Reviewerly/exHarmony huggingface dataset:
Pengaruh Price dan Product Quality Terhadap Costumer Loyalty Internet Indosat Ooredo di Sukabumi

This research is motivated by a decrease in the number of customers in the last 3 (three) years which indicates a decrease in customer loyalty to Indosat Ooredo. With the existing problems, this study aims to examine the effect of price and product quality on Indosat Ooredo's internet customer loyalty in Sukabumi. The research method used is quantitative descriptive, using a sample of 100 Indosat Ooredoo internet users in Sukabumi. The results of the study show that R square value is 67.1%, which indicates customer loyalty is determined positively and significantly by product quality and product quality with a regression equation Y = 4,709 + 0,100 X1 + 0,288 X2. Simultaneous analysis results show that price and product quality affect customer loyalty. The t test shows that there is no partial effect between the price of Indosat Ooredo Internet Loyalty Customers in Sukabumi. Partially there is a positive and significant effect between Product Quality on Indosat Ooredo's Internet Customer Loyalty in Sukabumi.
Research on Data Verification and Exchange of Heterogeneous Blockchains for Electricity Application

Abstract This paper proposes a verification and exchange method for cross-chain transactions of heterogeneous blockchains for electricity application scenarios and designs a chain-level cross-chain event verification mechanism. For a blockchain platform that requires cross-chain transactions, a Merkel-structured cross-chain event tree is systematically constructed with the collected cross-chain transactions in the generation stage of the block. The target blockchain platform verifies the credibility of cross-chain events based on the Merkel-structured cross-chain event tree and related credible proofs. Cross-chain events are captured through smart contract events, and cross-chain event Merkel trees and Merkel paths are constructed to achieve credible and rapid verification of cross-chain events. The cross-chain experiments are carried out in the main-side chain mode of electricity business scenario, and the integrity of cross-chain transaction is ensured by a cross-chain transaction consistency guarantee mechanism, so as to achieve the purpose of trusted circulation and secure transactions of electricity business data between different application chains.
Method for Synchronized Driving of Sign Language Avatar Using a Digital TV Signal

There are several people around the world with some kind of disability requiring more accessibility on multimedia devices. In this work the main focus is the use of a sign language Avatar to help the deaf or people with hearing loss to have more access to the video content transmitted by digital TV channels. This method aiming to allow the broadcaster to add glosses(Representation of a sign language sign), as a binary file, synchronized with DTV content, as audio and video (A/V), directly on the signal. Glosses represents the signs/gestures (words) that will be reproduced by the Avatar on the receiver side. The synchronization is guaranteed by timestamps multiplexed together to the transmitted stream. Having a DTV signal with this information, on receiver side, an embedded system extracts each gloss-binary file, together with the synchronization data to run the Avatar in a continuous process. This method presents several advantages when compared with usual broadcast methods and web based methods. Among these advantages are: it can be disabled by user, on receiver side, due the sign language interpreter is not attached directly on video media, as is normally used by broadcasters; the receiver does not need to access the internet to translate or get glosses, avoiding problems related to delays or overheads when performing constant communications with external servers to translate the content into sign language, in addition to making the system less prone to intrusion, the translation step is not necessary due the final glosses are added on DTV signal to be showed synchronized with video; and finally, it can also be used on premium content, such as movies, TV shows and documentaries, that normally do not have sign language content, further increasing the possibility of accessibility by the deaf audience.
<i>k</i> ‐Sparse extreme learning machine

Extreme learning machine (ELM) is a single layer feed-forward neural network with advantages of fast training and good generalization properties. However, when the size of the hidden layer is increased, both of these advantages are lost as the redundant information may cause overfitting. Traditional way to deal with the issue is to introduce regularisation which promote sparsity but in the output layer weight matrix. In this Letter, we proposed the use of sparsity inside the output of the hidden layer such as to use it as the only non-linearity in the hidden layer. In the proposed formulation, we use linear activation function inside the hidden layer and keep k highest activity-bearing neurons as a measure of sparsity. Using the principal component analysis, we project the resulting output layer matrix onto a low-dimensional space in order to further remove redundant and irrelevant information, and speed up the training process. In order to verify the feasibility and effectiveness of the proposed method, we test and compare it with a number of ELM variants using benchmark datasets. Compared with these methods, our results demonstrate that the proposed method achieves better accuracy performance consistently across many different benchmark datasets.
A region selection model to identify unknown unknowns in image datasets

