Reviewerly/exHarmony huggingface dataset:
The Influence of Used Good-Based Learning Media on the Value of Chracter Education and Student's Motivation to Study

The purpose of this study was to see the effect of used goods-based learning media on the value of character education and student motivation. Something that can give effect or result from an action so as to bring a result of an effort or action taken, in this case the effect can be seen from achieving a specific instructional goal that has been planned. The place of this research is SD Sinar Pembaharuan Hidup. The subjects of this study were 35 students of grade V SD. Based on the theoretical study and data analysis, 1) based on the test criteria, the three variables have an average value of at least 3, in this case it is categorized as good. 2) the significance test of 0.005&gt; 0.000, this test shows the effect of used goods-based division media on the value of character education and student motivation, 3) the f-value test shows that f-count&gt; f-table is 4.192&gt; 3.29 and 4.757&gt; 3,29 This statement shows the influence of learning media on the value of character education and student learning motivation.
Emotion Analysis of Arabic Tweets during COVID-19 Pandemic in Saudi Arabia

Social media has emerged as an effective platform to investigate people's opinion and feeling towards crisis situations.Along with Coronavirus crisis, range of different emotions reveal, including anger, sadness, fear, trust, and anticipation.In this paper, we investigate public's emotional responses associated with this pandemic using Twitter as platform to perform our analysis.We investigate how emotional perspective vary regarding lockdown ending in Saudi Arabia.We develop an emotion detection method to classify tweets into standard eight emotions.Furthermore, we present insights into the changes in the intensity of the emotions over time.Our finding shows that joy and anticipation are the most dominant among all emotions.While people express positive emotions, there are tones of fear, anger, and sadness revealed.Moreover, this research might help to better understand public behaviors to gain insight and make the proper decisions.
Robust quantum gates using smooth pulses and physics-informed neural networks

The presence of decoherence in quantum computers necessitates the suppression of noise. Dynamically corrected gates via specially designed control pulses offer a path forward, but hardware-specific experimental constraints can cause complications. Existing methods to obtain smooth pulses are either restricted to two-level systems, require an optimization over noise realizations, or limited to piecewise-continuous pulse sequences. In this paper, we present the first general method for obtaining truly smooth pulses that minimizes sensitivity to noise, eliminating the need for sampling over noise realizations and making assumptions regarding the underlying statistics of the experimental noise. We parametrize the Hamiltonian using a neural network, which allows the use of a large number of optimization parameters to adequately explore the functional control space. We demonstrate the capability of our approach by finding smooth shapes, which suppress the effects of noise within the logical subspace as well as leakage out of that subspace.
HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing

Virtualization is one of the key features of cloud computing, where the physical machines are virtually divided into several virtual machines in the cloud.The user's tasks are run on these virtual resources as per the requirements.When the user requests the services to the cloud, the user's tasks are allotted to the virtual resources depending on their needs.An efficient scheduling mechanism is required for optimizing the involved parameters.Scientific workflows deals with a large amount of data with dependency constraints and is used to simplify the applications in the diverse scientific domains.Scheduling the workflow in cloud computing is a well-known NP-hard problem.Deploying such data-and compute-intensive workflow on the cloud needs an efficient scheduling algorithm.In this paper, we have proposed a multi-objective model based hybrid algorithm (HPSOGWO), which combines the desirable characteristics of two well-known algorithms, particle swarm optimization (PSO), and grey wolf optimization (GWO).The results are analyzed under complex real-world scientific workflows such as Montage, CyberShake, Inspiral, and Sipht.We have considered the two essential parameters: total execution time and total execution cost while working in the cloud environment.The simulation results show that the proposed algorithm performs well compared to other state-of-the-art algorithms such as round-robin (RR), ant colony optimization (ACO), heterogeneous earliest time first (HEFT), and particle swarm optimization (PSO).
Progressive Semantic Face Deblurring

Previous face deblurring methods have utilized semantic segmentation maps as prior knowledge. Most of these methods generated the segmentation map from a blurred facial image, and restore it using the map in a sequential manner. However, the accuracy of the segmentation affects the restoration performance. Generally, it is difficult to obtain an accurate segmentation map from a blurred image. Instead of sequential methods, we propose an efficient method that learns the flows of facial component restoration without performing segmentation. To this end, we propose a multi-semantic progressive learning (MSPL) framework that progressively restores the entire face image starting from the facial components such as the skin, followed by the hair, and the inner parts (eyes, nose, and mouth). Furthermore, we propose a discriminator that observes the reconstruction-flow of the generator. In addition, we present new test datasets to facilitate the comparison of face deblurring methods. Various experiments demonstrate that the proposed MSPL framework achieves higher performance in facial image deblurring compared to the existing methods, both qualitatively and quantitatively. Our code, trained model and data are available at https://github.com/dolphin0104/MSPL-GAN.
Optimization of interval type-2 fuzzy system using the PSO technique for predictive problems

