Reviewerly/exHarmony huggingface dataset:
Falia Expression Protecting Environmental Ecosystem in Barangka District

Falia expression is a spoken language formulated in the linguistic and cultural facts supporting the community. This study aims to describe the falia expression of Muna society in Barangka district serves as a control to protect the forest, rivers, sea, flora, and fauna. The study used a qualitative approach with methods ecolinguistics. Source of research data is an oral speech in situations informant said that natural and candid as the primary data and research resources that have been published as secondary data. Time data collection was done in January 2019 until June 2019 in the region Barangka district. Data collection is conducted with involved participation techniques, observations, and interviews. Analysesare carried out during collection, reduction, presentation, and conclusions/verification. Data analysis is inductive-qualitative. The results showed that the role falia as of protecting the environment in Barangka district classified into five categories: First, falia as the protection control of forest; Second, falia as the protection control of river; Third, falia as the protection control of sea; Fourth, falia as the protection control of flora; Fifth, falia as the protection control of fauna. In general, falia expression classified in the imperative statements, either command or forbidden.
Partial Label Metric Learning Based on Statistical Inference

In partial label data, the ground-truth label of a training example is concealed in a set of candidate labels associated with the instance. As the ground-truth label is inaccessible, it is difficult to train the classifier via the label information. Consequently, manifold structure information is adopted, which is under the assumption that neighbor/similar instances in the feature space have similar labels in the label space. However, the real-world data may not fully satisfy this assumption. In this paper, a partial label metric learning method based on likelihood-ratio test is proposed to make partial label data satisfy the manifold assumption. Moreover, the proposed method needs no objective function and treats the data pairs asymmetrically. The experimental results on several real-world PLL datasets indicate that the proposed method outperforms the existing partial label metric learning methods in terms of classification accuracy and disambiguation accuracy while costs less time.
Adoption of Artificial Intelligence for Diagnosis and Treatment of &lt;i&gt;Staphylococcus aureus&lt;/i&gt; Infections Disease on Humans

The aim of this paper is to develop an expert system that could aid medical practitioner to effectively diagnose and treat Staphylococcus aureus infections disease on a human.The objective of the research includes to develop an expert system for quick diagnosis and detection of Staphylococcus aureus bacteria on human skin, a system that aids in accurate treatment of staph infectious diseases by doctors, helps in quick decision making in the hospital, improves accuracy in drug prescription, and a system that will bring about computerized storage process, and to enlighten the knowledge workers on how to implement a computer based decision support systems and importance of it in the health care.The research was motivated due to delay in diagnosis and identification of Staphylococcus aureus bacteria and the fast rate at which infectious disease is spreading, delay in treatment of these bacteria, increase of guess work by health practitioners leading to delay in decision making and lack of electronic storage facility in the hospitals.Top down approach was used in the system design of this research while adopting expert system as the methodology and the programming language used was Java and database design used was MySQL.The result after design was a computerized standalone application that assists health practitioners (Doctor's) in quick identification, diagnosis, prescription and treatment of Staphylococcus aureus bacteria on human skin.The expert system will facilitate quick decision making in the clinic.
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs

It has been advocated that medical imaging systems and reconstruction algorithms should be assessed and optimized by use of objective measures of image quality that quantify the performance of an observer at specific diagnostic tasks. One important source of variability that can significantly limit observer performance is variation in the objects to-be-imaged. This source of variability can be described by stochastic object models (SOMs). A SOM is a generative model that can be employed to establish an ensemble of to-be-imaged objects with prescribed statistical properties. In order to accurately model variations in anatomical structures and object textures, it is desirable to establish SOMs from experimental imaging measurements acquired by use of a well-characterized imaging system. Deep generative neural networks, such as generative adversarial networks (GANs) hold great potential for this task. However, conventional GANs are typically trained by use of reconstructed images that are influenced by the effects of measurement noise and the reconstruction process. To circumvent this, an AmbientGAN has been proposed that augments a GAN with a measurement operator. However, the original AmbientGAN could not immediately benefit from modern training procedures, such as progressive growing, which limited its ability to be applied to realistically sized medical image data. To circumvent this, in this work, a new Progressive Growing AmbientGAN (ProAmGAN) strategy is developed for establishing SOMs from medical imaging measurements. Stylized numerical studies corresponding to common medical imaging modalities are conducted to demonstrate and validate the proposed method for establishing SOMs.
LaSTUS/TALN at TRAC - 2020 Trolling, Aggression and Cyberbullying

