Reviewerly/exHarmony huggingface dataset:
Kesantunan Deklaratif dalam Kegiatan Webinar Pendidikan “Peran Guru dalam Mengembangkan Pembelajaran Jarak Jauh Menyikapi New Normal #1” di Youtube

Webinars are seminars or meetings held virtually using certain internet-based applications. In participating in webinar activities, many types of speech that can be used by webinar participants, one of which is declarative speech. Declarative speech can be examined from various aspect, including aspect of function and the principle of politeness. This study aims to describe, analyze, and interpret declarative politeness in the educational webinar activity “The Role pf Teachers in Developing Distance Learning in the Respecting the New Normal #1” on Youtube. This research uses a descriptive method. The data analysis technique in this study used content analysis techniques. Data collection techniques used in this study were documentation techniques, listening techniques, and note-taking techniques. The data in this study are in the form of declarative speech spoken by the webinar participants. Sources of data in this study are the entire speeches of the educational webinar participants “The Role of Teachers in Developing Distance Learning in Respecting New Normal #1” which was broadcast live on June 17, 2020. The results of this study indicate the following. First, the most declarative function found in the speeches of webinar participants in educational webinar activities is the function of declaring information. Based on the data analysis of the declarative speech function, the function of declaring information is widely found because most of the contents of this webinar activity are providing information or matters relating to information about the role of teachers in developing distance learning. Second, the maxims of politeness principles found in the declarative utterances of webinar participants in educational webinar activities are maxim of appreciation and maxim of sympathy. Based on the data analysis, the maxim of politeness principle, the maxim of appreciation and the maxim of sympathy were found because the webinar participants were more likely to show respect and sympathy for the speech partner.
Distinct Geometrical Representations for Temporal and Relational Structures in Knowledge Graphs

Geometric aspects of knowledge graph embedding models directly impact their capability to preserve knowledge from the original graph to the vector space. For example, the capability to preserve structural patterns such as hierarchies, loops, and paths present as relational structures in a knowledge graph depends on the underlying geometry. In these years, temporal information has gained lots of attention from researchers. While non-Euclidean geometry, e.g. Hyperbolic Geometry, has been shown to work well in static knowledge graph embedding models for such relational structures, this does not hold for temporal information in knowledge graphs. This is due to the different characteristics of temporal information: time can be seen mostly as a linear construct and using a geometry that is not suitable for this can adversely affect performance. To address this research gap, we provide a novel temporal knowledge graph embedding model that combines different geometries: the non-temporal part of the knowledge is mapped to a hyperbolic space and the temporal part is mapped to a Euclidean space. Our extensive evaluations on several benchmark datasets show a significant performance improvement in comparison to state-of-the-art models.
Towards Few-Shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-Guided Neural Process Approach

Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links observed. Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities. Therefore, recent inductive methods utilize the sub-graphs around unseen entities to obtain the semantics and predict links inductively. However, in the few-shot setting, the sub-graphs are often sparse and cannot provide meaningful inductive patterns. In this paper, we propose a novel relational anonymous walk-guided neural process for few-shot inductive link prediction on knowledge graphs, denoted as RawNP. Specifically, we develop a neural process-based method to model a flexible distribution over link prediction functions. This enables the model to quickly adapt to new entities and estimate the uncertainty when making predictions. To capture general inductive patterns, we present a relational anonymous walk to extract a series of relational motifs from few-shot observations. These motifs reveal the distinctive semantic patterns on KGs that support inductive predictions. Extensive experiments on typical benchmark datasets demonstrate that our model derives new state-of-the-art performance.
Comparing Apples and Oranges? On the Evaluation of Methods for Temporal Knowledge Graph Forecasting

Due to its ability to incorporate and leverage time information in relational data, Temporal Knowledge Graph (TKG) learning has become an increasingly studied research field. To predict the future based on TKG, researchers have presented innovative methods for Temporal Knowledge Graph Forecasting. However, the experimental procedures employed in this research area exhibit inconsistencies that significantly impact empirical results, leading to distorted comparisons among models. This paper focuses on the evaluation of TKG Forecasting models: We examine the evaluation settings commonly used in this research area and highlight the issues that arise. To make different approaches to TKG Forecasting more comparable, we propose a unified evaluation protocol and apply it to re-evaluate state-of-the-art models on the most commonly used datasets. Ultimately, we demonstrate the significant difference in results caused by different evaluation settings. We believe this work provides a solid foundation for future evaluations of TKG Forecasting models, thereby contributing to advancing this growing research area.
Towards Memory-Efficient Training for Extremely Large Output Spaces – Learning with 670k Labels on a Single Commodity GPU

