Reviewerly/exHarmony huggingface dataset:
Bayesian graph selection consistency under model misspecification

Gaussian graphical models are a popular tool to learn the dependence structure in the form of a graph among variables of interest. Bayesian methods have gained in popularity in the last two decades due to their ability to simultaneously learn the covariance and the graph. There is a wide variety of model-based methods to learn the underlying graph assuming various forms of the graphical structure. Although for scalability of the Markov chain Monte Carlo algorithms, decomposability is commonly imposed on the graph space, its possible implication on the posterior distribution of the graph is not clear. An open problem in Bayesian decomposable structure learning is whether the posterior distribution is able to select a meaningful decomposable graph that is “close” to the true non-decomposable graph, when the dimension of the variables increases with the sample size. In this article, we explore specific conditions on the true precision matrix and the graph, which results in an affirmative answer to this question with a commonly used hyper-inverse Wishart prior on the covariance matrix and a suitable complexity prior on the graph space. In absence of structural sparsity assumptions, our strong selection consistency holds in a high-dimensional setting where $p=O(n^{\alpha })$ for $\alpha <1/3$. We show when the true graph is non-decomposable, the posterior distribution concentrates on a set of graphs that are minimal triangulations of the true graph.
Hardware design of a trapped-ion quantum computer for Software-Tailored Architecture for Quantum co-design (STAQ) project

We present the hardware design of a trapped-ion quantum computer for 32 fully-connected 171 Yb + hyperfine qubits. The system is engineered for optomechanical stability to ensure high performance quantum gates utilizing a low vibration cryostat.
Training and Application of Neural-Network Language Model for Ontology Population

This paper considers one of the subtasks of ontology learning - the ontology population, which implies the extension of existing ontology by new instances without changing the ontology structure. A brief overview of existing ontology learning approaches and their software implementations is presented. A highly automated technology for ontology population based on training and application of the neural network language model to identify and extract potential instances of ontology classes from domain texts is proposed. The main stages of its application, as well as the results of its experimental evaluation and the main directions of its further improvement are considered.
Online Particle Smoothing With Application to Map-Matching

We introduce a novel method for online smoothing in state-space models that utilises a fixed-lag approximation to overcome the well known issue of path degeneracy. Unlike classical fixed-lag techniques that only approximate certain marginals, we introduce an online resampling algorithm, called particle stitching, that converts these marginal samples into a full posterior approximation. We demonstrate the utility of our method in the context of map-matching, the task of inferring a vehicle's trajectory given a road network and noisy GPS observations. We develop a new state-space model for the difficult task of map-matching on dense, urban road networks.
Quantum acceleration by an ancillary system in non-Markovian environments

We study the effect of an ancillary system on the quantum speed limit time in different non-Markovian environments. Through employing an ancillary system coupled with the quantum system of interest via hopping interaction and investigating the cases that both the quantum system and ancillary system interact with their independent/common environment, and the case that only the system of interest interacts with the environment, we find that the quantum speed limit time will become shorter with enhancing the interaction between the system and environment and show periodic oscillation phenomena along with the hopping interaction between the quantum system and ancillary system increasing. The results indicate that the hopping interaction with the ancillary system and the structure of environment determine the degree of which the evolution of the quantum system can be accelerated.
PENINGKATAN HASIL BELAJAR DARING PESERTA DIDIK SELAMA PANDEMI COVID-19 MELALUI PEMANFAATAN FITUR APLIKASI ZOOM DI SD NEGERI 77 PALEMBANG

This Classroom Action Study was aimed at describing the online learning outcomes improvement and learning process of Grade VI students in Public Elementary School 77 Palembang through the using of Zoom Aplication. The research was conducted through two cycles of action. The diagram shows that the exhaustiveness increased from cycle I at 47% to 88% after cycle II. The learning outcomes also improved from cycle I to II at 63.8 and 82.9 respectively. This study concluded that through the using of Zoom Application, there will be improvement at online learning outcomes in thematic learning.Keywords: Learning Outcomes, Online Learning, Zoom Application
User Preference Multi-criteria Recommendations Using Neural Collaborative Filtering Methods