This paper presents the participation of the LaSTUS/TALN team at TRAC-2020 Trolling, Aggression and Cyberbullying shared task. The aim of the task is to determine whether a given text is aggressive and contains misogynistic content. Our approach is based on a bidirectional Long Short Term Memory network (bi-LSTM). Our system performed well at sub-task A, aggression detection; however underachieved at sub-task B, misogyny detection.
Upper bounds on the rate in device-independent quantum key distribution

Quantum key distribution (QKD) is a method to distribute secret key among sender and receiver by the transmission of quantum particles (e.g. photons). Device-independent quantum key distribution (DIQKD) is a version of quantum key distribution with a stronger security demand in that sender and receiver do not need to rely on the inner workings of their device, which consequently can come from an untrusted vendor. We study the rate at which DIQKD can be carried out for a given quantum channel connecting sender and receiver and provide a technique to upper bound the achievable rate. As a result, we show that the rate of device independent secure key can for some channels be much smaller (even arbitrarily so) than that of QKD. We do so, by first providing definition of device independent secure key rate, which is of independent interest. For bipartite states we formulate a sufficient condition for a gap between QKD and DIQKD key rates and examples of 8-qubit bipartite state exhibiting the gap between device dependent and device independent key.
Deep State-Space Model for Noise Tolerant Skeleton-Based Action Recognition

Action recognition using skeleton data (3D coordinates of human joints) is an attractive topic due to its robustness to the actor's appearance, camera's viewpoint, illumination, and other environmental conditions. However, skeleton data must be measured by a depth sensor or extracted from video data using an estimation algorithm, and doing so risks extraction errors and noise. In this work, for robust skeleton-based action recognition, we propose a deep state-space model (DSSM). The DSSM is a deep generative model of the underlying dynamics of an observable sequence. We applied the proposed DSSM to skeleton data, and the results demonstrate that it improves the classification performance of a baseline method. Moreover, we confirm that feature extraction with the proposed DSSM renders subsequent classifications robust to noise and missing values. In such experimental settings, the proposed DSSM outperforms a state-of-the-art method.
Integration of new moving object segmentation and classification techniques using optimal salp swarm-based feature fusion with linear multi k-SVM classifier

Abstract The feature extraction technique is applied on least enclosing rectangle (LER) of the segmented object to increase the processing speed. The main intuition of this salp swarm algorithm relays on reducing the computational load of the proposed classifier by removing the repetitive and unrelated features from the feature vector. Also, increased training samples of similarly shaped classes when applied on the classifier can generate the misclassification results. Thus, a new layered kernel-based support vector machine (k-SVM) classifier is developed by means of integrating the k-neural network classifier and layered SVM classifier. Because of the high dimensional features, a difficulty occurs in the application of a single classifier. In order to ease the computational load, this multi classifier is integrated with a shadow elimination technique to classify the object categories of intelligent transportations system such as motorcycles, bicycles, cars, and pedestrians.
Algebraic Hardness versus Randomness in Low Characteristic

We show that lower bounds for explicit constant-variate polynomials over fields of characteristic $p > 0$ are sufficient to derandomize polynomial identity testing over fields of characteristic $p$. In this setting, existing work on hardness-randomness tradeoffs for polynomial identity testing requires either the characteristic to be sufficiently large or the notion of hardness to be stronger than the standard syntactic notion of hardness used in algebraic complexity. Our results make no restriction on the characteristic of the field and use standard notions of hardness. We do this by combining the Kabanets-Impagliazzo generator with a white-box procedure to take $p$-th roots of circuits computing a $p$-th power over fields of characteristic $p$. When the number of variables appearing in the circuit is bounded by some constant, this procedure turns out to be efficient, which allows us to bypass difficulties related to factoring circuits in characteristic $p$. We also combine the Kabanets-Impagliazzo generator with recent "bootstrapping" results in polynomial identity testing to show that a sufficiently-hard family of explicit constant-variate polynomials yields a near-complete derandomization of polynomial identity testing. This result holds over fields of both zero and positive characteristic and complements a recent work of Guo, Kumar, Saptharishi, and Solomon, who obtained a slightly stronger statement over fields of characteristic zero.
Training Teachers In The Prevention And Resolution Of Conflicts By Studying Tv Series

