Reviewerly/exHarmony huggingface dataset:
Decadal predictability in the North Atlantic as seen by EC-Earth3.

&amp;lt;p&amp;gt;We study the decadal predictability in the North Atlantic region using&amp;amp;#160; ensembles of historical and decadal prediction experiments with EC-Earth3&amp;amp;#160; and other CMIP models. In particular, the focus is on the NAO and the sub-polar gyre region. In general the impact of initialization is weak&amp;amp;#160; for lead-times larger than one to two years and we investigate different ways to isolate and estimate the statistical significance of this impact. For the sub-polar gyre region the prediction skill is found to be mainly due to an abrupt change in the late 90ies and models disagree on whether this skill is due to forcing or initial conditions. Also the predictability of the NAO is weak and varies with lead-time and length of the predicted period. We only see weak evidence of the 'signal-to-noise paradox'. The importance of the ensemble size is also studied.&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;lt;/p&amp;gt;
User Personas: Discovering Factors That Influence Genz’s Sedentary Lifestyle for the Development of a Mobile Exergame Design Model

The use of mobile devices for youths in tertiary education is found to be linked to sedentary behaviour. This paper attempts to create user personas on these youths and finding the factors that affect their sedentary lifestyles, which could assist the development of a mobile exergame design model. An online survey consisting of sociodemographic and IPAQ questionnaires was used to collect data on students in a public university. Three user personas were derived, which are based on their weekly physical activity levels: Low, Moderate, and High. It was found that income status, time management, daily smartphone usage, and fitness self-rating has a direct relationship with physical activity levels. The user personas share a common factor in that smartphone usage is essential to manage the youths’ daily activities regardless of their physical activities. The derived user personas would assist future mobile exergame design model development by concentrating on the influencing factors from the Low and Moderate physical activity level groups.
Research Guides: Athens - Remote Access to Carilion Subscriptions: Logging In

How to register for an account, login, customize your home page, troubleshoot issues, and more. Instructions on how to login to your MyAthens account
The Region Smoothing Swap Game

We introduce a topological combinatorial game called the Region Smoothing Swap Game. The game is played on a game board derived from the connected shadow of a link diagram on a (possibly non-orientable) surface by smoothing at crossings. Moves in the game are performed on regions of the diagram and can switch the direction of certain crossings' smoothings. The players' goals relate to the connectedness of the diagram produced by game play.
Toward a New Human Action Recognition Framework: Applying Deep Belief Networks to 3D Skeleton Sequences

In this paper, we introduce a new human action recognition framework which consists of feature extract, feature coding, network tuning, and action recognition. Spatial–temporal combined features are designed to represent human actions in both space and time, a method which results in higher accuracy than when individual spatial or temporal features are used. A new layer is added to the Deep Belief Network (DBN), which is used to unify the length of the spatial–temporal features. In the training stage, we suggest some improved methods, including model initialization and a fine-tuning of the DBN’s parameters. We evaluate our framework using the MSR-Action 3D public datasets. Our experiments indicate that spatial–temporal features can achieve an accuracy of 93.4%, an improvement of 1.09% and 11.71% when compared with the uncombined temporal and spatial features, respectively.
EVALUASI PELAKSANAAN PROGRAM BANTUAN OPERASIONAL SEKOLAH (BOS) PADA MADRASAH ALIYAH NEGERI 2 KOTA PALU

The purpose of this study was to determine the evaluation of the implementation of the School Operational Assistance Program (Bos) at State 2 Madrasah Aliyah in Palu City. The foundation of the theory used is William N. Dunn (2003) which states that there are six factors in evaluating the implementation of the program, namely effectiveness, efficiency, adequacy, alignment, responsiveness, and accuracy. This type of research is Qualitative Descriptive which seeks to interpret and interpret data that is pleasing to the facts, using purposive methods, data cursing and discussion with a qualitative analysis approach to collecting data and materials through observation and interviews with the number of 5 informants. Based on the results of the discussion that the Evaluation of the Implementation of the School Operational Assistance Program (BOS) at the State 2 Madrasah Aliyah in City of Palu for effectiveness, efficiency, alignment, responsiveness, and accuracy has been running optimally. However, for the adequacy factor it has not run optimally because the BOS budget is considered to be insufficient or insufficient to finance all school operational costs in order to improve the quality of education services to the community.
Medicine Supplier Autonomous Robot Navigation in COVID-19 Pandemic

