Reviewerly/exHarmony huggingface dataset:
Parameter-Parallel Distributed Variational Quantum Algorithm

Variational quantum algorithms (VQAs) have emerged as a promising near-term technique to explore practical quantum advantage on noisy intermediate-scale quantum (NISQ) devices. However, the inefficient parameter training process due to the incompatibility with backpropagation and the cost of a large number of measurements, posing a great challenge to the large-scale development of VQAs. Here, we propose a parameter-parallel distributed variational quantum algorithm (PPD-VQA), to accelerate the training process by parameter-parallel training with multiple quantum processors. To maintain the high performance of PPD-VQA in the realistic noise scenarios, a alternate training strategy is proposed to alleviate the acceleration attenuation caused by noise differences among multiple quantum processors, which is an unavoidable common problem of distributed VQA. Besides, the gradient compression is also employed to overcome the potential communication bottlenecks. The achieved results suggest that the PPD-VQA could provide a practical solution for coordinating multiple quantum processors to handle large-scale real-word applications.
Out-of-Distribution Detection with Semantic Mismatch under Masking

This paper proposes a novel out-of-distribution (OOD) detection framework named MoodCat for image classifiers. MoodCat masks a random portion of the input image and uses a generative model to synthesize the masked image to a new image conditioned on the classification result. It then calculates the semantic difference between the original image and the synthesized one for OOD detection. Compared to existing solutions, MoodCat naturally learns the semantic information of the in-distribution data with the proposed mask and conditional synthesis strategy, which is critical to identifying OODs. Experimental results demonstrate that MoodCat outperforms state-of-the-art OOD detection solutions by a large margin.
Toward Understanding WordArt: Corner-Guided Transformer for Scene Text Recognition

Artistic text recognition is an extremely challenging task with a wide range of applications. However, current scene text recognition methods mainly focus on irregular text while have not explored artistic text specifically. The challenges of artistic text recognition include the various appearance with special-designed fonts and effects, the complex connections and overlaps between characters, and the severe interference from background patterns. To alleviate these problems, we propose to recognize the artistic text at three levels. Firstly, corner points are applied to guide the extraction of local features inside characters, considering the robustness of corner structures to appearance and shape. In this way, the discreteness of the corner points cuts off the connection between characters, and the sparsity of them improves the robustness for background interference. Secondly, we design a character contrastive loss to model the character-level feature, improving the feature representation for character classification. Thirdly, we utilize Transformer to learn the global feature on image-level and model the global relationship of the corner points, with the assistance of a corner-query cross-attention mechanism. Besides, we provide an artistic text dataset to benchmark the performance. Experimental results verify the significant superiority of our proposed method on artistic text recognition and also achieve state-of-the-art performance on several blurred and perspective datasets.
On Interactive Explanations as Non-Monotonic Reasoning

Recent work shows issues of consistency with explanations, with methods generating local explanations that seem reasonable instance-wise, but that are inconsistent across instances. This suggests not only that instance-wise explanations can be unreliable, but mainly that, when interacting with a system via multiple inputs, a user may actually lose confidence in the system. To better analyse this issue, in this work we treat explanations as objects that can be subject to reasoning and present a formal model of the interactive scenario between user and system, via sequences of inputs, outputs, and explanations. We argue that explanations can be thought of as committing to some model behaviour (even if only prima facie), suggesting a form of entailment, which, we argue, should be thought of as non-monotonic. This allows: 1) to solve some considered inconsistencies in explanation, such as via a specificity relation; 2) to consider properties from the non-monotonic reasoning literature and discuss their desirability, gaining more insight on the interactive explanation scenario.
Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation

Training semantic segmentation models with few annotated samples has great potential in various real-world applications. For the few-shot segmentation task, the main challenge is how to accurately measure the semantic correspondence between the support and query samples with limited training data. To address this problem, we propose to aggregate the learnable covariance matrices with a deformable 4D Transformer to effectively predict the segmentation map. Specifically, in this work, we first devise a novel hard example mining mechanism to learn covariance kernels for the Gaussian process. The learned covariance kernel functions have great advantages over existing cosine similarity-based methods in correspondence measurement. Based on the learned covariance kernels, an efficient doubly deformable 4D Transformer module is designed to adaptively aggregate feature similarity maps into segmentation results. By combining these two designs, the proposed method can not only set new state-of-the-art performance on public benchmarks, but also converge extremely faster than existing methods. Experiments on three public datasets have demonstrated the effectiveness of our method.
Recurring Contingent Service Payment

