Reviewerly/exHarmony huggingface dataset:
PENERAPAN BAHAN AJAR AUDIO UNTUK ANAK TUNANETRA TINGKAT SMP DI INDONESIA

Blind people are people who can not see (totally blind) and can see only see the light (less alert).Limitations in seeing can be helped by audio media that emphasizes hearing.This media is suitable because blind children have very sharp hearing when practiced continuously.This very sharp hearing can be used for learning by using audio media.Blind Junior High School students have a higher concentration than elementary school children so this media will be suitable if used as a medium for junior high school students.This media will facilitate learning in junior high school students who are blind in learning and receiving material.Audio The purpose of the article is to determine the feasibility and effectiveness of the application of audio media as teaching material for blind students at the junior high level.This study uses a series of previous articles related to the title of this research.From the results of the study of previous articles quoting that by using audio media, blind students are more motivated and can easily learn, can also learn and understand sounds heard with audio so that blind students can better understand the material presented.
Broadcasting of entanglement via orthogonal and non-orthogonal state-dependent cloners

In this work, we extensively study the problem of broadcasting of entanglement as state-dependent versus state-independent cloners. We start by re-conceptualizing the idea of state-dependent quantum cloning machine (SD-QCM), and in that process, we introduce different types of SD-QCMs, namely orthogonal and non-orthogonal cloners. We derive the conditions for which the fidelity of these cloners will become independent of the input state. We note that such a construction allows us to maximize the cloning fidelity at the cost of having partial information of the input state. In the discussion on broadcasting of entanglement, we start with a general two-qubit state as our resource and later we consider a specific example of Bell diagonal state. We apply both state-dependent and state-independent cloners (orthogonal and non-orthogonal), locally and non-locally, on input resource state and obtain a range for broadcasting of entanglement in terms of the input state parameters. Our results highlight several instances where the state-dependent cloners outperform their state-independent counterparts in broadcasting entanglement. Our study provides a comparative perspective on the broadcasting of entanglement via cloning in two-qubit scenario, when we have some knowledge of the resource ensemble versus a situation when we have no such information.
PyGlove: Symbolic Programming for Automated Machine Learning

Neural networks are sensitive to hyper-parameter and architecture choices. Automated Machine Learning (AutoML) is a promising paradigm for automating these choices. Current ML software libraries, however, are quite limited in handling the dynamic interactions among the components of AutoML. For example, efficientNAS algorithms, such as ENAS and DARTS, typically require an implementation coupling between the search space and search algorithm, the two key components in AutoML. Furthermore, implementing a complex search flow, such as searching architectures within a loop of searching hardware configurations, is difficult. To summarize, changing the search space, search algorithm, or search flow in current ML libraries usually requires a significant change in the program logic. In this paper, we introduce a new way of programming AutoML based on symbolic programming. Under this paradigm, ML programs are mutable, thus can be manipulated easily by another program. As a result, AutoML can be reformulated as an automated process of symbolic manipulation. With this formulation, we decouple the triangle of the search algorithm, the search space and the child program. This decoupling makes it easy to change the search space and search algorithm (without and with weight sharing), as well as to add search capabilities to existing code and implement complex search flows. We then introduce PyGlove, a new Python library that implements this paradigm. Through case studies on ImageNet and NAS-Bench-101, we show that with PyGlove users can easily convert a static program into a search space, quickly iterate on the search spaces and search algorithms, and craft complex search flows to achieve better results.
Tangled String for Multi-Timescale Explanation of Changes in Stock Market

This work addresses the question of explaining changes in the desired timescales of the stock market. Tangled string is a sequence visualization tool wherein a sequence is compared to a string and trends in the sequence are compared to the appearance of tangled pills and wires bridging the pills in the string. Here, the tangled string is extended and applied to detecting stocks that trigger changes and explaining trend changes in the market. Sequential data for 11 years from the First Section of the Tokyo Stock Exchange regarding top-10 stocks with weekly increase rates are visualized using the tangled string. It was found that the change points obtained by the tangled string coincided well with changes in the average prices of listed stocks, and changes in the price of each stock are visualized on the string. Thus, changes in stock prices, which vary across a mixture of different timescales, could be explained in the time scale corresponding to interest in stock analysis. The tangled string was created using a data-driven innovation platform called Innovators Marketplace on Data Jackets, and is extended to satisfy data users here, so this study verifies the contribution of data market to data-driven innovation.
XCSF for Automatic Test Case Prioritization

