Reviewerly/exHarmony huggingface dataset:
Decoherences-protected implementation of quantum gates

We present a scheme to implement a universal set of quantum gates based on achievable interactions, and the gates can be protected against decoherences through dynamical-decoupling approach without encoding. By properly designing system evolutions, the desired system interactions commute with the elements forming dynamical decoupling pulses. Thus, the effect of decoherences can be eliminated by repeatedly applying the pulses, without noticeably affecting the system evolutions governed by the desired system interactions given small enough time interval between pulses. Moreover, due to the commutation between the elements forming the pulses and the desired system interactions, our scheme is resistant to different types of decoherences, and so not limited to specific decoherences. Our scheme also works well in the case that the desired system interactions cannot be achieved ideally due to imperfect control of system parameters, through the action of dynamical-decoupling pulses.
Text Growing on Leaf

Irregular-shaped texts bring challenges to Scene Text Detection (STD). Although existing contour point sequence-based approaches achieve comparable performances, they fail to cover some highly curved ribbon-like text lines. It leads to limited text fitting ability and STD technique application. Considering the above problem, we combine text geometric characteristics and bionics to design a natural leaf vein-based text representation method (LVT). Concretely, it is found that leaf vein is a generally directed graph, which can easily cover various geometries. Inspired by it, we treat text contour as leaf margin and represent it through main, lateral, and thin veins. We further construct a detection framework based on LVT, namely LeafText. In the text reconstruction stage, LeafText simulates the leaf growth process to rebuild text contour. It grows main vein in Cartesian coordinates to locate text roughly at first. Then, lateral and thin veins are generated along the main vein growth direction in polar coordinates. They are responsible for generating coarse contour and refining it, respectively. Considering the deep dependency of lateral and thin veins on main vein, the Multi-Oriented Smoother (MOS) is proposed to enhance the robustness of main vein to ensure a reliable detection result. Additionally, we propose a global incentive loss to accelerate the predictions of lateral and thin veins. Ablation experiments demonstrate LVT is able to depict arbitrary-shaped texts precisely and verify the effectiveness of MOS and global incentive loss. Comparisons show that LeafText is superior to existing state-of-the-art (SOTA) methods on MSRA-TD500, CTW1500, Total-Text, and ICDAR2015 datasets.
PERANCANGAN PERANGKAT LUNAK “SISTEM INFORMASI ABSENSI KARYAWAN” DI BAGIAN DOKUMEN CV. NIASA BANDUNG

CV Niasa is a company that engaged in the produce of foods namely chocolate and cakes. CV Niasa Provides chocolate and cakes with various types of shapes and flavors. However, the attendance system that the employee used is still very simple, in recording employee attendance. Is it very inefficient and ineffective because it spend a lot of time and the risk of error is relatively greater in recording of each employee’s absence. Therefore, it is necessary to create a system of employee attendance recording with the aim that the attendance recording activities will be more effective and efficient both in terms of time, and in terms of reducing the risk of error and can also facilitate the presentation of annual reports so that it can facilitate leaders to make decisions. In the use of the program, the author chose to use Visual Basic programming 2010, because Visual Basic 2010 is the best and easiest to use in any programming language. Based on the background above, the author intends to make a computerized attendance system with the hope that employee attendance activities become more effective and efficient. In connection with this, the author compose this journal with the title : PERANCANGAN PERANGKAT LUNAK “SISTEM ABSENSI KARYAWAN” Di BAGIAN DOKUMEN CV NIASA BANDUNG.CV Niasa is a company that engaged in the produce of foods namely chocolate and cakes. CV Niasa Provides chocolate and cakes with various types of shapes and flavors. However, the attendance system that the employee used is still very simple, in recording employee attendance. Is it very inefficient and ineffective because it spend a lot of time and the risk of error is relatively greater in recording of each employee’s absence. Therefore, it is necessary to create a system of employee attendance recording with the aim that the attendance recording activities will be more effective and efficient both in terms of time, and in terms of reducing the risk of error and can also facilitate the presentation of annual reports so that it can facilitate leaders to make decisions. In the use of the program, the author chose to use Visual Basic programming 2010, because Visual Basic 2010 is the best and easiest to use in any programming language. Based on the background above, the author intends to make a computerized attendance system with the hope that employee attendance activities become more effective and efficient. In connection with this, the author compose this journal with the title : PERANCANGAN PERANGKAT LUNAK “SISTEM ABSENSI KARYAWAN” Di BAGIAN DOKUMEN CV NIASA BANDUNG.
Systematic study on the dependence of the warm-start quantum approximate optimization algorithm on approximate solutions

