Reviewerly/exHarmony huggingface dataset:
Analysis of Employee Attendance Mobile Application Problems Based on User Reviews: A Case Study

E-government is a government administration that utilizes information and communication technology to provide services to its users. Civil Servants who are one of the E-government users are obliged to be an example in the implementation and use of its. One application of E-government for civil servants is a mobile presence application called Face Biometric Location Authentication (FABIOLA). It is used by employees of Agency for the Assessment and Application of Technology. The application had been downloaded more than 5000 times with a 4-star rating on Google Playstore. Although it has received a fairly good rating, this cannot be used as a reference because there are still many negative reviews. To identify the problem, a text mining methodology and association rules are used. From the results of text mining analysis, several aspects of problems related to applications are aplikasi, foto, gagal, android, and absen. From these 5 aspects, association rules were carried out to facilitate fishbone analysis. From the results of the fishbone analysis, several solutions to the problem are proposed: application reengineering, the use of a stable internet connection, following the appropriate usage steps, and fulfilling the application preinstallation requirements.
Performance Analysis of a Parallel Genetic Algorithm: A Case Study of the Traveling Salesman Problem

Genetic Algorithm (GA) is one of the most popular optimization techniques. Inspired by the theory of evolution and natural selection, it is also famous for its simplicity and versatility. Hence, it has been applied in diverse fields and domains. However, since it involves iterative and evolutionary processes, it takes a long time to obtain optimal solutions. To improve its performance, in this research work, we had parallelized GA processes to enable searching through the solution space with concurrent efforts. We had experimented with both CPU and GPU architectures. Speedups of GA solutions on CPU architecture range from 7.2 to 22.2, depending on the number of processing cores in the CPU. By contrast, speed-ups of GA solutions on GPU architecture can reach up to 172.4.
Network Traffic Classification Method Based on Subspace Triple Attention Mechanism

Network traffic classification plays an important role in network management. In order to improve classification accuracy of encrypted traffic, a method of encrypted network traffic classification based on subspace triple attention mechanism module is proposed. In this method, the network traffic data feature map is divided into several subspaces along the channel dimension. In each subspace, the one-dimensional feature coding calculation is carried out for the three channel branches respectively. ISCX public datasets, which including general and protocol encrypted network traffic data, is used for classification experiments. The results show that the proposed method can achieve better classification accuracy than other current methods on encrypted traffic datasets.
Rock Image Contrast Enhancement Method Based on Improved De-sharpening Mask

In engineering applications, traditional methods are usually used to acquire microscopic images, and due to objective factors such as uneven acquisition equipment and uneven illumination, there may be problems such as unclear acquisition images and insufficient local exposure. Traditional de-sharpening mask image enhancement algorithms are suitable for enhancing the edges and details of microscopic images, but are extremely sensitive to noise and do not enhance contrast and detail at the same time. In this paper, an improved sharpening masking algorithm is proposed, which sharpens the edges of rock images in the brightness channel of HSV color space, uses the difference between the non-local mean filter image and the original image to achieve high-frequency components, adaptively enhances the high-frequency components, improves the problem of image light unevenness through the contrast enhancement algorithm, and superimposes it with high-frequency components to effectively enhance the image. Experimental results show that the image processed by the algorithm has outstanding details and clear textures, which better suppresses the amplification of noise.
An adaptive black box attack algorithm based on improved differential evolution

As an important part of artificial intelligence technology, deep learning is widely used in various fields of contemporary society. The security of deep learning directly affects the effectiveness of its application in different fields. Effective attack algorithms can evaluate the security of deep learning models, and black box attacks are one of the important methods for testing the security of deep learning algorithms. An adaptive black box attack algorithm based on improved differential evolution is proposed to solve the problems of many queries, difficult selection of attack points that may cause higher attack costs in applications. The algorithm sets the variation factor as a linear decreasing function, uses the fitness function to adaptively control the change of the cross probability factor to improve the global search ability and accelerate the convergence rate, proposes a new variation strategy to enhance the ability of global search and local exploitation and the accuracy of searching attack points, and optimizes the loss function and the calculation method of gradient for defining decisions in deep learning models to improve the effectiveness and efficiency of black box attacks. The results of the comparison experiments show that the attack success rate is effectively improved and the average time and the average number of queries are reduced with the same attack success rate.
Research on raw speech isolated word recognition based on Sincnet-CNN model

