Reviewerly/exHarmony huggingface dataset:
Discussion on the Construction of Stadiums and Gymnasiums in Urban Circle Based on the Law of Water Movement

Chen, J., 2020. Discussion on the construction of stadiums and gymnasiums in urban circle based on the law of water movement. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 935–939. Coconut Creek (Florida), ISSN 0749-0208.Gymnasium is the most important place for people to exercise, train, participate in and watch sports competitions, and it is an essential hardware condition for sports undertakings. Due to the huge cost, high technical requirements, long construction period, low utilization rate after the games and difficult recovery of funds, most of the stadiums and gymnasiums were prepared by the government, which brought heavy pressure to the government. Based on the analysis of water movement law, this paper studies the development mode of urban sports circle and the spatial layout of sports resources, and puts forward the development principles and planning framework of sports circle on the basis of comprehensive consideration of several important factors affecting the development of urban sports circle. The purpose is to provide some reference for the layout of urban sports venues, urban and rural sports facilities planning and urban sports planning.
Abstractive Summarization via Discourse Relation and Graph Convolutional Networks

Currently, the mainstream abstractive summarization method uses a machine learning model based on encoder-decoder architecture, and generally utilizes the encoder based on a recurrent neural network. The model mainly learns the serialized information of the text, but rarely learns the structured information. From the perspective of linguistics, the text structure information is effective in judging the importance of the text content. In order to enable the model to obtain text structure information, this paper proposes to use discourse relation in text summarization tasks, which can make the model focus on the important part of the text. Based on the traditional LSTM encoder, this paper adds graph convolutional networks to obtain the structural information of the text. In addition, this paper also proposes a fusion layer, which enables the model to pay attention to the serialized information of the text while acquiring the text structure information. The experimental results show that the system performance is significantly improved on ROUGE evaluation after joining discourse relation information.
Content Sifting Storage: Achieving Fast Read for Large-scale Image Dataset Analysis

Analyzing large-scale image dataset requires all images to be read from disks first, leading to high read latency. Therefore, we propose a Content Sifting Storage (CSS) system, which aims to reduce the read latency by only reading sifted relevant data. CSS generates embedded content metadata via deep learning and manages the metadata via Semantic Hamming Graph, which achieves fast read based on content similarity meeting the given analysis. Extensive experimental results on image datasets show that compared with conventional semantic storage systems, our CSS can greatly reduce the read latency by 82.21% to 94.8% with more than 98% recall rate.
System for Recommending Facial Skincare Products

Acne is a skin problem that plagues many young people and adults.Even if it is cured, it leaves acne spots or acne scars and many individuals must use skincare products or undertake medical treatment.However, the use of inappropriate skincare products can exacerbate skin conditions.We propose the use of multifeature processing and classification of skin quality and acne status by machine learning to make recommendations for facial skincare products.The results for 15 subjects show that the proposed system achieves a consumer satisfaction index of 80%.
A Visualization System for Performance Analysis of Image Classification Models

Developing machine learning models for image classification problems involves various tasks such as model selection, layer design, and hyperparameter tuning for improving the model performance. However, regarding deep learning models, insufficient model interpretability renders it infeasible to understand how they make predictions. To facilitate model interpretation, performance analysis at the class and instance levels with model visualization is essential. We herein present an interactive visual analytics system to provide a wide range of performance evaluations of different machine learning models for image classification. The proposed system aims to overcome challenges by providing visual performance analysis at different levels and visualizing misclassification instances. The system which comprises five views - ranking, projection, matrix, and instance list views, enables the comparison and analysis different models through user interaction. Several use cases of the proposed system are described and the application of the system based on MNIST data is explained. Our demo app is available at https://chanhee13p.github.io/VisMlic/.
Mobile-Learning Experience as Support for Improving the Capabilities of the English Area for Engineering Students

There are studies that show that the use of technologies play an important role in the teaching-learning process, and the teaching of a foreign language is not an alien to such influence. This paper presents the results of the experience of the use of the Mobile-Learning approach as support for the improvement of the capabilities of the English area of Engineering students of the Universidad Tecnologica del Perú - Arequipa. The research method was quasi-experimental. The results of the Student's T test show us the improvement of the experimental group with the Mobile-Learning approach in two dimensions: grammar/vocabulary and oral production. We believe that this experience could be replicated in other institutions..
DISPLACED DISCUSSION: THE IMPLICATIONS OF REDDIT QUARANTINE AND THE MOVEMENT OF THEREDPILL TO SELF-HOSTING