In classification problems with large output spaces (up to millions of labels), the last layer can require an enormous amount of memory. Using sparse connectivity would drastically reduce the memory requirements, but as we show below, applied naïvely it can result in much diminished predictive performance. Fortunately, we found that this can be mitigated by introducing an intermediate layer of intermediate size. We further demonstrate that one can constrain the connectivity of the sparse layer to be of constant fan-in, in the sense that each output neuron will have the exact same number of incoming connections, which allows for more efficient implementations, especially on GPU hardware. The CUDA implementation of our approach is provided at https://github.com/xmc-aalto/ecml23-sparse .
Interpretation Attacks and Defenses on Predictive Models Using Electronic Health Records

The emergence of complex deep neural networks made it crucial to employ interpretation methods for gaining insight into the rationale behind model predictions. However, recent studies have revealed attacks on these interpretations, which aim to deceive users and subvert the trustworthiness of the models. It is especially critical in medical systems, where interpretations are essential in explaining outcomes. This paper presents the first interpretation attack on predictive models using sequential electronic health records (EHRs). Prior attempts in image interpretation mainly utilized gradient-based methods, yet our research shows that our attack can attain significant success on EHR interpretations that do not rely on model gradients. We introduce metrics compatible with EHR data to evaluate the attack’s success. Moreover, our findings demonstrate that detection methods that have successfully identified conventional adversarial examples are ineffective against our attack. We then propose a defense method utilizing auto-encoders to de-noise the data and improve the interpretations’ robustness. Our results indicate that this de-noising method outperforms the widely used defense method, SmoothGrad, which is based on adding noise to the data.
Metaverse as a Tool for the Achievement of SGDs: Challenges, Opportunities, and Applications

The Metaverse is a new virtual reality with the potential to act as a catalyst for achieving the Sustainable Development Goals (SDGs). It involves immersive and interactive technologies that can promote education, healthcare, and other services while supporting the inclusion and empowerment of all people. However, the Metaverse also raises several ethical and social implications that require careful consideration and addressing, such as privacy, security, consent, fairness, inclusivity, diversity, accessibility, and social cohesion. In this chapter, we explore the impact of the Metaverse on SDG implementation and how it can support them. We also discuss the ethical and social implications of the Metaverse, presenting case studies and examples that demonstrate these implications in action. Finally, we provide recommendations for ensuring that the Metaverse is used ethically, socially responsible, and aligned with the SDGs.
Relativity of Metaverse in Business Model: A Conceptual Analysis

The Metaverse is a new platform for producing and selling goods and services. Businesses can use the Metaverse as an extension of their current business model to their advantage. This study examines the Metaverse and how it can be used as a tool for business interactions. It explores the utilization of the Metaverse to give workers a more productive and effective workplace. Additionally, it looks at how companies can use virtual reality technology to their advantage regularly. Using virtual reality (VR) technology is crucial for companies to succeed in today's market. Companies are using VR-based products such as avatars and 3D environments to attract customers who desire more than just an online shopping experience. Although the concept of the Metaverse has been around for decades Anshari (2022), it's only recently that companies have started to take advantage of its potential.
Distributed Adaptive Optimization with Divisible Communication

Synchronous distributed training can scale the training of deep neural networks on large-scale data, thus it has been widely adopted in large-scale applications. Because it often suffers from the communication bottleneck, many methods have been proposed to reduce the communication cost. However, these communication reduction methods often lead to poor performance for the adaptive optimizer, largely due to its non-linearity. To address this challenging issue, we propose a novel method to divide the communication into the foreground and background communication. The foreground communication is more informative but can be of low cost to achieve communication efficiency, while the background communication runs in the background and requires no synchronization time. We use Adam as the base optimizer and achieve $$\times 1024$$ foreground compression ratio on CIFAR-10, $$\times 128$$ on non-iid CIFAR-10, $$\times 64$$ on ImageNet image classification tasks, and $$\times 128$$ on WMT’16 EN-DE machine translation task with comparable performance, which leads to $$\times 7$$ , $$\times 6.4$$ , $$\times 3.5$$ , and $$\times 7$$ training speedup, respectively. Moreover, we provide rigorous theoretical analysis to prove that our method obtains the same convergence rate as Adam and achieves linear speedup regarding the number of workers.
The Price Determinants of NFTs: The Case of Metaverse

