Reviewerly/exHarmony huggingface dataset:
Two-view Graph Neural Networks for Knowledge Graph Completion

We present an effective graph neural network (GNN)-based knowledge graph embedding model, which we name WGE, to capture entity- and relation-focused graph structures. Given a knowledge graph, WGE builds a single undirected entity-focused graph that views entities as nodes. WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes. WGE then proposes a GNN-based architecture to better learn vector representations of entities and relations from these two single entity- and relation-focused graphs. WGE feeds the learned entity and relation representations into a weighted score function to return the triple scores for knowledge graph completion. Experimental results show that WGE outperforms strong baselines on seven benchmark datasets for knowledge graph completion.
Hard Negative Sampling via Regularized Optimal Transport for Contrastive  Representation Learning

We study the problem of designing hard negative sampling distributions for unsupervised contrastive representation learning. We analyze a novel min-max framework that seeks a representation which minimizes the maximum (worst-case) generalized contrastive learning loss over all couplings (joint distributions between positive and negative samples subject to marginal constraints) and prove that the resulting min-max optimum representation will be degenerate. This provides the first theoretical justification for incorporating additional regularization constraints on the couplings. We re-interpret the min-max problem through the lens of Optimal Transport theory and utilize regularized transport couplings to control the degree of hardness of negative examples. We demonstrate that the state-of-the-art hard negative sampling distributions that were recently proposed are a special case corresponding to entropic regularization of the coupling.
$p$-Laplacian Based Graph Neural Networks

Graph neural networks (GNNs) have demonstrated superior performance for semi-supervised node classification on graphs, as a result of their ability to exploit node features and topological information simultaneously. However, most GNNs implicitly assume that the labels of nodes and their neighbors in a graph are the same or consistent, which does not hold in heterophilic graphs, where the labels of linked nodes are likely to differ. Hence, when the topology is non-informative for label prediction, ordinary GNNs may work significantly worse than simply applying multi-layer perceptrons (MLPs) on each node. To tackle the above problem, we propose a new $p$-Laplacian based GNN model, termed as $^p$GNN, whose message passing mechanism is derived from a discrete regularization framework and could be theoretically explained as an approximation of a polynomial graph filter defined on the spectral domain of $p$-Laplacians. The spectral analysis shows that the new message passing mechanism works simultaneously as low-pass and high-pass filters, thus making $^p$GNNs are effective on both homophilic and heterophilic graphs. Empirical studies on real-world and synthetic datasets validate our findings and demonstrate that $^p$GNNs significantly outperform several state-of-the-art GNN architectures on heterophilic benchmarks while achieving competitive performance on homophilic benchmarks. Moreover, $^p$GNNs can adaptively learn aggregation weights and are robust to noisy edges.
How to Find a Good Explanation for Clustering?

$k$-means and $k$-median clustering are powerful unsupervised machine learning techniques. However, due to complicated dependences on all the features, it is challenging to interpret the resulting cluster assignments. Moshkovitz, Dasgupta, Rashtchian, and Frost [ICML 2020] proposed an elegant model of explainable $k$-means and $k$-median clustering. In this model, a decision tree with $k$ leaves provides a straightforward characterization of the data set into clusters. We study two natural algorithmic questions about explainable clustering. (1) For a given clustering, how to find the "best explanation" by using a decision tree with $k$ leaves? (2) For a given set of points, how to find a decision tree with $k$ leaves minimizing the $k$-means/median objective of the resulting explainable clustering? To address the first question, we introduce a new model of explainable clustering. Our model, inspired by the notion of outliers in robust statistics, is the following. We are seeking a small number of points (outliers) whose removal makes the existing clustering well-explainable. For addressing the second question, we initiate the study of the model of Moshkovitz et al. from the perspective of multivariate complexity. Our rigorous algorithmic analysis sheds some light on the influence of parameters like the input size, dimension of the data, the number of outliers, the number of clusters, and the approximation ratio, on the computational complexity of explainable clustering.
Theater as Data: Computational Journeys into Theater Research

"Theater as Data: Computational Journeys into Theater Research." Contemporary Theatre Review, 32(1), pp. 101–102
Radar Signal Sorting Based on Intra-pulse Features

