Reviewerly/exHarmony huggingface dataset:
Phrase Prioritization Algorithm with Supporting Data Structure and Its Implementation

Search Engines are the most common data on the World Wide Web Although Search Engine Optimization deals with growing visibility of search results web pages, this study work focuses on the search algorithm that will provide the most appropriate, relevant and needed records with the anticipated search query. A comparative research of the intersection algorithm and its variants provided the requirement for the fresh algorithm. CRANTOP database is a conventional WWW database that not only provides a database of documents and queries, but also provides appropriate queries for real appropriate documents. The study was conducted on that database, available in the Appendix, and the analysis is based on the values of precision, recall and F-measure. The proposal algorithm, which preference of the search queries, is at the core of the study job. This algorithm indicates fetching phrases methodology and then looking for all possible sentences in order to increase the recall value. In this respect, the most significant problem is the use of such sparse matrix representation, which promotes the effective search of sentences into the papers. For doing it, sparse matrix representation was suggested, which contains Columns corresponding to the papers, making searching for any possible sentence effective. Rows Linked Representations preserve the vibrant nature of papers and insert and delete phrases from papers. In addition, emphasis was placed on general words and query words into the dictionary, resulting in precision search outcomes. Synonyms were also regarded for document retrieval, thereby enhancing comprehension.
Innovative approaches to manage entomophages production

Annotation Purpose. Improving the efficiency of entomophages production by developing innovative approaches for it management. Methods. System approach, intelligent information technologies. Results. Innovative approaches for it management of entomophages production have been developed, which consist of: determining and ranking the factors that have the greatest impact on the quality of entomological products, and factors that lead to substandard products; the creation of structural and parametric complexes for assessing product quality; real-time assessment of the intensity of entomocultures development processes; automation of management abiotic parameters stepwise development of insects (temperature and relative humidity of the box for growing of insects) in real time; determining the quality of entomological products in conditions of incomplete information, taking into account the impact of a combination of abiotic and biotic production parameters; calculation of optimal values of production parameters in conditions of uncertainty; systematization of knowledge about the interaction of heterogeneous parameters in the production of entomophages. Conclusions. The proposed innovative approaches to manage the production of entomophages can increase its efficiency by forming optimal management strategies, using the technological experience of specialists and modern intelligent information technologies in particular, SCADA-systems, fuzzy logic theory and cognitive analysis. Keywords: innovative approaches, production of entomophages, intelligent information technologies.
Predicting Critical Transitions in Multiscale Dynamical Systems Using Reservoir Computing

We study the problem of predicting rare critical transition events for a class of slow–fast nonlinear dynamical systems. The state of the system of interest is described by a slow process, whereas a faster process drives its evolution and induces critical transitions. By taking advantage of recent advances in reservoir computing, we present a data-driven method to predict the future evolution of the state. We show that our method is capable of predicting a critical transition event at least several numerical time steps in advance. We demonstrate the success as well as the limitations of our method using numerical experiments on three examples of systems, ranging from low dimensional to high dimensional. We discuss the mathematical and broader implications of our results.
KAJIAN SLOGAN DONALD TRUMP “MAKE AMERICA GREAT AGAIN” MELALUI TEORI SEMANTIK GEOFFREY LEECH.

This study discusses Donald Trump's slogan during his 2016 presidential campaign. The slogan Make America Great Again was viewed from the perspective of seven types of semantic meanings from Geoffrey Leech. The meaning was explored in six Trump speeches at different places and times. The seven meanings are denotative, connotative, social, affective, reflective, collocation, and thematic meanings. Denotative meaning refers to the future time, that a subject in the slogan will make the United States great again, which at the present its condition is considered bad. The connotative meaning covers many sides of the living conditions of America, but mainly are economic problems. In social terms, Trump uses language, mental images, ideas, attitudes and hierarchies like his audience, an attempt to identify himself with his people referring to American traditional values. In terms of affective meaning, Trump associates himself with other American citizens as a single entity; despite the fact that America and the American people emerged as different agents, for Trump both were agents. So that the slogan expression has an implicit meaning We will make America great While the reflective meaning of the slogan is in the word great, namely improvement, better life. The collocation meaning coming with the slogan is a great economic plan, and start winning again. The thematic meaning of Trump's slogan is imperative. Implicitly, it is also an invitation with the word Let's that has a direct, straightforward, and decisive mode.
Correlative Study of Image Magnification Techniques