TraditionalNithin Goud, Kaira recommendationRamanjaneyulu, Y. V. systemsSathya Babu, Korra workPatra, Bidyut Kr. with a single rating provided by a user on an item. However, in many domains such as tourism, hotels, etc., a user would love to give rating for every criterion of an item based on his several experiences throughout the journey and at the same time, a user would love to be recommended considering all the features an item. Hence, the single rating recommendation systems are a bit inadequate for recommending items to the user in these situations and especially when user preferences change dynamically specifically on the criteria of the items that are to be recommended. So to tackle these anomalies in user behaviour, we propose a modified version of deep neural collaborative filtering method that is capable of predicting the criterion ratings of the items. To compare the similarity between criteria ratings and user’s dynamic shift in preferences on the criteria of items, we use some standard similarity techniques. The proposed network is designed to learn from user–item interaction data to predict the criterion ratings. To evaluate our proposed approach, we predict the overall rating of an item through using its criterion ratings with artificial neural networks (ANN). The proposed approach is tested on Yahoo! Movies dataset and TripAdvisor dataset. The proposed approach outperformed many of the baseline recommendation system models.
Comparative study of wavelet based techniques for electromagnetic noise evaluation and removal

Signals acquired from an industrial environment with many sources of electromagnetic interferences may be polluted by white noise. Polluted data segments with many steady consecutive periods can be used (sometimes unsuccessful) for the estimation of a denoised period from the steady acquired data by using the mean signal method. For data segments with at least 4 periods, when only certain segments (shorter than a period) can be considered steady, hybrid algorithms can be used to automatically evaluate the power of noise and afterward to perform the noise removal by using wavelet thrashing trees. This paper deals with 2 additional denoising techniques. The 1-st one is based on the Wavelet Package Transform and allows for the separation of the noise components which pollute a data segment of at least one period. The second approached denoising technique is also addressing one period data segments and estimates ﬁrstly the power of noise by using the energies of the vectors of details from the ﬁrst 2 levels of a tree used by decompositions with the Stationary Wavelet Transform. The estimated power of noise is afterward used to establish the number of levels in the wavelet thrashing trees. In this last stage, two wavelet mothers were used. Simulated and real test signals were used and performance comparisons were performed.
Comprehensive analysis of risk factors in Internet agricultural finance based on neural network model

Agricultural industrialization is a major reform and practice in the process of agricultural development and requires theoretical guidance. However, the current theoretical research on financial support for the development of agricultural industrialization is insufficient, which to a certain extent seriously affects the development speed of agricultural industrialization. This paper studies the nature of the part and tail probability of dependent random variable sequences with different distributions, and focuses on the random variable sequences with wide dependent structures, and obtains the relevant probability estimation formulas. At the same time, this paper also considers the application of the main results in complete convergence. Moreover, based on the research on the nature of dependent random variable sequences, the dependent risk model is discussed, which combines Internet finance with the development of agricultural industrialization. In addition, this article uses agricultural industrialization theory and Internet finance theory to study the support of Internet finance for the development of agricultural industrialization in my country. The research results show that the model constructed in this paper has a certain effect.
Secure and Decentralized Management of Health Records

The electronic Health (eHealth) record transforms the conventional healthcare domain into a digital healthcare domain that advances the service to the people. The eHealthcare gained more attention these days due to its benefits such as ease in transferring the record, available at all times, and effortless search and access. However, security and user privacy slow down the broader implementation of eHealthcare in various hospitals and countries. To utilize the effectiveness of eHealthcare and protect the data against various attacks, this chapter proposes a blockchain-based electronic health record management system. The encrypted eHealth records are stored on the cloud or Inter Planetary File System (IPFS) and the meta-data of the records on the blockchain to ensure data availability, integrity, and confidentiality. The proposed system provides immutable logging of access information for audit and regulatory compliance. In addition, the patient’s redundant account problem and data inaccessibility during the patient’s unresponsive state are addressed. The proposed system is implemented by modifying the goethereum implementation to analyze the overhead and applicability. The implementation results, security, and overhead analysis substantiate the proposed eHealthcare management system.
User-Friendly Automatic Transcription of Low-Resource Languages: Plugging ESPnet into Elpis

