Reviewerly/exHarmony huggingface dataset:
HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL

Nowadays, the internet and social media grow fast. This condition has positive and negative effects on society. They become media to communicate and share information without limitation. However, many people use that easiness to broadcast news or information which do not accurate with the facts and gather people's opinions to get benefits or we called a hoax. Therefore, we need to develop a system that can detect hoax. This research uses the neural network method with Long Short-Term Memory (LSTM) model. The process of the LSTM model to identify hoax has several steps, including dataset collection, pre-processing data, word embedding using pre-trained Word2Vec, built the LSTM model. Detection model performance measurement using precision, recall, and f1-measure matrix. This research results the highest average score of precision is 0.819, recall is 0.809, and f1-measure is 0.807. These results obtained from the combination of the following parameters, i.e., Skip-gram Word2Vec Model Architecture, Hierarchical Softmax, 100 as vector dimension, max pooling, 0.5 as dropout value, and 0.001 of learning rate.
Planning Algorithms for Zero-Sum Games with Exponential Action Spaces: A Unifying Perspective

In this paper we review several planning algorithms developed for zero-sum games with exponential action spaces, i.e., spaces that grow exponentially with the number of game components that can act simultaneously at a given game state. As an example, real-time strategy games have exponential action spaces because the number of actions available grows exponentially with the number of units controlled by the player. We also present a unifying perspective in which several existing algorithms can be described as an instantiation of a variant of NaiveMCTS. In addition to describing several existing planning algorithms for exponential action spaces, we show that other instantiations of this variant of NaiveMCTS represent novel and promising algorithms to be studied in future works.
The probabilistic backbone of data-driven complex networks: an example in climate

Abstract Complex systems often exhibit long-range correlations so that typical observables show statistical dependence across long distances. These teleconnections have a tremendous impact on the dynamics as they provide channels for information transport across the system and are particularly relevant in forecasting, control, and data-driven modeling of complex systems. These statistical interrelations among the very many degrees of freedom are usually represented by the so-called correlation network, constructed by establishing links between variables (nodes) with pairwise correlations above a given threshold. Here, with the climate system as an example, we revisit correlation networks from a probabilistic perspective and show that they unavoidably include much redundant information, resulting in overfitted probabilistic (Gaussian) models. As an alternative, we propose here the use of more sophisticated probabilistic Bayesian networks, developed by the machine learning community, as a data-driven modeling and prediction tool. Bayesian networks are built from data including only the (pairwise and conditional) dependencies among the variables needed to explain the data (i.e., maximizing the likelihood of the underlying probabilistic Gaussian model). This results in much simpler, sparser, non-redundant, networks still encoding the complex structure of the dataset as revealed by standard complex measures. Moreover, the networks are capable to generalize to new data and constitute a truly probabilistic backbone of the system. When applied to climate data, it is shown that Bayesian networks faithfully reveal the various long-range teleconnections relevant in the dataset, in particular those emerging in El Niño periods.
The Correlation between Teaching Evaluation and Lecturers’ Performances

The purpose of the study is to explore the relationship between teaching evaluation and lecturers’ performances. To achieve this purpose, the study analysed the correlations between teaching evaluation scores by student and evaluation on lecturers’ performances by dean, and between teaching evaluation scores by student and students’ final examination results. Census study was applied to collect all data, which involved 124 lecturers and eight deans. The study used data from the online system, which involved evaluation made for 246 courses by all active students in semester June – November 2018. Descriptive and inferential statistics were applied in this study and analysed using SPSS version 21.0. The result showed that there is no significant relationship between teaching evaluation scores by student and evaluation on lecturers’ performances by dean. This study also found that there is no significant relationship between teaching evaluation scores by student and students’ final examination results. Based on the findings, the study inferred there is bias in the evaluation instruments and there are external factors contributed to the outcome of insignificant correlations in both analyses, thus the effect or impact of the evaluation system to academic division is not significant
Automatic Modulation Classification Based on In-Phase Quadrature Diagram Constellation Combined with a Deep Learning Model

Objectives: This study aims to present a framework for Automatic Modulation recognition using Deep learning without feature extraction.Methods: We study seven modulations using the In-Phase Quadrature constellation polluted by Additive White Gaussian Noise.We apply the K-means algorithm to normalize data transmitted and polluted by noise; the new diagram obtained is considered as an image and coded in pixel before entering in a Deep Neural Network where we apply 20% dropout on hidden layers to avoid overfitting.The simulation is carried out in Matlab.Findings: Experiment performed on selected modulations following the proposed framework gives a good percentage of recognition equal to 96.12%.Our algorithm Deep Neural Network imaGe gives the best performance results at epoch equal to 2,000,000.Applications: The outcome will be beneficial for researchers in Software-Defined Radio for civilian and military applications like electronic attacks and electronic protection.
Optimizing Extreme Learning Machine using GWO Algorithm for Sentiment Analysis

