Reviewerly/exHarmony huggingface dataset:
Weighted Hybrid Recommendation System for Toba Tourism Based on Google Review Data

Many tourist attraction information on the web and social platform benefit tourists but also confuse them because tourists must choose manually from huge information suitable for their preferences. Travelers need recommendation systems that can extract information from the web and provide personalized services. This paper proposed a weighted hybrid tourism recommendation system that combines collaborative and content-based filtering to overcome the drawback when using them separately. The System is built based on four main features: tourism place, reviewer, reviewer rating, and overview extracted from Google Review Data. On Content-based Filtering, the review is preprocessed, then combined with the tourist place category and description extracted from tourist websites to solve data sparsity problems (formed overview feature). TF-IDF method weights each term in the overview to determine the relative importance of the tourist review. The similarity between tourist attractions is calculated using Cosine similarity and grouped using the KNN algorithm to develop a user profile for recommendations. The Collaborative Filtering phase applies Cosine Similarity and KNN algorithm to see similar users based on the tourist place that has already been rated. The Hybrid Recommendation System combines these two by applying weight combinations to find the best model that improves recommendation performance. The proposed System is developed by Python and evaluated on 65 Toba tourism place datasets (scraping from Google Places). The evaluation results show that the proposed tourist recommendation system improves accuracy (RMSE value 1.0616) compared to traditional techniques.
Optimized meta-VQE Algorithm for Better Trend Learning of Ground State Energy

Using variational quantum eigensolver (VQE) algorithm to realize ground state energy calculation on noisy intermediate-scale quantum (NISQ) devices is widely applied in quantum physics, quantum chemistry, and other fields. It can simulate chemical molecules to solve the energy solution required by chemical reactions. This paper proposes an optimized cost function for the original meta-VQE and combines it with the quantum architecture search algorithm to break the limitation of the fixed structure. To expand the penalty degree of error and obtain more accurate results, two indicators are added to the original cost function including the slope error of discrete predicted value and accurate value as well as the error of the first value. The results show that the optimized algorithm can learn more characteristics of the ground state energy changing trend in the noise channel, and obtain a more accurate estimate of equilibrium bond length.
Students' Timely Graduation Attributes Prediction Using Feature Selection Techniques, Case Study: Informatics Engineering Bachelor Study Program

Timely graduation is a problem that is often experienced by study programs at higher education institutions, where several factors can be the cause. This study applies data mining feature selection techniques to analyze attributes from student academic data which are likely affecting students' on-time graduation. The feature selection techniques used are Correlation-based Feature Selection, Information Gain Based Feature Selection, and Learner Based Feature Selection. The accuracy of each feature selection method is measured using the Naïve Bayes classification algorithm. The results of the classification test using Naïve Bayes with the application of feature selection using Correlation-based Feature Selection and Information Gain Based Feature Selection get almost the same level of accuracy as the classification test using Naïve Bayes without the application of feature selection, but the application of feature selection using Learner Based Feature Selection in the Naive Bayes algorithm, when reducing the number of features there is a possibility of increasing accuracy by eliminating features that have little relevance, namely 70.06% from 66.53%.
Improving Prometer, A Measure For Programmer Performance

The performance of a programmer can be defined as skills in solving problems or challenges. A programmer produces source codes. There are types of source code: physical line of code (PLOC) is the source code without comments and whitespace whereas the logical line of code (LLOC) is the source code in the form of statements. In the previous work, an attempt to measure programmer performance was proposed and implemented in a tool called Prometer. The tool uses LOC as the sole metric. In this work, we added PLOC and LLOC to give a more complete measurement result. We collected historical data from GitHub repositories. We cloned and parsed the repositories to find authors and the contributed codes. We count the number of PLOC and LLOC besides LOC. Lastly, we generated the performance score based on their influence score. The programmer performance is obtained from the sum of scores on each metric.
Making DelProctor Proctoring Applications Using OpenCV

DelProctor is a test supervisor application by confirming the identity of the examinee. Confirmation of this identity is done using the OpenCV library for image processing. The image processing load is carried out on the client computer to reduce resource requirements on the server. This application was developed by following the software engineering stages to study the existing proctoring system. The client-server architecture was chosen to be able to implement this application. Webview is also used in making applications to load the Moodle LMS page where the exam is held. Limitation of exams on the Moodle LMS is done by creating a plugin to ensure exams are carried out on the DelProctor application. The results of the detection of fraudulent actions during the implementation of the exam can be reviewed by the supervisor after the exam is completed.
Rating Manipulation Prevention: Utilization of Naïve Bayes in Identifying Mobile Phones on Android-Based Review Applications

