Reviewerly/exHarmony huggingface dataset:
Overviews of Internet of Things Applications in China’s Hospitality Industry

During the current post-epidemic period, hygiene requirements and health needs in the hospitality industry keep increasing, and consumers become more concerned about the cleanliness of hotels and have stronger demands for contactless services in hotels. The growth and popularity of IoT technology in China make it more accessible to a wider range of service industries and provides the basis for the application of IoT in the hospitality industry. The application of IoT devices in hotels mainly includes intelligent robots, intelligent guest control, systems, etc., which helps to realize contactless services in hotels. This research reviews the entire development cycle of the application of IoT in the hospitality industry, from the founding, development, and expansion of companies to their IPO and post-IPO maturity. From a perspective combined with IoT technology, we can cope with the changing industry models, business models, and operation modes in the hospitality industry more efficiently. The development of IoT in hotels will move towards humanization and service-orientation. Through the role of human–machine linkage and the reasonable management of IoT equipment, IoT technology can reasonably promote the efficiency of manual work, while injecting “warmth” and “quality” into IoT equipment.
Verilay: A Verifiable Proof of Stake Chain Relay

Blockchain relay schemes enable cross-chain state proofs without requiring trusted intermediaries. This is achieved by applying the source blockchain's consensus validation protocol on the target blockchain. Existing chain relays allow for the validation of blocks created using the Proof of Work (PoW) protocol. Since PoW entails high energy consumption, limited throughput, and no guaranteed finality, Proof of Stake (PoS) blockchain protocols are increasingly popular for addressing these shortcomings. We propose Verilay, the first chain relay scheme that enables validating PoS protocols that produce finalized blocks, for example, Ethereum 2.0, Cosmos, and Polkadot. The concept does not require changes to the source blockchain protocols or validator operations. Signatures of block proposers are validated by a dedicated relay smart contract on the target blockchain. In contrast to basic PoW chain relays, Verilay requires only a subset of block headers to be submitted in order to maintain full verifiability. This yields enhanced scalability. We provide a prototypical implementation that facilitates the validation of Ethereum 2.0 beacon chain headers within the Ethereum Virtual Machine (EVM). Our evaluation proves the applicability to Ethereum 1.0's mainnet and confirms that only a fraction of transaction costs are required compared to PoW chain relay updates.
Criteria for studying dependent and independent samples in the field of education

Introduction. The article is devoted to monitoring the quality of education and continues the research presented in the author’s previous article. The goal of this work is to improve the author's criterion presented in the above mentioned work in two directions: the first is to strengthen its practical applicability; the second is to deepen research, covering all possible manifestations of problems in the field of quantitative characteristics within the education system (academic performance). Materials and Methods. The methodology of solving this problem and achieving the goal is based on the integration of two important areas of science – mathematics and pedagogy, as well as on the new results obtained by the author in the field of research of dependent and independent samples, which together form the basis of the theory of measuring academic performance. Results. The author has formulated the criterion in order to increase its practical applicability. Consequently, half of the tasks on the problem under study are solved by direct application of the criterion. In the field of deepening the research, a second special class of problems has been found which, together with the first, exhausts all the variety of manifestations of the problem under study in the field of dependent and independent samples. In addition, a two-parameter criterion is introduced, contributing to a more objective way of determining a promising educational technology among competing ones and taking into account the chronology of events. The latter is very important due to the fact that many well–known and widely used criteria (Wilcoxon –Mann - Whitney) are "blind", give the same result for different pairs (X, Y) and (Y, X) and do not follow the chronological order. The recommendation provided by incompetent researchers to rearrange the samples in pairs is very harmful and leads to falsification of results. Conclusions. The introduction of the author's criterion not only contributes to the selection of a decent educational technology, but also reduces the volume of scientific and methodological material on mathematical statistics serving the education system tenfold. It is justified by the fact that instead of 12-15 criteria containing about 100 rules and formulas, only one criterion can be used. In addition, this criterion does not contain contradictions, restrictions on the number of students and categories, etc.
Learned Semantic Index Structure Using Knowledge Graph Embedding and Density-Based Spatial Clustering Techniques