Reddit.com is a social media site with huge volumes and varieties of content, both hosted on their platform, and imported from other providers. It is also a place of counterpublic community action. Sub-Reddits like r/the_donald, and r/theredpill expound, in turn, alt-right and anti-feminist views. Along with various posts, links, and outside content, the communities support millions of words of user discussion. These communities are policed, at least in part, by their exposure to the community at large, and push-back from other people and groups to their content. When a community sees that its action on a site like Reddit is limited, and perceives that its ideas and ideologies are being silenced, what happens? This paper uses data from a digital ethnography of r/TheRedPill, a sub-community of Reddit dedicated to “discussion of sexual strategy in a culture increasingly lacking a positive identity for men.” The community was quarantined by Reddit in September of 2018, and since that time has engaged in regular discussion about the movement of community discussion and forums away from Reddit to their own hosting site www.trp.red. Beyond simply allowing versus limiting speech, the case of r/TheRedPill provides an opportunity to engage in a discussion about whether progressive sanction is the right way to manage the intersection of counterpublic views with the needs of tech firms. This paper hopes to further that discussion using a community that is less generally associated with hate speech, and can therefore exist on the margins of acceptability.
UFollower: A Model for Smart Cities Based on Ubiquitous Security and Surveillance

Crime is one of the most critical problems in urban centers, especially in large cities. In this sense, technological solutions are needed to provide security to citizens, contributing to the reduction of crime rates. The present work proposes the UFollower model (Ubiquitous Follower) which meets this scenario. The scientific contribution of this work consists of the use of Context Histories and User Profiles for data analysis focused on ubiquitous security. The use of prediction mechanisms and historical contexts allowed to reach up to 70.35% of inference rate for a particular crime (domestic violence). The comparison with the related works indicates that UFollower is the only proposal that presents the issue of public security with context histories and user profile management. The evaluation was conducted through scenarios, allowing to evaluate the related hypotheses. In this sense, a context simulator was built where twenty objects interacted to allow the evolution. Among them, there are people, vehicles, cameras, and wearables.
A proposed authentication and group-key distribution model for data warehouse signature, DWS framework

In the virtual world, the authentication process is used to prove the identity for both of the server and the clients. Therefore, the authentication is an urgent issue for sharing data between users in the data warehouse systems. This paper presents the enhancement of Data Warehouse Signature (DWS) framework through proposing a novel authentication and group-key distribution model, based on the modular symmetric polynomials, to distribute a secure group-key between the manager and the chosen executors. To the best of our knowledge, we are the first to propose a group-key based on the symmetric polynomial and the modular arithmetic. This leads to increase the security level of the proposed model from t, where t is the symmetric polynomial degree, to ∑i=1cci×t, where c is the number of executors in the DWS system. In this paper, enhancing the DWS achieves the high performance using a parallel computing in our middleware. The proposed model has the lowest cost of computation, communication and complexity overheads among the existing security models. Additionally, security analysis shows that the proposed model achieves the key security, prevents insider and outsider attacks, and provides forward and backward secrecy.
The Impact of Rapid Population Growth on Economic Growth: Evidence From Ethiopia

Rapid population growth is increasing from time to time as a result of high fertility and migration rate.This has a greater impact on the quality life of the society, in case of increasing number of unemployed peoples, lack of job opportunity, decrease in land share, infrastructure and so on.The objective of this study aimed to examine the impact of rapid population on economic growth of Ethiopia country by employing Autoregressive distributed lag model for the time period spanning from 1975 to 2019.The variables used in model were, population, consumer price index, gross investment proxied by gross capital formation and trade openness as explanatory variables and RGDP proxy for economic growth used as dependent variable.To check stationarity properties of the data augmented dickey fuller unit root test was used and the result indicates all the variables are non-stationary at level and become stationary at first difference.Regarding cointegration test, ARDL bound test used to test the existence of long-run association among the variables and the result confirmed that there is long run relationship among RGDP and explanatory variables.The empirical result obtained from ARDL Model revealed that gross capital formation and consumer price index have found to be positive and statistical significant impact on RGDP in both short run and long run where as our variable of interest meaning population growth is negatively affecting real GDP in the short run but, in the long run it is found to be positive and statistical significantly affecting economic growth.
The new Weakness of RSA and The Algorithm to Solve this Problem

