Reviewerly/exHarmony huggingface dataset:
IMPLEMENTING LEARNING MODEL BASED ON INTERACTIVE LEARNING COMMUNITY FOR EFL STUDENTS OF MUHAMMADIYAH UNIVERSITY

The main objective of this study was to investigate speaking ability of Muhammadiyah University students. It is the most intense language ability which is used in communicating with others. Speaking material plays an important role in various language productions. Such conversation can be in the form of discussion, speech, debate, dialogue, storytelling, retelling. The speakers use oral language to express their desires, feelings, thoughts with others, so that people can understand what they want, but there are still many misunderstandings that can affect the students’ mastery of accuracy, fluency, flexibility and comprehensibility. A total number of 35 students in third semester studens have been involved in this study, who are sampled by using total sampling technique. From this study, it was found that leaning community can motivate students to speak up effectivelly. The students’ competence in speaking skills of Unismuh Makassar are not fully in line with curriculum expectations in terms of the critical thinking. Therefore, to achieve the goal of teaching speaking skills, lecturer are required to use the methods that can encourage the leaners’ interest and motivate more until their speaking skills can be improved. The key words of the learning community method are learning and sharing experiences with others, working with others to create better learning among members of groups enhance the awareness, confidance, and sharing experience. The finding of this study can improve the speaking abiity from learning community stages futhermore it has been answer the researh question in terms of the application of Learning Community can attract studets participation in oral skill.
Variational Bayes In Private Settings (VIPS)

Many applications of Bayesian data analysis involve sensitive information such as personal documents or medical records, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving framework for Variational Bayes (VB), a widely used optimization-based Bayesian inference method. Our framework respects differential privacy, the gold-standard privacy criterion, and encompasses a large class of probabilistic models, called the Conjugate Exponential (CE) family. We observe that we can straightforwardly privatise VB’s approximate posterior distributions for models in the CE family, by perturbing the expected sufficient statistics of the complete-data likelihood. For a broadly-used class of non-CE models, those with binomial likelihoods, we show how to bring such models into the CE family, such that inferences in the modified model resemble the private variational Bayes algorithm as closely as possible, using the Pólya-Gamma data augmentation scheme. The iterative nature of variational Bayes presents a further challenge since iterations increase the amount of noise needed. We overcome this by combining: (1) an improved composition method for differential privacy, called the moments accountant, which provides a tight bound on the privacy cost of multiple VB iterations and thus significantly decreases the amount of additive noise; and (2) the privacy amplification effect of subsampling mini-batches from large-scale data in stochastic learning. We empirically demonstrate the effectiveness of our method in CE and non-CE models including latent Dirichlet allocation, Bayesian logistic regression, and sigmoid belief networks, evaluated on real-world datasets.
Accelerated and Robust Generation of <i>W</i> State by Parametric Amplification and Inverse Hamiltonian Engineering

A scheme to realize the accelerated and robust generation of W state in a cavity quantum electrodynamics (QED) system by combining parametric amplification (PA) with inverse Hamiltonian engineering (IHE) is proposed. The atom-cavity coupling strength can be exponentially enhanced via parametrically squeezing the cavity mode, which facilitates the generation of W state. Moreover, the evolution of the system can be optimized with suppressing the populations of the intermediate states. Numerical simulations show that the scheme is fast and high-fidelity, immune to systematic parameter deviations, robust against spontaneous emission of atoms, and decay of cavities. Therefore, this scheme may provide some useful applications in entanglement generation.
Electroformed, Self‐Connected Tin Oxide Nanoparticle Networks for Electronic Reservoir Computing

Abstract Interconnected SnO x nanoparticle (NP) networks are electroformed within a semi‐insulating SnO x thin‐film and between lateral electrodes. During this top‐down process, Joule heating, disproportionation, and de‐wetting of the SnO x thin‐film precede the formation of the NP networks. The same lateral electrodes used for electroforming are used to probe the network and reveal its complex electrical characteristics. Higher‐order harmonic generation is observed and the internal short‐term memory effects of the NP networks enable temporal inputs to be mapped into reservoir states for subsequent linear readout without training. Reservoir computing functionality is demonstrated with no requirement for high‐vacuum or cryo‐cooled environments.
Motion estimation of high density crowd using fluid dynamics

