Reviewerly/exHarmony huggingface dataset:
A study on keyword search framework based on end-to-end speech recognition in low-resource languages

The progression of voice keyword search has been intrinsically linked with the evolution of speech recognition technologies since their inception. In recent years, with the widespread application of end-to-end techniques in speech recognition, the academic community has become increasingly interested in the potential of end-to-end techniques for voice keyword search. However, the study of keyword retrieval in adhesive speech under low-resource conditions remains a relatively overlooked domain. To address the unique attributes and inherent challenges of low-resource languages, this study has designed a keyword search framework based on end-to-end speech recognition specifically for such languages. By leveraging phonetically similar speech and integrating multiple pre-trained language models, performance improvements in the target language were achieved. Moreover, the integration of the attention mechanism further optimized the feature extractor in speech recognition, ensuring the model&apos;s higher efficiency and accuracy when dealing with these complex linguistic scenarios. In conclusion, this study offers a viable solution for voice keyword search in low-resource languages.
The research and design of electrical engineering experiments auxiliary instruction system with speech recognition

In the traditional electrical engineering experiment teaching scenarios, there are shortcomings such as the lack of experiment instructors for the complicated steps of experiments, resulting lack of detailed guidance to all students. In order to addres s th e issue, this paper introduces speech recognition technology into the electrical experiment teaching scenarios and improves the recognition accuracy of speech recognition under noise environment with the improved MFCC speech recognition algorithm. By testing the accuracy of speech recognition under different noise conditions, it is shown that compared with the traditional speech recognition algorithm, the improved speech recognition algorithm achieves the best recognition rate when the signal to noise ratio is 15 dB, and even when the signal to noise ratio is 5 dB, the recognition rate is also improved by 20% on average. The research in this paper provides an application reference of the artificial intelligence application in electrical experiment assisted instruction.
MySQL data analysis and its application in E-commerce user behavior analysis

MySQL is a free open source database management system of Oracle Corporation. In enterprises, MySQL can manage all kinds of data in the operation of enterprises, instead of traditional manual operation to deal with enterprise related information, greatly improve the efficiency of enterprise information processing, greatly meet the information management needs of small, medium and large enterprises. Data analysis is a kind of behavior to turn the massive and unclear data into regular and effective data that can extract useful information. Data analysis not only provides descriptive analysis of data, but also predictive analysis and decision making solutions. Data analysis is of great value for both personal applications and commercial enterprises. In this paper, we use MySQL, Tableau, Excel software tools and AARRR model, funnel model and RFM model to analyze user activity, user retention rate, product conversion rate and user value of online shopping platform user behavior to summarize user behavior data and make rationalization suggestions for the platform. The research results of this paper have certain reference significance for enterprises to invest human and material resources reasonably and create greater sales profits in the future.
Person re-identification algorithm based on identity information matrix

In order to overcome the domain shift problem in person re-identifacation algorithms, the paper proposes a person re-identifacation algorithm oriented towards identity information matrix. Firstly, the algorithm trained two dictionaries through low-rank decomposition. One dictionary represents interference information other than person in the image, and the other dictionary represents person feature information after removing interference information. Then, the person coding coefficients obtained through person feature information training are combined with identity information to train the classifier. At the same time, the consistency matrix and uncorrelation matrix of identity information are introduced to improve the effectiveness of identity information. Finally, the algorithm is tested based on a classifier. The experiment shows that the rank1 value of the algorithm is within PRID2011, VIPeR, i_LIDS and CHUK01 datasets achieved 41.20%, 50.32%, 82.27%, and 75.41% respectively, which is superior to other newer person re-identifacation methods. The paper introduces an identity information matrix to overcome the domain shift problem caused by changes in background, lighting, and other factors, which can improve the person re-identifacation rate to a certain extent.
New media user behavior prediction and content recommendation based on deep learning

With the development of information age, mobile apps have penetrated into every aspect of modern people&apos;s life, accompanied by the explosion of user information, video information and product information. How people get the information they are interested in, and how apps and web pages can push users more interesting content from massive data based on their browsing history and other relevant user characteristics have become popular research nowadays. In recent years, user click prediction models and recommendation models designed by combining deep neural networks have been far more effective than traditional machine learning models. With the explosive growth of data sets, effective processing of data information combined with neural network models has become the key to improve the effectiveness of the models. Therefore, in this paper, we improve and optimize the user behavior click-throughrates (CTR) prediction algorithm on apps/web pages by starting from the user click-throughrates (CTR) model in sparse scenarios. Finally, it is proved experimentally that verifying the feature interaction information is the key to the user click-through prediction model, which provides new ideas and methods for the design of the user click-through prediction model.
Research on abnormal behavior recognition algorithm based on 3D-resnet deep neural network