Non-Fungible Tokens (NFTs) were first introduced in 2013, and they reached their peak in 2021. Since then, investors and scholars have shown much greater interest in the NFT market. NFTs are Blockchain-based cryptographic assets that can be identified from one another due to specific metadata and identification codes. They are divided into categories such as collectibles, gaming, art, utilities, and the metaverse. Since the market is in its infancy, there is scarce research on NFTs. In this study, the price determinants of the Metaverse segment of NFTs are investigated using the Adaptive Network-Based Fuzzy Inference System (ANFIS) technique. The results of all groups are compared, and it is found that the Bitcoin and Ethereum prices make the best input variables for forecasting NFT prices.
Demystifying Metaverse in Business: A Conceptual Study

Although the term “metaverse” has gained popularity in modern literature, it is often disregarded by many businesspeople. The metaverse offers a vast array of alternatives, but its limitless nature can also be confusing for business owners. While navigating the maze of terms such as NFT, blockchain, avatar, and web 3.0 may be difficult for some, entrepreneurs should keep in mind that the metaverse is considered the next generation of the Internet and will bring new opportunities for their businesses. The pandemic has accelerated digitalization and new technology, adding a new dimension to the metaverse. This study aims to evaluate the increasingly problematic use of metaverse services in many business sectors and provide realistic strategic scenarios for effective planning and implementation of commercial metaverse services. The article focuses on how the metaverse service can be used as a business model, with particular emphasis on its role in marketing, healthcare, and financial services. To this end, the relevant literature on metaverse services is evaluated from multiple angles.
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations

This work introduces interpretable regional descriptors, or IRDs, for local, model-agnostic interpretations. IRDs are hyperboxes that describe how an observation’s feature values can be changed without affecting its prediction. They justify a prediction by providing a set of “even if” arguments (semi-factual explanations), and they indicate which features affect a prediction and whether pointwise biases or implausibilities exist. A concrete use case shows that this is valuable for both machine learning modelers and persons subject to a decision. We formalize the search for IRDs as an optimization problem and introduce a unifying framework for computing IRDs that covers desiderata, initialization techniques, and a post-processing method. We show how existing hyperbox methods can be adapted to fit into this unified framework. A benchmark study compares the methods based on several quality measures and identifies two strategies to improve IRDs.
Cross Model Parallelism for Faster Bidirectional Training of Large Convolutional Neural Networks

Large convolutional neural networks (CNNs) have been successful in data mining tasks, but it is hard to train these large-scale models. Model parallelism (MP) places a large CNN to several workers (GPUs) to fit in the memory, but its computation efficiency is low as only one worker is activated at a time and the other workers are idle during training. Pipeline model parallelism (PMP) improves model parallelism by pipelining mini-batches, checkpointing some intermediate activations, and using delayed backward error gradients. But all these techniques have certain limitations, add to the computation cost, and may deteriorate the model performance. To address these important issues and improve the efficiency of model parallelism, we propose a novel cross model parallelism (CMP) method without requiring additional computation overheads and jeopardizing the performance. In cross model parallelism, we reversely place two models to workers and bidirectionally train them at the same time to improve the training throughput. A novel averaging method to synchronize the two models is also proposed in cross model parallelism. Theoretical analysis shows that cross model parallelism converges as fast as model parallelism regarding training epochs. Extensive deep learning experimental results show that our proposed cross model parallelism can achieve a speedup of up to $$\times 1.5$$ compared with model parallelism regarding training time.
Construction and Training of Multi-Associative Graph Networks

Modern methods and networks of supervised learning use a vast amount of computational resources when adapting to large datasets. They are unable to incorporate new training examples into trained models quickly and to represent internal knowledge for quick adaptation to other computational tasks without retraining. The human brain is capable of representing and retrieving vast amounts of information and can create associations between its various pieces based on frequent relationships and patterns. The backpropagation algorithm is not the best and only way to train neural networks, especially since its use is limited to feed-forward architectures. Brain structures can be modeled using graph architectures that are not feed-forward but recursive with many feedback connections. This paper introduces Multi-Associative Graph Networks that enable the representation of associated training data and objects transformed from relational databases. These graphs store the data along with the most useful relationships to facilitate computational intelligence processes. We describe the associative transformation algorithm allowing for the transformation of any relational database into this graph network, reproducing stored relationships and enriching them with newly detected ones. We also introduce the tuning algorithm that learns to associate different priorities with different neurons representing objects to improve the relational dependencies and classification results. Finally, we draw conclusions from the comparisons to other state-of-the-art models.
Learning to Augment Graph Structure for both Homophily and Heterophily Graphs