Unknown Unknowns (UUs) are a kind of test data points on which predictive model confidence is high but the prediction incorrect. It is crucial to identify these instances for better understanding the limitation of predictive models and to avoid critical errors. However, existing methods utilize a fixed model to identify UUs in test data, resulting in limited performance and high cost. To address these limitations, we propose a regional candidate selection algorithm that seamlessly combines a deep neural network and humans to identify UUs in visual data. Specifically, we identify several candidate regions in the data space where UUs have high probability of being present. This is achieved by comparing labels learned by a deep network and predictions obtained with the original classification model. Moreover, inspired by active learning, diversity and training loss are utilized to obtain suitable query sequences. We evaluate our method using a publicly available image dataset. Experimental results conducted on this dataset demonstrate the improved performance of the proposed method over different baselines under different conditions and its robustness to noisy labels.
Software Cost Estimation Using Flower Pollination Algorithm

Software Development Organizations (SDO) develop a massive number of projects per year. One of the elementary and significant features of any SDO is to use a tool that can precisely estimate the software cost. It directly affects nearly all management activities including resource allocation, project planning, and project bidding. Imprecise estimation causes troubles e.g. dropping the worth of the project, waste the company’s budgets and can outcome in the disaster of the project. During the last few decades’ researchers have developed a large number of models for software cost estimation (SCE). However, SCE is still a challenging task. Algorithmic and non-algorithmic approaches were firstly used to achieve the goal. Each of them has their own merits and demerits but still, these are considered as primary tools for SCE. This study proposes Flower Pollination Algorithm (FPA) for SCE. Mean Magnitude of Relative Error (MMRE) is used as an evaluation metric for benchmarking the proposed model with the existing model. All the results of FPA are compared with the COCOMO model. Experimental results show a better performance of FPA as compare to COCOMO. Three datasets from NASA software projects are selected, NASA93, NASA63, and NASA60. On NASA93 dataset the improvement is 10.17%, on NASA63 the improvement is 77.38% and on NASA60 it is 22.96%.
Smart Supervision Based on A New Generation of Information Technology Integration and Its Application

The new generation of information technology represented by artificial intelligence, block chain, cloud computing and big data is infiltrating various fields of smart supervision. In recent years, smart supervision based on a new generation of information technology has achieved results in the fields of government procurement, traditional food and medical insurance, whereas it relies on a single technical form. Therefore, how to organically integrate various forms of new-generation information technology with smart supervision may become a future direction. Taking the new generation of information technology represented by "ABCD" as the starting point, this article proposes that smart supervision which integrates multiple technical forms further improves the accuracy and real-time of smart supervision and the effectiveness of the construction of smart cities. Meantime, it can effectively coordinate regional development, vigorously promote the comprehensive reform of "intelligence+", strengthen data correlation research, solve the problem of asymmetry in regulatory information, mine the information behind the data, improve the utilization rate of regulatory data, give full play to the power of social think tanks, and make the best of the media convergence, which would accelerate the process of China's information modernization and improve national quality of life.
Statistically Efficient Greedy Equivalence Search.

We establish the theoretical foundation for statistically efficient variants of the Greedy Equivalence Search algorithm. If each node in the generative structure has at most k parents, we show that in the limit of large data, we can recover that structure using greedy search with operator scores that condition on at most k variables. We present simple synthetic experiments that compare a backward-only variant of the new algorithm to GES using finite data, showing increasing benefit of the new algorithm as the complexity of the generative model increases.
Discussion on the application of artificial intelligence technology in the construction of physical education class in Higher Vocational College

This paper analyzes the current situation of physical education classroom in Ningbo City Vocational and technical college, and focuses on the application of artificial intelligence in the online teaching of physical education classroom, including the application of micro class, MOOC class, multimedia computer and virtual reality technology in the online teaching of physical education classroom. It is proposed that we should have a correct understanding of artificial intelligence and combine it teaching methods with the traditional classroom model to improve the online teaching effect. This paper provides a theoretical basis for the further development of related teaching research.
A Study and Analysis of Touch Screen Technologies

The use of touch screen in our day-to-day life is always there. People without touch screen are only a few. Touch screen were built for ease of work and for saving time. It is an assistive technology. This interface can be beneficial to those that have difficulty in using other input devices such as a mouse or keyboard. When used in conjunction with software such as on-screen keyboards, or other assistive technology, they can make computing resources more available to people that have difficulty in using computers. Touch Screen is widely used and emerging technology that is sensitive to human touch, allowing a user to interact with the computer by touching pictures or words on the screen. It provides a very good user interface with applications that normally require a mouse. The touch screen interface is going to revolutionize the electronic interactive devices in a big way. The purpose of this study is to analyse the various technologies used to build the touch screen and it's also helpful for the future gen those who make a study on it.
Secure Color Image Transmission Based on the Impulsive Synchronization of Fractional-Order Chaotic Maps Over a Single Channel