This paper reports on progress integrating the speech recognition toolkit ESPnet into Elpis, a web front-end originally designed to provide access to the Kaldi automatic speech recognition toolkit. The goal of this work is to make end-to-end speech recognition models available to language workers via a user-friendly graphical interface. Encouraging results are reported on (i) development of an ESPnet recipe for use in Elpis, with preliminary results on data sets previously used for training acoustic models with the Persephone toolkit along with a new data set that had not previously been used in speech recognition, and (ii) incorporating ESPnet into Elpis along with UI enhancements and a CUDA-supported Dockerfile.
A user interest community evolution model based on subgraph matching for social networking in mobile edge computing environments

Abstract With the rapid development of mobile edge computing, mobile social networks are gradually infiltrating into our daily lives, in which the communities are an important part of social networks. Internet of People such as online social networks is the next frontier for the Internet of Things. The combination of social networking and mobile edge computing has an important application value and is the development trend of future networks. However, how to detect evolutionary communities accurately and efficiently in dynamic heterogeneous social networks remains a fundamental problem. In this paper, a novel User Interest Community Evolution (UICE) model based on subgraph matching is proposed for accurately detecting the corresponding communities in the evolution of the user interest community. The community evolutionary events can be quickly captured including forming, dissolving, evolving and so on with the introduction of core subgraph. A variant of subgraph matching, called Subgraph Matching with Dynamic Weight (SMDW), is proposed to solve the problem of updating the core subgraph due to the change of core user’s interest when tracking evolutionary communities. Finally, the experiments based on the real datasets have been designed to evaluate the performance of the proposed model by comparing it with the state-of-art methods in this area and complete data processing through the local edge computing layer. The experimental results demonstrate that the UICE model presented in this paper has achieved better accuracy, higher efficiency and better scalability against existing methods.
Is Distance Teaching-Learning-Evaluation Triad a Form of Digital Art?

The pandemic situation at the beginning of March 2020 forced teachers to develop alternative teaching methods, and most important to find the best ways to keep teaching for every student no matter the situation, as for example, the lack of computer knowledge or hardware/software support. Teachers worldwide struggled to support, encourage, find the best ways not only to help students to keep learning but support them emotionally. At the end of the academic year, teachers made efforts to develop fair, appropriate evaluation procedures adapted to distance education. This paper summarizes the Distance Teaching-Learning-Evaluation (DTLE) evolution in Russia and Romania and some methods developed from March to December 2020 to support the educational activity. Some benefits, challenges and difficulties are identified during the same period of time in different DTLE scenarios, from the point of view of teachers and students, as well. Examples of new adapted methods, dedicated to the DTLE scenarios are al-so addressed in this work.
Dynamic Policy Management System for High Availability in a Multi-site Cloud

Edge computing promises to improve the network operational cost and improve the quality of service by pushing computation resources to the network edge. Each edge cloud consists of heterogeneous servers. The Service Function Chain deployed in Edge sites needs to be flexible to meet the QoS requirement. The service function's location and policy can vary based on latency, so stringent latency constraints are applied to a service. Although there are solutions to monitor systems flexibly, the policy for service function when migrated to a new edge site is still a challenge. In particular, Network operators may need to quickly change the network service's policy to adapt to the new underlying infrastructure. In this paper, we propose a Dynamic Policy Management, which monitors the whole infrastructure and network virtualization environments to detect the anomaly and dynamically update maintenance action to guarantee the high availability of network services, such as auto-scaling and migration.
Collaborative quantum computation with redundant graph state

Quantum computation is a computing model based on quantum theory, which can outperform the classical computation in solving certain problems. With the increase of the complexity of quantum computing tasks, it becomes important to distribute quantum computing resources to multi-parties to cooperatively fulfill the complex tasks. Here in this paper a scheme based on the one-way quantum computing model is proposed to realize collaborative quantum computation. The standard one-way quantum computing model is based on graph states. With graph states used as resources, one can realize a universal quantum computer through using single-qubit measurements and feed-forward. In contrast to the standard one-way computation, the main resource for collaborative quantum computation is a redundant graph state (also a multi-particle highly entangled state). Unlike in the traditional graph state where each particle corresponds to a specific node, in a redundant graph state, several particles correspond to a single node, which means that each node of the graph has several redundant copies. With the help of a redundant graph state, several parties can share a graph state flexibly at will. A redundant graph state is prepared and then distributed to several parties where each of them obtains a full copy of all nodes. By communicating with each other and measuring the particles in different ways, a standard graph state is prepared and distributed among these parties. The collaborative computation then finishes through the common one-way quantum computing operations. Besides the general scheme, a concrete optical implementation of a two-party cooperative single-qubit quantum state preparation based on a six-photon redundant graph state is also put forward. Such a redundant graph state is proposed to be prepared by using the spontaneous parametric down-conversion entangled source and quantum interference. With this redundant graph state, a standard three-node graph state can be shared with the two parties in an arbitrary way. This scheme does not only make the collaborative quantum computation across several parties possible and flexible, but also guarantee the privacy of each party’s operations. This feature would be particularly useful in the case where the computing resource is obtained from an outside provider. This scheme paves the way for realizing quantum computation in more general and complicated applications.
Key extraction in a chaos-based image cipher and wavelet packets