Recently, a pragmatic approach toward achieving semantic search has made significant progress with knowledge graph embedding (KGE). Although many standards, methods, and technologies are applicable to the linked open data (LOD) cloud, there are still several ongoing problems in this area. As LOD are modeled as resource description framework (RDF) graphs, we cannot directly adopt existing solutions from database management or information retrieval systems. This study addresses the issue of efficient LOD annotation organization, retrieval, and evaluation. We propose a hybrid strategy between the index and distributed approaches based on KGE to increase join query performance. Using a learned semantic index structure for semantic search, we can efficiently discover interlinked data distributed across multiple resources. Because this approach rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. The performance of the proposed index structure is compared with some existing methods on real RDF datasets. As a result, the proposed indexing method outperforms existing methods due to its ability to prune a lot of unnecessary data scanned during semantic searching.
Factors Influencing Students’ Continuance Intention to Learn in Blended Environments at University

(1) Background: The main goal of this study was to determine the factors that have an influence on the continuance intention to learn in blended environments. (2) Methods: For our study, we created a research model based on the Expectation–Confirmation Model (ECM) and Theory of Planned Behavior (TPB), which involves a total of seven latent constructs and contains a total of eight hypotheses. A total of 301 undergraduate and postgraduate students studying at Humanity Institute of Peter the Great Polytechnic University voluntarily participated in the study. The online survey consisted of 22 items that determined the seven indicators studied and was conducted in the spring semester of 2021. For our analysis and hypothesis testing, we used PSS 24.0 and SmartPLS 3.0 programs. (3) Results: According to the results of this study, all the proposed hypotheses were confirmed, which confirmed the influencing power of research model indicators. Also, it was revealed that such indicators as confirmation and attitude are the key factors that affect the continuance intention to learn in a blended environment. (4) Conclusions: As the COVID-19 pandemic is an ever-changing situation, it is important to understand student perceptions of blended learning and manage their continuance intention to learn in such environments. This study contributes to such knowledge and provides insightful implications for academia.
Label-Efficient Hybrid-Supervised Learning for Medical Image Segmentation

Due to the lack of expertise for medical image annotation, the investigation of label-efficient methodology for medical image segmentation becomes a heated topic. Recent progresses focus on the efficient utilization of weak annotations together with few strongly-annotated labels so as to achieve comparable segmentation performance in many unprofessional scenarios. However, these approaches only concentrate on the supervision inconsistency between strongly- and weakly-annotated instances but ignore the instance inconsistency inside the weakly-annotated instances, which inevitably leads to performance degradation. To address this problem, we propose a novel label-efficient hybrid-supervised framework, which considers each weakly-annotated instance individually and learns its weight guided by the gradient direction of the strongly-annotated instances, so that the high-quality prior in the strongly-annotated instances is better exploited and the weakly-annotated instances are depicted more precisely. Specially, our designed dynamic instance indicator (DII) realizes the above objectives, and is adapted to our dynamic co-regularization (DCR) framework further to alleviate the erroneous accumulation from distortions of weak annotations. Extensive experiments on two hybrid-supervised medical segmentation datasets demonstrate that with only 10% strong labels, the proposed framework can leverage the weak labels efficiently and achieve competitive performance against the 100% strong-label supervised scenario.
iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection

Machine learning methods such as deep neural networks (DNNs), despite their success across different domains, are known to often generate incorrect predictions with high confidence on inputs outside their training distribution. The deployment of DNNs in safety-critical domains requires detection of out-of-distribution (OOD) data so that DNNs can abstain from making predictions on those. A number of methods have been recently developed for OOD detection, but there is still room for improvement. We propose the new method iDECODe, leveraging in-distribution equivariance for conformal OOD detection. It relies on a novel base non-conformity measure and a new aggregation method, used in the inductive conformal anomaly detection framework, thereby guaranteeing a bounded false detection rate. We demonstrate the efficacy of iDECODe by experiments on image and audio datasets, obtaining state-of-the-art results. We also show that iDECODe can detect adversarial examples. Code, pre-trained models, and data are available at https://github.com/ramneetk/iDECODe.
Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay

