GNNs, it is difficult to perform frequent updates to provide the most up-to-date recommendations. There have been several attempts towards training GNN models in an incremental fashion to enable faster training times and permit more frequent model updates using the latest training data. The main technique is knowledge distillation, which aims to allow model updates while preserving key aspects of the model that were learned from the historical data. In this work, we develop a novel Graph Structure Aware Contrastive Knowledge Distillation for Incremental Learning in recommender systems, which is tailored to focus on the rich relational information in the recommendation context. We combine the contrastive distillation formulation with intermediate layer distillation to inject layer-level supervision. We demonstrate the effectiveness of our proposed distillation framework for GNN based recommendation systems on four commonly used datasets, showing consistent improvement over state-of-the-art alternatives.
Generating facial expression adversarial examples based on saliency map

The security demands in humanization should be considered as important, because artificial intelligence is developing rapidly in this area. Recent studies have shown the vulnerability of many deep learning models to adversarial examples; however, only a few studies on facial expression adversarial examples have been conducted. Thus, in this paper, we propose a novel method for generating facial expression adversarial examples using facial saliency maps and facial masking maps. Extensive numerical experiments demonstrate the outstanding performance of our method in terms of attack accuracy, structural similarity index measure score, and computational time, compared with other leading methods, such as the fast gradient sign method, projected gradient descent method, and Carlini–Wagner attacks.
Stopping criteria for, and strong convergence of, stochastic gradient descent on Bottou-Curtis-Nocedal functions

Stopping criteria for Stochastic Gradient Descent (SGD) methods play important roles from enabling adaptive step size schemes to providing rigor for downstream analyses such as asymptotic inference. Unfortunately, current stopping criteria for SGD methods are often heuristics that rely on asymptotic normality results or convergence to stationary distributions, which may fail to exist for nonconvex functions and, thereby, limit the applicability of such stopping criteria. To address this issue, in this work, we rigorously develop two stopping criteria for SGD that can be applied to a broad class of nonconvex functions, which we term Bottou-Curtis-Nocedal functions. Moreover, as a prerequisite for developing these stopping criteria, we prove that the gradient function evaluated at SGD’s iterates converges strongly to zero for Bottou-Curtis-Nocedal functions, which addresses an open question in the SGD literature. As a result of our work, our rigorously developed stopping criteria can be used to develop new adaptive step size schemes or bolster other downstream analyses for nonconvex functions.
WikiCheck

With the growth of fake news and disinformation, the NLP community has been working to assist humans in fact-checking. However, most academic research has focused on model accuracy without paying attention to resource efficiency, which is crucial in real-life scenarios. In this work, we review the State-of-the-Art datasets and solutions for Automatic Fact-checking and test their applicability in production environments. We discover overfitting issues in those models, and we propose a data filtering method that improves the model's performance and generalization. Then, we design an unsupervised fine-tuning of the Masked Language models to improve its accuracy working with Wikipedia. We also propose a novel query enhancing method to improve evidence discovery using the Wikipedia Search API. Finally, we present a new fact-checking system, the WikiCheck API that automatically performs a facts validation process based on the Wikipedia knowledge base. It is comparable to SOTA solutions in terms of accuracy and can be used on low-memory CPU instances.
Facial Recognition in Collaborative Learning Videos

Face recognition in collaborative learning videos presents many challenges. In collaborative learning videos, students sit around a typical table at different positions to the recording camera, come and go, move around, get partially or fully occluded. Furthermore, the videos tend to be very long, requiring the development of fast and accurate methods.
ADC: Adversarial attacks against object Detection that evade Context consistency checks

Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples, which are slightly perturbed input images which lead DNNs to make wrong predictions. To protect from such examples, various defense strategies have been proposed. A very recent defense strategy for detecting adversarial examples, that has been shown to be robust to current attacks, is to check for intrinsic context consistencies in the input data, where context refers to various relationships (e.g., object-to-object co-occurrence relationships) in images. In this paper, we show that even context consistency checks can be brittle to properly crafted adversarial examples and to the best of our knowledge, we are the first to do so. Specifically, we propose an adaptive framework to generate examples that subvert such defenses, namely, Adversarial attacks against object Detection that evade Context consistency checks (ADC). In ADC, we formulate a joint optimization problem which has two attack goals, viz., (i) fooling the object detector and (ii) evading the context consistency check system, at the same time. Experiments on both PASCAL VOC and MS COCO datasets show that examples generated with ADC fool the object detector with a success rate of over 85% in most cases, and at the same time evade the recently proposed context consistency checks, with a "bypassing" rate of over 80% in most cases. Our results suggest that "how to robustly model con- text and check its consistency," is still an open problem.
Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images

We address the problem of multi-person 3D body pose and shape estimation from a single image. While this problem can be addressed by applying single-person approaches multiple times for the same scene, recent works have shown the advantages of building upon deep architectures that simultaneously reason about all people in the scene in a holistic manner by enforcing, e.g., depth order constraints or minimizing interpenetration among reconstructed bodies. However, existing approaches are still unable to capture the size variability of people caused by the inherent body scale and depth ambiguity. In this work, we tackle this challenge by devising a novel optimization scheme that learns the appropriate body scale and relative camera pose, by enforcing the feet of all people to remain on the ground floor. A thorough evaluation on MuPoTS- 3D and 3DPW datasets demonstrates that our approach is able to robustly estimate the body translation and shape of multiple people while retrieving their spatial arrangement, consistently improving current state-of-the-art, especially in scenes with people of very different heights. Code can be found at: https://github.com/nicolasugrinovic/size_depth_disambiguation