RSA is one of the best well-known public key cryptosystems.This methodology is widely used at present because there is not any algorithm which can break this system that has all strong parameters within polynomial time.However, it may be easily broken when at least one parameter is weak.In fact, many weak parameters are already found and are solved by some algorithms.Some examples of weak parameters consist of a small private key, a large private key, a small prime factor and a small result of the difference between two prime factors.In this paper, the new weakness of RSA is proposed.Assuming Euler's totient value, Φ (n), can be rewritten as Φ (n) = ad + b, where d is the private key and , a b ∈  , if a divides both of Φ (n) and b and the new exponent for the decryption equation is a small integer, this condition is assigned as the new weakness for breaking RSA.Firstly, the specific algorithm which is created for this weakness directly is proposed.Secondly, two equations are presented to find a, b and d.In fact, one of two equations must be implemented to find a and b at first.After that, the other equation is chosen to find d.The experimental results show that if this weakness has happened and the new exponent is small, original plaintext, m, will be recovered very fast.Furthermore, number of steps to recover d are very small when a is large.However, if a is too large, d may not be recovered because m which must be always written as m = h a is higher than modulus.
Komposit Grafit-Polistirena Diperkuat Poliuretan sebagai Penyerap Gelombang Akustik :

Noise, harshness, and vibration (NHV) are problems encountered when driving a vehicle. This can cause serious problems for drivers if exposed to NHV for a long time. In order to avoid the NHV, vehicle are equipped with acoustic tile which functions to absorb vibration from the sound wave and reduce the noise caused by the vehicle’s engine. Polyurethane foam is commonly used as a sound absorption material because of its high sound absorption efficiency. The study aimed at investigating polystyrene (PS) and graphite (G) fillers with polyurethane matrix for improving acoustic property. The research started with optimization of composite that characterized using sound absorption intensity parameters at frequencies of 50 Hz-6000 Hz, and the optimum results obtained at matrix/filler 92.8%wt. The composite morphology is analyzed using SEM-EDX, optical microscopy, and the density. Mechanical test is conducted using Universal Testing Machine (UTM) and achieved the optimum compressive strength at 60G/40PS with 0.65 MPa. The absorption of acoustic waves property was tested in the frequency range 50 Hz-6300 Hz using an impedance tube and obtained the optimum composition at 0G/100PS.
Distance variable improvement of time-series big data stream evaluation

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.
On Bayesian sparse canonical correlation analysis via Rayleigh quotient framework.

We propose a semi-parametric Bayesian method for the principal canonical pair that employs the scaled Rayleigh quotient as a quasi-log-likelihood with the spike-and-slab prior as the sparse constraints. Our approach does not require a complete joint distribution of the data, and as such, is more robust to non-normality than current Bayesian methods. Moreover, simulated tempering is used for solving the multi-modality problem in the resulting posterior distribution. We study the numerical behavior of the proposed method on both continuous and truncated data, and show that it compares favorably with other methods. As an application, we use the methodology to maximally correlate clinical variables and proteomic data for a better understanding of covid-19 disease. Our analysis identifies the protein Alpha-1-acid glycoprotein 1 (AGP 1) as playing an important role in the progression of Covid-19 into a severe illness.
Research on Multi-source Heterogeneous Big Data in Extra-large Enterprises

Extra-large enterprises, due to their huge scale and complex businesses, face serious challenges in the big data time. This paper introduces the Operating and Monitoring Information System (OMIS) in the State Grid Corporation of China to try to use big data in the extra-large enterprises. OMIS consists of full coverage data flow path, compound general library of model and algorithm, multi-mode computing platform and interface components. It solves a series of key problems that are data barrier, transmission, analysis, computing, usability in extra large enterprises. OMIS has connected headquarters, provinces and cities, covered 27 provinces. Benefited from interface components, a programme for data extraction, model train and parallel computing can be implemented by several codes conveniently in OMIS. In the experiments, OMIS can extract 1.73TB line loss data in about 93 hours from 27 provinces, provide multiple algorithms to detect abnormal low-voltage substation areas, support high performance computing by parallelization. Finally, OMIS reaches 3.71 speedup ratio on five nodes.
Analysis of Differences in Students' Key Competencies in Relation to Types of College Admission Screening: Focused on the Case of C University

