Reviewerly/exHarmony huggingface dataset:
Strategi Affiliate Marketing pada Sistem Informasi Penerimaan Calon Mahasiswa Baru

The high level of educational development in Indonesia, especially in East Nusa Tenggara, spurs every university to continue to innovate to provide the best information technology-based services quickly and easily. Universities that have fast and easy services are one of the attractions for prospective new students to join the college. One of the strategies used to increase the number of new prospective student admissions is to use digital media that handles the registration process. In addition, implementing an affiliate marketing strategy to expand the network and promotional reach. Each affiliate is given a commission in the form of cashback and a regular honorarium every semester for affiliate students who successfully register and actively study at STIKOM Uyelindo Kupang. This will certainly attract many actors to join as affiliates. The performance of affiliate marketing can be seen from the increase in the number of new students by 21% from the previous year which has not implemented an affiliate marketing strategy.
Implementasi Metode WebQual 4.0 untuk Analisis Kualitas Website Universitas Boyolali

&#x0D; &#x0D; &#x0D; &#x0D; As a higher education institution, Universitas Boyolali (UBY) has an official website with the address https://uby.ac.id. The main website conveys information to the academic community and the public. Website quality measurement is used as a basis for making improvements, so that website performance and quality are better. One of the methods for website measurement is using the WebQual 4.0 method. WebQual 4.0 Method measures a website's quality and focuses on three main dimensions: usability, information quality, and interaction service quality.&#x0D; This study aims to use the Webqual 4.0 method to measure quality with the website research object owned by Boyolali University, namely https://uby.ac.id. The study used 174 respondents using the non-probability incidental sample method for students, teaching staff, and lecturers at Boyolali University who had visited or accessed the website. The analysis results show that the Usability Dimension gets a percentage value of 78.62%, the Information Quality dimension is 78.22%, and service interaction is 77.75%. These results indicate that a percentage of respondents stated that they were satisfied with the quality of the UBY website.&#x0D; &#x0D; &#x0D; &#x0D;
Psychological and didactic fundamentals of modern educational technologies of visualization

The relevance of the studied issues is that the introduction of modern technologies of visualization of educational information in the educational environment of higher school is a tool for ensuring the quality of professional training. Accordingly, there is a need to determine the psychological and didactic fundamentals of this process in order to use them as efficiently as possible. The purpose of the given study is to determine the psychological and didactic fundamentals of modern educational technologies of visualization and to formulate practical recommendations for their implementation in the educational process of higher school. Based on the outlined features of the cognitive activity of modern generation (the ability to quickly switch between different semantic fragments, high speed of information processing, preference for perceiving information in a figurative form, inability to perceive linear information, etc.), the most significant cognitive needs of modern students are characterized. Prospective technologies for visualizing educational information (mind maps, scribing, video scribing, etc.) and methodical features of their use in the process of professional training are characterized. The tasks of modern visualization tools are defined (tool for improving the educational process, concentrated presentation of educational material, provision of the correspondence of educational material corresponds to the psychophysiological characteristics of students and rational organization of educational and cognitive activities of students). The author's experience in the use of modern visualization technologies, which are based on the application of modern software products, in the process of professional training of future specialists has been presented.
Methods of forming a responsible attitude to the system of professional values in future teachers

The article describes the method of forming a responsible attitude to the system of professional values in future teachers. The essence of a responsible attitude to professional pedagogical activity is manifested in: the future teacher's awareness of the value of his profession, his own place in the professional environment, the formation of motives for professional behavior; the teacher has integrated subject and psychological-pedagogical knowledge, formed professional abilities and skills, experience in the practical implementation of professional activities; skillful organization by the teacher of his activity and effective management of it; the teacher's abilities to analyze pedagogical situations and pedagogical reflection; feelings of satisfaction from the results of pedagogical activity, striving for creative implementation of the teacher's duties and overcoming difficulties on the way to professional development. In the process of teaching the educational disciplines «Pedagogy», «Methodology of educational work», «History of pedagogy», «Fundamentals of pedagogical mastery», the methodology of forming a responsible attitude to the system of pedagogical values in future teachers was introduced. The requirements for the teacher's professional competencies were analyzed on the basis of modern documents on education. We determined the content of the educational material, identified semantic connections between its elements. The development of the procedural component consisted in the study of professional experience, the performance of cognitive and practical tasks. In the process of organizing educational activities, both traditional (lectures, practical, laboratory classes) and interactive (group work, business games, problem-based learning, project method) forms of work were introduced.
Artificial Intelligent Player for Bullet Hell Games Based on Deep Q-Networks