Motion estimation (ME) being a fundamental process of crowd behavior analysis experienced real challenges at high densities due to visual ambiguities and occlusion problems etc. Various surveys reported in the past years summarize conventional ME methods for crowd behaviors at low/medium densities. In this paper, we focus on state-of-the-art fluid dynamics (FD) ME methods developed over the last one and the half-decade for high-density crowd analysis. A detailed discussion is provided on the development of FD ME methods explaining the strengths and weaknesses and viability of FD ME methods for anomaly detection at high crowd densities. Comprehensive experiments are performed comparing the performance of conventional and FD ME at varying crowd densities. Experimentation results show that conventional ME methods fail at high-density crowd whereas FD ME methods could estimate motion only at the global level. Still, research is required to meet the challenges of local ME at high crowd densities.
A Multi-directional Approach for Missing Value Estimation in Multivariate Time Series Clinical Data

Missing values are common in clinical datasets which bring obstacles for clinical data analysis. Correctly estimating the missing parts plays a critical role in utilizing these analysis approaches. However, only limited works focus on the missing value estimation of multivariate time series (MTS) clinical data, which is one of the most challenge data types in this area. We attempt to develop a methodology (MD-MTS) with high accuracy for the missing value estimation in MTS clinical data. In MD-MTS, temporal and cross-variable information are constructed as multi-directional features for an efficient gradient boosting decision tree (LightGBM). For each patient, temporal information represents the sequential relations among the values of one variable in different time-stamps, and cross-variable information refers to the correlations among the values of different variables in a fixed time-stamp. We evaluated the estimation method performance based on the gap between the true values and the estimated values on the randomly masked parts. MD-MTS outperformed three baseline methods (3D-MICE, Amelia II and BRITS) on the ICHI challenge 2019 datasets that containing 13 time series variables. The root-mean-square error of MD-MTS, 3D-MICE, Amelia II and BRITS on offline-test dataset are 0.1717, 0.2247, 0.1900, and 0.1862, respectively. On online-test dataset, the performance for the former three methods is 0.1720, 0.2235, and 0.1927, respectively. Furthermore, MD-MTS got the first in ICHI challenge 2019 among dozens of competition models. MD-MTS provides an accurate and robust approach for estimating the missing values in MTS clinical data, which can be easily used as a preprocessing step for the downstream clinical data analysis.
Controlled Query Evaluation in Description Logics Through Instance Indistinguishability

We study privacy-preserving query answering in Description Logics (DLs). Specifically, we consider the approach of controlled query evaluation (CQE) based on the notion of instance indistinguishability. We derive data complexity results for query answering over DL-LiteR ontologies, through a comparison with an alternative, existing confidentiality-preserving approach to CQE. Finally, we identify a semantically well-founded notion of approximated query answering for CQE, and prove that, for DL-LiteR ontologies, this form of CQE is tractable with respect to data complexity and is first-order rewritable, i.e., it is always reducible to the evaluation of a first-order query over the data instance.
Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel.

Bayesian optimisation (BO) has been widely used for hyperparameter optimisation but its application in neural architecture search (NAS) is limited due to the non-continuous, high-dimensional and graph-like search spaces. Current approaches either rely on encoding schemes, which are not scalable to large architectures and ignore the implicit topological structure of architectures, or use graph neural networks, which require additional hyperparameter tuning and a large amount of observed data, which is particularly expensive to obtain in NAS. We propose a neat BO approach for NAS, which combines the Weisfeiler-Lehman graph kernel with a Gaussian process surrogate to capture the topological structure of architectures, without having to explicitly define a Gaussian process over high-dimensional vector spaces. We also harness the interpretable features learnt via the graph kernel to guide the generation of new architectures. We demonstrate empirically that our surrogate model is scalable to large architectures and highly data-efficient; competing methods require 3 to 20 times more observations to achieve equally good prediction performance as ours. We finally show that our method outperforms existing NAS approaches to achieve state-of-the-art results on NAS datasets.
Being Short, Sweet, and Sour: Congruent Visuo-Olfactory Stimulation Enhances Illusory Embodiment

Bodily self-identification has shown to be easily altered through spatiotemporally congruent multimodal signals. While such manipulations are mostly studied through visuo-tactile or visuo-motor stimulation, here we investigated whether congruent visuo-olfactory cues might enhance illusory self-identification with an arbitrary object. Using virtual reality, healthy individuals saw a grapefruit from its supposed first-person perspective that was touched in synchrony with their own body. The touch attempted to replicate what was seen as softly squeezing the grapefruit. Crucially, when we additionally presented the smell of a grapefruit in synchrony with the squeezing, they self-identified stronger with the fruit than when they smelled strawberry.
Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices

A new parameter optimization method for a hybrid quantum-classical algorithm shows how it can exploit novel mechanisms to speed up computational time by orders of magnitude.
Rep the Set: Neural Networks for Learning Set Representations

In several domains, data objects can be decomposed into sets of simpler objects. It is then natural to represent each object as the set of its components or parts. Many conventional machine learning algorithms are unable to process this kind of representations, since sets may vary in cardinality and elements lack a meaningful ordering. In this paper, we present a new neural network architecture, called RepSet, that can handle examples that are represented as sets of vectors. The proposed model computes the correspondences between an input set and some hidden sets by solving a series of network flow problems. This representation is then fed to a standard neural network architecture to produce the output. The architecture allows end-to-end gradient-based learning. We demonstrate RepSet on classification tasks, including text categorization, and graph classification, and we show that the proposed neural network achieves performance better or comparable to state-of-the-art algorithms.
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces

This paper focuses on the problem of unsupervised alignment of hierarchical data such as ontologies or lexical databases. This is a problem that appears across areas, from natural language processing to bioinformatics, and is typically solved by appeal to outside knowledge bases and label-textual similarity. In contrast, we approach the problem from a purely geometric perspective: given only a vector-space representation of the items in the two hierarchies, we seek to infer correspondences across them. Our work derives from and interweaves hyperbolic-space representations for hierarchical data, on one hand, and unsupervised word-alignment methods, on the other. We first provide a set of negative results showing how and why Euclidean methods fail in this hyperbolic setting. We then propose a novel approach based on optimal transport over hyperbolic spaces, and show that it outperforms standard embedding alignment techniques in various experiments on cross-lingual WordNet alignment and ontology matching tasks.
Quantum correlations dynamics in two coupled semiconductor InAs quantum dots

We investigate the dynamics of quantum discord and concurrence between two excitonic qubits placed inside two coupled semiconductor quantum dots independently interacting with dephasing reservoirs. We explore their behavior against the dimensionless time and the temperature in both Markovian and non-Markovian environments. Moreover, we analyze the external electric field effects and the Forster interaction effects on these correlations. We show that, although the quantum correlations amount is strongly influenced by the variation of the electric field and the Forster interaction, their non-Markovian behavior is still preserved under the variation of these two parameters. Furthermore, we show that for large values of temperature and dimensionless time, unlike concurrence which vanishes, nonzero discord can still be observed.
An improved BM3D algorithm based on anisotropic diffusion equation

Traditional 3D block matching (BM3D) algorithms are among the best denoising methods at present; however, they exhibit the issue of ringing around image edges, which makes them unable to protect image edges and details. Therefore, this paper proposes an BM3D noise processing algorithm for the diffusion equation to reduce image noise without affecting image details, specifically at the edges. This method first uses anisotropic diffusion (AD) filtering for image preprocessing, and then uses the edge direction instead of horizontal direction to search for similar blocks. The AD model is mainly improved to achieve better edges and detailed processing effects. Firstly, with the improved AD direction, a 5 × 5 edge enhancement operator model is implemented in eight directions, and the corresponding gradient information is obtained. This operator improves the processed image edges to achieve clear contours and good continuity. Next, a new calculation method for the diffusion function, whose coefficient is constructed using a hyperbolic tangent function, is introduced. The proposed method is based on the link between the image gradient and diffusion function, and it is mathematically proven that the diffusion function converges faster than the diffusion function of the model proposed by Perona and Malik. Experimental results indicate that the improved model can effectively retain the image edges and texture details, avoid edge ringing, and provide significant improvements in terms of the subjective visual effects and objective numerical indicators.
Two-qubit state recovery from amplitude damping based on weak measurement

In this paper, we propose a feed-forward control approach to protect arbitrary two-qubit pure and mixed initial states using the weak measurement. A feed-forward operation and measurements are used before the noise channel, and afterward, a reversed operation and measurements are applied to recover the state back to its initial state. In the case of two-qubit pure states, we use the unraveling trick to describe the state of the system in each step of the control procedure. For two-qubit mixed states, a completely positive trace-preserving map is implemented. Finally, the fidelity and success probability are used to evaluate the effect of protection. The complete recovery conditions for the measurement strengths are derived, under which we achieve the optimal fidelity and the success probability of recovering the initial pure and mixed states.
The taming of the rew: a type theory with computational assumptions