Fair exchange protocols let two mutually distrustful parties exchange digital data in a way that neither party can cheat. They have various applications such as the exchange of digital items, or the exchange of digital coins and digital services between a buyer/client and seller/server. In this work, we formally define and propose a generic blockchain-based construction called "Recurring Contingent Service Payment" (RC-S-P). It (i) lets a fair exchange of digital coins and verifiable service reoccur securely between clients and a server while ensuring that the server is paid if and only if it delivers a valid service, and (ii) ensures the parties' privacy is preserved. RC-S-P supports arbitrary verifiable services, such as "Proofs of Retrievability" (PoR) or verifiable computation and imposes low on-chain overheads. Our formal treatment and construction, for the first time, consider the setting where either client or server is malicious. We also present a concrete efficient instantiation of RC- S-P when the verifiable service is PoR. We implemented the concrete instantiation and analysed its cost. When it deals with a 4-GB outsourced file, a verifier can check a proof in only 90 milliseconds, and a dispute between a prover and verifier is resolved in 0.1 milliseconds. At CCS 2017, two blockchain-based protocols were proposed to support the fair exchange of digital coins and a certain verifiable service; namely, PoR. In this work, we show that these protocols (i) are susceptible to a free-riding attack which enables a client to receive the service without paying the server, and (ii) are not suitable for cases where parties' privacy matters, e.g., when the server's proof status or buyer's file size must remain private from the public. RC- S-P simultaneously mitigates the above attack and preserves the parties' privacy.
The 2-localization of a model category

In this paper we study a 2-dimensional version of Quillen's homotopy category construction. Given a category $\mathscr{A}$ and a class of morphisms $Σ\subset \mathscr{A}$ containing the identities, we construct a 2-category $\mathcal{H}o(\mathscr{A})$ obtained by the addition of 2-cells determined by homotopies. A salient feature here is the use of a novel notion of cylinder introduced in \cite{e.d.2}. The inclusion 2-functor $\mathscr{C} \longrightarrow \mathcal{H}o(\mathscr{A})$ has a universal property which implies that it will be the 2-localization of $\mathscr{A}$ at $Σ$ as soon as the arrows of $Σ$ become equivalences in $\mathcal{H}o(\mathscr{A})$. This is then used to obtain 2-localizations of a model category $\mathscr{A}{C}$, with $Σ= \mathcal{W}$, the weak equivalences, and $\mathscr{A} = \mathscr{C}_{fc}$, the full subcategory of fibrant-cofibrant objects, as well as with $\mathscr{A} = \mathscr{C}$. The set of connected components of the hom categories yields Quillen's results. We follow the general lines established in \cite{e.d.2}, \cite{e.d.} for model bicategories. The development here is not just the examination of the general theory in a particular case. It is not concerned with and avoids the problems which arise when dealing with non invertible 2-cells. Also, the use here of functorial factorization adds further simplifications by eliminating the need of pseudofunctors. New proofs are produced which are not a mere simplified adaptation of the ones of the general case.
Automatically Categorising GitHub Repositories by Application Domain

GitHub is the largest host of open source software on the Internet. This large, freely accessible database has attracted the attention of practitioners and researchers alike. But as GitHub's growth continues, it is becoming increasingly hard to navigate the plethora of repositories which span a wide range of domains. Past work has shown that taking the application domain into account is crucial for tasks such as predicting the popularity of a repository and reasoning about project quality. In this work, we build on a previously annotated dataset of 5,000 GitHub repositories to design an automated classifier for categorising repositories by their application domain. The classifier uses state-of-the-art natural language processing techniques and machine learning to learn from multiple data sources and catalogue repositories according to five application domains. We contribute with (1) an automated classifier that can assign popular repositories to each application domain with at least 70% precision, (2) an investigation of the approach's performance on less popular repositories, and (3) a practical application of this approach to answer how the adoption of software engineering practices differs across application domains. Our work aims to help the GitHub community identify repositories of interest and opens promising avenues for future work investigating differences between repositories from different application domains.
GAMIFIED ACTIVITIES IN STUDENT’S LEARNING ENGAGEMENT IN AN ONLINE ENGLISH CLASS