Within the framework of the Master's Degree in Secondary School Teaching, one of the challenges arising in the training of secondary school teachers is to ensure that the future teachers experience teaching from a systemic perspective, grasping the full complexity of the educational phenomena and the conflict processes. The students in the three groups studying the course on Conflict Prevention and Resolution at the University of Zaragoza (2018-2019) were trained in conflict analysis and resolution skills. 171 students took part, grouped into 31 teams. Each team performed a task involving the analysis of the conflicts that arose in an episode of a current television series. The data obtained in the group discussion were categorised inductively with the support of Atlas ti. software and five categories were obtained: learning, weaknesses, difficulties, challenges and value of the experience. The most relevant categories were learning and value of the experience. The participants highlighted the usefulness of the series for acquiring a systemic perspective of the conflict, distinguishing between types of conflict and the monitoring and handling of conflict situations in their future professional lives as teachers. They emphasized that the realistic educational situations presented in the series were highly motivating and encouraged critical and reflective learning. On the basis of the qualitative analysis a pilot questionnaire was designed to evaluate students' experience in a more systematic way in future courses.
Pengembangan Sistem Informasi Rumah Sakit Sebagai Upaya Pencegahan Fraud Dalam Rangka Peningkatan Layanan Kesehatan pada Rumah Sakit Pemerintah

The current National Health Insurance fund policy invites many problems in its implementation. JKN funds leakage caused various government efforts to minimize it through fraud prevention. Management Information System Development can minimize this so that good public service governance can be created over the implementation of JKN funds in government hospitals in the South Sumatra Region through transparency and accountability.This study aims to prove that the Development of Management Information Systems influences the Governance of Public Services at Government Hospitals in the South Sumatra Region through fraud prevention. The results of the study said that the Development of Management Information Systems which is proxied by the Innovation of Management Information Systems affects the Governance of Public Services
Mind the Gap: Ceremonies for Applied Secret Sharing

Abstract Secret sharing schemes are desirable across a variety of real-world settings due to the security and privacy properties they can provide, such as availability and separation of privilege. However, transitioning secret sharing schemes from theoretical research to practical use must account for gaps in achieving these properties that arise due to the realities of concrete implementations, threat models, and use cases. We present a formalization and analysis, using Ellison’s notion of ceremonies, that demonstrates how simple variations in use cases of secret sharing schemes result in the potential loss of some security properties, a result that cannot be derived from the analysis of the underlying cryptographic protocol alone. Our framework accounts for such variations in the design and analysis of secret sharing implementations by presenting a more detailed user-focused process and defining previously overlooked assumptions about user roles and actions within the scheme to support analysis when designing such ceremonies. We identify existing mechanisms that, when applied to an appropriate implementation, close the security gaps we identified. We present our implementation including these mechanisms and a corresponding security assessment using our framework.
Adversarial Attacks and Defense on Texts: A Survey

Deep learning models have been used widely for various purposes in recent years in object recognition, self-driving cars, face recognition, speech recognition, sentiment analysis, and many others. However, in recent years it has been shown that these models possess weakness to noises which force the model to misclassify. This issue has been studied profoundly in the image and audio domain. Very little has been studied on this issue concerning textual data. Even less survey on this topic has been performed to understand different types of attacks and defense techniques. In this manuscript, we accumulated and analyzed different attacking techniques and various defense models to provide a more comprehensive idea. Later we point out some of the interesting findings of all papers and challenges that need to be overcome to move forward in this field.
Semi-supervised Embedding Learning for High-dimensional Bayesian Optimization

Bayesian optimization is a broadly applied methodology to optimize the expensive black-box function. Despite its success, it still faces the challenge from the high-dimensional search space. To alleviate this problem, we propose a novel Bayesian optimization framework (termed SILBO), which finds a low-dimensional space to perform Bayesian optimization iteratively through semi-supervised dimension reduction. SILBO incorporates both labeled points and unlabeled points acquired from the acquisition function to guide the embedding space learning. To accelerate the learning procedure, we present a randomized method for generating the projection matrix. Furthermore, to map from the low-dimensional space to the high-dimensional original space, we propose two mapping strategies: $\text{SILBO}_{FZ}$ and $\text{SILBO}_{FX}$ according to the evaluation overhead of the objective function. Experimental results on both synthetic function and hyperparameter optimization tasks demonstrate that SILBO outperforms the existing state-of-the-art high-dimensional Bayesian optimization methods.
Pengaruh Model Guided Inquiry terhadap Hasil Belajar Siswa pada Pembelajaran Matematika Siswa SD Kota Bengkulu

