Reviewerly/exHarmony huggingface dataset:
RETRACTED: Improving the performance of speech clustering methods using I-vectors

Abstract Identification of speaker for the speech segment is a challenging task in speech clustering. Similar segments (i.e., those segments are spoken by the same speaker) are grouped into a cluster. Thus, segment labelling is very important in speaker detection. Existing systems use the Gaussian mixture model (GMM) for deriving the GMM mean supervector of speech segments in segment labelling process. i-vectors are tougher than GMM mean supervectors and i-vectors are between GMM mean supervectors high-dimensional space and low-dimensional manifolds. Existing GMM based speech clustering method is modified by i-vectors for achieving an effective speech clustering method. In order to show the feasibility of the proposed work, Experiments are performed on real time speech datasets.
Care of non COVID patients in COVID-19 era. (COVID-19 special issue.)

The consequences of the COVID-19 pandemic are extensive and far-reaching Non COVID communicable diseases continue to spread and non-communicable diseases continue to progress People may access healthcare facilities little bit late due to fear of contracting COVID-19 and present with severe symptoms, even with complications Nepal has been facing dual burden of both non-communicable and communicable diseases The number of COVID-19 patients has continuously been rising in Nepal since the start of May 2020 There is an anticipated surge of infectious disease such as malaria, dengue fever, enteric fever, scrub typhus, leptospirosis during summer and monsoon seasons in Nepal There will be surge of cases of acute undifferentiated febrile illness (AUFI) during monsoon As fever is one of the very common symptoms of COVID-19, so COVID-19 needs to be considered in differential diagnoses of acute undifferentiated febrile illness
KORELASI PENGUASAAN KOSAKATA BIDANG LINGKUNGAN HIDUP DENGAN KETERAMPILAN MENULIS TEKS BERITA SISWA KELAS VIII MTs.N 2 PESISIR SELATAN

ABSTRACT The purpose of this research there are three. First, describe the environmental field vocabulary mastery of grade VIII MTS. n. 2 southern coast. Second, it describes the writing skills text beritasiswa class VIII MTS. n. 2 SelatanKetiga Coast, describing the correlation field environmental vocabulary mastery with the text of the news writing skills of students of class VIII MTS. n. 2 southern coast. This research type is quantitative descriptive method. The design used is a korelasional design. Population research is grade VIII MTS. n. 2 southern coast that are listed in the 2018/2019, i.e., 223 students. The research sample is determined by proportional random sampling of 20% of the population, namely 45 pupils. The results of the research there are three. First, mastery of the vocabulary of the field of the environment students of class VIII MTS. n. 2 located on the southern coast of qualifications either (B) with an average value of 78.81. Secondly, the text of the news writing skills of students of class VIII MTS. n. 2 located on the southern coast of qualifications either (B) with an average of 79.54. Third, there is a correlation of mastery of the vocabulary of the field of the environment with the text of the news writing skills of students of class VIII MTS. n. 2 southern coast on a 95% confidence level with n-1 degrees of freedom. the value of thitung &gt; ttabel (5.01 &gt; 1.62). Kata Kunci: Korelasi, Kosakata Bidang Lingkungan Hidup, Menulis Teks Berita
Exploring Cross-lingual Sublanguage Classification with Multi-lingual Word Embeddings

Cross-lingual text classification is an important task due to the globalization and the increased availability of multilingual data. This thesis explores the method of implementing cross-lingual cl ...
Robust Learning via Golden Symmetric Loss of (un)Trusted Labels

Learning robust deep models against noisy labels becomes ever critical when today's data is commonly collected from open platforms and subject to adversarial corruption. The information on the label corruption process, i.e., corruption matrix, can greatly enhance the robustness of deep models but still fall behind in combating hard classes. In this paper, we propose to construct a golden symmetric loss (GSL) based on the estimated corruption matrix as to avoid overfitting to noisy labels and learn effectively from hard classes. GSL is the weighted sum of the corrected regular cross entropy and reverse cross entropy. By leveraging a small fraction of trusted clean data, we estimate the corruption matrix and use it to correct the loss as well as to determine the weights of GSL. We theoretically prove the robustness of the proposed loss function in the presence of dirty labels. We provide a heuristics to adaptively tune the loss weights of GSL according to the noise rate and diversity measured from the dataset. We evaluate our proposed golden symmetric loss on both vision and natural language deep models subject to different types of label noise patterns. Empirical results show that GSL can significantly outperform the existing robust training methods on different noise patterns, showing accuracy improvement up to 18% on CIFAR-100 and 1% on real world noisy dataset of Clothing1M.KeywordsRobust trainingDeep learning modelsSymmetric loss functionNoisy labels
Generating Plannable Lifted Action Models for Visually Generated Logical Predicates