Traditional video surveillance requires a large amount of manpower and material resources, requiring manual attendance and only providing a single monitoring function. Under long-term and high traffic data conditions, duty personnel are prone to fatigue and overlook abnormal behaviors of security risks in videos. Therefore, video systems that provide intelligent recognition of abnormal behaviors in videos are particularly important, as they can help security management departments achieve intelligent security management, the abnormal behavior recognition algorithm is the core soul of the system. This article studies the use of deep learning algorithm ``3D residual deep neural network&apos;&apos; (referred to as 3D-Resnet) to identify three types of abnormal behavior risks in video surveillance, including: fighting, robbery, and arson hazards. The designed algorithm is applied in an intelligent recognition system for video abnormal behavior, and the experimental results show that the recognition accuracy is over 90%. The designed algorithm can effectively detect new input videos in the intelligent recognition system for video abnormal behavior, and has a good recognition effect on abnormal behavior recognition. According to experimental comparison, the neural network algorithm of 3D combined residual structure is significantly superior to C3D neural network and Two-stream neural network in identifying abnormal behavior.
Digital Service of Practice licensing for Medical Personnel and Health Facilities with Dashboard System

Health workers in the rules must have a requirement in the form of a Practice License in carrying out medical and health activities at a medical practice site. Likewise, health facilities that want to open medical practices must be recorded and have a license to practice issued by the local district / city Health Office. The license to practice medical personnel and health facilities so far in the city of Bukittinggi in the service process is still done manually, making it ineffective for data management and difficult to obtain information. Therefore, a technological innovation is needed for the process of digitizing the service of issuing a web-based license to assist the process of licensing services for licenses to practice medical personnel and health facilities that are fast and effective in producing such correspondence needs. This web-based application was developed using the waterfall model because the stages of the process are passed step by step to get good results. This application on the main page is also displayed with the concept of a dashboard system, making it easier for users to view general data in the application such as data on medical personnel and health facilities that will expire their medical licenses. This application has been tested and used for services to the government and helps in the process of correspondence and evaluating the control process on the licensing of health workers.
Sistem Rekomendasi Penentuan Titik Usaha Kafe Menggunakan Data Spasial dan Algoritma Topsis

− The cafe business is a business that has very promising opportunities because this cafe business has a very wide target market, namely not only coffee lovers, but also ordinary people, especially millennials such as students and university students. Sinja City is a city with good business potential, but the problem is that there is no system that can determine suitable business locations, especially determining café business locations. This research aims to develop a recommendation system that can help prospective cafe entrepreneurs determine the optimal location to open their cafe business. This system uses a spatial data-based approach and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to produce appropriate recommendations [1]. The results of this research produce a list of recommendations for the best locations to open a cafe business. This recommendation system can help cafe entrepreneurs make more informed decisions and minimize the risks associated with choosing a business location. In addition, the use of spatial data and the TOPSIS method allows this system to produce recommendations that are more accurate and relevant to actual geographic conditions. Apart from that, the use of spatial data and the TOPSIS method allows this system to produce more accurate recommendations with a trial with 4 alternatives using the TOPSIS method. The highest ranking result is alternative 1 as a recommendation for determining the location of a cafe business location
Perancangan Aplikasi Naviku untuk Memberikan Informasi Navigasi Kepada Tunanetra Menggunakan Metode Test Driven Development

Visually impaired is a term for someone who has lost or reduced the function of vision. People with visual disabilities often experience difficulties in daily activities, for example when walking or navigating. They need the help of others to help them go to a room or move from one place to another. Currently, existing navigation aids do not fully meet the needs of the blind. They still need the help of others to get detailed information about visual objects such as the layout of rooms in buildings, traffic signs, and vehicles. Based on these problems, an Android-based navigation application needed that utilizes Text-to-Speech (TTS) technology to help blind people navigate independently by utilizing navigation information provided by voice. In order to help the development process, applications are developed by adopting Agile Software Development principles using the Test Driven Development (TDD) method. The features developed begin with a test fail phase where the developer creates unit test code before the production code is implemented, then refactor the code, and tests afterward (test passes). The success status of the features being tested gets success status with a rate of 100% for all features. Then in the TTS test, testing was carried out with 2 TTS engines, namely Google TTS and Samsung TTS, the results showed that the Word Error Rate (WRE) of Google TTS was 2,86, lower than Samsung TTS which got 4,06.
Sistem Informasi Geografis Pemetaan Kawasan Pembibitan Buah