An interval type-2 fuzzy logic system (IT2FLS) can function well with uncertain data, with which a type-1 fuzzy logic system (T1FLS) is ineffective because its membership function rests upon crisp values. However, similar to T1FLSs, there are challenges associated with IT2FLSs in selecting parameters, which can significantly affect the accuracy of the classification results with their relatively high sensitivity. This paper discusses and proposes a hybrid model based on IT2FLS and particle swarm optimization (PSO) for prediction problems. The main objective of this paper is to find the optimal solution for the unknown fuzzy systems using labelled data for training the fuzzy system. The PSO technique was used to find the optimal parameters of the Gaussian membership functions which utilized for IT2FLSs. The authors tested two data sets for each of the two prediction problems, namely: burnt forest area prediction and wine quality prediction. The predictive results were compared with other predictive methods including random forest (RF), support vector machines (SVM), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and IT2FLS with parameters generated by using the fuzzy c-means algorithm (IT2FLS-FCM). Experiment results showed that the proposed method could significantly improve accuracy compared to several other predictive techniques.
Automatic machine learning algorithms for fraud detection in digital payment systems

Data on global financial statistics demonstrate that total losses from fraudulent transactions around the world are constantly growing. The issue of payment fraud will be exacerbated by the digitalization of economic relations, in particular the introduction by banks of the concept of "Bank-as-a-Service", which will increase the burden on payment services.The aim of this study is to synthesize effective models for detecting fraud in digital payment systems using automated machine learning and Big Data analysis algorithms.Approaches to expanding the information base to detect fraudulent transactions have been proposed and systematized. The choice of performance metrics for building and comparing models has been substantiated.The use of automatic machine learning algorithms has been proposed to resolve the issue, which makes it possible in a short time to go through a large number of variants of models, their ensembles, and input data sets. As a result, our experiments allowed us to obtain the quality of classification based on the AUC metric at the level of 0.977‒0.982. This exceeds the effectiveness of the classifiers developed by traditional methods, even as the time spent on the synthesis of the models is much less and measured in hours. The models' ensemble has made it possible to detect up to 85.7 % of fraudulent transactions in the sample. The accuracy of fraud detection is also high (79‒85 %).The results of our study confirm the effectiveness of using automatic machine learning algorithms to synthesize fraud detection models in digital payment systems. In this case, efficiency is manifested not only by the resulting classifiers' quality but also by the reduction in the cost of their development, as well as by the high potential of interpretability. Implementing the study results could enable financial institutions to reduce the financial and temporal costs of developing and updating active systems against payment fraud, as well as improve the effectiveness of monitoring financial transactions
Reply to: Overconfidence in Bayesian analyses of galaxy rotation curves

Cameron {\it et al.}~2019 (hereafter C19) recommends a more cautious and rigorous approach to statistical analysis in astronomy. We welcome this particular side of their communication as it helps stimulating the effort towards the adoption of better statistical methods in galaxy rotation curves, an effort to which we contributed with Rodrigues {\it et al.}~2018 (hereafter R18). Indeed, R18 was the first work that, in order to conclude on the universality of the acceleration scale $a_0$, studied the posterior distributions on $a_0$ of a large set of galaxies. As C19 agrees, the credible intervals were found within the Bayesian framework, that is, the marginalized posteriors on $a_0$ were found using Bayes' theorem to update the priors in light of the observational data; this process was done without introducing any approximation. Considering R18, C19 also remarks that: i) better methods to select the nuisance parameters and the corresponding priors could be used; ii) a quality cut based on $\chi^2$ values should not be used, and iii) the compatibility of the posteriors should be assessed in a more robust way. In the following, after first clarifying the context of our work, we address these criticisms.
Guiding Neuroevolution with Structural Objectives