We propose FOSAE++, an unsupervised end-to-end neural system that generates a compact discrete state transition model (dynamics / action model) from raw visual observations. Our representation can be exported to Planning Domain Description Language (PDDL), allowing symbolic state-of-the-art classical planners to perform high-level task planning on raw observations. FOSAE++ expresses states and actions in First Order Logic (FOL), a superset of so-called object-centric representation. It is the first unsupervised neural system that fully supports FOL in PDDL action modeling, while existing systems are limited to continuous, propositional, or property-based representations, and/or require manually labeled input for actions/predicates/propositions.
Optimizing Speaker Embeddings using Meta-Training Sets

This paper presents a method to learn speaker embeddings for text-independent speaker verification. The proposed method aims to optimize embeddings for unseen enrollment/test speakers by training a network with a meta-training set. The main procedure consists of two steps. The first step generates a meta-training set, a set of episodes each with a pair of intraepisode training and testing sets. The second step optimizes network parameters so that the average verification performance over the generated episodes is maximized. An advantage of our approach lies in its complementarity to studies focusing on network structure and we demonstrate its effectiveness with recent ResNet-based models in experiments on the VoxCeleb dataset.
HUBUNGAN MOTIVASI MEMELIHARA KEBERSIHAN DAN SIKAP KEBERSIHAN LINGKUNGAN DENGAN KEPEMIMPINAN KEPALA SEKOLAH DALAM PROGRAM KEBERSIHAN LINGKUNGAN DI SEKOLAH

This study aims: To determine the relationship of motivation in maintaining cleanliness of the school environment with leadership in the school's environmental hygiene program, to determine the relationship of the principal's attitude to environmental cleanliness with his leadership in the school's environmental hygiene program, to determine the relationship of motivation to maintain cleanliness and the attitude of the principal towards environmental cleanliness with his leadership in environmental hygiene programs in schools. The research method used in this research is descriptive with correlational statistical analysis. The population in this study were all Principals of the First Public and Private Schools in the MKKS Karangnunggal region of Tasikmalaya Regency as many as 36 people (Principals of Public and Private Middle Schools). As for the sampling technique with total sampling so the number of samples in this study were 36 Head of Public and Private Middle Schools. Data collection instruments to be analyzed, using a questionnaire. The data analysis technique used is the correlation with a significance level of 0.05. Based on the results of data processing obtained: there is a very strong relationship between motivation to maintain cleanliness with the leadership of the principal in accordance with the terminated coefficient (r) obtained at 0.737 with a regression equation Y = 0.597 + 0.935X 1 . There is a very strong relationship between attitude towards cleanliness with the leadership of the principal in accordance with the terminated coefficient price (r) obtained at 0.741 with a regression equation Y = 8.932 + 0.885X 2 . There is a very strong relationship between motivation to maintain cleanliness and attitudes towards environmental hygiene with the leadership of the principal in the environmental hygiene program at school in accordance with the termination coefficient (r) obtained at 0.742 with a regression equation Y = 5.618 + 0.589X 1 + 0.617X 2 .
Peningkatan Prestasi Belajar Akuntansi Dasar Melalui Penerapan Model Pembelajaran Kooperatif Tipe Student Team Achievement Devision (STAD) Berbantuan Media Power Point Pada Kelompok Belajar Di Desa Guyung Kecamatan Gerih Kabupaten Ngawi

The purpose of this study was to describe: the application of the cooperativelearning model type Student Team Achievement Devision (STAD) assisted bypower point media to improve student achievement in basic accounting subjects instudy groups in Guyung Village, Gerih District, Ngawi Regency. This type ofresearch is Classroom Action Research (PTK) which is conducted in 2 cycles. Thesubjects of this study were 15 students of the study group in Guyung Village,Gerih District, Ngawi Regency. Data collection techniques used were tests andobservation. The results showed that in the pre-cycle the students' average testscore was 62.67 with a 33.33% completeness percentage or as many as 5 studentsout of 15 students. In the first cycle the average score of the test increased by66.67 with a percentage of 53.33% completeness or as many as 8 students out of15 students. In the second cycle the average test score increased to 79 with apercentage of 86.67% completeness or as many as 13 students out of 15 students.In this study, it can be concluded that the application of the Student TeamAchievement Devision (STAD) learning model in basic accounting subjects2assisted by power point media can improve learning achievement in student studygroup in Guyung Village, Gerih District, Ngawi Regency.
Η επίδραση της διαδικασίας της μάθησης στη γήρανση του νευρικού συστήματος