This study aims to determine the effect of guided inquiry model on student learning outcomes in Mathematics learning in grade V SD Gugus XII Kota Bengkulu. This type of research is a quasi-experimental research with the design of the study of the matching only pretest-posttest control group design. The population in this study is all students of class V SD group XII consisting of 5 elementary schools. Based on the homogeneity test results, the sample in this study were the students of grade V SDN 99 as the experimental class and the students of class V SDN 81 as the control class. The research instrument used is a matter of objective test consisting of 20 items. Data analysis used is descriptive analysis, prerequisite test and inferential analysis that is t-test. Based on data analysis of research results, obtained the average value of posttest result of experimental class using guided inquiry model that is 78,120 and control class using conventional learning that is 62,789. T-test of both sample classes with significance level? = 0.05, obtained count = 4,730 whereas ttable = 2,000, tcount&gt; ttable so that H0 is rejected and Ha accepted. The increase of study result percentage in experiment class is higher than control class that is 83.36% in experimental class and control class 33.98%. Thus it can be concluded there is a significant effect of student learning outcomes that follow the learning using guided inquiry model.
SISTEM APLIKASI LOGIKA FUZZY UNTUK PENENTUAN OPTIMASI RAGI TEMPE PADA PROSES FERMENTASI TEMPE KEDELAI MENGGUNAKAN METODE FUZZY MAMDANI (STUDI KASUS : PENGRAJIN TEMPE KEDELAI DESA BULU CINA)

The main ingredient in the making of Tempe in addition to soy beans is the yeast Tempe. The role of Tempe yeast is veryimportant because the main factor that supports the success of tempeh is optimal yeast levels of the Tempe. In addition, theyeast levels of Tempe can also affect the duration of fermentation. If the level of yeast Tempe and fermentation time can beoptimal then the fermentation process can run well, so it will produce good quality Tempe and decent for consumption.Based on these issues, the author devised an application system built using the Matlab software in which the authors of thisstudy used the fuzzy method and the Matlab GUI as the application's appearance.
Logistic Regression for Massive Data with Rare Events

This paper studies binary logistic regression for rare events data, or imbalanced data, where the number of events (observations in one class, often called cases) is significantly smaller than the number of nonevents (observations in the other class, often called controls). We first derive the asymptotic distribution of the maximum likelihood estimator (MLE) of the unknown parameter, which shows that the asymptotic variance convergences to zero in a rate of the inverse of the number of the events instead of the inverse of the full data sample size. This indicates that the available information in rare events data is at the scale of the number of events instead of the full data sample size. Furthermore, we prove that under-sampling a small proportion of the nonevents, the resulting under-sampled estimator may have identical asymptotic distribution to the full data MLE. This demonstrates the advantage of under-sampling nonevents for rare events data, because this procedure may significantly reduce the computation and/or data collection costs. Another common practice in analyzing rare events data is to over-sample (replicate) the events, which has a higher computational cost. We show that this procedure may even result in efficiency loss in terms of parameter estimation.
Rethinking Dialogue State Tracking with Reasoning

Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on considering dependencies that occur on dialogues, and are lacking of reasoning capabilities. This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method significantly outperforms the state-of-the-art methods by 38.6% in terms of joint belief accuracy for MultiWOZ 2.1, a large-scale human-human dialogue dataset across multiple domains.
Partial Domain Adaptation for Relation Extraction Based on Adversarial Learning

Relation extraction methods based on domain adaptation have begun to be extensively applied in specific domains to alleviate the pressure of insufficient annotated corpus, which enables learning by utilizing the training data set of a related domain. However, the negative transfer may occur during the adaptive process due to differences in data distribution between domains. Besides, it is difficult to achieve a fine-grained alignment of relation category without fully mining the multi-mode data structure. Furthermore, as a common application scenario, partial domain adaptation (PDA) refers to domain adaptive behavior when the relation class set of a specific domain is a subset of the related domain. In this case, some outliers belonging to the related domain will reduce the performance of the model. To solve these problems, a novel model based on a multi-adversarial module for partial domain adaptation (MAPDA) is proposed in this study. We design a weight mechanism to mitigate the impact of noise samples and outlier categories, and embed several adversarial networks to realize various category alignments between domains. Experimental results demonstrate that our proposed model significantly improves the state-of-the-art performance of relation extraction implemented in domain adaptation.
Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling

We explore to what extent knowledge about the pre-trained language model that is used is beneficial for the task of abstractive summarization. To this end, we experiment with conditioning the encoder and decoder of a Transformer-based neural model on the BERT language model. In addition, we propose a new method of BERT-windowing, which allows chunk-wise processing of texts longer than the BERT window size. We also explore how locality modelling, i.e., the explicit restriction of calculations to the local context, can affect the summarization ability of the Transformer. This is done by introducing 2-dimensional convolutional self-attention into the first layers of the encoder. The results of our models are compared to a baseline and the state-of-the-art models on the CNN/Daily Mail dataset. We additionally train our model on the SwissText dataset to demonstrate usability on German. Both models outperform the baseline in ROUGE scores on two datasets and show its superiority in a manual qualitative analysis.