A digital image is often required to be rescaled. When the pixels in the image are mapped to a larger grid, new coordinate positions will be formed. The interpolation procedures are used to approximate the value for the unknown sample points set between the initial sample points. The concept is to replace the missing values using the known values of the initial sample points. The interpolation techniques are intended to effectively preserve the characteristic features of an image. There is an indeed greater number of interpolation algorithms available which have their strengths and challenges. In this study, interpolation techniques based on the nearest neighbour, bilinear interpolation, and high-quality magnification (hq3x) scaling were examined. The qualitative and quantitative properties of these three interpolation techniques were analysed. The parameters considered are the SSIM and SNR measure. In the experiment, the performance of the three methods was compared and measured from objective and visual assessments. The aim of this study is to analyse the visual quality of the output to determine the suitable method. From the analysis, it is observed that hqx scaling performs better than nearest neighbour interpolation and bilinear approach performs fairly closer to hqx scaling.
Computing semantic relatedness using latent semantic analysis and fuzzy formal concept analysis

Measuring semantic similarity/semantic relatedness is an important task in computational linguistic, natural language processing and ontology creation. In this paper, a new hybrid method using LSA and FFCA is proposed for computing the semantic-relatedness. Latent semantic analysis (LSA) is used to extract the attributes of the concepts and these attributes are further mapped to FFCA to compute semantic relatedness. The latent semantic analysis is used for finding the neighbouring words or attributes and their correlation value. The concepts and their attributes are mapped to FCA table and then to FFCA table by using the correlation value as membership. A fuzzy similarity measure is then used to compute the semantic relatedness between these concepts/words. The proposed method is evaluated on word similarity bench mark dataset WS-353 and found an accuracy of 0.85.
Need analysis developing EFL, Paragraph writing materials at Kalimantan L2 Learners

This study attempts to explore the target needs and the learning needs. This research was conducted at the second semester English department students of Palangka Raya State Islamic Institute of 2017/ 2018 academic years. The respondent was 20 EFL paragraph writing learners. The research findings were as follows: (a) in terms of the target language needs, it revealed that majority of learners (45%) were studying Paragraph Writing course; (b) it was found that majority of the respondents (50%) stated that the skills to develop through the paragraph writing course was the understanding the paragraph development; (c) it was found that majority of respondents said that grammar (45%) and mind mapping (40%) was the students’ difficulty in writing paragraph; (d) in terms of learning style, the learners preferred to get assistance from Internet than other sources, and they preferred to self-learning in the classroom activities. In terms of the learners’ appropriate teaching methods it was found that (a) Internet was preferred dominantly by the respondents (75%) as source to be included as instructional materials; (b) the source of corrective feedback preferred mostly by the respondents (80%) was teacher feedback. Therefore, it is recommended that internet-based materials are preferred for developing EFL writing materials. The materials should be made excellent integration of resources available on the web.
Penerapan Sistem Informasi Akuntansi Pada Pelayanan Apotek Pharm 24 Yogyakarta

AbstractThe running of business activities, of course, requires important components such as accounting that determines whether the business is running smoothly or not. With accounting, business owners will know the possibilities that will happen in the future. To find out this possibility, companies need financial reports to obtain information about the company's losses or profits. However, there are still many business fields that have problems in presenting financial statements that are inaccurate and time-consuming. Based on this, the authors conducted research on the accounting data processing process at Pharm 24 Pharmacy which still uses manual recording where the authors make observations and interviews with the system at the Pharmacy, then the recording is applied to a computerized recording system using the MYOB Premier V. Accounting Application. 16. The purpose of this study is to determine the resulting accounting process after computerized processing of accounting data and then make comparisons with a manual accounting recording system. The results of this study can assist the Pharm 24 Pharmacy in processing their business accounting data to make it more effective and efficient in recording so that the resulting financial reports are more accurate.Keywords: Accounting; Financial statements; Data processing; Application; MYOB Premier V.16
Robots in Travel, Tourism and Hospitality: Key Findings from a Global Study

This report presents part of the results of the first global study on customers’ perceptions on robots in travel, tourism, and hospitality implemented by the authors in 2018-2019. Specifically, the report presents the methodology of the research and elaborates on the key results regarding the activities that customers perceive as appropriate for robotisation, their preferences to be served by robots, and their willingness-to-pay for robot-delivered services. Finally, the report provides recommendations to travel, tourism, and hospitality companies how to implement successfully robotic technologies in their operations.
Effective and scalable data discovery with NextiaJD

This work is partly supported by Barcelona’s City Council under grant agreement 20S08704. Javier Flores is supported by contract 2020-DI-027 of the Industrial Doctorate Program of the Government of Catalonia and Consejo Nacional de Ciencia y Tecnologia (CONACYT, Mexico).
LibGuides: Summer Reading Program : Early Literacy