This paper describes an application ofautonomous navigation robot system in current pandemic of Coronavirus by supplying medicine to make sure the touchless service by freely navigating under fixed and dynamic conditions regarding objects as obstacles and obtains the map of the indoorenvironment to successfully plans its path towards the desired goal.We are presenting a SLAM (Simultaneous Localization and Mapping)algorithm combined with the particle filter to estimate the location of robot in map of unknown environment and that map is being generated using odometry data from differential drive robot which solves the problem of mapping and localization accurately.Moreover,a Dijkstra algorithm coupled with the reactive motion planning algorithm known as Trajectory Rollout is used.The robot is navigating in the environment by PID controller implemented for the control of actuators.Results have been verified by real time experiment.
Improved Emoji Identification for Sentence using LSTM in RNN

We proposed an improved sentence classification using emoji. Word embedding are used to assign emoji for sentences. The benchmark existing models speak to the word as a position vector in word embedding space. These researchers convert each word in the sentence into their word vectors and afterward take a mean of word vectors. They were used pre-trained word embeddings for representing words in a sentence. Thus, existing proposals cannot catch the multiple aspects and context of the sentence. Furthermore, due to taking the normal of word vectors of given sentence, existing strategies perform less precision at sentence order for accurate emoji. In this paper, we proposed an improved sentence classification which is used LSTM units in RNN model and train the existing embeddings during model creation based on training set. This proposal improved word embedding vector when the model can be updated with new training examples. It likewise has generally high segregating power, on account of sentence is represented in various feature space by representing to each feature dimension with potential highlights. Experimental results on various data sets show that an accuracy of about100% on training set and about 98% on test set. Overall, the proposed model can make moderately incredible various interpretable highlights and word vector embeddings and gives the extension to actualize new strategies to improve the sentence characterization. Further, it very well may be utilized to improve the exhibition of sentence representation, document classification and representation and text characterization.
Mental Health in Chilean Students during Confinement by Covid-19

In the wake of the pandemic, Chilean students had to switch to online instruction to decrease the spread of Covid-19. Given this scenario, students are expected to have had some degree of emotional distress. This article's objective was to identify and analyze publications that address mental health in the confinement of Chilean students in 2020. The analysis was a mixed documentary, with a descriptive design through a keyword search that allowed identifying analysis topics. The procedure to carry out the review had three stages: (i) identification of publications, (ii) selection, and (iii) eligibility. Since this is a young and specific topic, the search was made via the Google Scholar engine to expand the results. A record of 16 articles was found under mental health and Chilean students' topics in the search engine. Using the inclusion criteria, 12 articles were discarded through the analysis matrix, leaving four studies for review. By analyzing the selected texts in their entirety, the same number of articles was maintained. It is concluded that there is a lack of national research on the effects of mental health on students in general. Although the reviewed articles contribute since they problematize and emphasize students' issues, descriptive studies are required. It is hoped that after a year of pandemic confinement, more studies can be found to understand the phenomenon in detail.
Multi-Stage Protection Using Pixel Selection Technique for Enhancing Steganography

Steganography and data security are extremely important for all organizations. This research introduces a novel stenographic method called multi-stage protection using the pixel selection technique (MPPST). MPPST is developed based on the features of the pixel and analysis technique to extract the pixel's characteristics and distribution of cover-image. A pixel selection technique is proposed for hiding secret messages using the feature selection method. The secret file is distributed and embedded randomly into the stego-image to make the process of the steganalysis complicated.  The attackers not only need to deter which pixel values have been selected to carry the secret file, they also must rearrange the correct sequence of pixels. MPPST generates a complex key that indicates where the encrypted elements of the binary sequence of a secret file are. The analysis stage undergoes four stages, which are the calculation of the peak signal-to-noise ratio, mean squared error, histogram analysis, and relative entropy. These four stages are used to demonstrate the characteristics of the cover image. To evaluate the proposed method, MPPST is compared to the standard technique of Least Significant Bit (LSB) and other algorithms from the literature. The experimental results show that MPPST outperforms other algorithms for all instances and achieves a significant security enhancement.
An Efficient Method for Mining Distributed Frequent Itemsets: MDFI

Discovering frequent Itemsets is an interesting problem in the context of parallel and distributed databases. Computation cost and communication/synchronization overhead are important elements in distributed Frequent Itemsets. In this work, we propose an efficient algorithm for mining distributed frequent Itemsets (MDFI) which can significantly reduce the number of candidates Itemsets and communication costs by adopting a Master/Slaves scheme of communication. We present performance comparisons for our algorithm against Apriori and FP-growth algorithms using two datasets with different minimum support.
Implementing a fast unbounded quantum fanout gate using power-law interactions