Testing is a crucial part in the development of a new product.Due to the change from manual testing to automated testing, companies can rely on a higher number of tests.There are certain cases such as smoke tests where the execution of all tests is not feasible and a smaller test suite of critical test cases is necessary.This prioritization problem has just gotten into the focus of reinforcement learning.A neural network and an XCS classifier system have been applied to this task.Another evolutionary machine learning approach is the XCSF which produces, unlike XCS, continuous outputs.In this work we show that XCSF is superior to both the neural network and XCS for this problem.
LSTM-Based Anomaly Detection: Detection Rules from Extreme Value Theory

In this paper, we explore various statistical techniques for anomaly detection in conjunction with the popular Long Short-Term Memory (LSTM) deep learning model for transportation networks. We obtain the prediction errors from an LSTM model, and then apply three statistical models based on (i) the Gaussian distribution, (ii) Extreme Value Theory (EVT), and (iii) the Tukey’s method. Using statistical tests and numerical studies, we find strong evidence against the widely employed Gaussian distribution based detection rule on the prediction errors. Next, motivated by fundamental results from Extreme Value Theory, we propose a detection technique that does not assume any parent distribution on the prediction errors. Through numerical experiments conducted on several real-world traffic data sets, we show that the EVT-based detection rule is superior to other detection rules, and is supported by statistical evidence.
LiMiT: The Literal Motion in Text Dataset

Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically.We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion.We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models.We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.
Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement

The structure of the deep artificial neural network is similar to the structure of the biological neural network, which can be well applied to the 3D visual image recognition of aerobics movements. A lot of results have been achieved by applying deep neural networks to the 3D visual image recognition of aerobics movements, but there are still many problems to be overcome. After analyzing the expression characteristics of the convolutional neural network model for the three-dimensional visual image characteristics of aerobics, this paper builds a convolutional neural network model. The model is improved on the basis of the traditional model and unifies the process of aerobics 3D visual image segmentation, target feature extraction, and target recognition. The convolutional neural network and the deep neural network based on autoencoder are designed and applied to aerobics action 3D visual image test set for recognition and comparison. We improve the accuracy of network recognition by adjusting the configuration parameters in the network model. The experimental results show that compared with other simple models, the model based on the improved AdaBoost algorithm can improve the final result significantly when the accuracy of each model is average. Therefore, the method can improve the recognition accuracy when multiple neural network models with general accuracy are obtained, thereby avoiding the complicated parameter adjustment process to obtain a single optimal network model.
Unsupervised quality control of segmentations based on a smoothness and intensity probabilistic model

Monitoring the quality of image segmentation is key to many clinical applications. This quality assessment can be carried out by a human expert when the number of cases is limited. However, it becomes onerous when dealing with large image databases, so partial automation of this process is preferable. Previous works have proposed both supervised and unsupervised methods for the automated control of image segmentations. The former assume the availability of a subset of trusted segmented images on which supervised learning is performed, while the latter does not. In this paper, we introduce a novel unsupervised approach for quality assessment of segmented images based on a generic probabilistic model. Quality estimates are produced by comparing each segmentation with the output of a probabilistic segmentation model that relies on intensity and smoothness assumptions. Ranking cases with respect to these two assumptions allows the most challenging cases in a dataset to be detected. Furthermore, unlike prior work, our approach enables possible segmentation errors to be localized within an image. The proposed generic probabilistic segmentation method combines intensity mixture distributions with spatial regularization prior models whose parameters are estimated with variational Bayesian techniques. We introduce a novel smoothness prior based on the penalization of the derivatives of label maps which allows an automatic estimation of its hyperparameter in a fully data-driven way. Extensive evaluation of quality control on medical and COCO datasets is conducted, showing the ability to isolate atypical segmentations automatically and to predict, in some cases, the performance of segmentation algorithms.
Witnessing entanglement in experiments with correlated noise