Quantum approximate optimization algorithm (QAOA) is a promising hybrid quantum-classical algorithm to solve combinatorial optimization problems in the era of noisy intermediate-scale quantum computers. Recently warm-start approaches have been proposed to improve the performance of QAOA, where approximate solutions are obtained by classical algorithms in advance and incorporated into the initial state and/or unitary ansatz. In this work, we study in detail how the accuracy of approximate solutions affect the performance of the warm-start QAOA (WS-QAOA). We numerically find that in typical MAX-CUT problems, WS-QAOA tends to outperform QAOA as approximate solutions become closer to the exact solutions in terms of the Hamming distance. We reveal that this could be quantitatively attributed to the initial state of the ansatz. We also solve MAX-CUT problems by WS-QAOA with approximate solutions obtained via QAOA, having a better result than QAOA especially when the circuit is relatively shallow. We believe that our study may deepen understanding of the performance of WS-QAOA and also provide a guide as to the necessary quality of approximate solutions.
Bayesian learning of feature spaces for multitasks problems

This paper introduces a novel approach for multi-task regression that connects Kernel Machines (KMs) and Extreme Learning Machines (ELMs) through the exploitation of the Random Fourier Features (RFFs) approximation of the RBF kernel. In this sense, one of the contributions of this paper shows that for the proposed models, the KM and the ELM formulations can be regarded as two sides of the same coin. These proposed models, termed RFF-BLR, stand on a Bayesian framework that simultaneously addresses two main design goals. On the one hand, it fits multitask regressors based on KMs endowed with RBF kernels. On the other hand, it enables the introduction of a common-across-tasks prior that promotes multioutput sparsity in the ELM view. This Bayesian approach facilitates the simultaneous consideration of both the KM and ELM perspectives enabling (i) the optimisation of the RBF kernel parameter $\gamma$ within a probabilistic framework, (ii) the optimisation of the model complexity, and (iii) an efficient transfer of knowledge across tasks. The experimental results show that this framework can lead to significant performance improvements compared to the state-of-the-art methods in multitask nonlinear regression.
Skeleton structure inherent in discrete-time quantum walks

In this paper, we claim that a common underlying structure--a skeleton structure--is present behind discrete-time quantum walks (QWs) on a one-dimensional lattice with a homogeneous coin matrix. This skeleton structure is independent of the initial state, and partially, even of the coin matrix. This structure is best interpreted in the context of quantum-walk-replicating random walks (QWRWs), i.e., random walks that replicate the probability distribution of quantum walks, where this newly found structure acts as a simplified formula for the transition probability. Additionally, we construct a random walk whose transition probabilities are defined by the skeleton structure and demonstrate that the resultant properties of the walkers are similar to both the original QWs and QWRWs.
Non-Gaussian Process Regression

Standard GPs offer a flexible modelling tool for well-behaved processes. However, deviations from Gaussianity are expected to appear in real world datasets, with structural outliers and shocks routinely observed. In these cases GPs can fail to model uncertainty adequately and may over-smooth inferences. Here we extend the GP framework into a new class of time-changed GPs that allow for straightforward modelling of heavy-tailed non-Gaussian behaviours, while retaining a tractable conditional GP structure through an infinite mixture of non-homogeneous GPs representation. The conditional GP structure is obtained by conditioning the observations on a latent transformed input space and the random evolution of the latent transformation is modelled using a Lévy process which allows Bayesian inference in both the posterior predictive density and the latent transformation function. We present Markov chain Monte Carlo inference procedures for this model and demonstrate the potential benefits compared to a standard GP.
Inference and Learning for Generative Capsule Models

Capsule networks (see e.g. Hinton et al., 2018) aim to encode knowledge of and reason about the relationship between an object and its parts. In this paper we specify a generative model for such data, and derive a variational algorithm for inferring the transformation of each model object in a scene, and the assignments of observed parts to the objects. We derive a learning algorithm for the object models, based on variational expectation maximization (Jordan et al., 1999). We also study an alternative inference algorithm based on the RANSAC method of Fischler and Bolles (1981). We apply these inference methods to (i) data generated from multiple geometric objects like squares and triangles ("constellations"), and (ii) data from a parts-based model of faces. Recent work by Kosiorek et al. (2019) has used amortized inference via stacked capsule autoencoders (SCAEs) to tackle this problem -- our results show that we significantly outperform them where we can make comparisons (on the constellations data).
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training

The quantification of uncertainty is important for the adoption of machine learning, especially to reject out-of-distribution (OOD) data back to human experts for review. Yet progress has been slow, as a balance must be struck between computational efficiency and the quality of uncertainty estimates. For this reason many use deep ensembles of neural networks or Monte Carlo dropout for reasonable uncertainty estimates at relatively minimal compute and memory. Surprisingly, when we focus on the real-world applicable constraint of $\leq 1\%$ false positive rate (FPR), prior methods fail to reliably detect OOD samples as such. Notably, even Gaussian random noise fails to trigger these popular OOD techniques. We help to alleviate this problem by devising a simple adversarial training scheme that incorporates an attack of the epistemic uncertainty predicted by the dropout ensemble. We demonstrate this method improves OOD detection performance on standard data (i.e., not adversarially crafted), and improves the standardized partial AUC from near-random guessing performance to $\geq 0.75$.
Quantum reservoir neural network implementation on coherently coupled quantum oscillators