This study analyzed differences in students' key competencies in relation to types of college admission screening, using the case of C University.The school record-based college admission screening was initiated to normalize public education, but it is at the center of controversy.Selecting and educating talented students in each university according to the criteria will be the core of university education, so it is very meaningful to compare the differences in the core competencies of the students.The key competence examination test questionnaire developed by C university was used.The test questionnaire consisted of 67 questionnaire items, which were measured based on a total of 5 Likert scales.The students were divided into three groups according to college admissions.The survey was conducted online for the period from September 17 to September 21 in 2018.And an analysis was performed on the data of 2,697 students who participated in the key competence examination survey among those enrolled in the university as of 2018.In conclusion, the core competencies of students depended on the admission criteria that the university used as the selection criterion.In conclusion, the core competencies of students differed according to the admissions criteria in the criteria that the university used as the selection criteria.Therefore, the government should relax regulations on admission standards of each university and provide university autonomy so that each university can select and nurture competent students.
The Role of Technology

Successful collaborative working is built on a combination of people, processes and technology, which means addressing some of the concerns voiced by potential Information and communication technology (ICT) users. Collaboration technology can support both internal and external integration processes. While resistance to using collaborative technology may reflect concerns about working in a more transparent partnering-type environment, it may also evidence more specific concerns about selecting and using a new technology to handle mission-critical information. In a partnering context, the client and team should seek a technology that will support their key processes and information requirements in the long-term. The Application service providers can then focus on core activities such as software development, devising improvements and new features based on the collective experiences of both construction ICT partners and direct customers. However, the technology can equally be used on multi-project programmes, for example by teams employed under five-year framework agreements.
Performance Level Evaluation of Cryptographic Algorithms

Cryptographic Algorithms persuade a significant contribution in data security. But they require notable portion of resources like processor time, system memory, power consumption, latency time etc. So, it is needed to find out best cryptographic algorithm by evaluating performance of each of them. This paper evaluates the performance of cryptographic algorithms namely AES, triple DES, Blowfish, RSA, MD5 as well as SHA based on parameters like time required to encipher, time required to decipher and memory used by them. The literature surveys of these algorithms are made and then the system for the measurement of performance is proposed and implemented.
Learning-Based Signal Detection for OFDM Systems with I/Q Imbalance

The in-phase/quadrature (I/Q) branches imbalance leads to mirror subcarrier interference and worsens the performance of zero-intermediate frequency (zero-IF)-based orthogonal frequency division multiplexing (OFDM) systems. To tackle the signal detection issue of in-phase/quadrature imbalance (IQI) at the transceiver, a deep learning-based approach is proposed by using the convolutional neural network. Specifically, the network model and parameters are well-designed based on the features of the channel impulse response and the mirror interference of IQI. To verify the designed model, it is first trained by simulated data under the off-line training and then used directly to recover the on-line transmitted data. The simulation results demonstrate that the proposed method shows excellent performance in processing OFDM signals under the case of IQI and it is more robust than traditional methods even without cyclic prefix.
Technique for Enhancing the Imperceptibility and Robustness of Digital Audio Watermarking

After discussing all these, our main research discussion starts from this chapter, in that digital audio watermarking algorithm is discussed. Initially, based on the survey done, the various methods available for digital audio watermarking are taken for consideration. From those methods, we found one approach that is relatively better than other approaches are proposed. It is based on the combination of DWT and SVD methods. So, we have implemented that algorithm first and found that it is better but still there are some improvements needed in that to increase the robustness because we found that this algorithm is not fitted for some kind of attacks as well some different types of watermark images. After that, we compare our results with existing methods and we found better results than other methods. But still, some improvements are needed in our work like in echo attack it is not giving the desired result. So, again we proposed another new approach that is the combination of DWT and SVD with DFT to increase the robustness and DSSS algorithm to provide the security to our algorithm. Our results show tremendous improvement against various malicious attacks and also improved the robustness.