The dissertation through the literature review, aims to study the effect of the learning process on the aging of the nervous system. It is divided into six parts: In the first part, an attempt is made to define and categorize aging. The factors to which this inevitable condition is due are analyzed through the six theories of aging. In addition, there is talk about the mechanisms of aging, but also the changes in the human body caused by the whole process.In the second part, you study the neurobiology of aging along with the physiological and biochemical changes observed in the nervous system. In the third part, theories of learning are examined from the point of view of the behavioral, cognitive and sociocultural approach. Also, the theories of memory are analyzed, as well as its types. In the fourth part, the neurophysiology of learning and memory is presented in detail.The fifth part refers to the inhibition of aging through learning. In the sixth part, daily exercises are suggested to activate mainly the right hemisphere of the brain to increase memory, perception and concentration. In addition, software is suggested to activate the cognitive functions of the brain. Reference is also made to artificial intelligence and brain aging. Finally, the importance of the effect of learning on the aging process is analyzed.
DOKUMENTER “PENGOBATAN CIMANDE” SEBAGAI KEARIFAN LOKAL MASYARAKAT JAWA BARAT

The documentary masterpiece of the cultural portrait of the author creates a documentary on the medication of Cimande that has a connection with the Cimande pencak silat. With this documentary work, the creator want to give the impression that the medication of cimande is a traditional medication from west java which could heal broken bones with Cimande oil media. Cimande's medication is not to be commercialized, but it is social to help each other. The concept that the creator implements in the making of the documentary is the concept of narrative approach, which divides the documentary flow into three sections which facilitates audience understanding in receiving information from documentary this. The information presented in this documentary is a fact derived from the results of the research of the creator, so that the public can get accurate and credible information.
An Agree-based account of Definiteness in Arabic Construct States

The present paper provides an account of definiteness spread in Standard Arabic (SA) Construct States (CS) within the recent model of the Minimalist Program (MP). The MP is assumed to address the issue of definiteness from the shell structure perspective. The spread of the (in)definiteness value of the embedded noun DP to the entire noun phrase will be claimed to operate within a split determiner (D) and nominal (N) shell structure. Benmamoun (2008), Fassi Fehri (1993-1999), Shlonsky (2003, 2012), and Shormani (2016-2017) among others have argued for or against definiteness spread in SA CSs. However, in this paper, we will argue that definiteness spread can be analyzed not only via N-raising to D and phrasal movement, but also empirically according to shell structure analysis. Therefore, we will argue, in conformity with edge feature valuation, for an Agree-Attract based approach that would license spreading with respect to a Definite (+Def feature) or specification (+Spec feature) which would replace the N-to-D and N-to-Spec approaches on empirical grounds. Finally, the current paper will demonstrate that the shell structure analysis of dP/nP structure of SA CSs might serve the study of the minimal syntax of SA DPs in general.
Analisis Dan Desain E-Comerce Pada Hasil Produk Pengolahan Ikan Asin

In industrial warehouse production that produces salted fish or or dried fish in the Gudang rejeki Keramat in Tanjung Balai can produce salted fish. As much as two tons a day and under certain conditions there is often fish production that accumulates in the warehouse because the processing is still using traditional methods. This method is related to food storage resilience that must be done is by accelerating the distribution of salted fish to customers, namely by utilizing information technology in marketing such as e-commerce, convenience is often an attraction such as easy access to choice of goods, ease of purchasing. Ease of payment to ease of getting goods or delivery of goods. The internet that allows store access from anywhere is one simple example of the convenience that e-commerce providers offer. Prospective buyer can now access the store from anywhere and when consumers find it easy to purchase product online, then security is compromised when making payments, including shipping goods that arrive properly, then convenience can be created. Convenience will bring customer satisfied, of course they will come back to buy online.
Global Optimization of SMHS to Enhance The Performance of Diversity and Sensitivity via Comparing with HS and other Existing Methods