In this paper, we propose a novel single-channel secure transmission scheme based on the impulsive synchronization of fractional-order discrete chaotic systems. A discrete fractional-order impulsive observer has been developed at the receiver level in order to reconstruct the states of a fractional-order Hénon map used at the transmitter level. Robust chaos-based encryption and decryption algorithms have been used to cipher an original color image and retrieve it as soon as the synchronization is established between the transmitter and the receiver.
Blockchain Technology as a Competitive Advantage for Higher Education Institutions in the Philippines

Abstract This research explores blockchain as a competitive advantage (CA) for higher education institutions (HEIs) in the Philippines. The respondents’ awareness of the relevance of blockchain use was also tested. The researcher aims to verify if blockchain is a foundational technology to be implemented in academe. Theoretical framework was formulated based on the knowledge procurement cycle and distributed learning ledger. The researcher proposes that conceptual framework, centered on blockchain (Ethereum), will enhance recordkeeping in education. Frequency, percentage, and weighted mean determined the degree of relevance of blockchain as CA of Philippine HEIs. One-way ANOVA determined respondents’ significant relationship (SR) with regard to blockchain awareness and identified SR of blockchain as CA over other colleges in terms of cost and differentiation. The results showed that students were most aware of the relevance of blockchain while professors were least aware. Both null hypotheses were accepted. There is no SR on the respondents’ awareness on the use of blockchain. Respondents considered technology relevant and a foundational technology in recordkeeping. The research proposes EduChain Activity Flow Model to enhance recordkeeping in the academe. It will offer ample job opportunities to students in the field of blockchain. Also, the researcher suggests that administrators may incorporate blockchain training as part of faculty development. This research will pave the way for future researchers to study other aspects of blockchain in education. Keywords: blockchain, blockchain technology, EduChain Activity Flow Model
Single-shot Digital Phase-shifting Moiré Pattern for 3D Metallic Surface Imaging

A four-step digital phase-shifting method using Moiré patterns and single shot recording is proposed and conducted to map submillimeter-scale 3D surface features.
Proof-of-principle experimental demonstration of twin-field quantum key distribution over asymmetric channels

We demonstrate a first experiment of twin-field quantum key distribution over asymmetric channels. We use asymmetric signal intensities and show that the secret key rate can beat the repeaterless bound at 50 (30+20) dB total loss.
Bidding in Spades

We present a Spades bidding algorithm that is superior to recreational human players and to publicly available bots. Like in Bridge, the game of Spades is composed of two independent phases, \textit{bidding} and \textit{playing}. This paper focuses on the bidding algorithm, since this phase holds a precise challenge: based on the input, choose the bid that maximizes the agent's winning probability. Our \emph{Bidding-in-Spades} (BIS) algorithm heuristically determines the bidding strategy by comparing the expected utility of each possible bid. A major challenge is how to estimate these expected utilities. To this end, we propose a set of domain-specific heuristics, and then correct them via machine learning using data from real-world players. The \BIS algorithm we present can be attached to any playing algorithm. It beats rule-based bidding bots when all use the same playing component. When combined with a rule-based playing algorithm, it is superior to the average recreational human.
A Hybrid Imbalanced Data Learning Framework to Tackle Opinion Imbalance in Movie Reviews

Opinion Mining is an important buzzword in recent times for research and industry for data science applications. Many concerns in opinion imbalance particularly in movie reviews were analyzed and handled for efficient recommendations. Opinion imbalance in terms of binary class, which often compromises the classifier prediction results, was scarcely studied. In this work, a Hybrid Imbalanced Data Learning Framework (HIDLF) is proposed to handle the opinion imbalance in the movie review dataset and then classify the movie reviews through the proposed HIDLT-SVM algorithm, which is a part of HIDLF for effective movie review classification. Experimental comparisons of the proposed work are done on movie reviews with Logistic Regression, CART, and REP Tree. Different evaluation metrics are used for capable classification of opinions from the movie reviews. The results recommend that the planned HIDLT-SVM framework performs better than the competent algorithms.
Impact of decomposition and kriging models on the solar irradiance downscaling accuracy in regions with complex topography