Online Customer Reviews (OCR) has become an important role in our online decision-making. We read reviews before deciding to buy things, watch films, visit places, etc. Thus, it is important to us to only read credible reviews that are not misleading. But, fake reviews are everywhere. It's on the marketplaces, social media. Fake reviews are toxic both for the customer and business owner. It's important to tackle them. A common technique used to tackle fraud activity is device fingerprinting. It's a way to uniquely identify devices in order to be able to monitor their activity. But people do actually have concerns regarding device fingerprinting. It threatens privacy. So we tried to implements a device fingerprinting method that considered does not threaten privacy. Wu et al. propose a naïve bayes based classifier to uniquely identify android phones. We try to utilize it to prevent a certain phone used to write too many reviews for a business within a week. Concluded that this algorithm only effective if the data within classes are evenly distributed and/or enough to generate a fair similarity.
Integrated Travel Route Information System with Ferry Schedule by Using Dijkstra Algorithm

The purpose of this study is to build a travel route information system with synchronization of ferry crossing schedules that will help tourists and residents who will go to Samosir to determine travel routes based on the time and ship schedules available at the port and help develop accessibility development in Samosir, especially in travel route information. The development of this system applies the agile Kanban method to the to-do process on the kanban board. We also used Dijkstra algorithm here for routing due to small number of port for efficiency. The results obtained are web-based and mobile-based travel route information systems. In this case, the web is used to manage information data that will be displayed on the mobile application, while the mobile is used by tourists to find out a list of travel routes from both land and water routes that have been synchronized with the ferry departure schedule, as well as providing information on tourist attractions and the location of supporting facilities with user's destination. By using this application user could shorten the search time up to approximately 15 minutes.
Improving Multi-face Detection Using Bimodality Approach on Viola Jones

Multi-face detection has a different level of difficulty with single face detection because it requires the ability to detect at various scales/sizes. Viola Jones can detect front-facing multi faces very well with AdaBoost's support, one of the most popular algorithms for creating a strong classifier in detection, even though there are still problems with the rate of false positives appearing. This study will discuss the optimization of Viola Jones performance by modifying the post-stage of Viola Jones classifier using the concept of a Bimodality approach that works in a split and merge. The dataset used is in the form of an image file that will be tested using an application built using the ACCORD Library which has been modified according to the proposed method. Tests will be carried out in stages on several color modes and also with and without applying the proposed method to then compare the test results. The test results show an increase in accuracy from 68% to 75% compared to the original method.
Design and Build a Visitor Data Collection System Using 2 Authentication Factors

This paper was created to develop an information system for the data collection the number of visitors which aims to ensure the number of visitors does not exceed the maximum capacity of a location. The system is divided into 2 parts, (1) METRO Web Application, and (2) a scanning System. The web application is made using PHP, while the scan system will be built using Python. Both will be connected to the Rest API so the client (Raspberry Pi) and server (computer) can communicate. As for calculating the number of visitors, 2 authentication factors are used, namely ID and face. The visit data of each user will then be presented in the form of numbers and graphs on the built METRO web application. From the experiments' results, the accuracy of matching scanned faces with face images stored in the datasheet is 83.33%.
User Experience Analysis of DiTenun Website-based Application and Mobile-based Application Using User Persona and User Experience Questionnaire (UEQ)

The DiTenun application is an intelligent tool that focuses on creating digital weaving motifs through AI (artificial intelligence)-based applications. The DiTenun application has never been subjected to user experience evaluation, hence it is not aware of any user experience challenges. This study used user personas and the User Experience Questionnaire (UEQ) to examine the application's user experience from the user's perspective and identify issues that DiTenun users encountered on both the website and mobile devices. when using the DiTenun app. With personas, it was found that user frustration when using the application was due to a lack of insight into using technology. The results of the initial UEQ evaluation of the website were Bad for attractiveness, perspicuity, and dependability, Below the Average for efficiency and novelty, and Above Average for stimulation. Excellent for all mobile aspects. There are 5 recommendations for mobile design improvements and 20 for website design improvements based on user experience. An increase in each UEQ scale on the website in all aspects at the Excellent level, as well as on Mobile Excellent with a rising value, was seen after further evaluation and improved designs.
Applying 360° Live Virtual Reality Tour in Taman Safari Indonesia Tourism Industry: Enterprise Architecture Approach