Abstract The structure and performance of neural networks are intimately connected, and by use of evolutionary algorithms, neural network structures optimally adapted to a given task can be explored. Guiding such neuroevolution with additional objectives related to network structure has been shown to improve performance in some cases, especially when modular neural networks are beneficial. However, apart from objectives aiming to make networks more modular, such structural objectives have not been widely explored. We propose two new structural objectives and test their ability to guide evolving neural networks on two problems which can benefit from decomposition into subtasks. The first structural objective guides evolution to align neural networks with a user-recommended decomposition pattern. Intuitively, this should be a powerful guiding target for problems where human users can easily identify a structure. The second structural objective guides evolution towards a population with a high diversity in decomposition patterns. This results in exploration of many different ways to decompose a problem, allowing evolution to find good decompositions faster. Tests on our target problems reveal that both methods perform well on a problem with a very clear and decomposable structure. However, on a problem where the optimal decomposition is less obvious, the structural diversity objective is found to outcompete other structural objectives—and this technique can even increase performance on problems without any decomposable structure at all.
Constrained BERT BiLSTM CRF for understanding multi-sentence entity-seeking questions

Abstract We present the novel task of understanding multi-sentence entity-seeking questions (MSEQs), that is, the questions that may be expressed in multiple sentences, and that expect one or more entities as an answer. We formulate the problem of understanding MSEQs as a semantic labeling task over an open representation that makes minimal assumptions about schema or ontology-specific semantic vocabulary. At the core of our model, we use a BiLSTM (bidirectional LSTM) conditional random field (CRF), and to overcome the challenges of operating with low training data, we supplement it by using BERT embeddings, hand-designed features, as well as hard and soft constraints spanning multiple sentences. We find that this results in a 12–15 points gain over a vanilla BiLSTM CRF. We demonstrate the strengths of our work using the novel task of answering real-world entity-seeking questions from the tourism domain. The use of our labels helps answer 36% more questions with 35% more (relative) accuracy as compared to baselines. We also demonstrate how our framework can rapidly enable the parsing of MSEQs in an entirely new domain with small amounts of training data and little change in the semantic representation.
Parallel Interactive Networks for Multi-Domain Dialogue State Generation

The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multidomain dialogue state tracking (MDST) models.In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies.Specifically, we integrate an interactive encoder to jointly model the in-turn dependencies and cross-turn dependencies.The slot-level context is introduced to extract more expressive features for different slots.And a distributed copy mechanism is utilized to selectively copy words from historical system utterances or historical user utterances.Empirical studies demonstrated the superiority of the proposed PIN model.
Experimental INVESTIGATION OF influence WEDM PARAMETERS ON SURFACE ROUGHNESS AND FLATNESS DEVIATION

Wire electrical discharge machining (EDM) is a modern technology that is widely used in the production of difficult to cut conductive materials.Stochastic nature of electrical discharges causing to difficult to identify the influence WEDM parameters on surface roughness and dimensional accuracy of machining parts.Predicting favorable machining conditions for the required surface finish and dimensional accuracy plays a key role in the process.Response surface methodology (RSM) was used to build empirical models on the influence of the discharge voltage VB, pulse interval SB, and workpiece height on the surface roughness (Ra) and the flatness deviation during the machining of tool steel X153CrMoV12.The conducted analysis of the material removal physics in relation to investigated parameters indicated potential sources of shape errors of cutting parts.
A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects

This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of Human Activity Recognition. We measure both the classification performance and resource consumption (runtime, memory, and power) of five usual stream classification algorithms, implemented in a consistent library, and applied to two real human activity datasets and three synthetic datasets. Regarding classification performance, the results show the overall superiority of the Hoeffding Tree, the Mondrian forest, and the Naïve Bayes classifiers over the Feedforward Neural Network and the Micro Cluster Nearest Neighbor classifiers on four datasets out of six, including the real ones. In addition, the Hoeffding Tree and—to some extent—the Micro Cluster Nearest Neighbor, are the only classifiers that can recover from a concept drift. Overall, the three leading classifiers still perform substantially worse than an offline classifier on the real datasets. Regarding resource consumption, the Hoeffding Tree and the Mondrian forest are the most memory intensive and have the longest runtime; however, no difference in power consumption is found between classifiers. We conclude that stream learning for Human Activity Recognition on connected objects is challenged by two factors which could lead to interesting future work: a high memory consumption and low F1 scores overall.
Social-Sensor Composition for Tapestry Scenes

The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events. One of its potential applications is to leverage crowdsourced social media images to create a tapestry scene for scene analysis of designated locations and time intervals. The existing attempts however ignore the temporal-semantic relevance and spatio-temporal evolution of the images and direction-oriented scene reconstruction. We propose a novel social-sensor cloud (SocSen) service composition approach to form tapestry scenes for scene analysis. The novelty lies in utilising images and image meta-information to bypass expensive traditional image processing techniques to reconstruct scenes. Metadata, such as geolocation, time and angle of view of an image are modelled as non-functional attributes of a SocSen service. Our major contribution lies on proposing a context and direction-aware spatio-temporal clustering and recommendation approach for selecting a set of temporally and semantically similar services to compose the best available SocSen services. Analytical results based on real datasets are presented to demonstrate the performance of the proposed approach.
Too much finance or too many weak instruments?