Sriwedari Village, Salaman District, is one of the villages in the Salaman region which is known as a fruit nursery center. Favorite fruit seeds in this area, including durian, avocado and longan fruit seeds. The diversity of types of fruit seeds, often causes problems for farmers who need fruit seeds as desired, because the information needed is not yet available, to overcome this it is necessary to build a geographic information system for mapping fruit nursery areas that are used to make it easier for users to find the fruit seeds needed. The method used in developing this system is prototyping, using the Model-View-Controller (MVC) framework. The results of this study are in the form of a geographic information system used by fruit nursery entrepreneurs to inform their whereabouts on the map and the types of seeds they provide. This Geographic Information System makes it easy for users to find the desired fruit seeds by displaying several entrepreneurs who have the seeds the user wants. Based on the results of the tests that have been carried out, the user perception is Strongly Agree of 0.73% which indicates the system is easy to use and easy to understand. Black box testing also shows that this system is successful. This research is expected to help users find information about fruit seedlings in the village of Sriwedari Salaman.
Analisis Sentimen dan Sistem Pendukung Keputusan Menginap di Hotel Menggunakan Metode CRISP-DM dan SAW

Research combining sentiment analysis approaches with decision support systems in the hospitality industry is still very limited. This study aims to analyze hotel guest sentiment based on location, cleanliness, service, and value criteria using Cross Industry Standard Process for Data Mining (CRISP-DM) to identify five and ten famous words based on sentiment data and the sentiment classification model with the best performance. Furthermore, the results of identifying five and ten famous words based on sentiment data related to location, cleanliness, service, and value ratings were processed using the Simple Additive Weighting (SAW) method. The results of this study show that the performance of SVM algorithms using SMOTE operators has accuracy values (99.74%), precision values (100%), recall values (99.48%), f-measure values (99.74%), and AUC (100%). This shows that sentiment classification results using the SVM algorithm with SMOTE operators can be used as a sentiment data classification model with 30% training and 70% test data. This model is relevant to be used as a model for the classification process of 1680 review data obtained from the Tripadvisor website at Grand Zuri Malioboro Hotel Yogyakarta, Tentrem Hotel Yogyakarta, Grand Ambarukmo Hotel, El Royale Yogyakarta Malioboro, and Ceria Hotel @alun alun Yogyakarta. Specifically, aspects that concern guests relate to staff, breakfast, room, and service. In addition, the results of the implementation of the SAW method show that Grand Zuri Malioboro Hotel Yogyakarta ranks first with a total value of 0.907663917. Otherwise, Ceria Hotel @alun Yogyakarta Square ranks second with a total value of 0.897193878. Meanwhile, Hotel Tentrem Yogyakarta ranks third with a total value of 0.861265289. This shows that hotel guests who assess the criteria of location, cleanliness, service, and value by giving value weights as rated on the Tripadvisor website and provide importance weights with similar values, where location (0.20), cleanliness (0.25), service (0.30), value (0.25) then the alternative with the highest weight is Hotel Grand Zuri Malioboro Yogyakarta.
Sentimen Analisis Pembatalan Indonesia Menjadi Tuan Rumah Piala Dunia U-20 Menggunakan Metode Naïve Bayes

Football is the most popular sport in the world, including Indonesia. Several football tournaments have been postponed due to Covid-19, including the U-20 World Cup which will be held this year in Indonesia. However, the tournament was rejected by several parties who did not want one of the participating countries to take part in the tournament. As a result, the FIFA federation canceled Indonesia as the host for the 2023 U-20 World Cup. The polemic that has occurred in the last few weeks has generated many opinions and opinions from social media users, one of which is Twitter. This study focuses on classifying opinions from tweets on Twitter regarding the cancellation of Indonesia from hosting the U-20 World Cup. This sentiment classification uses the Lexicon Based method to determine positive, negative and neutral sentiments, classification uses the Multinomial Naïve Bayes method for calculating accuracy with the Confusion Matrix. Based on the system built, an accuracy of 85% is obtained with a precision of 85%, a recall of 85%, and an f1-score value of 83%.
Sistem Pendukung Keputusan Pemilihan Aplikasi Belajar Online Menggunakan Metode Additive Ratio Assessment