A fast protocol is proposed for a multiqubit quantum gate using engineered power-law interacting Hamiltonians that gives a superpolynomial speedup in implementation time over nearest-neighbor systems. The protocol demonstrates the computational hardness of sampling from output states of strongly long-range systems evolving for polylogarithmic times (assuming that factoring is hard).
ON SOME BOUNDS OF THE MINIMUM EDGE DOMINATING ENERGY OF A GRAPH

Number of hidden neurons is necessary constant for tuning the neural network to achieve superior performance. These parameters are set manually through experimentation. The performance of the network is evaluated repeatedly to choose the best input parameters.Random selection of hidden neurons may cause underfitting or overfitting of the network. We propose a novel fuzzy controller for finding the optimal value of hidden neurons automatically. The hybrid classifier helps to design competent neural network architecture, eliminating manual intervention for setting the input parameters. The effectiveness of tuning the number of hidden neurons automatically on the convergence of a back-propagation neural network, is verified on speech data. The experimental outcomes demonstrate that the proposed Neuro-Fuzzy classifier can be viably utilized for speech recognition with maximum classification accuracy.
Towards objectively evaluating the quality of generated medical summaries.

We propose a method for evaluating the quality of generated text by asking evaluators to count facts, and computing precision, recall, f-score, and accuracy from the raw counts. We believe this approach leads to a more objective and easier to reproduce evaluation. We apply this to the task of medical report summarisation, where measuring objective quality and accuracy is of paramount importance.
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE TEAMS GAMES TOURNAMENT (TGT) UNTUK MENINGKATKAN HASIL BELAJAR IPS MURID KELAS IV SD INPRES BARRANG CADDI II KOTA MAKASSAR

Abstract: The Application of Cooperative Learning Model Type Teams Games Tournament (TGT) to Improve Social Studies Learning Outcomes of Students in Class IV SD Inpres Barrang Caddi II Makassar . The purpose of this study was to improve social studies learning outcomes by implementing cooperative learning types of teams games tournament (TGT) in fourth grade students of SD Inpres Barrang Caddi II Makassar. The approach used in this research was a qualitative approach and the type of research was Classroom Action Research (CAR). The focus on this classroom research was: 1. Teacher, namely how teachers plan learning steps to be achieved to maximum 2. Students, namely to improve student learning outcomes in social science subjects ( IPS) through the cooperative approach of Teams Games Tournament (TGT) model IV SD Inpres Barrang Caddi II Makassar. Data collection techniques were observation and learning achievement tests. Learning outcomes of social science (IPS) grade IV students of SD Inpres Barrang Caddi II in Makassar City had improved after the two teams learning tournament (TGT) held for two cycles. Improved learning outcomes were shown by an increase of evaluation test results in each cycle. In the first cycle the average score reached 63.33, and the second cycle increased to 70.37. While the classical completeness of cycle I, which was 44.44%, had not met the indicators of success. But in the second cycle classical completeness increased to 85.18% and had met the indicators of success. Keywords : Student Learning Outcomes, Teams Games Tournament Model (TGT).
PENGENALAN EKSPRESI RAUT WAJAH BERBASIS JARINGAN SARAF TIRUAN BACKPROPAGATION DENGAN METODE PRINCIPAL COMPONENT ANALYSIS

The development of artificial neural networks is related to statistical and biometric analysis which is one of the applications that can require artificial neural network models. Recognition of facial patterns is an important part of identifying a person. The face can be divided into areas such as the nose, eyes and mouth. Face pattern recognition is a research area that can be applied to the principal component analysis (PCA) method. The training process carried out by the eigenface calculation uses PCA and the results of this study show that facial pattern recognition based on the proportion of memorization and generalization for the use of the method without PCA is better than facial pattern recognition using PCA. Pattern recognition without using the PCA method, the level of memorization and generalization reaches 100% at the 40th iteration and 0.0099 error with a learning rate and momentum of 0.8, while facial pattern recognition using the PCA method, the memorization and generalization level reaches 100% in the iteration. to -1000 and error 0.00103 with learning rate and momentum 0.9.
Analysis of Service Levels in Café Black and White using Fuzzy Service Quality

This study is an analysis of the level of service in a Hitam putih cafe using fuzzy service quality. Hitam Putih cafe is a business that is engaged in the culinary field and also a coffee shop. At this time there is no system that can measure the level of visitor or consumer satisfaction with the services received and the services expected. Every consumer certainly wants to get good and optimal service. Therefore, it is necessary to measure to determine whether or not visitors are satisfied with the services provided by the hitam putih cafe. In this study, the method used to measure the level of service is the fuzzy service quality method, which is to determine the results of the gap between perceived and expected services. The system design was made web-based with the PHP programming language and the database used was XAMPP. Based on the resulting system design, it can be seen the level of service in the hitam putih cafe using fuzzy service quality so that visitors can find out the level of service in accordance with the service received and the service expected.
Detection Method for Crowd Abnormal Behavior Based on Long Short-Term Memory Network