In order to effectively speed up the model training time, reduce the model training parameters and improve the accuracy of raw speech isolated word recognition. An interpretable convolutional filter structure (sincnet) combined with convolutional neural network (CNN) is proposed for the task of raw speech isolated word recognition. On the premise of ensuring the speech recognition rate, the model structure becomes lightweight and the computational complexity is reduced. The experimental results show that compared with the traditional neural network model, the proposed model can effectively improve the performance of raw speech isolated word recognition.
Density Peak Clustering Algorithm Based on High Density Connection with Entropy Optimization

In this paper, an MM-HDC (Max Mean and High Density Connection) method was proposed to find the initial clustering center based on the maximum mean distance and fuse each cluster based on the high density Connection. Firstly, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Delta\rho=70\%$</tex> was set to select the initial clustering centers and the mean distance was introduced. The selection of cluster centers will be stopped until the distance between the desired new mean center and some previously selected cluster centers is less than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{\ast}d_{c}$</tex> , and the selection of initial cluster centers is completed. Then use the allocation policy of k-means to clustering all the data points by the distance between each initial clustering center and data points, constantly updated after cluster center, center for migration, until the old and the new cluster centers position changed little (the distance is very small), then stop update clustering center, and the last of the clustering results as the final clustering results. Finally, iterative fusion method is used for center fusion to get better clustering results. Experimental results of classical data sets show that the MM-HDC method is superior to the DPC algorithm and k-means algorithm, and the improved density peak clustering algorithm has higher accuracy. Moreover, The MM-HDC algorithm can obtain satisfactory results on the data set with special shape or uneven distribution.
An Application Traffic Identification Method Based on Deep ResNet

Application traffic identification is of great significance to improve network service quality and cyberspace security. Although deep learning has made great progress in the field of traffic identification, many existing methods rely on manually designed features for identification, or rely on inflexible neural networks for limited classification, which makes the implementation of large-scale traffic identification challenging. To solve this problem, this paper proposes a method based on deep ResNet and L2-triplet loss, which learns features from raw traffic data by taking traffic data as images, and outputs traffic features as feature embeddings. Using these feature embeddings, known and unknown application traffic identification can be further realized. This paper also uses feature constraints to improve the adaptability of neural network model in traffic identification task. On the USTC-TFC2016 dataset, the proposed method achieves a good identification performance.
Research on a text data preprocessing method suitable for clustering algorithm

In the clustering process, the eigenvalues in the data set have mixed type attributes such as numerical and text, and the measurement methods are inconsistent. In this paper, the distance between samples is easily affected by the eigenvalues of a certain dimension. This includes affecting clustering performance and the inability of continuous algorithms to deal with discrete data. These two problems focus on two points in the algorithm of this paper. First, each characteristic attribute of the dataset is analyzed. The type and number of ranges for each attribute is counted. Attributes that are not affected by the clustering algorithm are deleted. Secondly, the text feature attributes with more than 2 range are extended to multiple new feature attributes. Each attribute has only two value fields, replaced by 0 or 1 respectively. This approach makes all textual and numeric attributes use a uniform metric. This method was used to preprocess the mushroom dataset. This keeps the values in the dataset in the same range. Clustering algorithm is used to classify it. In the experiment, the classification accuracy of k-means++ algorithm is improved from 70.9% to 89.2% compared with LabelEncoder method. It also applies to more algorithms. This proves that our method works.
Electronic System Comprehensive Design Experiment-Intelligent Trash Can Design

Aiming at the problem of cultivating applied talents to serve the society, the electronic system comprehensive design experiment takes MCU as the core, and designs an intelligent trash can. The trash can can automatically control the opening and closing of the trash can lid according to the infrared sensor to detect whether someone puts garbage; At the same time, it has the function of manual one-key control of opening and closing the trash can lid and the overflow sound and light alarm function; The trash can has GPS positioning, GPRS wireless communication function, the garbage processing center can query the status and geographic location information of the trash can in real time through the Internet of Things platform. This pro-ject is closely related to the practical application of life and inte-grates multiple knowledge points. The student-centered experi-mental teaching mode improves students' autonomy in learning, stimulates students' creativity, and improves students' ability to solve practical problems. The teaching effect is good.
Study on Artifact Classification Identification Based on Deep Learning