Sentiment analysis on social media is one of the most popular text mining application and many researchers have devoted more efforts in this interesting field.Sentiment analysis is a method for analyzing data and extracting the feeling it represents.Twitter is considered one of the most common social media forums used by people on various occasions to express their opinions and express feelings.Twitter's sentiment analysis has grown gradually over the past few decades.Due to the format of small tweet, a new dimension is created for problems such as slang usage, abbreviations, etc.This paper proposes a hybrid approach that optimizes extreme learning machine (ELM) classifier with one of the most recent swarm intelligence algorithms which is grey wolf optimization algorithms (GWO).GWO is used to self-adaptation of hidden neurons weights rather than manually selection.Also avoid the overfitting problem and make the model more generalized and robust.Results represented in this paper showed that the proposed hybrid model GWO-ELM overcame the problems found in classical ELM model and achieved best accuracy against all compared models.
Reinforcement learning beyond the Bellman equation: Exploring critic objectives using evolution

Living organisms learn on multiple time scales: evolutionary as well as individual-lifetime learning. These two learning modes are complementary: the innate phenotypes developed through evolution s...
The searching algorithm for detecting a Markovian target based on maximizing the discounted effort reward search

Abstract This paper presents the searching algorithm to detect a Markovian target which moves randomly in M -cells. Our algorithm is based on maximizing the discounted effort reward search. At each fixed number of time intervals, the search effort is a random variable with a normal distribution. More than minimizing the non-detection probability of the targets at time interval i , we seek for the optimal distribution of the search effort by maximizing the discounted effort reward search. We present some special cases of one Markovian and hidden target. Experimental results for a Markovian, hidden target are obtained and compared with the cases of applying and without applying the discounted effort reward search.
EFL written competence through twitter in mobile version in compulsory secondary education

The use of Information and Communication Technologies (henceforth, ICT) in foreign language learning and, to be more specific, in English language learning has increased quite considerably since the commencement of the 21st century and, particularly, in the second decade of the 21st century with the introduction of mobile learning (m-learning, henceforth) at our daily lives. Our aim, at this current paper, is to confirm whether use of the Twitter social network via a mobile device helps Spanish learners of English as a foreign language (henceforth, EFL) improve their quality of EFL written expression and, especially, their summarizing skill in the English language. We conducted a classroom experiment with students in Year 4 of compulsory secondary education at the time of the experiment. The participating students were level B1 according to the Common European Framework of Reference for Languages (henceforth, CEFR). Two research questions were established. Based on these, the following preliminary hypothesis was formulated: Use of the mobile version of Twitter has a positive impact on improving written expression and, to be more concrete, the summarizing skill in EFL. The research model, which we selected, was action research, in accordance with which quantitative data were analysed to justify the outcomes of this empirical research. The data were satisfactory since learners improved significantly at the end of this current classroom experiment. This research on the use of Twitter in its mobile version could imply a starting point to conduct further empirical studies in this respect on English language teaching and learning in non-university education, such as primary education, compulsory secondary education and Baccaulerette.
High-Efficiency Microsatellite-Using Super-Resolution Algorithm Based on the Multi-Modality Super-CMOS Sensor

To realize the application of super-resolution technology from theory to practice, and to improve microsatellite spatial resolution, we propose a special super-resolution algorithm based on the multi-modality super-CMOS sensor which can adapt to the limited operation capacity of microsatellite computers. First, we designed an oblique sampling mode with the sensor rotated at an angle of 26.56 ∘ ( arctan 1 2 ) to obtain high overlap ratio images with sub-pixel displacement. Secondly, the proposed super-resolution algorithm was applied to reconstruct the final high-resolution image. Because the satellite equipped with this sensor is scheduled to be launched this year, we also designed the simulation mode of conventional sampling and the oblique sampling of the sensor to obtain the comparison and experimental data. Lastly, we evaluated the super-resolution quality of images, the effectiveness, the practicality, and the efficiency of the algorithm. The results of the experiments showed that the satellite-using super-resolution algorithm combined with multi-modality super-CMOS sensor oblique-mode sampling can increase the spatial resolution of an image by about 2 times. The algorithm is simple and highly efficient, and can realize the super-resolution reconstruction of two remote-sensing images within 0.713 s, which has good performance on the microsatellite.
Quantum algorithms for graph problems with cut queries