Data-Free Knowledge Distillation (KD) allows knowledge transfer from a trained neural network (teacher) to a more compact one (student) in the absence of original training data. Existing works use a validation set to monitor the accuracy of the student over real data and report the highest performance throughout the entire process. However, validation data may not be available at distillation time either, making it infeasible to record the student snapshot that achieved the peak accuracy. Therefore, a practical data-free KD method should be robust and ideally provide monotonically increasing student accuracy during distillation. This is challenging because the student experiences knowledge degradation due to the distribution shift of the synthetic data. A straightforward approach to overcome this issue is to store and rehearse the generated samples periodically, which increases the memory footprint and creates privacy concerns. We propose to model the distribution of the previously observed synthetic samples with a generative network. In particular, we design a Variational Autoencoder (VAE) with a training objective that is customized to learn the synthetic data representations optimally. The student is rehearsed by the generative pseudo replay technique, with samples produced by the VAE. Hence knowledge degradation can be prevented without storing any samples. Experiments on image classification benchmarks show that our method optimizes the expected value of the distilled model accuracy while eliminating the large memory overhead incurred by the sample-storing methods.
A Unified Framework for Real Time Motion Completion

Motion completion, as a challenging and fundamental problem, is of great significance in film and game applications. For different motion completion application scenarios (in-betweening, in-filling, and blending), most previous methods deal with the completion problems with case-by-case methodology designs. In this work, we propose a simple but effective method to solve multiple motion completion problems under a unified framework and achieves a new state-of-the-art accuracy on LaFAN1 (+17% better than previous sota) under multiple evaluation settings. Inspired by the recent great success of self-attention-based transformer models, we consider the completion as a sequence-to-sequence prediction problem. Our method consists of three modules - a standard transformer encoder with self-attention that learns long-range dependencies of input motions, a trainable mixture embedding module that models temporal information and encodes different key-frame combinations in a unified form, and a new motion perceptual loss for better capturing high-frequency movements. Our method can predict multiple missing frames within a single forward propagation in real-time and get rid of the post-processing requirement. We also introduce a novel large-scale dance movement dataset for exploring the scaling capability of our method and its effectiveness in complex motion applications.
BScNets: Block Simplicial Complex Neural Networks

Simplicial neural networks (SNNs) have recently emerged as a new direction in graph learning which expands the idea of convolutional architectures from node space to simplicial complexes on graphs. Instead of predominantly assessing pairwise relations among nodes as in the current practice, simplicial complexes allow us to describe higher-order interactions and multi-node graph structures. By building upon connection between the convolution operation and the new block Hodge-Laplacian, we propose the first SNN for link prediction. Our new Block Simplicial Complex Neural Networks (BScNets) model generalizes existing graph convolutional network (GCN) frameworks by systematically incorporating salient interactions among multiple higher-order graph structures of different dimensions. We discuss theoretical foundations behind BScNets and illustrate its utility for link prediction on eight real-world and synthetic datasets. Our experiments indicate that BScNets outperforms the state-of-the-art models by a significant margin while maintaining low computation costs. Finally, we show utility of BScNets as a new promising alternative for tracking spread of infectious diseases such as COVID-19 and measuring the effectiveness of the healthcare risk mitigation strategies.
FisheyeHDK: Hyperbolic Deformable Kernel Learning for Ultra-Wide Field-of-View Image Recognition

Conventional convolution neural networks (CNNs) trained on narrow Field-of-View (FoV) images are the state-of-the art approaches for object recognition tasks. Some methods proposed the adaptation of CNNs to ultra-wide FoV images by learning deformable kernels. However, they are limited by the Euclidean geometry and their accuracy degrades under strong distortions caused by fisheye projections. In this work, we demonstrate that learning the shape of convolution kernels in non-Euclidean spaces is better than existing deformable kernel methods. In particular, we propose a new approach that learns deformable kernel parameters (positions) in hyperbolic space. FisheyeHDK is a hybrid CNN architecture combining hyperbolic and Euclidean convolution layers for positions and features learning. First, we provide intuition of hyperbolic space for wide FoV images. Using synthetic distortion profiles, we demonstrate the effectiveness of our approach. We select two datasets - Cityscapes and BDD100K 2020 - of perspective images which we transform to fisheye equivalents at different scaling factors (analogue to focal lengths). Finally, we provide an experiment on data collected by a real fisheye camera. Validations and experiments show that our approach improves existing deformable kernel methods for CNN adaptation on fisheye images.
Efficient optical reservoir computing for parallel data processing