Deep learning is a hot technology developed in the field of artificial intelligence in recent years. It extracts complex content, simulates the hierarchical structure of the human brain, and constantly adjusts the parameters to find the optimal prediction results. This paper introduces the implementation principle and process of deep learning, uses the deep learning method to study the artifact classification and identification, and completes the artifact classification and identification experiment through the training model of various artifacts. The experimental results show that the sufficient training of the samples can have a high identification accuracy, but the identification accuracy needs to be further strengthened in practical application environments.
Kampusku: Information Portal Mobile Application Design of Private Universities in Indonesia

Education is learning that humans take throughout their lives to gain experience and knowledge. College is the highest level that can be reached in attaining education and degrees. After students graduate from Senior High School, students can continue their studies towards higher education. High school students have a high interest in private universities. However, private universities are likely unable to facilitate information to students practically. The purpose of making this application is to facilitate students who wished to continue their studies at higher education institutions with information from various private universities in Indonesia. The stages of this research method used the Design Thinking process. Therefore, to help students who have difficulty choosing private universities, the author wishes to create information portal application of private universities in Indonesia.
Infiltration Wells Program in Jakarta: Twitter Sentiment Analysis

The infiltration wells program in Jakarta,Indonesia, is one of the issues that has become a hot topic on Twitter after a political figure’s car fell into one of the infiltration wells in South Jakarta. As a result, a growing number of people have spoken out about the program’s benefits and drawbacks, which later cause pros and cons. This study aims to determine public sentiment on Twitter about the Jakarta infiltration wells program and to determine the accuracy and performance of the Naive Bayes, Support Vector Machine, and K-Nearest Neighbor as the classification algorithms used in this research. With SMOTE, balanced data of 591 positive and 591 negative tweets was obtained, with testing data of 138 tweets. The result shows the highest accuracy of 93.32 percent, as well as high performance was reached with SVM, followed by Naive Bayes in second place, and KNN in third place. The result of this study also finds that most of the tweets have negative sentiments, mostly about the program inability to handle floods, the formation of puddles and damages on roads, high allocation program budgets, and protests of residents who had not been compensated for their assistance in building the infiltration wells.
Liquid Drip Detection in Power Plant based on Machine Vision

In the power industry, equipment that has been running for a long time will have poor airtight performance over time, and the liquid in the equipment or pipeline will leak out, causing drip leakage.With the rapid development of computer vision technology, especially the development of convolutional neural networks and target detection models, target detection algorithms based on deep learning are increasingly being used in the power industry. In this paper, the YOLOV3 algorithm based on the PyTorch framework is used to detect the falling process of the dripping liquid. However, because the real power plant environment cannot be inspected on the spot, it can only simulate the falling process of the dripping liquid in the power plant by itself. The leakage of the water from the household faucet is used to simulate the leakage of the power plant equipment, and the pictures of the water droplets and the video of the falling process of the water droplets are taken with the mobile phone, which are used for the production, training and target detection of the subsequent data sets. By taking pictures of water droplets and performing data augmentation, a data set of droplet pictures is constructed, and the labelImg software is used to label the droplets in the pictures. The data set has only one droplet classification. Then, the data set of the augmented dripping pictures is made into the PASCL VOC data set, and the YOLOv3 model is used for training. The MAP of the training result is 0.996. When using the model obtained after training to detect the dripping video, it can be accurately identified. And locate the dripping target in the video frame.
Potential Gradient Measurement System in Ground Grid Field Area

Substation grounding network is an important measure to maintain the safe and reliable operation of power system and ensure the safety of operators. The condition of grounding device is directly related to the safe operation of power system. It is very important to test the characteristic parameters of grounding device scientifically and reasonably and evaluate its condition accurately. At present, the testing work of grounding device in domestic power system is weak, some key technical concepts are vague, the means are backward, and the testing instrument is not complete.
Design and Deployment of Border Security in Multimedia Network