This study focuses on the application of Deep Q-Networks (DQN) to train AI agents to play bullet hell games. We built a training environment and utilized ray casting to collect input data for the network. Two similar network model architectures were evaluated and compared to maximize the learning efficiency of our AI agent. The trained AI demonstrates commendable performance and the ability to learn and adapt strategies into gameplay. However, while the AI agent displayed potential in mastering gameplay dynamics, there remain several challenges to integrating the agent to complete commercial bullet hell games. These challenges may provide directions for future research.
Research on Negative Transfer Problem Caused by Cascading Auxiliary Tasks in Ad Ranking

In current advertising and recommendation industry applications, precise ranking of traffic and content is of paramount importance. With businesses concentrating on a broader array of metrics, the existing ranking model has transitioned from a single-task paradigm to a multi-task one. In order to model users' sequential actions, incorporating cascading tasks to aid in understanding the CVR (Conversion Rate) task has become the most prevalent and effective approach. Nevertheless, adopting this strategy might detrimentally influence the CVR task due to the integration of CTR (Click-Through Rate) task data. This paper delves into the reasons behind the negative transfer issue stemming from cascading auxiliary tasks and emphasizes that this problem arises from the inclusion of CTR task information. Building on this, we introduce an adaptive gradient separation cascading task learning algorithm. Through real-time computation of the gradient similarity and fusion parameters of associated tasks, it autonomously segregates and amalgamates the gradients of the CVR task and the cascading task. Experiments utilizing the traffic log dataset from the Didi advertising platform demonstrate that our proposed method markedly outperforms its counterparts.
Survey on Chinese Word Segmentation

With the rapid development of information technique, the amount of text is expanding, which puts forward higher requirements for the accuracy and the speed of Chinese word segmentation. Word segmentation technology is widely used in search engines, AI customer service, man-machine dialogue and other scenarios. This paper summarizes the existing research and challenges. Finally, we looks forward to the possible research directions in the future, and provides a large number of references.
Deep Learning-Based Modulation Recognition for Imbalanced Classification

Automatic modulation recognition (AMR) is a crucial task in intelligent receiver systems, with significant applications in both civilian and military scenarios. Deep learning-based modulation recognition algorithms have achieved high recognition accuracy and good robustness. However, the performance of DL-AMR heavily relies on a large amount of training data. When the training dataset has a limited number of samples or an imbalanced distribution across different classes, the accuracy of automatic modulation recognition for minority class samples may significantly decrease. Therefore, a CNN model with a focal loss as the output layer's loss function is proposed to address the issue of decreased recognition accuracy for minority classes in the deep learning model when faced with imbalanced training data. Simulation results demonstrate that this model improves the recognition accuracy of classes with a limited number of samples.
An approach for detecting phishing websites by using search engine

Among the massive information, there are many illegal elements who use the network to cheat users' trust and profit from it, and phishing websites are one of them. The address, content and layout of phishing websites are very similar to those of real websites, so netizens who have no security awareness are easily deceived and have serious consequences. With the increasing harm of phishing websites, how to judge whether a website is a phishing website has become a research hotspot. In order to detect whether a website is a phishing website, it is necessary to find out its real website, so we propose a new method. This method carries out TF-IDF analysis on the phishing to be detected, and extracts some words to represent the website. After the extracted phrases are input into different search engines, the output web pages are integrated and sorted, and the top ranked web pages are extracted to find out the fake objects of phishing websites to be detected. Experiments show that this method can effectively identify phishing websites.
Research on Animation Sentiment Analysis based on Deep Neural Network

Recently, deep neural networks have achieved promising performances and become most widely used learning model for prediction applications. However, there are rarely investigations towards animation sentiment analysis through learning models. In this work, we make the first attempt on sentiment analysis of animations through a deep neural network. In contrast with traditional neural networks against real-world images or time-series videos, our model needs to address two primary challenges. Initially, our neural network can dispose the animation images rather than real-world objectives through the iteration of learning process. Subsequently, our model can analyze the sentiment of animation objectives, which can assist animators to modify and enhance the sentiment of videos. From our simulation analysis and comparison results, we can conclude our model can achieve the sentiment analysis of animations with reasonable accuracy and training costs.
Wordle the Puzzle: A Prediction and Classification Model based on Com-prehensive RNN