Recent years have witnessed great successes in performing graph structure learning for Graph Neural Networks (GNNs). However, comparatively little work studies structure augmentation for graphs, where the augmented structures are only used for training and are not available during inference. This is mainly due to that structure augmentation is a discrete combinatorial optimization problem rather than a continuous optimization problem like structure learning. In this paper, we propose Learning to Augment (L2A), a novel structure augmentation framework that learns customized augmentation strategies for graphs with different homophily levels. Specifically, L2A simultaneously performs the maximum likelihood estimation of GNN parameters and the learning of optimal structure augmentations in a variational inference framework. Moreover, L2A applies two auxiliary self-supervised tasks to exploit both global position and label distribution information in the graph structure to further reduce the reliance on annotated labels and improve applicability to heterophily graphs. Extensive experiments have shown that L2A can produce truly encouraging results at various homophily levels compared with other leading methods and can learn customized structure augmentation strategies across various GNNs architectures and graph datasets. Codes are available at: https://github.com/LirongWu/L2A .
Auction-Based Task Allocation and Motion Planning for Multi-Robot Systems with Human Supervision

Abstract This paper presents a task allocation strategy for a multi-robot system with a human supervisor. The multi-robot system consists of a team of heterogeneous robots with different capabilities that operate in a dynamic scenario that can change in the robots’ capabilities or in the operational requirements. The human supervisor can intervene in the operation scenario by approving the final plan before its execution or forcing a robot to execute a specific task. The proposed task allocation strategy leverages an auction-based method in combination with a sampling-based multi-goal motion planning. The latter is used to evaluate the costs of execution of tasks based on realistic features of paths. The proposed architecture enables the allocation of tasks accounting for priorities and precedence constraints, as well as the quick re-allocation of tasks after a dynamic perturbation occurs –a crucial feature when the human supervisor preempts the outcome of the algorithm and makes manual adjustments. An extensive simulation campaign in a rescue scenario validates our approach in dynamic scenarios comprising a sensor failure of a robot, a total failure of a robot, and a human-driven re-allocation. We highlight the benefits of the proposed multi-goal strategy by comparing it with single-goal motion planning strategies at the state of the art. Finally, we provide evidence for the system efficiency by demonstrating the powerful synergistic combination of the auction-based allocation and the multi-goal motion planning approach.
Boosting Adaptive Graph Augmented MLPs via Customized Knowledge Distillation

While Graph Neural Networks (GNNs) have shown convinced performance on handling non-Euclidean network data, the high inference latency caused by message-passing mechanism hinders their deployment on real-time scenarios. One emerging inference acceleration approach is to distill knowledge derived from teacher GNNs into message-passing-free student multi-layer perceptrons (MLPs). Nevertheless, due to the graph heterophily causing performance degradation of teacher GNNs, as well as the unsatisfactory generalization ability of student MLPs on graph data, GNN-MLP like designs often achieve inferior performance. To tackle this challenge, we propose boosting adaptive GRaph Augmented MLPs via Customized knowlEdge Distillation (GRACED), a novel approach to learn graph knowledge effectively and efficiently. Specifically, we first design a novel customized knowledge distillation strategy to modify the guided knowledge to mitigate the adverse influence of heterophily to student MLPs. Then, we introduce an adaptive graph propagation approach to precompute aggregation feature for node considering both of homophily and heterophily to boost the student MLPs for learning graph information. Furthermore, we design an aggregation feature approximation technique for inductive scenarios. Extensive experiments on node classification task and theoretical analyses demonstrate the superiority of GRACED by comparing with the state-of-the-art methods under both transductive and inductive settings across homophilic and heterophilic datasets.
ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning

The recent contrastive learning methods, due to their effectiveness in representation learning, have been widely applied to modeling graph data. Random perturbation is widely used to build contrastive views for graph data, which however, could accidentally break graph structures and lead to suboptimal performance. In addition, graph data is usually highly abstract, so it is hard to extract intuitive meanings and design more informed augmentation schemes. Effective representations should preserve key characteristics in data and abandon superfluous information. In this paper, we propose ENGAGE (ExplaNation Guided data AuGmEntation), where explanation guides the contrastive augmentation process to preserve the key parts in graphs and explore removing superfluous information. Specifically, we design an efficient unsupervised explanation method called smoothed activation map as the indicator of node importance in representation learning. Then, we design two data augmentation schemes on graphs for perturbing structural and feature information, respectively. We also provide justification for the proposed method in the framework of information theories. Experiments of both graph-level and node-level tasks, on various model architectures and on different real-world graphs, are conducted to demonstrate the effectiveness and flexibility of ENGAGE. The code of ENGAGE can be found here ( https://github.com/sycny/ENGAGE ).