The current radar signal sorting methods are exposed to the problems of poor sorting effect and easy interference in the increasingly complex electromagnetic environment. A new sorting method has recourse to the characteristics of complexity and spectral resemblance. Primarily, the received pulse needs to be normalized and denoised, then the resemblance coefficient, box dimension and sparseness are extracted from the preprocessed signals in the frequency domain. And the three sets of features are combined into a joint feature vector, and the clustering algorithm is used to achieve the classification of 8 kinds of modulation signals. Finally, the experimental simulation shows the characteristic parameters have splendid inter-class separation, with the signal to noise ratio (SNR) of 0 dB and a minimum sorting accuracy of 85% among different modulation types of signals, this approach has considerable engineering value.
Pedagogical and psychological features of increasing the creative activity of students in art classes

This article contains information about the technologies that teachers can use in fine arts classes, pedagogical methods as well as pedagogical principles and technologies used to increase the creative activity of students in fine arts classes takes. It also provides a clear analysis of what constitutes creative activity, its essence, and the views that have emerged as a result of research by European scholars on ways to develop it. The main purpose of the article is to consider the pedagogical and psychological features of the formation and development of creative activity in the student and to develop new proposals. In addition, the analysis of the relationship between pedagogical principles and psychological activity concluded that pedagogical education is inseparable from psychological features.
Video Content Classification using Deep Learning

Video content classification is an important research content in computer vision, which is widely used in many fields, such as image and video retrieval, computer vision. This paper presents a model that is a combination of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) which develops, trains, and optimizes a deep learning network that can identify the type of video content and classify them into categories such as "Animation, Gaming, natural content, flat content, etc". To enhance the performance of the model novel keyframe extraction method is included to classify only the keyframes, thereby reducing the overall processing time without sacrificing any significant performance.
Representation Selective Self-distillation and wav2vec 2.0 Feature  Exploration for Spoof-aware Speaker Verification

Text-to-speech and voice conversion studies are constantly improving to the extent where they can produce synthetic speech almost indistinguishable from bona fide human speech. In this regard, the importance of countermeasures (CM) against synthetic voice attacks of the automatic speaker verification (ASV) systems emerges. Nonetheless, most end-to-end spoofing detection networks are black-box systems, and the answer to what is an effective representation for finding artifacts remains veiled. In this paper, we examine which feature space can effectively represent synthetic artifacts using wav2vec 2.0, and study which architecture can effectively utilize the space. Our study allows us to analyze which attribute of speech signals is advantageous for the CM systems. The proposed CM system achieved 0.31% equal error rate (EER) on ASVspoof 2019 LA evaluation set for the spoof detection task. We further propose a simple yet effective spoofing aware speaker verification (SASV) method, which takes advantage of the disentangled representations from our countermeasure system. Evaluation performed with the SASV Challenge 2022 database show 1.08% of SASV EER. Quantitative analysis shows that using the explored feature space of wav2vec 2.0 advantages both spoofing CM and SASV.
Computing with Cognitive States

Basic experimental findings about human working memory can be described by an algebra built on high-dimensional binary states, representing information items, and two operations: multiplication for binding and addition for bundling. In contrast to common VSA algebras, bundling is not associative. Consequently bundling a sequence of items preserves their sequential ordering. The cognitive states representing a memorised list exhibit a primacy as well as a recency gradient. The typical concave-up and asymmetrically shaped serial position curve is derived as a linear combination of those gradients. Quantitative implications of the algebra are shown to agree well with empirical data from basic cognitive tasks including storage and retrieval of information in human working memory.
Formal Techniques for Cryptography

This chapter discusses mathematical aspects used in cryptography and throughout this book. First, it starts with some definitions of specific terminology; then, it covers some minimal notions of algebra; and finally, the chapter presents elliptic curves. This chapter contains a compilation of different technical (mathematical) tools used in cryptography gathered from different resources [1]–[9].
Thinking Beyond Distributions in Testing Machine Learned Models

Testing practices within the machine learning (ML) community have centered around assessing a learned model's predictive performance measured against a test dataset, often drawn from the same distribution as the training dataset. While recent work on robustness and fairness testing within the ML community has pointed to the importance of testing against distributional shifts, these efforts also focus on estimating the likelihood of the model making an error against a reference dataset/distribution. We argue that this view of testing actively discourages researchers and developers from looking into other sources of robustness failures, for instance corner cases which may have severe undesirable impacts. We draw parallels with decades of work within software engineering testing focused on assessing a software system against various stress conditions, including corner cases, as opposed to solely focusing on average-case behaviour. Finally, we put forth a set of recommendations to broaden the view of machine learning testing to a rigorous practice.
Integrated Semantic and Phonetic Post-correction for Chinese Speech  Recognition