This article focuses on the Wordle game, a word puzzle game that has become extremely popular on social media. To further enhance the gaming experience, three models have been proposed to solve the problem: a model for predicting the number of players, a model for predicting the distribution of number of attempts, and a model for classifying word difficulty. For the problem of predicting the number of players, the ARIMIA model was proposed. To address the problem of predicting the distribution of number of attempts, the Gated Recurrent Neural Network model was proposed. In order to effectively improve the user experience, we have further mined the data. In regards to the problem of classifying word difficulty, the AHP-Kmeans clustering model was proposed. Finally, this paper contributes to the difficulty classification of words based on different word attributes.
Research on NFC Anti-Counterfeit Traceability Based on Signcryption AlgorithmResearch on NFC Anti-Counterfeit Traceability

As COVID-19 slowly disappears from public life, people's purchasing power and demand for luxury goods will reach new heights. The continuous emergence of counterfeit luxury goods directly affects brand sales and brand reputation, which has become a global concern. Existing anti-counterfeiting solutions for luxury goods still face problems such as high anti-counterfeiting costs, high operational difficulty and low counterfeiting difficulty. To address these issues, this article proposes a lightweight anti-counterfeiting solution, an anti-counterfeiting traceability method based on NFC technology and signcryption algorithm. It makes the product inspection process safer and more efficient by adopting a signcryption algorithm for double verification, which increases the difficulty and cost of counterfeiting.
Application research of association rule based on Apriori algorithm

Apriori algorithm and fp-growth are both classical algorithms of association rules. This paper collects the employment situation of graduates of a college of Electrical engineering in recent three years, and analyzes the data based on the association rules of the Apriori algorithm. Apriori filters a lot of irrelevant data sets, but this algorithm also has a disadvantage, is that every time the candidate elimination data algorithm is completed, it needs to scan all the data records, which causes the algorithm may consume a little more time. This paper provides some suggestions and references for the application of Apriori algorithm.
Efficient text detection via Re-parameterization and Tversky coefficientEfficient and flexible text detection

Currently, scene text detection models commonly suffer from a high precision but low recall issue. In other words, the false negative rate is much higher than the false positive rate, which limits the overall performance improvement of the models. This situation may lead to a series of issues such as missing important information. We propose an efficient and flexible text detection model to address the diversity of challenges in various usage scenarios. First of all, in order to meet the ever-increasing real-time needs while keeping its performance as unaffected as possible, we introduced the idea of structural re-parameterization, using parameter conversion to decouple training and inference, and realized multi-branch training and single-branch inference. In order to fuse multi-scale features efficiently and losslessly, we design a lightweight sub-pixel convolutional upsampling unit (LSUN) and an attention feature fusion module (AFM). LSUN will gradually adjust according to the feedback of the loss, and can learn the upsampling mode and feature expression suitable for specific tasks to produce more accurate and detailed upsampling results. AFM enables the network to selectively emphasize important features and suppress unimportant ones. Focal-TverskyLoss controls the punishment of the model for false positive samples(FPs) and false negative samples(FNs) through hyperparameters, and adjusts the penalty to drive the self-improvement of the model, balancing precision and recall.
Research on Pedestrian Crossing Intention Prediction Based on Deep Learning

Using computer vision techniques to detect, identify and track pedestrians in video sequences has important significance. For the pedestrian detection task, YOLOX is used as the pedestrian detection algorithm, and Ghost convolution module, asymmetric convolution, CBAM module, and DIOU loss function are introduced to improve YOLOX; the improved YOLOX is used as the detector of ByteTrack, and after classifying the detecting frames with high and low confidence, the Hungarian algorithm is used to match the Kalman filter predicted trajectory and the detection frame are matched using the Hungarian algorithm to realize the function of pedestrian tracking. The improved YOLOX model improves the precision by 0.82%, the detection speed by 17%, the recall by 0.9%, the model F1 by 1%, and the AP value by 0.27%; the improved ByteTrack improves by 5% compared to the original version.
Students' perception of the use of infographics at Canva application in accomplishing TOEFL class assignment

Canva application is an application that provides some features that allow the user to make some interesting designs. This application was used in a TOEFL class assignment in sixth semester of the English Study Program IAIN Madura in the year 2021-2022; the lecturer asked her students to make an infographic dealing with the material that was discussed in the class by using Canva application. How the students perceive in accomplishing the TOEFL class assignment by using an infographic at Canva application and how they used it are the focuses discussed in this study. The mixed method design was used as a data collection procedure by obtaining the data through interview, observation, questionnaire, and documentation. The sixth-semester students of English Education Department IAIN Madura in the year 2021-2022 who join the TOEFL class become the primary source, while their assignment result, especially their infographic, is secondary source of this study in knowing how the students use Canva application. The result shows that most sixth-semester students have a positive form of perception. They feel enjoy and comfortable and find it easier to do the assignment since it provides interesting features and design, easy to apply and modify. The students acknowledged that the use of the Canva application in making infographics worthy enough to resume any materials in a picturesque, readable, and memorable manner. Most students also use Canva Application with infographic design features with free templates.
Multi-Path Network Architecture for Low-Light Image Enhancement