It is significant for maintaining public safety to find abnormal behavior among crowd in time and exactly. However, traditional deep learning algorithms still have difficulties to be solved in terms of group abnormal behavior detection, mainly including two aspects. On the one hand, there is a large amount of background redundancy in the monitoring video data, which leads to longer training time and negative effects of the detection accuracy. On the other hand, the abnormal samples in the monitoring video data are relatively low, which will cause the model to generate a higher false alarm rate. Aiming at the above problems, this paper proposes a crowd abnormal behavior detection method based on long short-term memory network (LSTM). First, an improved ViBe image foreground extraction method is proposed to reduce the negative impact of irrelevant background on abnormal behavior detection. Then, use the improved LSTM algorithm of the focal loss function to detect abnormal behavior of the crowd and increase the attention of the LSTM algorithm to abnormal samples, thereby improving the detection accuracy. Finally, the validity of the model is verified on the UMN dataset. Experiments show that the method in this paper can get good results even when the data samples are unbalanced, and the AUC on the UMN datasets reaches 99.83%.
The Use of Mobile Assisted Language Learning in English Learning of Fourth Semester English Department Students at IAIN Samarinda

Mobile Assisted Languge Learning, (MALL,) is a learning tool focused on language learning that used mobile phone. MALL in English learning is handy for students who in need to enhancing their English skills. Especially English department students at IAIN Samarinda, they were required to master the primary English language skill to support the learning process. The research objective was to knew students perspective toward the use of MALL in English learning, how do studnets use MALL to support their English learning activities in English and their perpective toward the advantages and disadvantages of utilizing MALL. The instrument was interview conducted by a brief introduction to the subject of the study and then selected students based on their last GPA. Based on the data finding, MALL are indeed useful and bring advantages for students. They share their experience in using MALL for English learning to facilitate, save time and energy, easy theory learning and information access, and improve motivation. However, besides the advantages MALL can provide, it also has its disadvantages. The students shared that the mobile phone is too small so that it is difficult to type, the application of online classes is not yet perfect, the source of material that cannot be trusted completely and the difficulty of internet access that requires money is limited for some students
T-­BERT:臺灣語言模型–以臺灣在地語言預訓練BERT模型

Pretrained language models have seen a significant improvement in nu­merous Natural Language Processing (NLP) tasks. Many communities have released their models pretrained on variety of languages as well as differ­ent domain copora within each language, typically employing variants of the Bidirectional Encoder Representations from Transformers (BERT). However, existing BERT models for Taiwan’s languages are only pretrained on Chinese Wikipedia. Even though we can further pretrain BERT models on the domain corpora from the released model, its vocabulary is also not suitable for Tai­wan’s lanugage.In this thesis, we propose T­-BERT, a language model based on BERT but pretrained on Taiwan’s three main languages: Taiwanese Mandarin (Tradi­tional Chinese), Taiwan Southern Min, Taiwanese Hakka, with a vocabulary generated for these three languages.We create two downstream tasks: LTN news classification, and iCorpus news classification for evaluating model’s performance on Taiwanese Man­darin and Taiwan Southern Min respectively.We show that T-­BERT model outperforms both Chinese BERT and multi­lingual BERT (mBERT), both released officially by Google, on the two down­stream tasks, with 16.88% increased f1 score on iCorpus dataset, and 0.32% increased f1 score on the LTN dataset.
Semantically Enriching Embeddings of Highly Inflectable Verbs for Improving Intent Detection in a Romanian Home Assistant Scenario

Word embeddings are known to encapsulate semantic similarity and have become the preferred representation solution for NLP models. However, they fail to identify the type of semantic relationship, which – in some applications – might be crucial. This paper adapts an existing solution for enhancing word embedding representations such as to better separate between synonyms and antonyms in an intent detection task applied to a Romanian home assistant scenario. Accounting for the morphological richness of the Romanian language, our method proposes an additional augmentation step, in order to generate conjugated pairs of antonym and synonym verbs. The generated pairs are run through the counterfitting step (inspired from literature), for which we propose a justified improvement for one of the hyperparameters. The evaluations performed on the home assistant scenario have shown that the pre-processing step has an essential role in reducing opposing intent errors in the classification model (by almost two thirds).