Virtual Reality is a computer-programmed environment where the user can feel and immerse themselves in their surroundings with scenes and objects that appear to be real but actually fake. Right now, Virtual Reality technology is starting to become more popular not only by the people who use it for entertainment purposes, but also popular for the business companies. During the Covid-19 Pandemic, all businesses in Tourism like Taman Safari Indonesia were having trouble getting visitors. Not only that, there are also visitors who couldn't visit the park due to their locations being far away from the park locations. In this study, the researchers will implement this Virtual Reality into the Taman Safari Bogor Tour Business in order to increase their visitors by using enterprise architecture. The researchers at this study used the Essential Enterprise Architecture framework model, because in the Essential Enterprise Architecture framework, it helps explain the Business Architecture, Information System Architecture, and the Information Technology Architecture that will be needed in the Virtual Reality implementation phase. With the implementation of the Virtual Reality Tour into the Taman Safari Indonesia Tourism Industry, it could helped solve the low visitors come due to Covid-19 Pandemic and also gives an opportunity to gather more visitors who lives far away from the park locations including visitors from other country.
Performance Improvement Of Viola-Jones Using Slicing Aided Hyper Inference (SAHI) For Multi-Face Detection

Performing multi-face detection using Viola- Jones is a challenge to provide more accurate and faster detection results. Viola-Jones is able to provide fairly good detection results as one of the most popular face detection algorithms although it provides slower face detection times when using high-resolution digital images. This study will discuss improving the performance of the Viola-Jones algorithm by adding the Slicing Aided Hyper Inference (SAHI) method at the pre-processing stage by detecting objects in certain slicing areas, accelerating and increasing accuracy for face detection. The dataset used is in the form of a WIDER digital image file. The dataset is tested using an application built with the ACCORD library by adding the proposed method. The test was carried out in stages to compare Viola-Jones with the proposed method of SAHI using several scan block-sizes in terms of accuracy and detection process time. The test results show an increase in the average value of accuracy in the proposed method with a scan block of 128 pixels from 81.97% to 86.65% and a decrease in the detection process time slightly faster with a difference of 320 milliseconds from the Viola-Jones method.
Long-tailed Multi-label Text Classification via Label Co-occurrence-Aware Knowledge Transfer

Multi-label classification is an extension of traditional multi-class classification. Unlike multi-class classification, where only one label can be allocated to an instance, multi-label classification will use multiple labels to describe an instance in more detail. However, in real-world applications, training samples typically exhibit a long-tailed class distribution, where a small portion of classes have massive sample points but the others are associated with only a few samples. To address the challenge of insufficient training data on tail label classification, we propose Label Co-Occurrence-Aware Knowledge Transfer (LCOAKT) to use label co-occurrence information to transfer knowledge learned in head classes to the equivalent semantically comparable tail classes. Extensive experiments show that significantly improves performance on rare classes while maintaining outstanding head class performance and outperforming stateof-the-art methods.
Relational Graph Attention Network Based on Bi-Directional Attention Flow for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis(ABSA) aims to predict the sentiment orientation of a specific aspect. Most syntax-based Graph Neural Networks encode the whole tree to obtain syntactic information, Empirically, only part of the parse tree is relevant to this task. Besides, modeling syntactic structure destroys the original word order structure, which leads to partial semantic absence and fails to judge sentiment polarity of the aspects corresponding to the non-opinion word. To address the problems, we propose a Relational Graph Attention Network based on Bi-Directional Attention Flow(BAF-RGAT), Firstly, we generate an aspect-oriented tree structure rooted it at the aspect, Relational Graph Attention Network is applied to encode the new tree and aggregate directed edge information. Secondly, The Bi-Directional Attention Flow network is employed to calculate the bidirectional attention weights between aspect and context words, which comprehensively learns the semantic interactions to generate greater information representations. Then, we introduce an effective consistency training strategy to regularize dropout, alleviating the inconsistency between the training and inference stages caused by the randomness of exploiting dropout. Experimental results on five datasets illustrate the proposed model has comparable effectiveness and superiority.
Application of man-machine hybrid intelligence in ground unmanned combat