Let G be an n-vertex graph with m edges. When asked a subset S of vertices, a cut query on G returns the number of edges of G that have exactly one endpoint in S. We show that there is a bounded-error quantum algorithm that determines all connected components of G after making O(log(n)6) many cut queries. In contrast, it follows from results in communication complexity that any randomized algorithm even just to decide whether the graph is connected or not must make at least Ω(n/log(n)) many cut queries. We further show that with O(log(n)8) many cut queries a quantum algorithm can with high probability output a spanning forest for G.En route to proving these results, we design quantum algorithms for learning a graph using cut queries. We show that a quantum algorithm can learn a graph with maximum degree d after O(d log(n)2) many cut queries, and can learn a general graph with many cut queries. These two upper bounds are tight up to the poly-logarithmic factors, and compare to Ω(dn) and Ω(m/log(n)) lower bounds on the number of cut queries needed by a randomized algorithm for the same problems, respectively.The key ingredients in our results are the Bernstein-Vazirani algorithm, approximate counting with "OR queries", and learning sparse vectors from inner products as in compressed sensing.
Scalable Evaluation of Quantum-Circuit Error Loss Using Clifford Sampling

A major challenge in developing quantum computing technologies is to accomplish high precision tasks by utilizing multiplex optimization approaches, on both the physical system and algorithm levels. Loss functions assessing the overall performance of quantum circuits can provide the foundation for many optimization techniques. In this Letter, we use the quadratic error loss and the final-state fidelity loss to characterize quantum circuits. We find that the distribution of computation error is approximately Gaussian, which in turn justifies the quadratic error loss. It is shown that these loss functions can be efficiently evaluated in a scalable way by sampling from Clifford-dominated circuits. We demonstrate the results by numerically simulating 10-qubit noisy quantum circuits with various error models as well as executing 4-qubit circuits with up to ten layers of 2-qubit gates on a superconducting quantum processor. Our results pave the way toward the optimization-based quantum device and algorithm design in the intermediate-scale quantum regime.
A Study on the New Design Thinking for Industrial Revolution 4.0, Requirements and Graduate Readiness

The Fourth Industrial Revolution can be regarded as Industry 4.0 connected with the developments, innovative approaches and technological advancements held in the year 2011 at Germany.It is the amalgamation of many digital technologies such artificial intelligence (AI), robotics, Internet of Things (IoT) cloud computing, big data, 3D printing etc.The present study is qualitative in nature and focuses on graduate readiness, the challenges faced by the engineering students of SOA university, Bhubaneswar to meet the industry 4.0 requirements because technical degree is not only the gateway to their success in industry rather they should equip themselves with various soft skills like English language development, communication, personality development, leadership, critical thinking, problem solving and team building skills to meet the expectations of their employers.The objective of this study is to analyze the current education practices and the industry requirements and come out with best possible solutions for industry readiness of graduate engineers for a successful corporate career.The study has put some valuable insights on the problems faced by the 5 th semester students related to language acquisition and their readiness for future industrial demands.Furthermore, universities should also come out with appropriate suggestions and recommendations to enable graduate engineers for future industry readiness.
Self-Supervised Animation Synthesis Through Adversarial Training

In this paper, we propose a novel deep generative model for image animation synthesis. Based on self-supervised learning and adversarial training, the model can find labeling rules and mark them without origin sample labels. In addition, our model can generate continuous changing images based on the automatically labels learning. The labels learning model can be implemented on a large number of out-of-order samples to generate two types of pseudo-labels, discrete labels and continuous labels. The discrete labels can generate different animation clips, and the continuous labels can generate different frames in the same clip. Embedding pseudo-labels with latent variables into latent space, our model discovers regularities and features from latent space. Animation features are fully characterized by the pseudo-labels learned from the self-supervised module. Using upgraded adversarial training steps, the model learns to map animation features to pseudo-labels from the latent space and then organizes pseudo-labels embedding into latent variables to generate animation features. By adapting dimensions of pseudo-labels, we match fine features with latent variables. Such as using the two types of pseudo-labels, our model can also generate different styles of videos from the same dataset. The specific implementation tricks depend on the different pseudo-label dimensions and the number of pseudo-label dimensions. Comparing the results of our model with other state-of-the-art approaches, the model does not use complicated components, such as 3D convolution layers and recurrent neural networks. Our experimental results show that an appropriate number of the pseudo-label dimensions can better characterize animation features. In this case, an animation which reached human-level perception can be synthesized. The performance of animation synthesis has reached relatively superior results on several challenging datasets.
An Approach to NMT Re-Ranking Using Sequence-Labeling for Grammatical Error Correction