Since the global financial crisis of 2008, a strand of the literature has documented a threshold beyond which financial development tends to affect growth adversely. However, the The evidence rests heavily on internal instrument identification strategies, whose reliability has received surprisingly little attention so far in the finance-growth literature. Therefore, the present paper conducts a reappraisal of the non-linear conclusion twofold. First, in light of new data, second, by a thorough assessment of the identification strategy. Evidence points out that a series of unaddressed issues affecting the system-gmm setup results in spurious threshold regressions and overfitting of outliers.
Who is the Film's Director? Authorship Recognition Based on Shot Features

We show how to perform automatic attribution of movie's authorship by the statistical analysis of two simple formal features: the length of camera takes (shot duration), and the distance between the camera and the subject (shot scale). Experiments include 143 films by eight distinguishable directors over 70 years of historiography of author cinema.
Implementing a Two‐Photon Three‐Degrees‐of‐Freedom Hyper‐Parallel Controlled Phase Flip Gate Through Cavity‐Assisted Interactions

Abstract Hyper‐parallel quantum information processing is a promising and beneficial research field. Herein, a method to implement a hyper‐parallel controlled‐phase‐flip (hyper‐CPF) gate for frequency‐, spatial‐, and time‐bin‐encoded qubits by coupling flying photons to trapped nitrogen vacancy (NV) defect centers is presented. The scheme, which differs from their conventional parallel counterparts, is specifically advantageous in decreasing against the dissipate noise, increasing the quantum channel capacity, and reducing the quantum resource overhead. The gate qubits with frequency, spatial, and time‐bin degrees of freedom (DOF) are immune to quantum decoherence in optical fibers, whereas the polarization photons are easily disturbed by the ambient noise.
PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision

User modeling is critical for many personalized web services.Many existing methods model users based on their behaviors and the labeled data of target tasks.However, these methods cannot exploit useful information in unlabeled user behavior data, and their performance may be not optimal when labeled data is scarce.Motivated by pre-trained language models which are pre-trained on large-scale unlabeled corpus to empower many downstream tasks, in this paper we propose to pretrain user models from large-scale unlabeled user behaviors data.We propose two selfsupervision tasks for user model pre-training.The first one is masked behavior prediction, which can model the relatedness between historical behaviors.The second one is next K behavior prediction, which can model the relatedness between past and future behaviors.The pre-trained user models are finetuned in downstream tasks to learn task-specific user representations.Experimental results on two realworld datasets validate the effectiveness of our proposed user model pre-training method.
Batch Entropy Supervised Convolutional Neural Networks for Feature Extraction and Harmonizing for Action Recognition

Developed digital signage solution for cloud-
Probing for Multilingual Numerical Understanding in Transformer-Based Language Models

Natural language numbers are an example of compositional structures, where larger numbers are composed of operations on smaller numbers.Given that compositional reasoning is a key to natural language understanding, we propose novel multilingual probing tasks tested on DistilBERT, XLM, and BERT to investigate for evidence of compositional reasoning over numerical data in various natural language number systems.By using both grammaticality judgment and value comparison classification tasks in English, Japanese, Danish, and French, we find evidence that the information encoded in these pretrained models' embeddings is sufficient for grammaticality judgments but generally not for value comparisons.We analyze possible reasons for this and discuss how our tasks could be extended in further studies.
Generic Body Expression Recognition Based on Synthesis of Realistic Neutral Motion

Most automatic expression analysis systems attempt to recognize a conventional set of expressions such as happiness, sadness, anger, surprise and fear, etc. Although this set of expressions is the most typical of the face, it is not the most representative/relevant for what the body expressions tell us. This paper presents a novel and generic approach for the recognition of body expressions using human postures. Our method is based on the notion of neutral motion generated from a given expressive one. In a second time, we estimate a residue function, as the difference between the two associated motions, namely the expressive and the neutral motion. More precisely, this function that is inspired by studies from psychology domain, gives a “neutrality” score of a motion. Using this “neutrality score”, we propose a cost function which enables to synthesis the neutral motion from any input expressive motion. The synthesis of neutral motion process is based on two nested Principal Component Analysis providing a space where moving and selecting realistic human animations become possible. Proposed approach is evaluated on four databases with heterogeneous movements and body expressions and it achieved recognition results for body expression recognition that exceed state of the art.