This study was conducted to find out the efficacy of gamified activities as students' learning engagement in an online English class.It employed a descriptive design, and it was participated by seventy (70) Grade 11 students under online distance learning of Laguna Senior High School.A survey questionnaire was used as the main instrument in obtaining the pertinent information.It determined the status of gamified activity elements such as enjoyable, educational, interactive, and participative on students' motivation, interest, competence, and behavior.It also identified the level of student learning engagement during online classes and whether this variable significantly affects the student's engagement during online classes.The results of the study revealed that the status of gamified activity elements was very high.The findings also exposed that the level of students learning engagement during online classes with regards to students' interest was very high while students' motivation, competence, and behavior were high.It also showed that there is a significant effect of using gamified activities on students' learning engagement in online English classes when it comes to gamified elements, educational, interactive, and participative.However, in terms of the variable, enjoyable, it was observed that it brings positive effects to students but lest no significance.Based on the result of the study, the variable, educational, interactive, and participative were observed to be significant while the variable enjoyable was observed to be not significant thus the null hypothesis was partially rejected.Still, this calls for the acceptance of the incites that there is a significant effect of gamified activities on students' learning engagement during an online class.This recommends for the teachers utilize the findings from this study to design activities that will suit the student's needs during this time of the pandemic.School management may also consider the findings from this study to craft projects/activities to address problems related to decreasing the attention span of students during an online class.
Assessment of the level of basic environmental knowledge of technical university students as the basis of environmental competence in a foreign-language educational environment

This article discusses the issue of determining the level of environmental basic knowledge of students as the basis of environmental competence at the stage of higher education. It is obvious that people who already have a certain level of environmental knowledge and skills, with their own environmental thinking, come to get higher education. Thus, it is very important to determine the real level of environmental competence of students at the beginning of their professional training in higher education. The authors of the article conducted an oral survey to analyze the attitude of 1st-year students of a technical university to environmental education by means of a foreign language, then students were offered testing to determine their level of basic environmental knowledge, and completed the study with another oral survey of students to identify readiness to receive environmental knowledge in a foreign language. The conducted experiment showed an average level of background environmental knowledge, but at the same time demonstrated the interest of the subjects in studying environmental fundamentals by means of a foreign language. The need for environmentally competent highly qualified specialists poses one of the main tasks for the higher technical school — the training of engineers with good environmental knowledge. The authors propose to improve environmental competence through the introduction of an integrated educational and methodological complex "Ecology-English" by means of a foreign language.
Biblioteki i media w czasie pandemii COVID-19. Bibliografia selektywna (od września 2021 do marca 2022 roku)

Selective bibliography of monographies and articles from representative scientific and professional journals in the field of information science and book science as well as social communication and media, which were published in Poland between November 2021 and march 2022.
FrOoDo: Framework for Out-of-Distribution Detection

FrOoDo is an easy-to-use and flexible framework for Out-of-Distribution detection tasks in digital pathology. It can be used with PyTorch classification and segmentation models, and its modular design allows for easy extension. The goal is to automate the task of OoD Evaluation such that research can focus on the main goal of either designing new models, new methods or evaluating a new dataset. The code can be found at https://github.com/MECLabTUDA/FrOoDo.
Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images

Deep learning shows promise for medical image analysis but lacks interpretability, hindering adoption in healthcare. Attribution techniques that explain model reasoning may increase trust in deep learning among clinical stakeholders. This paper aimed to evaluate attribution methods for illuminating how deep neural networks analyze medical images. Using adaptive path-based gradient integration, we attributed predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models. The technique highlighted possible biomarkers, exposed model biases, and offered insights into the links between input and prediction. Our analysis demonstrates the method's ability to elucidate model reasoning on these datasets. The resulting attributions show promise for improving deep learning transparency for domain experts by revealing the rationale behind predictions. This study advances model interpretability to increase trust in deep learning among healthcare stakeholders.
Quantum-enhanced multiparameter estimation and compressed sensing of a field

We show that a significant quantum gain corresponding to squeezed or over-squeezed spin states can be obtained in multiparameter estimation by measuring the Hadamard coefficients of a 1D or 2D signal. The physical platform we consider consists of twolevel atoms in an optical lattice in a squeezed-Mott configuration, or more generally by correlated spins distributed in spatially separated modes. Our protocol requires the possibility to locally flip the spins, but relies on collective measurements. We give examples of applications to scalar or vector field mapping and compressed sensing.
Event Notifications in Value-Adding Networks