The optimization process is concerned with the finding of the best solution from all possible solutions for a given problem. In this paper, the focus is on the importation step of HS algorithm using Scramble Mutation. The essential idea of new type of modification on variable. The selection method of Scramble mutation hybridized onto harmony search. The resulting variant of HS is called the Scramble Mutation Harmony Search algorithm (SMHS). For optimization problems to avoid premature convergence problem due by using one of the mutations to increase the number of solutions proposed, thus improving the performance of the Harmony Search (HS) algorithm, by maintaining diversity in the use of different rates to modify the proposed solutions. Compare the SMHS with HS and other existing methods to validate the efficiency of the SMHS. The results obtained by comparing the SMHS with basic HS and two other methods (i.e. MHS and DLHS), using ten benchmark functions illustrated that SMHS outperformed the basic HS for majority of the functions. It is therefore concluded that the SMHS algorithms is highly sensitive for the HMCR and obtains the best results at high value of HMCR. For harmony memory size, the HS performs better when HMS is relatively small. In the same vein, the SMHS performs better when the number of domain ( ) is compatible with HMS.
Grading and localization of histological features for bioengineered kidney constructs

Transplantation and dialysis remain the standard treatment for kidney failure. However, transplantation demands outpace demand, transplants may be rejected, and dialysis limits the patient quality of life. Advances in tissue engineering and regenerative medicine offer an alternative therapeutic option in which patient-derived renal cells can be biopsied, cultured, and expended with a scaffold into a new, therapeutic tissue construct. Histological analyses play an important role in validating such therapies. However, there is currently no method for measuring the histological similarity between bioengineered tissues and their genuine counterparts. Here, we present a preliminary method for scoring the similarity between native and newly formed tissues. Using a dataset of H&amp;E-stained kidneys (n = 116) graded as low, medium, or high similarity to real tissue, a convolutional neural network was trained via 6-fold cross-validation to classify artificial tissues. Results indicate high accuracy across all classes – 98.44% for low, 92.86% for medium, and 84.12% for high. Furthermore, visualizations of these predictions superimposed over original H&amp;E corroborate results. Further experimentation and refinement of our model through the inclusion of real tissue as a high similarity class resulted in 83.72%, 71.43%, and 84.00% accuracy for low, medium, and high similarity.
Evaluation of Single-Span Models on Extractive Multi-Span Question-Answering

Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire dataset is approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods.
Phishing e o código penal brasileiro: como tipificar a conduta? Uma análise do Acórdão em Apelação Criminal nº 5002347- 69.2010.404.7000, do Tribunal Regional Federal da 4ª região, com base na novatio legis in mellius

The present work aims at the analysis of the aspects of the phishing conduct, focusingon its classification in the Brazilian Criminal Law. Starting from the concept ofcybercrime, as well as the concepts of proper and improper cybercrimes, ajurisprudential and doctrinal study was addressed, whose questions about theapplicability of the Penal Code on cybercrimes were raised. Therefore, there were alsocomments about the legislative diplomas applicable to phishing, with theacknowledgement of a gap in the legal system regarding the classification of theconduct. Despite the supervenience of a specific law for cybercrimes, the Law no12.737/2012, there is still a legal vacuum concerning phishing. It was sought, likewise,the safeguard on the jurisprudence to unravel doubts about the application of criminallaw in facts which uses the artifice, as it occurs in the Internet Banking frauds. It wasalso made the necessary distinction between the conducts of fraud and aggravatedtheft through fraud in the informatics sphere, in order to resolve the confusion aboutthe classification of the Internet Banking fraud. In this tone, as the questions about thenature of cybercrimes and their legal object were analyzed, the more delimited was theconclusion of the hypothesis. Furthermore, it was found that the application of art. 154-A, CP on phishing conducts is closely linked to the means employed by the agent forits consummation. The same is true in relation to the agent’s performance, and it iscertain that, in the event of his competing for another more serious crime, the invasionof a computer device crime remains absorbed by the final crime, by virtue of theprinciple of consummation.
Face Recognition Based on Multi-source Superpixel Fusion

Face recognition is an important field of artificial intelligence. Single face recognition is one of the indispensable and challenging directions. There have been many existing single face recognition algorithms, such as PCA, 2DPCA, LDA, SVD, HMM, maximum likelihood linear regression, neural network and so on. These algorithms mainly solve the problem of single face recognition from three perspectives: expanding samples, expanding feature space and creating a learning general framework. The general performance of the algorithm for learning the general framework and extending the feature space is better than that of the method for expanding samples. In this paper,a face recognition learning algorithm based on multi-source superpixel fusion is proposed, which combines extended feature space and learning general framework. This algorithm mainly combines the proposed multi-source fusion representation model with super-pixel face features, and uses the theory of multi-source fusion model to solve the shortcomings of super-pixel face features. In particular, LBP algorithm is introduced into super-pixel segmentation to reduce the impact of illumination, rotation and occlusion on superpixel segmentation. Finally, the feasibility and validity of the super-pixel face recognition learning algorithm based on multi-source fusion model are demonstrated by simulation experiments.