Due to the recent advances of natural language processing, several works have applied the pre-trained masked language model (MLM) of BERT to the post-correction of speech recognition. However, existing pre-trained models only consider the semantic correction while the phonetic features of words is neglected. The semantic-only post-correction will consequently decrease the performance since homophonic errors are fairly common in Chinese ASR. In this paper, we proposed a novel approach to collectively exploit the contextualized representation and the phonetic information between the error and its replacing candidates to alleviate the error rate of Chinese ASR. Our experiment results on real world speech recognition datasets showed that our proposed method has evidently lower CER than the baseline model, which utilized a pre-trained BERT MLM as the corrector.
A Complementary Study on General Interval Type-2 Fuzzy Sets

Liang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> in 2000 defined interval type-2 fuzzy sets (IT2FSs), which constitute a subset of type-2 fuzzy sets. While the membership degrees in the former are functions from [0, 1] to [0, 1] (fuzzy truth values), the membership degrees in IT2FSs only take their values in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lbrace {\text{0}},{\text{1}}\rbrace$</tex-math></inline-formula> . Although all the initial work on IT2FSs involved convex membership degrees only, in 2015, Bustince <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> began the study on IT2FSs in general, including certain sets with nonconvex membership degrees. However, these are obviously early stages, with a lot of open problems regarding the theoretical structure of IT2FSs. For example, as far as we know, no negation operator has been obtained in this context. Therefore, it seems appropriate to continue with the study started in previous papers, delving deeper into the properties and operations of IT2FSs. Consequently, this work studies the structure of the set of functions from [0, 1] to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lbrace {\text{0}},{\text{1}}\rbrace$</tex-math></inline-formula> (expanding the set considered by Bustince <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ), from which we have removed the constant function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf{0}$</tex-math></inline-formula> , to offer a different study to the one carried out by Walker and Walker. More specifically, we consider join and meet operations, partial order derived from each one, and the negation operators in that set. Among other results, we provide new characterizations of join and meet operations and of partial orders on the set of functions from [0, 1] to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \lbrace {\text{0}},{\text{1}} \rbrace $</tex-math></inline-formula> ; we also present the first negation operators on this set.
Plotkin's call-by-value λ-calculus as a modal calculus

In the authors' previous analysis of the calling paradigms call-by-name and call-by-value through Girard's and Gödel's embeddings into the S4 modal logic, an asymmetry remains: the two paradigms are unified by the call-by-box paradigm of the modal target, but only for call-by-name can one say that the paradigm exists, up to isomorphism, inside the modal target. In this paper, we show that, by pushing further the modal analysis, a symmetric situation is revealed, in that ordinary and Plotkin's λ-calculi are shown to truly co-exist inside a simple modal calculus.
The Crime of Using Fake Diplomas Conducted by Members of the Regional House of Representatives in Indonesia: A Study

One of the requirements to become a member of the House of Representatives is to possess a minimum high school diploma so that people who desire to have the certificate try hard to obtain it either legally or illegally.Falsifying certifications is an illegal method giving rise to criminal sanctions, as conducted by Abdul Kadir from Kraksaan Probolinggo.The type of research used in this thesis was normative juridical (legal research) research with statutory and conceptual approaches and qualitative descriptive analysis method.The fact of the use of a fake diploma by the member of the Regional House of Representatives of Probolinggo began with a report made
Challenges Facing Online Learning During COVID-19

Technology became the main pillar of our life. This importance has increased during the spread of COVID-19. This study came to reveal the challenges that face online learning during COVID-19 period. To achieve the study aims, the researchers used a qualitative approach to explore the factors and the teaching strategies that influence students, engagement in the online learning from the perspective of teachers and students. They were selected from different schools and background were selected to participate in this study survey. SPSS was used to analyze the data. The results showed that there is no difference between male and female in using online learning. There are challenges such as the negative attitudes toward online learning. They don’t have enough competences to use it. The result showed that students, teachers, parents didn’t prepare well to the closure. The study recommended that the Ministry of education should enforce the schools’ infrastructure on the other. Besides, students have to employ their smart and digital tools in benefit way. They also have to recognize the importance of online learning during crises as a suitable solution to many challenges by enhancing their ICT skills.
The Box Size Confidence Bias Harms Your Object Detector