Quantum reservoir computing is a promising approach for quantum neural networks, capable of solving hard learning tasks on both classical and quantum input data. However, current approaches with qubits suffer from limited connectivity. We propose an implementation for quantum reservoir that obtains a large number of densely connected neurons by using parametrically coupled quantum oscillators instead of physically coupled qubits. We analyse a specific hardware implementation based on superconducting circuits: with just two coupled quantum oscillators, we create a quantum reservoir comprising up to 81 neurons. We obtain state-of-the-art accuracy of 99 % on benchmark tasks that otherwise require at least 24 classical oscillators to be solved. Our results give the coupling and dissipation requirements in the system and show how they affect the performance of the quantum reservoir. Beyond quantum reservoir computing, the use of parametrically coupled bosonic modes holds promise for realizing large quantum neural network architectures, with billions of neurons implemented with only 10 coupled quantum oscillators.
INFACT: An Online Human Evaluation Framework for Conversational Recommendation

Conversational recommender systems (CRS) are interactive agents that support their users in recommendation-related goals through multi-turn conversations. Generally, a CRS can be evaluated in various dimensions. Today's CRS mainly rely on offline(computational) measures to assess the performance of their algorithms in comparison to different baselines. However, offline measures can have limitations, for example, when the metrics for comparing a newly generated response with a ground truth do not correlate with human perceptions, because various alternative generated responses might be suitable too in a given dialog situation. Current research on machine learning-based CRS models therefore acknowledges the importance of humans in the evaluation process, knowing that pure offline measures may not be sufficient in evaluating a highly interactive system like a CRS.
Co-CIAT: Collaborative Environment for Modeling with CIAN Notation

This paper presents Co-CIAT, a collaborative graphics editor for the specification of groupware systems with CIAN notation. The paper describes the process of creating Co-CIAT, as an example of the application of the Learn-CIAM model-driven development method, which has enabled the semi-automatic generation of this collaborative editor, but also the incorporation of awareness mechanisms and other coordination and communication components necessary to support this type of group tasks. Co-CIAT, being an online tool, allows any multidisciplinary team to work without the need to reserve space, travel, etc., which results in cost reduction.
Application of Carbonate Lithofacies Logging Identification Method in Gucheng Area

The Cambrian system in Gucheng area is characterized by its overall well depth, long strata, few coring data and complex and varied diagenesis types, which result in the changes of rock skeleton, physical properties and radioactive element content of the original rocks with the same sedimentary structure, therefore, it is a challenge for well logging to identify lithofacies accurately. This study presents a based on core data, rock structure factor and minimum near classification algorithm (KNN) a carbonate facies integrated logging identification method, the method to identify the sedimentary facies in facies and depositional cycle the space division of application, in line with the typical carbonate sedimentation model combined phase sequence and sedimentary characteristics of energy distribution, It is consistent with the sedimentary facies zones identified by the typical facies markers of rock slices, and can be used as an important supplementary means for fine study of sedimentary facies, and has a good application effect in the Cambrian system in Gucheng area.
Modern challenges in the training of future specialists: the possibilities of artificial intelligence and data science

Purpose of the article. The intensive growth of modern technologies, such as artificial intelligence, blockchain, quantum technologies, Internet of Things (IoT), etc., requires a transition to model of continuous studying of person throughout his life. To respond to modern challenges in the field of education in Russia, an educational organization of a new type, University 2035, was created. The article describes the technological capabilities of the institutional transformation of education using the example of a digital platform developed by University of 2035. Materials and methods. The article describes the route of the user of the University platform 2035 in the formation of a personal development trajectory, outlines the principles of data processing about the listener. Results. It is shown that such platform services as digital diagnostics of a student, collection, and analysis of digital footprint, as well as creating a digital profile of competencies make it possible to form and correct trajectory of human development, which makes it possible to acquire the competencies of the most relevant technologies of the modern world most effectively. Conclusion. With the help of the digital services of the University 2035 platform, anyone can form their personal development trajectory based on their current aspirations and goals for the future, and then implement this trajectory during their studies.
Criteria for performing total knee arthroplasty without postoperative intensive care monitoring v2

After reviewing the medical literature, we defined objective criteria to indicate intensive care monitoring after total knee arthroplasty. These criteria were adopted in January 2020 and since then none of the 80 patients who underwent total knee arthroplasty at the Pedro Ernesto University Hospital and who met the criteria required intensive care monitoring.
Segmenting the Future