This LibGuide has information and resources for the current summer reading program. It has been created for RCLS member library staff.
RANCANGAN COOPERATIVE LEARNING TIPE NUMBERED HEADS TOGETHER UNTUK MENINGKATKAN KERJASAMA PESERTA DIDIK PADA PEMBELAJARAN IPS KELAS V SEKOLAH DASAR

This research aims to describe the implementation design of the cooperative learning model Numbered Heads Together to improve student cooperation in social studies learning in class V Elementary School. In this research, the subject and participants consisted of three people, namely the lecturer Mr. Sendi Fauzi Giwangsa M.Pd as a content expert, and the lecturer, Dra. Hj. Ani Hendriani. M.Pd as an elementary school expert, and Ibu Intan S.Pd as a practical. This research uses descriptive research method where the results and expert responses are descriptive or explanatory. Research and data processing techniques use the Delphi technique by implementing steps such as creating a learning hypothesis design (RHP) stage one, giving RHP to experts to get agreement and suggestions for improvement, researchers receive agreement and suggestions for improvement from experts, researchers make RHP improvements, then given to the expert to get a second RHP agreement, and the researcher receives the result of the expert's agreement. Based on the research steps that have been carried out, the researchers get the results that the cooperative learning model of the Numbered Heads Together type is considered relevant to improve student cooperation in social studies learning on economic material in grade V of elementary school, as for the learning which must include three stages, namely the opening activity, core activities and closing activities. Keywords: Cooperative Learning Design, Numbered Heads Together, and Cooperation.
Central Louisiana Technical Community College Library: Patient Care Technician: Open Educational Resources

This LibGuide will provide you with basic research information for the Patient Care Technician Program.Use the tabs to the left to get information on finding books, finding articles from magazines and journals, and locating recommended websites with u
MRI images series segmentation using the geodesic deformable model

Image segmentation is regarded as the most crucial medical imaging process because it extracts the region of interest and thus helps upgrade medical diagnosis. The Geodesic deformable model is a curve that deforms within digital images to extract object shapes. The Geodesic deformable model has been used very successfully in the process of single image segmentation but it fails to segment a series of images. In this research, the Geodesic deformable model is developed to overcome its limitation by controlling the speed of the curve deformation. The developed model is implemented on several series of MRI images that include tumors of varying shape complexity. Experimental results show that the developed model performed very well and successfully segmented the series of MRI images which outperform the baseline model.
Review Paper on “Real time Speech-Driven Facial Animation Using GANs”

Speech driven facial animation can be regarded as a speech-to-face translation. Speech driven facial motion synthesis involves Speech analysis and face modeling. This method makes use of still image of a person and speech signals to produce an animation of a talking character. Our method makes use of GAN classifier to obtain better lip synchronizing with audio. GAN methodology also helps to obtain realistic facial expressions thereby making a talking character more effective. Factors such as lip-syncing accuracy, sharpness, and ability to create high -quality faces and natural blinks are taken into consideration by this system. GANs are mainly used in case of image generation as adversarial loss generates sharper and more depictive images. Along with images, GANs can also handle videos easily.
Significance of improving communication skills

The term ‘communication’ has got its own importance in the current scenario. In the present age of information and communication technology (ICT), it has become essential for every individual to possess and master communication skills. The present research article aims at understanding the significance of communication skills and it also discusses some important ways to improve these skills. When we speak of communication, we normally think about the day-to-day tasks and discussions we all engage in. As Peter Ustinov says, “Communication is the act of simply being understood”. One of the basic aims of communication is to convey our thoughts and ideas and make the people understand these ideas. Hence, there is an urgent need of improving communication skills to present one’s thoughts and ideas effectively.
Deep learning to predict the modulation schemes of real OFDM signals

The goal of this effort is to train Deep Learning (DL) models using synthetic Orthogonal Frequency-Division Multiplexing (OFDM) datasets to predict the modulation schemes of real OFDM signals without transfer learning. To facilitate our study, we generated a synthetic dataset, OFDM-O, that consists of 480k instances across four different modulations which include BP SK, QP SK, QAM16, and QAM64. Each instance with 16 OFDM symbols consists of 1280 IQ symbols. Since real OFDM instances have lengths of [2, 5, 44] OFDM symbols, the DL models are trained using short instances in order to overcome the instance length mismatch. Two datasets generated dynamically during training, OFDM-ro and OFDM-riq, are derived from dataset OFDM-O, by randomly choosing 5 consecutive OFDM symbols or 400 consecutive IQ symbols from each instance in OFDM-O at each epoch. 1-D Residual Neural Network (ResNet) models trained using three datasets achieve overall accuracies of 97.8%, 84.5% and 77.6% for OFDM-O, OFDM-ro and OFDM-riq, respectively. Cross validation of the three datasets shows that the ResNet model trained using OFDM-riq predicts the validation datasets of OFDM-O and OFDM-ro with high accuracy. Furthermore, a two-step validation is proposed during training of DL models where DL models are first validated with a synthetic validation dataset and then validated with real OFDM instances. Including a validation set with real signal allows us to terminate training before the DL model is over fit to the synthetic signals. The ResNet model trained using OFDM-riq correctly predicts 5 out of 7 short instances and all 5 long instances in the testing dataset of real signals. Both mis-classifications come from short instances of 2 OFDM symbols. Overall, the ResNet model trained using OFDM-riq can successfully predict the modulation schemes of real OFDM signals with high accuracy.
Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags

Sentiment analysis tools and models have been developed extensively throughout the years, for European languages. In contrast, similar tools for Indian Languages are scarce. This is because, state-of-the-art pre-processing tools like POS tagger, shallow parsers, etc., are not readily available for Indian languages. Although, such working tools for Indian languages, like Hindi and Bengali, that are spoken by the majority of the population, are available, finding the same for less spoken languages like, Tamil, Telugu, and Malayalam, is difficult. Moreover, due to the advent of social media, the multi-lingual population of India, who are comfortable with both English ad their regional language, prefer to communicate by mixing both languages. This gives rise to massive code-mixed content and automatically annotating them with their respective sentiment labels becomes a challenging task. In this work, we take up a similar challenge of developing a sentiment analysis model that can work with English-Tamil code-mixed data. The proposed work tries to solve this by using bi-directional LSTMs along with language tagging. Other traditional methods, based on classical machine learning algorithms have also been discussed in the literature, and they also act as the baseline systems to which we will compare our Neural Network based model. The performance of the developed algorithm, based on Neural Network architecture, garnered precision, recall, and F1 scores of 0.59, 0.66, and 0.58 respectively.
It’s Commonsense, isn’t it? Demystifying Human Evaluations in Commonsense-Enhanced NLG Systems

Common sense is an integral part of human cognition which allows us to make sound decisions, communicate effectively with others and interpret situations and utterances. Endowing AI systems with commonsense knowledge capabilities will help us get closer to creating systems that exhibit human intelligence. Recent efforts in Natural Language Generation (NLG) have focused on incorporating commonsense knowledge through large-scale pre-trained language models or by incorporating external knowledge bases. Such systems exhibit reasoning capabilities without common sense being explicitly encoded in the training set. These systems require careful evaluation, as they incorporate additional resources during training which adds additional sources of errors. Additionally, human evaluation of such systems can have significant variation, making it impossible to compare different systems and define baselines. This paper aims to demystify human evaluations of commonsense-enhanced NLG systems by proposing the Commonsense Evaluation Card (CEC), a set of recommendations for evaluation reporting of commonsense-enhanced NLG systems, underpinned by an extensive analysis of human evaluations reported in the recent literature.
E-VOTING APPLICATION USING BIOMETRICS & SMS OTP VERIFICATION

The project title is “E-VOTING APPLICATION USING BIOMETRICS &SMS OTP VERIFICATION”.The problem of voting is still critical in terms of safety and security. This paper deals with the design and development of a web-based voting system using finger print  and Aadhaar card in order to provide a high performance with high security to the voting system. Also, we use web technology to make the voting system more practical .The proposed Online Voting System allows the voters to scan their fingerprint ,which is then matched with an already save damage with in a data base that is retrieved from Aadhaar card database of the government. The voting system is managed in a simpler way as all the users must login by Aadhaar card number and One Time password and click on his/her favorable candidates to cast the vote. This will increase the voting percentage in India and reduces the cost of voting process. By using biometric fingerprint it provides enough security which reduces he false votes
Dependency Parsing Evaluation for Low-resource Spontaneous Speech

How well can a state-of-the-art parsing system, developed for the written domain, perform when applied to spontaneous speech data involving different interlocutors? This study addresses this question in a low-resource setting using child-parent conversations from the CHILDES databse. Specifically, we focus on dependency parsing evaluation for utterances of one specific child (18 - 27 months) and her parents. We first present a semi-automatic adaption of the dependency annotation scheme in CHILDES to that of the Universal Dependencies project, an annotation style that is more commonly applied in dependency parsing. Our evaluation demonstrates that an outof-domain biaffine parser trained only on written texts performs well with parent speech. There is, however, much room for improvement on child utterances, particularly at 18 and 21 months, due to cases of omission and repetition that are prevalent in child speech. By contrast, parsers trained or fine-tuned with in-domain spoken data on a much smaller scale can achieve comparable results for parent speech and improve the weak parsing performance for child speech at these earlier ages