The purpose of an entanglement witness experiment is to certify the creation of an entangled state from a finite number of trials. The statistical confidence of such an experiment is typically expressed as the number of observed standard deviations of witness violations. This method implicitly assumes that the noise is well-behaved so that the central limit theorem applies. In this work, we propose two methods to analyze witness experiments where the states can be subject to arbitrarily correlated noise. Our first method is a rejection experiment, in which we certify the creation of entanglement by rejecting the hypothesis that the experiment can only produce separable states. We quantify the statistical confidence by a p-value, which can be interpreted as the likelihood that the observed data is consistent with the hypothesis that only separable states can be produced. Hence a small p-value implies large confidence in the witnessed entanglement. The method applies to general witness experiments and can also be used to witness genuine multipartite entanglement. Our second method is an estimation experiment, in which we estimate and construct confidence intervals for the average witness value. This confidence interval is statistically rigorous in the presence of correlated noise. The method applies to general estimation problems, including fidelity estimation. To account for systematic measurement and random setting generation errors, our model takes into account device imperfections and we show how this affects both methods of statistical analysis. Finally, we illustrate the use of our methods with detailed examples based on a simulation of NV centers.
Entangling non planar molecules <i>via</i> inversion doublet transition with negligible spontaneous emission

We analyze theoretically the entanglement between two non-planar and light identical molecules (<italic>e.g.</italic>, pyramidal NH<sub>3</sub>) that present inversion doubling due to the internal spatial inversion of their nuclear conformations by tunneling.
JLHS: A Joint Linear Frequency Modulation and Hyperbolic Frequency Modulation Approach for Speed Measurement

In the waveform design, the distance measurement and resolution are a pair of irreconcilable contradictions.Linear Frequency Modulation (LFM) can alleviate this contradiction.LFM is widely used in radar and sonar, however, its Doppler tolerance is not ideal.Hyperbolic Frequency Modulation (HFM) signal has a particularly strong tolerance towards Doppler frequency shift.When the unidirectionally modulated HFM signal is in distance measurement, the Doppler delay of the matched filtering output cannot be eliminated, and there is a ranging error.After matched filtering of the positive and negative frequency modulation (HFM+LFM) echo signal based on the same frequency band, the Doppler-induced delay is the same but opposite in direction, and the delay is closely related to the frequency, bandwidth, and pulse width of the transmitted signal.By using the inverse time delay difference of the positive and negative frequency modulation, the ranging error in the ranging of unidirectionally modulated LFM signal can be eliminated.In this paper, a Joint Linear frequency modulation and Hyperbolic frequency modulation approach for Speed measurement (JLHS) is proposed, which employs the same frequency band of positive and negative frequency modulation signals for speed measurement and ranging.Extensive simulation results show that the proposed approach can better estimate the speed and distance of moving targets, and it has reference value for engineering application.
Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference

While discriminative neural network classifiers are generally preferred, recent work has shown advantages of generative classifiers in term of data efficiency and robustness.In this paper, we focus on natural language inference (NLI).We propose GenNLI, a generative classifier for NLI tasks, and empirically characterize its performance by comparing it to five baselines, including discriminative models and large-scale pretrained language representation models like BERT.We explore training objectives for discriminative fine-tuning of our generative classifiers, showing improvements over log loss fine-tuning from prior work (Lewis and Fan, 2019).In particular, we find strong results with a simple unbounded modification to log loss, which we call the "infinilog loss".Our experiments show that GenNLI outperforms both discriminative and pretrained baselines across several challenging NLI experimental settings, including small training sets, imbalanced label distributions, and label noise.
Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks

Understating spatial semantics expressed in natural language can become highly complex in real-world applications. This includes applications of language grounding, navigation, visual question answering, and more generic human-machine interaction and dialogue systems. In many of such downstream tasks, explicit representation of spatial concepts and relationships can improve the capabilities of machine learning models in reasoning and deep language understanding. In this tutorial, we overview the cutting-edge research results and existing challenges related to spatial language understanding including semantic annotations, existing corpora, symbolic and sub-symbolic representations, qualitative spatial reasoning, spatial common sense, deep and structured learning models. We discuss the recent results on the above-mentioned applications –that need spatial language learning and reasoning – and highlight the research gaps and future directions.
Entity-fishing: A DARIAH Entity Recognition and Disambiguation Service