We propose and experimentally demonstrate an optical reservoir computing system in free space, using second-harmonic generation for nonlinear kernel functions and a scattering medium to enhance reservoir nodes interconnection. We test it for one-step and multi-step predication of Mackey–Glass time series with different input-mapping methods on a spatial light modulator. For one-step prediction, we achieve 1.8 × 10 −3 normalized mean squared error (NMSE). For the multi-step prediction, we explore two different mapping methods: linear-combination and concatenation, achieving 16-step prediction with NMSE as low as 3.5 × 10 −4 . Robust and superior for multi-step prediction, our approach and design have potential for parallel data processing tasks such as video prediction, speech translation, and so on.
Machine learning for the security of healthcare systems based on Internet of Things and edge computing

Using the Internet of Medical Things (IoMT) for treatment and diagnosis has exponentially grown due to its diverse use cases and efficient planning with defined resources. IoMT in the e-healthcare system enables continuous monitoring of a patient's medical indicators, which eases routine patient follow-ups and increases the productivity of human life. Nowadays, biomedical data can be easily collected from patients remotely thanks to the integration of wireless communications, wearable devices, and big data. In edge computing-based healthcare applications, the importance of privacy protection is increasing because of the openness and data sensitivity of communication channels. Dealing with the privacy and security issues for medical devices used for real-time processing and analysis is necessary through big data analysis. In this chapter, we investigate the security risks of big data platforms in health care and how machine learning can mitigate security risks.
Modified temperature-based global solar radiation models for estimation in regions with scarce experimental data

Reliable data on the availability of solar energy is needed, as solar energy is an essential resource for sustainable development worldwide. However, ground-based radiometric stations are scarce, at least in large areas far from population and in developing countries, so there are difficulties in validating methods for estimating solar radiation. Indirect models mitigate the problem by providing radiation data from other meteorological variables, which can be measured with low-cost equipment and calibrated with data from secondary station networks. However, models' accuracy decreases if estimations are required far away from the calibration stations. It is hoped that modified models that include the influence of geographical and topographical variables can attenuate this drawback in data-scarce regions. This paper evaluates the accuracy and generality of 14 existing models of monthly global solar radiation based on temperature, which is a routinely measured variable. At first, models are locally calibrated at 105 stations in three large areas in Spain. Then, from the local coefficients of eight stations selected in each area, general equations are derived for the coefficients of each model as function of the ratio between elevation and distance to the sea. The predictions of these modified models, i.e., using coefficients derived from general equations, are compared both for the eight base stations and the remaining ones used for validation. In the comparisons, not only errors averaged in groups of stations are considered, but also local results. Several models perform well in some areas, but a simple homogeneous model is the only one whose indicators are good in all areas and hardly vary when using general coefficients derived from the data measured at all available stations.
CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation

We propose a novel data-augmentation technique for neural machine translation based on ROT-k ciphertexts.ROT-k is a simple letter substitution cipher that replaces a letter in the plaintext with the kth letter after it in the alphabet.We first generate multiple ROT-k ciphertexts using different values of k for the plaintext which is the source side of the parallel data.We then leverage this enciphered training data along with the original parallel data via multi-source training to improve neural machine translation.Our method, CipherDAug, uses a co-regularization-inspired training procedure, requires no external data sources other than the original training data, and uses a standard Transformer to outperform strong data augmentation techniques on several datasets by a significant margin.This technique combines easily with existing approaches to data augmentation, and yields particularly strong results in low-resource settings. 1
bitsa_nlp@LT-EDI-ACL2022: Leveraging Pretrained Language Models for Detecting Homophobia and Transphobia in Social Media Comments

Online social networks are ubiquitous and userfriendly.Nevertheless, it is vital to detect and moderate offensive content to maintain decency and empathy.However, mining social media texts is a complex task since users don't adhere to any fixed patterns.Comments can be written in any combination of languages and many of them may be low-resource.In this paper, we present our system for the LT-EDI shared task on detecting homophobia and transphobia in social media comments.We experiment with a number of monolingual and multilingual transformer based models such as mBERT along with a data augmentation technique for tackling class imbalance.Such pretrained large models have recently shown tremendous success on a variety of benchmark tasks in natural language processing.We observe their performance on a carefully annotated, real life dataset of YouTube comments in English as well as Tamil.Our submission achieved ranks 9, 6 and 3 with a macro-averaged F1-score of 0.42, 0.64 and 0.58 in the English, Tamil and Tamil-English subtasks respectively.The code for the system has been open sourced 1 .