This paper introduces the design of boundary security in Multimedia Network in detail. It needs to complete the deployment of boundary division, boundary detection and boundary defense. In terms of boundary division, the firewall is used to create the security domain to avoid security accidents spreading to other areas; In the aspect of boundary detection, real-time boundary intrusion detection is realized to make up for the deficiency of firewall; In terms of border defense, as a part of defense and monitoring, it is used to strengthen users’ network security management ability and enable users to timely understand the security status of the network. At last, this paper uses the method of real-time detection according to the time period to test the function of the boundary security system, and verifies the performance of the boundary security system through the obtained data.
Performance Analysis of the Triple Exponential Smoothing Method During the Covid19 Pandemic on Tourist Visit Data

Data forecasting methods are essential in the business world to determine the company’s future steps. However, the COVID-19 pandemic has hit the tourism economy hard, resulting in a slump in income. In this study, trials were conducted to analyze the reliability of forecasting methods on data affected by the COVID-19 pandemic. The method used is the Triple Exponential Smoothing method involving two models, namely Additive and Multiplicative. In this paper, the test is carried out using actual data derived from data from a service company engaged in tourist crossing transportation. Each method’s alpha, beta, and gamma values are determined based on the parameters that produce the smallest error value. The experiment results show the predictability of the Triple Exponential Smoothing method by measuring the prediction error value based on the Mean Absolute Percentage Error (MAPE) value, which was 7.56% in the Additive model and 10.32% in the Multiplicative model before the pandemic happened. However, both methods’ prediction measurements during a pandemic produce poor forecasts with an error percentage above 40%. Meanwhile, during the decline in pandemic cases, the value of the Triple Exponential Smoothing Multiplicative method was closer to the actual data with a prediction error value of 33.02%. Therefore, the Triple Exponential Smoothing Multiplicative method is more resistant and suitable for implementing into a forecasting system with actual data that influences pandemic events.
Decentralized Identity and Password Authentication System based on Block Chain

The most widely used user authentication method on the Internet is abstract password authentication, That is to say, the user and his name - password to each server established, and then use it to authenticate. Although they are widely used because of their simple operation and high experience, the security of authentication provided by them is not high, and the server operator is positioned as a trusted party who can fully control the user’s identity. To solve this problem, many identity recognition systems adopt the concepts of public key encryption and decentralization,but the requirement that users generate and manage public and private key pairs, and that users often do not have such expertise, has become a key factor in the failure of PKI for many end users. To sum up, this paper proposes a decentralized identity and password authentication system (DIA) based on block chain as an identity and authentication framework. Users can register their own user name password pairs and use them as general credentials. The system provides a global name space, meaningful user names, and the ability to resist user name conflict attacks. User’s identity to any server can be applied to authenticate a user, to the server without having to disclose any information relating to the password,, so it is impossible to carry out offline dictionary attack on the password.
A recurrent neural network model for sign language classification

Sign language classification is the most critical task of sign language information recognition. This task involves the extraction of information features of sign language materials. It mainly focuses on describing the underlying features of sign language and improving the accuracy of sign language classification. The sign language classification model based on recurrent neural network has the problems of complex model input data processing, complex network model and slow training speed. This paper presents a classification model based on recurrent neural network bidirectional long-term and short-term memory network and puts forward an optimized scheme from network data processing, network structure and network training method. Experiments show the effectiveness of the proposed model.
Research on evaluation method of image style transfer algorithm

The task of style transfer is to transfer the style of the target image to the required image. In recent years, there have been increasing studies on image style transfer. and now it is often used in animation production, software interface beautification, image expansion and the creation of a variety of styles of decorative patterns. As these algorithms are proposed, how to evaluate algorithms will become an important problem, only with evaluation can comparative analysis be carried out and algorithms can continue to develop and progress. The importance of evaluation can not be underestimated. This paper firstly systematically combs and introduces the evaluation method of algorithm from subjective and objective aspects. Subjective methods are divided into two parts: questionnaire and User Study, which mainly rely on people's subjective perception and are easily influenced by subjects. The objective method evaluates the algorithm from a more accurate perspective, mainly including relying on statistics and evaluation indicators. Secondly, this paper introduces the experiments using several evaluation methods and evaluates their effects. Finally, it points out the problems existing in the current evaluation methods to provide the direction for the follow-up research.