In the digital era, there are many educational platforms or learning applications that offer easy learning. However, with so many existing online learning applications, care is needed to choose one that suits your needs. To select an online learning application, users must look for information one by one on the profiles and features of existing online learning applications. This results in the difficulty and duration of making choices. This study aims to build a decision support system through the application of the Additive Ratio Assessment (ARAS) method for selecting online learning applications so that it can facilitate the selection and does not require a long time. The ARAS method looks for the best solution by comparing the utility function of each option with the optimum utility function value. Based on the existing case studies through the implementation of the ARAS method, the best alternative is Ruangguru (A4) with a score of 0.8819, followed by Kelaskita (A2) with a score of 0.8469, Zenius (A3) with a score of 0.8397, and Udemy (A1) with a value of 0.6282. The built-in decision support system produces valid calculations because the calculation results obtain the same value as the manual calculation results. Then, the usability test results produced an average value of 88.33%. This means that the system that is built is able to facilitate users in every functional area.
Penerapan Metode Design Thinking dalam Perancangan UI/UX Aplikasi Mobile SIPROPMAWA

&#x0D; &#x0D; &#x0D; Sipropmawa is a web-based system designed to manage, monitor, and oversee the online submission of proposals. However, the use of the Sipropmawa website is still suboptimal and lacks attention to user interface (UI) and user experience (UX) design, especially when accessed through mobile devices, resulting in user discomfort when using the application. Therefore, the motivation behind this testing is to update the UI/UX of the Sipropmawa application from a website to a mobile-based application by implementing the design thinking method. The choice was made to adopt a design thinking approach because it emphasizes creativity in creating new solutions and places a strong emphasis on understanding user needs. This study involves the collection and analysis of data, which includes five stages of the design thinking method: empathize, define, ideate, prototype, and testing. The implementation of the design thinking method in the development of the user interface (UI/UX) yields results that can achieve the goals of the method, which is to enhance user satisfaction and utility. In this regard, the support relies on the results of usability testing, which achieved a final score of 86 and obtained a B classification on the rating scale. Thus, this indicates that users highly appreciate the usability of the Sipropmawa mobile application. The UI/UX design of the Sipropmawa application is expected to serve as a model for future development of the Sipropmawa application.&#x0D; &#x0D; &#x0D;
Efficient Ensemble-based Phishing Website Classification Models using Feature Importance Attribute Selection and Hyper parameter Tuning Approaches

The internet is now a common place for different business, scientific and educational activities. However, there are bad elements in the internet space that keep using different attack techniques to perpetrate evils. Among these categories are people who use phishing techniques to launch attacks in the enterprise networks and internet space. The use of machine learning (ML) approaches for phishing attacks classification is an active research area in the field of cyber security. This is because phishing attack detection is a good example of intrusion identification tasks. These machine learning techniques can be categorized as single and ensemble learners. Ensemble learners have been identified to be more promising than the single classifiers. However, some of the ways to achieve an improved ML-based detection models are through feature selection/dimensionality reduction as well as hyper parameter tuning. This study focuses on the classification of phishing websites using ensemble learning algorithms. Random Forest (RF) and Extra Trees ensembles were used for the phishing classification. The models built from the algorithms are optimized by applying a feature importance attribute selection and hyper parameter tuning approaches. The RF-based phishing classification model achieved 99.3% accuracy, 0.996 recall, 0.983 f1-score, 0.996 precision and 1.000 as AUC score. Similarly, Extra Trees-based model attained 99.1% accuracy, 0.990 as recall, F1-score was 0.981, precision of 0.990 while AUC score is 1.000. Thus, the RF-based phishing classification model slightly achieved better classification results when compared with the Extra Trees own. The study concluded that attribute selection and hyper parameter tuning approaches employed are very promising.
Parameter-free Neural Field-based Optimal Design of Nonuniform Transmission Lines

This paper presents a novel method for resolution free, free-form shape optimization for nonuniform transmission lines using artificial neural networks. We use a low dimensional representation of the geometries by learning a neural field embedding with a contrastive loss function to group similar geometries. A second neural network predicts the scattering parameters from the encoded geometry for a given frequency point, that are used to define the optimization objective in the frequency domain. The whole pipeline is fully-differentiable enabling the use of fast gradient based optimization methods. The proposed model architecture shows promising results for the simple test case of optimizing a transmission line taper. The method is fast, stable and flexible to apply to different geometries with different constraints and requirements.