Countless applications depend on accurate predictions with reliable confidence estimates from modern object detectors. It is well known, however, that neural networks including object detectors produce miscalibrated confidence estimates. Recent work even suggests that detectors' confidence predictions are biased with respect to object size and position, but it is still unclear how this bias relates to the performance of the affected object detectors. We formally prove that the conditional confidence bias is harming the expected performance of object detectors and empirically validate these findings. Specifically, we demonstrate how to modify the histogram binning calibration to not only avoid performance impairment but also improve performance through conditional confidence calibration. We further find that the confidence bias is also present in detections generated on the training data of the detector, which we leverage to perform our de-biasing without using additional data. Moreover, Test Time Augmentation magnifies this bias, which results in even larger performance gains from our calibration method. Finally, we validate our findings on a diverse set of object detection architectures and show improvements of up to 0.6 mAP and 0.8 mAP50 without extra data or training.
Permutation Equivariant Generative Adversarial Networks for Graphs

One of the most discussed issues in graph generative modeling is the ordering of the representation. One solution consists of using equivariant generative functions, which ensure the ordering invariance. After having discussed some properties of such functions, we propose 3G-GAN, a 3-stages model relying on GANs and equivariant functions. The model is still under development. However, we present some encouraging exploratory experiments and discuss the issues still to be addressed.
Scalable 3D Semantic Segmentation for Gun Detection in CT Scans

With the increased availability of 3D data, the need for solutions processing those also increased rapidly. However, adding dimension to already reliably accurate 2D approaches leads to immense memory consumption and higher computational complexity. These issues cause current hardware to reach its limitations, with most methods forced to reduce the input resolution drastically. Our main contribution is a novel deep 3D semantic segmentation method for gun detection in baggage CT scans that enables fast training and low video memory consumption for high-resolution voxelized volumes. We introduce a moving pyramid approach that utilizes multiple forward passes at inference time for segmenting an instance.
Coordination and Flexibility in the Management of Software Development Processes for Start-Up Companies

The processes of digitalization in the cloud and the provision of services from Start-up Companies not only implies changes within their internal software development, but also involves making profound changes in the way of applying new strategies and managing the service offered to companies distributed in different continents, shortening distances through the development of quality software. On the other hand, strategies are affected by external conditions such as the influence of changing decision-making, market trends and changing competitive advantages, as well as social attitudes that are linked to the confidence of users in digital and the adoption of technology in their environment. These particularities associated with the management of software development processes for startups, requires special attention, therefore, this study focuses on the analysis of artifacts, coordination capabilities, flexibility and its importance in software development. From this analysis, it is proposed a model that will facilitate the linkage and integration in software development with an approach that includes the most relevant aspects and characteristics in the implementation of coordination and flexibility capabilities in Start-ups companies, offering them benefits in the delivery of software products and services.
Research on the Data Security Enhancement Method Based on Encryption Paradigm

As the network information developing, the network information security has become a focal issue in the field of information technology. Through the analysis of network security status, data encryption is used to guarantee the received information security. This paper introduces the knowledge of cryptography and proposes a ring signcryption scheme based on attributes. The signcryption method improves the efficiency compared with the traditional method of signature before encryption. A ring is established through the attribute set and relaxation attribute set in the system. The advantage of the proposed method is that the message receiver can determine the source of the message through the ring to confirm the reliability of the message while it also protects the identity of the signcrypter.
Video Streaming in Cooperative Vehicular Networks

Video services in vehicular networks play a significant role in our daily traveling. In this paper, we propose a cooperative communication scheme to facilitate video data transmission, utilizing the mobility of vehicles and the cooperation among infrastructure and vehicles. To improve the video quality of experience (QoE), i.e., reduce the interruption ratio, quality variation and improve the playback quality, we design a Back Compensation (BC) video transmission strategy with the knowledge of vehicle status information. In addition, we analyze the throughput with one-hop and target-cluster-based cooperation schemes and obtain their closed-form expressions, respectively, which is useful for video encoding design in the central server. Simulation results demonstrate that the proposed approach can improve the video performance significantly and verify the accuracy of our analytical results.