Compared with combat in other fields, ground combat has the characteristics of complex environment, fierce confrontation and difficult coordination. The inherent shortcomings of artificial intelligence in understanding, interpretability and controllability restrict the development speed of unmanned ground combat. In order to give full play to the advantages of artificial intelligence, overcome deficiencies and promote the development of ground unmanned combat, this paper starts from the advantages of man-machine hybrid intelligence, Combined with OODA(Observatio, Orientation, Decision, Action) ring theory, this paper analyzes the application and prospect of man-machine hybrid intelligence in ground unmanned combat, and finally briefly analyzes the key technologies that should be grasped in the development of man-machine hybrid intelligence, hoping to provide inspiration and reference for the research of ground unmanned combat.
A novel explainable structure for text classification

With the development of deep learning, text classification has achieved very good results, but the poor interpretability of the model still limits its application in practical scenarios to a certain extent. Many explainable text classifiers extract words from a sentence and then observe their effect on increasing or decreasing classification accuracy. However, in many cases, the relationship between words in a sentence is interdependent and closely related. On account of the above, selecting words individually often has little effect on the classification results. To address the above situation, we propose a new model which treats interpretability as an intrinsic property, using constituent trees to generate continuous interpretable words instead of isolated words and it achieves good results on several datasets.
Core Family Values as Correlate of Juvenile Involvement in Internet Fraud in Delta State, Nigeria

The study examined core family values as correlate of juvenile involvement in internet fraud in Delta state, Nigeria. A comprehensive background of the study amongst others was examined that discussed the problem of the study. Several related literature was reviewed in line with the sub-themes of the study that examined the family values in Nigeria, Family Factors Influencing Juvenile Involvement in Crime, internet fraud and types of internet fraud and Causes of Internet Fraud among Juvenile. The research design adopted for the study was the correlational survey design. This was applied through the use of the questionnaire and in-depth interview; the research instrument used for data collection. The questionnaire was administered to sample of 591 respondents and 24 respondents for in-depth interview through the use of multi-stage sampling method that was employed to randomly select respondents. The descriptive statistics and Pearson correlation technique were used to analyze the data obtained from the study. Descriptive statistics was used to analyze the descriptive data comprising the bio-data in frequencies and percentages while the hypotheses were tested using Pearson Product-Moment Correlation (PPMC). The study concluded that the lower the level of core family values, the higher the juvenile involvement in internet fraud and vice versa. This was the basis upon which contribution to knowledge and recommendations were put forward for further studies.
Analysis of Facial Expressions in Class Based on Lightweight Convolutional Neural Network

Researches show that analyzing student’s facial expressions in class is contribute to improving the accuracy of learning behavior diagnosis which can effectively improve the quality of teaching. In order to improve the accuracy and real-time of facial expression recognition in class, we design a lightweight convolutional neural network(GEGNet) based on VGG16 framework with novel feature fusion and attention mechanism. At first, channel pruning and layer pruning are used to reduce redundant features, and Ghost convolution is adopted to replace the traditional convolution which can reduce the number of parameters. Secondly, efficient channel attention(ECA) is integrated into the feature extraction process which enhance feature extraction capability. Furthermore, a fusion of deep and shallow features is performed before regression module to improve the recognition accuracy of the model. The experiment accuracy of GEGNet on the homemade dataset reached 94.25% which is better than VGG16 model. In addition, the calculation volume of GEGnet is 8.5% as much as VGG16, and the parameters is 9.9% of VGG16. Our model also achieves the same performance in the comparative experiments on CK+ and FER2013, which furtherly proves the effectiveness of our model.
Internet Data Risk Model of Sports Events Based on Convolutional Neural Network

In the field of new media and big data, sports events and news media need to be integrated and promoted, and there are more and more sports information, especially sports information on the Internet. The purpose of this paper is to study the risk model of sports event Internet data based on convolutional neural network. A progress label prediction network based on ResNet-50 is designed to obtain risk label sequences of test videos. A sports event action dataset from the first perspective is established. Experiments on this dataset show that the sports event internet data risk model proposed in this paper can achieve high-precision action localization. Comparing the results with the three traditional methods, found that compared to 85% of BP, the model in this paper is more effective in predicting the risk of Internet data in sports events. There is a certain improvement in the accuracy of the model, which fully demonstrates the effectiveness and practical significance of the model.