This paper presents an attempt to provide a generic named-entity recognition and disambiguation module (NERD) called entity-fishing as a stable online service that demonstrates the possible delivery of sustainable technical services within DARIAH, the European digital research infrastructure for the arts and humanities. Deployed as part of the national infrastructure Huma-Num in France, this service provides an efficient state-of-the-art implementation coupled with standardised interfaces allowing an easy deployment on a variety of potential digital humanities contexts. Initially developed in the context of the FP9 EU project CENDARI, the software was well received by the user community and continued to be further developed within the H2020 HIRMEOS project where several open access publishers have integrated the service to their collections of published monographs as a means to enhance retrieval and access. entity-fishing implements entity extraction as well as disambiguation against Wikipedia and Wikidata entries. The service is accessible through a REST API which allows easier and seamless integration, language independent and stable convention and a widely used service-oriented architecture (SOA) design. Input and output data are carried out over a query data model with a defined structure providing flexibility to support the processing of partially annotated text or the repartition of text over several queries. The interface implements a variety of functionalities, like language recognition, sentence segmentation and modules for accessing and looking up concepts in the knowledge base. The API itself integrates more advanced contextual parametrisation or ranked outputs, allowing for the resilient integration in various possible use cases. The entity-fishing API has been used as a concrete use case to draft the experimental stand-off proposal, which has been submitted for integration into the TEI guidelines. The representation is also compliant with the Web Annotation Data Model (WADM). In this paper we aim at describing the functionalities of the service as a reference contribution to the subject of web-based NERD services. In this paper, we detail the workflow from input to output and unpack each building box in the processing flow. Besides, with a more academic approach, we provide a transversal schema of the different components taking into account non-functional requirements in order to facilitate the discovery of bottlenecks, hotspots and weaknesses. We also describe the underlying knowledge base, which is set up on the basis of Wikipedia and Wikidata content. We conclude the paper by presenting our solution for the service deployment: how and which the resources where allocated. The service has been in production since Q3 of 2017, and extensively used by the H2020 HIRMEOS partners during the integration with the publishing platforms.
A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events

An adverse drug event (ADE) is an injury resulting from medical intervention related to a drug.ADE detection from text can be either fine-grained (ADE entity recognition) or coarse-grained (ADE assertive sentence classification), with limited efforts leveraging interdependencies among these two granularities.We instead design a multi-grained joint deep network model MGADE to concurrently solve both ADE tasks MGADE takes advantage of their symbiotic relationship, with a transfer of knowledge between the two levels of granularity.Our dual-attention mechanism constructs multiple distinct representations of a sentence that capture both task-specific and semantic information in the sentence, providing stronger emphasis on the key elements essential for sentence classification.Our model improves stateof-art F1-score for both tasks: (i) entity recognition of ADE words (12.5% increase) and (ii) ADE sentence classification (13.6% increase) on MADE 1.0 benchmark of EHR notes.
Adversarial Grammatical Error Correction

Recent works in Grammatical Error Correction (GEC) have leveraged the progress in Neural Machine Translation (NMT), to learn rewrites from parallel corpora of grammatically incorrect and corrected sentences, achieving state-of-the-art results. At the same time, Generative Adversarial Networks (GANs) have been successful in generating realistic texts across many different tasks by learning to directly minimize the difference between human-generated and synthetic text. In this work, we present an adversarial learning approach to GEC, using the generator-discriminator framework. The generator is a Transformer model, trained to produce grammatically correct sentences given grammatically incorrect ones. The discriminator is a sentence-pair classification model, trained to judge a given pair of grammatically incorrect-correct sentences on the quality of grammatical correction. We pre-train both the discriminator and the generator on parallel texts and then fine-tune them further using a policy gradient method that assigns high rewards to sentences which could be true corrections of the grammatically incorrect text. Experimental results on FCE, CoNLL-14, and BEA-19 datasets show that Adversarial-GEC can achieve competitive GEC quality compared to NMT-based baselines.
A Survey of GAN-Generated Fake Faces Detection Method Based on Deep Learning

In recent years, with the rapid growth of generative adversarial networks (GANs), a photo-realistic face can be easily generated from a random vector.Moreover, the faces generated by advanced GANs are very realistic.It is reasonable to acknowledge that even a well-trained viewer has difficulties to distinguish artificial from real faces.Therefore, detecting the face generated by GANs is a necessary work.This paper mainly introduces some methods to detect GAN-generated fake faces, and analyzes the advantages and disadvantages of these models based on the network structure and evaluation indexes, and the results obtained in the respective data sets.On this basis, the challenges faced in this field and future research directions are discussed.
Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing

This paper studies a novel model that simplifies the disambiguation of connectives for explicit discourse relations.We use a neural approach that integrates contextualized word embeddings and predicts whether a connective candidate is part of a discourse relation or not.We study the influence of those context specific-embeddings.Further, we show the benefit of training the tasks of connective disambiguation and sense classification together at the same time.The success of our approach is supported by state-of-the-art results.