The novelty of this work is the statistical perspective of the cryptanalyst being able to perform a chosen plaintext attack on a private communication scheme involving chaos-based algorithms. The attacker finds himself in the position of analyzing the pseudo-random matrix that was used for encryption. The contribution is done in the context of the well-known fields of secret-key cryptology and that of wavelet packet decomposition of images. The analysis is exemplified on the thoroughly investigated, in the existing literature, simplest chaotic system, the logistic map. Kolmogorov-Smirnov tests, histogram testing, autocorrelation functions and nonlinear singular value decomposition are used. In addition, a new enciphering wavelet-based algorithm is proposed and analyzed.
Can Monolingual Pretrained Models Help Cross-Lingual Classification?

Multilingual pretrained language models (such as multilingual BERT) have achieved impressive results for cross-lingual transfer. However, due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. In this work, we present two approaches to improve zero-shot cross-lingual classification, by transferring the knowledge from monolingual pretrained models to multilingual ones. Experimental results on two cross-lingual classification benchmarks show that our methods outperform vanilla multilingual fine-tuning.
Snapshot Navigation in the Wavelet Domain

Many animals rely on robust visual navigation which can be explained by snapshot models, where an agent is assumed to store egocentric panoramic images and subsequently use them to recover a heading by comparing current views to the stored snapshots. Long-range route navigation can also be explained by such models, by storing multiple snapshots along a training route and comparing the current image to these. For such models, memory capacity and comparison time increase dramatically with route length, rendering them unfeasible for small-brained insects and low-power robots where computation and storage are limited. One way to reduce the requirements is to use a compressed image representation. Inspired by the filter bank-like arrangement of the visual system, we here investigate how a frequency-based image representation influences the performance of a typical snapshot model. By decomposing views into wavelet coefficients at different levels and orientations, we achieve a compressed visual representation that remains robust when used for navigation. Our results indicate that route following based on wavelet coefficients is not only possible but gives increased performance over a range of other models.
Machine Learning in the Optical Domain Enabled by Reservoir Computing

Photonic reservoir computing can substitute digital signal processing and equalize linear and nonlinear impairments. Especially photonic integrated variants e.g. ring-resonator based devices may become attractive. Feasibility and limitations are evaluated, and potential solutions are proposed.
Unsupervised Deep Language and Dialect Identification for Short Texts

Automatic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines.Language identification is considered a solved task in many cases; however, in the case of very closely related languages, or in an unsupervised scenario (where the languages are not known in advance), performance is still poor.In this paper, we propose the Unsupervised Deep Language and Dialect Identification (UDLDI) method, which can simultaneously learn sentence embeddings and cluster assignments from short texts.The UDLDI model understands the sentence constructions of languages by applying attention to character relations which helps to optimize the clustering of languages.We have performed our experiments on three shorttext datasets for different language families, each consisting of closely related languages or dialects, with very minimal training sets.Our experimental evaluations on these datasets have shown significant improvement over state-of-the-art unsupervised methods and our model has outperformed state-of-the-art LI and DI systems in supervised settings.
Research on knowledge graph model of diversified online resources and personalized recommendation

Abstract This paper proposes an improved knowledge graph model to match the distinctiveness in the education field, considering the lack of robustness and resource diversity in the existing research on knowledge graphs in the education field. A recommendation algorithm based on personalized features is given on the new knowledge graph model to improve recall and precision. Finally, we verify the robustness and expression ability in course resource diversity. The results show that the recall rate of curriculum resources is improved to 0.95-0.96, personalized satisfaction is up to 0.96 and the difference of recommended resources is up to 0.9.