An approach to N-best hypotheses re-ranking using a sequence-labeling model is applied to resolve the data deficiency problem in Grammatical Error Correction (GEC). Multiple candidate sentences are generated using a Neural Machine Translation (NMT) model; thereafter, these sentences are re-ranked via a stacked Transformer following a Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Field (CRF). Correlations within the sentences are extracted using the sequence-labeling model based on the Transformer, which is particularly suitable for long sentences. Meanwhile, the knowledge from a large amount of unlabeled data is acquired through the pre-trained structure. Thus, completely revised sentences are adopted instead of partially modified sentences. Compared with conventional NMT, experiments on the NUCLE and FCE datasets demonstrate that the model improves the F 0.5 score by 8.22% and 2.09%, respectively. As an advantage, the proposed re-ranking method has the advantage of only requires a small set of easily computed features that do not need linguistic inputs.
Buyer Inspired Meta-Heuristic Optimization Algorithm

Abstract Nature inspired swarm based meta-heuristic optimization technique is getting considerable attention and established to be very competitive with evolution based and physical based algorithms. This paper proposes a novel Buyer Inspired Meta-heuristic optimization Algorithm (BIMA) inspired form the social behaviour of human being in searching and bargaining for products. In BIMA, exploration and exploitation are achieved through shop to shop hoping and bargaining for products to be purchased based on cost, quality of the product, choice and distance to the shop. Comprehensive simulations are performed on 23 standard mathematical and CEC2017 benchmark functions and 3 engineering problems. An exhaustive comparative analysis with other algorithms is done by performing 30 independent runs and comparing the mean, standard deviation as well as by performing statistical test. The results showed significant improvement in terms of optimum value, convergence speed, and is also statistically more significant in comparison to most of the reported popular algorithms.
OPTIMIZATION OF RANDOM FOREST ALGORITHM FOR BREAST CANCER DETECTION

Today, cancer is a big issue and the most common disease all over the world.Cancer starts due to the abnormal growth of cells in your body.So, cancer takes place anywhere in the body.There are more than 100 types of cancers.The most common cancers are blood cancer, skin cancer, lung cancer, breast cancer etc. Nowadays, women can die because of breast cancer.There are several techniques like machine learning algorithms, big data & hadoop algorithms, and data mining algorithms to addressing breast cancer.Many techniques claim that their results were faster and more accurate.This paper presents an optimized random forest algorithm for cancer detection.Experimental results show that random forest gives the accuracy of 98.60%.All experiments are executed within anaconda package the scientific python/R development environment and spider software.
Optimizing Live Layered Video Multicasting Over LTE With Mobile Edge Computing

Live video streaming has become one of the key applications in mobile wireless networks. To offload the bandwidth requirement in both backhaul and radio access networks, the integration of Mobile Edge Computing (MEC) and multicasting have become a natural candidate. However, less attention has been paid to the user Quality of Experience-driven (QoE-driven) approach to optimize the radio resource management of multicasting in mobile wireless networks. In this work, we study the optimal radio resource management, including modulation and coding scheme (MCS) selection, radio resource blocks allocation, and Forward Error Correction (FEC), for multicasting in LTE networks with the assistance of MEC. We formulate it as a convex optimization problem and propose a weighted sub-gradient (WSG) method to find the near-optimal solution. In addition, we also propose a heuristic algorithm based on the concept of Maximizing marginal Gain and Minimizing marginal Loss (MGML). Our simulation results show that both approaches are able to achieve near-optimal solutions and outperform previous work, including MSML [11] and OLM [14]. Our simulation results also show that WSG yields the best QoE fairness index while MGML yields the best system utility in most scenarios.
The Next Generation of Neural Networks

The most important unsolved problem with artificial neural networks is how to do unsupervised learning as effectively as the brain. There are currently two main approaches to unsupervised learning. In the first approach, exemplified by BERT and Variational Autoencoders, a deep neural network is used to reconstruct its input. This is problematic for images because the deepest layers of the network need to encode the fine details of the image. An alternative approach, introduced by Becker and Hinton in 1992, is to train two copies of a deep neural network to produce output vectors that have high mutual information when given two different crops of the same image as their inputs. This approach was designed to allow the representations to be untethered from irrelevant details of the input.