Predicting the future is an important aspect for decision-making in robotics or autonomous driving systems, which heavily rely upon visual scene understanding. While prior work attempts to predict future video pixels, anticipate activities or forecast future scene semantic segments from segmentation of the preceding frames, methods that predict future semantic segmentation solely from the previous frame RGB data in a single end-to-end trainable model do not exist. In this paper, we propose a temporal encoder-decoder network architecture that encodes RGB frames from the past and decodes the future semantic segmentation. The network is coupled with a new knowledge distillation training framework specific for the forecasting task. Our method, only seeing preceding video frames, implicitly models the scene segments while simultaneously accounting for the object dynamics to infer the future scene semantic segments. Our results on Cityscapes and Apolloscape outperform the baseline and current state-of-the-art methods. Code is available at https://github.com/eddyhkchiu/segmenting_the_future/.
Development of Cooperative Translation Learning Model to Improve English Learning Achievement

The objectives of the study are: 1) to describe the design of the Cooperative Translation Based Learning model to improve students’ achievement in class XII; 2) to assess the feasibility of the Cooperative Translation Based Learning model to improve students’ achievement in class XII; and 3) to measure the effectiveness of the Cooperative Translation Based Learning model to improve students’ achievement in class XII. The type of research is Research and Development (R&amp;D). The data of this research are English learning activities in class XII of SMKIT Nur Hasan Boarding School; data on the achievement of learning English in class XII of SMKIT Nur Hasan Boarding School. Data were collected through tests, observations, and questionnaires. Data were analyzed using inferential analysis and qualitative analysis. The feasibility test for developing the Cooperative Translation Based Learning model shows 96.7% with very practical criteria. Under the practicality criteria in the large group trial, the learning module “Cooperative Translation Based Learning for Writing Skill Grade XII” in learning to write English Business letters is stated to be very practical and feasible. The effectiveness test of the Cooperative Translation Based Learning model shows that the output of the Paired Samples Test has a Sig. (2-tailed) is 0.000 &lt; 0.05; therefore, H0 is rejected and H1 is accepted. It can be concluded that there is a difference in the average value of learning outcomes between pre-test and post-test results. Therefore, developing the learning module “Cooperative Translation Based Learning for Writing Skill Grade XII” can improve writing learning skills.
Efektivitas Penggunaan Media Pembelajaran Google Classroom Terhadap Hasil Belajar Siswa Kelas X Mata Pelajaran Akuntansi

Online learning during the COVID-19 pandemic is an alternative to do so that learning activities can continue. The implementation of this learning requires learning media such as Google Classroom. This application has the advantage of facilitating structured and systematic learning activities. SMKN Darul Ulum Muncar is one of the schools that has not implemented the use of learning media. So researchers are interested in conducting research related to the use of Google Classroom to determine its effect on learning outcomes. This research is an experimental study with a Quasi-Experimental model using the Nonequivalent Control Group Design. The sample required consists of control and experimental groups. The results of the independent sample t-test analysis showed that the sig. (2 tailed) 0.00 &lt; 0.05 so H0 is rejected and Ha is accepted, meaning that there is a significant difference in student learning outcomes between the control and experimental groups. This difference can be seen based on the average value of the experimental group, which is 81.6, which is higher than the control group, which is 68.1. So, it can be concluded that the use of Google Classroom is effective on learning outcomes. However, there are still some obstacles such as difficulties in the coordination process, limitations in supervising student activities, difficulties in shaping students' character and skills, and inadequate student facilities
Pengaruh Peer Instruction Flipped Classroom dan Literasi Digital Terhadap Hasil Belajar Ekonomi Kelas XI BDP di SMKN 1 Surakarta

This research was carried out based on the phenomenon of online learning during the Covid-19 pandemic whose learning outcomes were below the KKM (Minimal Completeness Criteria). This condition is caused by the less innovative learning model and the lack of digital literacy in Eleven-grade students of Online Business and Marketing at SMK Negeri 1 Surakarta. The purpose of this study was to determine the effect of peer instruction flipped classroom and digital literacy on economic learning outcomes in online business materials. This research was in the form of a quasi-experimental research model with pretest and posttest group design. The analysis of the research data used the Independent Sample T-Test and N-Gain Test. The results showed that there were significant effects between peer instruction flipped classroom and digital literacy on cognitive learning outcomes. In collecting various research data, researchers used pre-test, post-test, and questionnaires. The results were processed using SPSS (Statistical Package for the Social Sciences) and were described in descriptive statistical analysis. The results of this study can be used as a solution in conducting a learning process that uses peer instruction flipped classrooms and digital literacy in a balanced way to create effective learning.