Linkages between research outputs are crucial in the scholarly knowledge graph. They include online citations, but also links between versions that differ according to various dimensions and links to resources that were used to arrive at research results. In current scholarly communication systems this information is only made available post factum and is obtained via elaborate batch processing. In this paper we report on work aimed at making linkages available in real-time, in which an alternative, decentralised scholarly communication network is considered that consists of interacting data nodes that host artifacts and service nodes that add value to artifacts. The first result of this work, the "Event Notifications in Value-Adding Networks" specification, details interoperability requirements for the exchange of real-time life-cycle information pertaining to artifacts using Linked Data Notifications. In an experiment, we applied our specification to one particular use-case: distributing Scholix data-literature links to a network of Belgian institutional repositories by a national service node. The results of our experiment confirm the potential of our approach and provide a framework to create a network of interacting nodes implementing the core scholarly functions (registration, certification, awareness and archiving) in a decentralized and decoupled way.
Neural network layers as parametric spans

Properties such as composability and automatic differentiation made artificial neural networks a pervasive tool in applications. Tackling more challenging problems caused neural networks to progressively become more complex and thus difficult to define from a mathematical perspective. We present a general definition of linear layer arising from a categorical framework based on the notions of integration theory and parametric spans. This definition generalizes and encompasses classical layers (e.g., dense, convolutional), while guaranteeing existence and computability of the layer's derivatives for backpropagation.
DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning

Federated learning (FL) is an emerging promising paradigm of privacy-preserving machine learning (ML). An important type of FL is cross-silo FL, which enables a small scale of organizations to cooperatively train a shared model by keeping confidential data locally and aggregating weights on a central parameter server. However, the central server may be vulnerable to malicious attacks or software failures in practice. To address this issue, in this paper, we propose DeFL, a novel decentralized weight aggregation framework for cross-silo FL. DeFL eliminates the central server by aggregating weights on each participating node and weights of only the current training round are maintained and synchronized among all nodes. We use Multi-Krum to enable aggregating correct weights from honest nodes and use HotStuff to ensure the consistency of the training round number and weights among all nodes. Besides, we theoretically analyze the Byzantine fault tolerance, convergence, and complexity of DeFL. We conduct extensive experiments over two widely-adopted public datasets, i.e. CIFAR-10 and Sentiment140, to evaluate the performance of DeFL. Results show that DeFL defends against common threat models with minimal accuracy loss, and achieves up to 100x reduction in storage overhead and up to 12x reduction in network overhead, compared to state-of-the-art decentralized FL approaches.
Large-Scale Product Retrieval with Weakly Supervised Representation Learning

Large-scale weakly supervised product retrieval is a practically useful yet computationally challenging problem. This paper introduces a novel solution for the eBay Visual Search Challenge (eProduct) held at the Ninth Workshop on Fine-Grained Visual Categorisation workshop (FGVC9) of CVPR 2022. This competition presents two challenges: (a) E-commerce is a drastically fine-grained domain including many products with subtle visual differences; (b) A lacking of target instance-level labels for model training, with only coarse category labels and product titles available. To overcome these obstacles, we formulate a strong solution by a set of dedicated designs: (a) Instead of using text training data directly, we mine thousands of pseudo-attributes from product titles and use them as the ground truths for multi-label classification. (b) We incorporate several strong backbones with advanced training recipes for more discriminative representation learning. (c) We further introduce a number of post-processing techniques including whitening, re-ranking and model ensemble for retrieval enhancement. By achieving 71.53% MAR, our solution "Involution King" achieves the second position on the leaderboard.
Towards Computing an Optimal Abstraction for Structural Causal Models

Working with causal models at different levels of abstraction is an important feature of science. Existing work has already considered the problem of expressing formally the relation of abstraction between causal models. In this paper, we focus on the problem of learning abstractions. We start by defining the learning problem formally in terms of the optimization of a standard measure of consistency. We then point out the limitation of this approach, and we suggest extending the objective function with a term accounting for information loss. We suggest a concrete measure of information loss, and we illustrate its contribution to learning new abstractions.
Video Question Answering with Iterative Video-Text Co-Tokenization

Video question answering is a challenging task that requires understanding jointly the language input, the visual information in individual video frames, as well as the temporal information about the events occurring in the video. In this paper, we propose a novel multi-stream video encoder for video question answering that uses multiple video inputs and a new video-text iterative co-tokenization approach to answer a variety of questions related to videos. We experimentally evaluate the model on several datasets, such as MSRVTT-QA, MSVD-QA, IVQA, outperforming the previous state-of-the-art by large margins. Simultaneously, our model reduces the required GFLOPs from 150-360 to only 67, producing a highly efficient video question answering model.