Challenging non-uniform illumination conditions often lead to various issues in images captured by sensors, including irregular brightness, diminished contrast, and unwanted noise. This research introduces a novel approach to address these problems by employing a multi-path interaction network for enhancing the R, G, and B channels. The enhanced channels are subsequently fused to generate a color image, followed by a meticulous fine-tuning process. Within the multi-path interaction network, feature maps from encoding and decoding sub-networks facilitate the exchange of information between different paths, while preserving a high-resolution path to augment feature representation. Furthermore, a careful correction process is applied to the multi-path interaction network's output to prevent any artifacts resulting from the separation of R, G, and B channels. Empirical findings strongly support the efficacy of the proposed technique in significantly improving the visual quality of low-light images.
The Density-graded Clustering Algorithm

In recent years, the widely used Density Peak Clustering (DPC) algorithm has given a clustering strategy in which the center of clusters is usually surrounded by data objects with locally lower densities, but it also results in some otherwise low-density clusters not being clearly distinguished due to such a definition. In this paper, we propose a density hierarchical clustering strategy for the density characteristics of the dataset itself, and divide the dataset according to the density level to form a subset, and different subsets respond to different strategies, which can effectively solve the above problems. Finally, the method is applied to six datasets, all of which have achieved good results.
Multi-step photovoltaic power prediction based on cloudy day error compensation

In order to improve the accuracy of multi-step prediction of photovoltaic power under cloudy conditions, this paper proposed a prediction model that combines the K-medoids-Gaussian Mixture Model (KDGMM), Complementary Ensemble Empirical Mode Decompostion (CEEMD) and Stochastic Configuration Networks (SCN) prediction models, and On this basis, the improved northern goshawk optimization (INGO) algorithm is introduced to optimize the error compensation model of the Least Squares Support Vector Marine(LSSVM). Firstly, the meteorological data was divided into sunny, cloudy and rainy days by KDGMM clustering. For the problem of high fluctuation of photovoltaic power data on cloudy days, CEEMD decomposition was used to reduce the non-stationarity of photovoltaic power on cloudy days. The SCN prediction model is constructed to predict the photovoltaic power, and its initial prediction value and corresponding error sequence are obtained. Then the error sequence is put into the INGO-LSSVM model to get the predicted value of the error, and finally it is superimposed with the initial predicted value to get the final predicted value. Through experimental analysis, the proposed method has higher prediction accuracy than other prediction methods.
Design and implementation of UAV special flight task algorithm

To realize the UAV's automatic following of pedestrians, it is necessary to design and implement the UAV flight task algorithm. Firstly, the mainstream UAV hardware and software technologies are investigated, and F450 UAV is selected as the hardware platform and PX4 as the flight control software platform. Secondly, the PX4 flight control development environment is configured and implemented, and the simulation environment based on QGC+Gazebo is configured. Thirdly, on the basis of learning the UAV attitude control algorithm, the algorithm of horizontal rolling, vertical rolling and other special flight tasks are designed. Finally, the burning and testing of the special flight algorithms are realized in the simulation environment and F450 UAV environment. The experimental results show that the designed flight control algorithm is effective and can realize special flight tasks such as horizontal rolling and vertical rolling.
VPModel Pro: New Home Entertainment Experiences for Next-Generation Spatial Computing Platforms

This paper is oriented to the needs of XR home entertainment applications and demonstrates diverse application scenarios based on the VPModel Pro platform. Learning and exploring new fields such as product design and musical instruments often require high costs and thresholds, on the other hand, low-fidelity and low-interactivity product prototyping tools can dampen people's enthusiasm for exploration. The VPModel Pro platform proposed in this paper enables high-fidelity rendering of simple physical objects and integration with real physical prototypes in XR scenarios. The development of the platform involves technical difficulties such as object detection and real-time matching of virtual information to the target model, as well as gesture detection and action recognition during user operation. To realize the development of VPModel Pro, the platform uses physical hardware (HoloLens) and machine learning algorithms as development tools. We also invited 6 participants to experience the practical application of the VPModel Pro platform and explore new directions for the application of the platform. This paper describes the development process of VPModel Pro and the diverse applications based on the VPModel Pro platform, which provides a direction to explore XR technology towards the mass daily consumer market.