Many small island states are planning to invest heavily in solar photovoltaics in an attempt to curb their overreliance on fossil fuels for electricity generation. In order to efficiently exploit the abundant solar energy resource, these islands need reliable solar irradiance data. However, the orographic effects arising from their volcanic origins often result in strong variability and uncertainty in the solar resource. In this context, satellite-based models present an effective alternative to ground-based measurements. Different downscaling approaches have been applied that compensate for the large spatial resolution of satellite images and the terrain-related effects that they disregard. Nevertheless, the accuracy of these methods is influenced by the solar radiation decomposition model used. Moreover, the variogram model used in the kriging process to characterize the spatial dependence of the solar radiation has a significant effect on the results. In this study, we compare the performances of seven radiation decomposition models for the anisotropy analysis and seven variogram models for the spatial interpolation of the solar irradiance. A dense network of ground measurements at 43 stations is used to evaluate the accuracy of the different models. Results reveal that the Yao radiation model coupled with the Matern variogram provide the best results.
A Fast Marching Gradient Sampling Strategy for Motion Planning using an Informed Certificate Set

We present a novel fast marching gradient sampling strategy to accelerate the convergence speed of sampling-based motion planning algorithms. This strategy is based on an informed certificate set which consists of the robot states with exact collision status as well as the minimum distance and the gradient to the nearest obstacle. The informed certificate set covers almost the whole planning space such that it contains rich information for the planner. The best quality point in this set is selected as the marching seed to guide the search graph move steadily to the goal set. The distance and gradient information of the marching seed helps to generate a new sample with almost sure collision status. When a feasible solution has been found, this set can construct the restricted subset that can improve current path quality. This marching gradient sampling strategy is applied to the RRT and RRT* algorithms. Simulation experiments demonstrate that the convergence speed to a feasible solution or to the optimal solution is almost twice faster than that of the safety certificate algorithms.
Using neural networks to detect microseismicity and pick P-wave arrival times in Oklahoma

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Using neural networks to detect microseismicity and pick P-wave arrival times in OklahomaAuthors: Bingxu LuoHejun ZhuBingxu LuoUniversity of Texas at DallasSearch for more papers by this author and Hejun ZhuUniversity of Texas at DallasSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3417918.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractDetecting induced seismicity is challenging due to small magnitudes of events and noisy background. Traditional detection methods and most current machine learning algorithms are based on waveform data (in the time domain). In this paper, we apply short-time Fourier transform to convert waveform data into 2D time-frequency feature maps, which are then used as inputs for the convolutional neural network (CNN) to effectively detect small events in both frequency and time domains. We collect seismic data for two stations located in Oklahoma, which are used as training and validation data sets. Then we utilize the trained CNN for one-month continuous records from 51 stations located in Oklahoma. On average, 2,106 event windows are detected per station. In addition, we use another CNN model to automatically pick P wave arrival times. We demonstrate that the CNN model works better than the traditional STA/LTA method and can be implemented for real datasets.Presentation Date: Tuesday, October 13, 2020Session Start Time: 9:20 AMPresentation Time: 11:00 AMLocation: Poster Station 4Presentation Type: PosterKeywords: neural networks, induced seismicity, machine learning, microseismicPermalink: https://doi.org/10.1190/segam2020-3417918.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Bingxu Luo and Hejun Zhu, (2020), "Using neural networks to detect microseismicity and pick P-wave arrival times in Oklahoma," SEG Technical Program Expanded Abstracts : 1354-1358. https://doi.org/10.1190/segam2020-3417918.1 Plain-Language Summary Keywordsneural networksinduced seismicitymachine learningmicroseismicPDF DownloadLoading ...
AQtpUIR: Adaptive query term proximity based user information retrieval

Information-seeking process primarily relies on the relevance manifestation and usually based on term statistics. Document-term statistics are dominant, e.g. term-frequency (TF), inverse document frequency (IDF), document length (DL), etc. Query Term Proximity (QTP) is mostly under-explored for the relevance estimation in the information retrieval. In this paper, we systematically review the lineage of the notion of QTP and proposed a novel framework for relevance estimation. The proposed framework is referred as Adaptive QTP based User Information Retrieval (AQtpUIR), is intended to promote the relevant documents. Here, the relevance estimation is a weighted combination of statistics. The notions ‘term-term query proximity’ is a simple aggregation of contextual aspects of user search in relevance estimates and query formation. Intuitively, QTP is derived via pre-processing, inherent to indexing and text-processing, and utilized to promote the extracted documents among all retrieved documents. Thus also balance the exploitation-exploration tradeoff. The adaption of QTP balance the traditional retrieval tradeoff, e.g. relevance, novelty, result diversity (Coverage and Topicality), and highlight various inherent challenges and issue of the proposed work.