Dependently typed programming languages and proof assistants such as Agda and Coq rely on computation to automatically simplify expressions during type checking. To overcome the lack of certain programming primitives or logical principles in those systems, it is common to appeal to axioms to postulate their existence. However, one can only postulate the bare existence of an axiom, not its computational behaviour. Instead, users are forced to postulate equality proofs and appeal to them explicitly to simplify expressions, making axioms dramatically more complicated to work with than built-in primitives. On the other hand, the equality reflection rule from extensional type theory solves these problems by collapsing computation and equality, at the cost of having no practical type checking algorithm. This paper introduces Rewriting Type Theory (RTT), a type theory where it is possible to add computational assumptions in the form of rewrite rules. Rewrite rules go beyond the computational capabilities of intensional type theory, but in contrast to extensional type theory, they are applied automatically so type checking does not require input from the user. To ensure type soundness of RTT—as well as effective type checking—we provide a framework where confluence of user-defined rewrite rules can be checked modularly and automatically, and where adding new rewrite rules is guaranteed to preserve subject reduction. The properties of RTT have been formally verified using the MetaCoq framework and an implementation of rewrite rules is already available in the Agda proof assistant.
Self-supervised Learning for Deep Models in Recommendations.

Large scale neural recommender models play a critical role in modern search and recommendation systems. To model large-vocab categorical features, typical recommender models learn a joint embedding space for both queries and items. With millions to billions of items to choose from, the quality of learned embedding representations is crucial to provide high quality recommendations to users with various interests. Inspired by the recent success in self-supervised representation learning (SSL) research in both computer vision and natural language understanding, we propose a multi-task self-supervised learning framework for neural models in recommendations. Furthermore, we propose two self-supervised tasks applicable to models with categorical features within the proposed framework: (i) Feature Masking (FM) and (ii) Feature Dropout (FD). We evaluate our framework using two large-scale datasets with ~500M and 1B training examples respectively. Our results demonstrate that the proposed framework outperforms learning with the supervision task only and other state-of-the-art regularization techniques in the context of retrieval. The SSL framework shows larger improvement with less supervision compared to the counterparts.
Energy Aware Resource Efficient-(EARE) Server Consolidation Framework for Cloud Datacenter

Cloud datacenter offers economic and elastic computing benefits to the customers, where on-demand virtual machine (VM) allocation plays a significant role. Inefficient VM placement leads to resource wastage and high power consumption that raises the requirement of server consolidation. The feasibly optimal placement of VMs with the objectives of minimum power consumption and maximum resource utilization is the key to server consolidation. Though many multi-objective VM placement schemes are available in the literature, it mostly works on weighted sum approaches that transforms multi-objective problem (where some objectives maximize and others minimize) into single objective to give optimized solution. Hence, the existing approaches do not correctly justify the multi-objective VM placement problem. To provide correct solution of multi-objective and multi-constrained VM allocation problem, this work presents GA based evolutionary server consolidation framework by applying rank based non-dominated sorting for multiple objectives to generate pareto-optimal solution. It enables maximum resource utilization and minimum power consumption to accomplish effective server consolidation. The VM placement is done using genetic algorithm (GA) which encodes VM allocation information into chromosomes. The performance evaluation of the proposed work is carried out by execution of numerous experiments in simulated datacenter environment. The experimental outcome reveals that the proposed VM allocation framework improves resource utilization upto 38.54, 41.67, and 44.8% and minimize power consumption upto 11.32, 12, and 13.7% over random, best-fit, and first-fit heuristic-based approaches.
Rapid RNN-T Adaptation Using Personalized Speech Synthesis and Neural Language Generator

Rapid unsupervised speaker adaptation in an E2E system posits us new challenges due to its end-to-end unified structure in addition to its intrinsic difficulty of data sparsity and imperfect label [1]. Previously we proposed utilizing the content relevant personalized speech synthesis for rapid speaker adaptation and achieved significant performance breakthrough in a hybrid system [2]. In this paper, we answer the following two questions: First, how to effectively perform rapid speaker adaptation in an RNN-T. Second, whether our previously proposed approach is still beneficial for the RNN-T and what are the modification and distinct observations. We apply the proposed methodology to a speaker adaptation task in a state-of-art presentation transcription RNN-T system. In the 1 min setup, it yields 11.58 % or 7.95 % relative word error rate (WER) reduction for the sup/unsup adaptation, comparing to the negligible gain when adapting with 1 min source speech. In the 10 min setup, it yields 15.71 % or 8.00 % relative WER reduction, doubling the gain of the source speech adaptation. We further apply various data filtering techniques and significantly bridge the gap between sup/unsup adaptation.