Reviewerly/exHarmony huggingface dataset:
The Interface Development of Hospital Information System Management Using Goal-Directed Design Method

Prof. Soeparwi Animal Hospital is one of the animal hospitals located in Yogyakarta. In its current operations, the administrative flow is still running manually using paper records. This study developed the interface of the administration system subsystem of the hospital. The development of this information system interface emphasized the user experience aspect using the goal-directed design method. The evaluation of user satisfaction was done using the questionnaire of user interface satisfaction (QUIS) method and presentation of the final results. This study developed the administration subsystem interface of Prof. Soeparwi Animal Hospital, which could meet user needs, business processes, and integration between the hospital's subsystem. The interface evaluation resulted in a user satisfaction score of 8.1125 from the acceptance level of 5, the more efficient service flow, and it received a positive response for further development from the hospital management. Thus, it can be concluded that the interface succeeded in achieving the design goals through the goal-directed design method.
An Optimization Algorithm Based on Artificial Potential Field and Particle Swarm Optimization to Avoid Radiation Exposure Under Radioactive Environment

This study introduces a radiation avoidance algorithm to help radiological occupational personnel (ROP) avoid high radiation exposure in a radioactive environment. The premise of this study is that ROP can be designated as a movable point in a two-dimensional radioactive scene with known radioactive sources. A trajectory of ROP is generated by the radiation avoidance algorithm based on an artificial potential field (APF) and particle swarm optimization (PSO). In the algorithm, ROP is subjected to an attractive force from a target as well as multiple repulsive forces from multiple radioactive sources. The attractive force and repulsive forces drive ROP moving toward the target along the trajectory. APF has obvious difficulties with parameter selection and a local minima problem. So, we used the PSO algorithm to solve these difficulties of APF. Additionally, we developed a radiation avoidance simulation program using the C# programming language. Simulation experiments showed the proposed algorithm could be useful to meet the challenges of radiation avoidance applications that can be described as trajectory optimization problems.
Trustworthiness Assessment of Web Applications: Approach and Experimental Study using Input Validation Coding Practices

The popularity of web applications and their world-wide use to support business critical operations raised the interest of hackers on exploiting security vulnerabilities to perform malicious operations. Fostering trust calls for assessment techniques that provide indicators about the quality of a web application from a security perspective. This paper studies the problem of using coding practices to characterize the trustworthiness of web applications from a security perspective. The hypothesis is that applying feasible security practices results in applications having a reduced number of unknown vulnerabilities, and can therefore be considered more trustworthy. The proposed approach is instantiated for the concrete case of input validation practices, and includes a Quality Model to compute trustworthiness scores that can be used to compare different applications or different code elements in the same application. Experimental results show that the higher scores are obtained for more secure code, suggesting that it can be used in practice to characterize trustworthiness, also providing guidance to compare and/or improve the security of web applications.
Research on Application of Data Mining Technology in Risk Assessment Process of Audit

In the information age, great changes have taken place in the internal and external environment of enterprises. In the face of huge data from all sides, it has become a difficult point in the current audit work to find out the doubtful points in the audit. However, the development of information technology brings an opportunity to audit. The application of data mining technology and applicable data mining algorithm can help the smooth progress of risk assessment process in audit. This paper first expounds the advantages of data mining technology in risk assessment, then lists its implementation procedures in risk assessment, and finally gives an example to illustrate the application process of clustering algorithm in risk assessment.
Extraction-Based Text Summarization and Sentiment Analysis of Online Reviews Using Hybrid Classification Method

The field of sentiment mining and text summarization has evoked the interest of many scientists and researchers over the last few years, as the textual data has become useful for many real-world applications and challenges. Sentiment Analysis and Opinion Mining is the most popular field for analyzing and discovering insights from text data from various sources, such as Facebook, Twitter and Amazon, Zomato, etc. It involves a computational study of an individual's behavior in terms of buying interest and then extracting his opinions on the business entity of the company. This entity can be viewed as an event, individual, blog post or product experience. Scholars in the fields of natural language processing, data mining, machine learning and others have tested a variety of methods for automating sentiment analysis. These reviews are increasing on a daily basis, as a result of which the summarization of the reviews plays a role where the text is summarized as needed, which provides useful information from a large number of reviews. It is very difficult for a human being to extract and interpret useful data from a very large file. In the text analysis, the value of sentences is decided on the basis of the linguistic characteristics of sentences. This paper provides a comprehensive review of current and past work on sentiment analysis and text description. In this research work, a new hybrid classification system is proposed based on coupling classification methods using arcing classifiers and their quality is evaluated within terms of accuracy. The Classifier Collection was constructed using Naïve Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA). The proposed work consists of a comparative study of the efficacy of the ensemble technique for sentiment classification. The feasibility and benefits of the proposed approaches are demonstrated by a restaurant review that is widely used in the field of sentiment classification. A wide range of comparative studies is performed and, ultimately, some in-depth analysis is addressed and conclusions are drawn on the efficacy of the ensemble technique for sentiment classification.
Edge Computing Platform Management: Design for F2C and F2F for Small Businesses to Reduce Costs

With a focus on edge computing and the cloud, IoT and connected devices require both edge computing platform management for real-time decisions and cloud computing for optimization. From a business's perspective, this requires further investments in keeping devices updated. Big players in edge computing have proposed new devices and concepts for industries, resulting in the likelihood of increasingly expensive devices going forward. However, small businesses and factories cannot afford such investments. Thus, this paper proposes a simple and low-cost implementation of an edge computing platform for small businesses to automate their business in real time. Furthermore, having the perspective of historical performance enables real-time optimization of the entire business process.
An Expert System for Diagnosis of Rheumatic Disease Types Using Forward Chaining Inference and Certainty Factor Method

This study aims to develop an expert system using forward chaining inference and certainty factor methods in order to diagnose types of rheumatic disease. Types of rheumatic disease under investigation in this study cover Gout Arthritis, Rheumatoid Arthritis, and Osteoarthritis. These three types of rheumatic disease have been chosen because they constitute the most common ones suffered by Indonesian people. Steps in developing the expert system consist of Problem Identification aiming to analyze problem domain and functional need, Knowledge Acquisition used to obtain MB and MD values for each symptom of rheumatic diseases through experts interview, Designing used to design knowledge representation such as decision table and inference engine, Coding used to create the expert system by means of PHP programming language and MySQL database, and the last step is Testing. The result shows that the forward chaining inference and certainty factor method can be used to diagnose types of rheumatic diseases and the developed system can help experts, doctors, or doctor assistants to diagnose rheumatic diseases. In addition, the testing done using the BlackBox method shows that all functions in this expert system works in line with the functional requirement analysis. The accuracy value of this system is 80%, which means the combination of forward chaining inference and certainty factor method to diagnose types of rheumatic diseases has a good performance.
A Cloud-Based Data Analysis and Prediction System for University Admission

Prediction of the college admission status can be useful to the college in various perspectives. Admission information includes the total number of candidates applying for admission and the total number of students from each state in the upcoming year. Prediction can be done after closely analyzing earlier year records. Since there is a huge amount of data in our system, we manage the data in cloud storage and anyone can access this system from any remote location. In this paper, a neural network based model called Artificial neural network is used for prediction. Artificial neural systems (ANN), part of AI, is a model which uses historic patterns and create a data model by recognizing the hidden patterns present in the input data. ANN can remember information from past years and it can forecast the future information based on past information. In our proposed system, ANN with Backpropagation algorithm is used to predict the total number of competitors in the upcoming year.
A curiosity-based mobile robot path planning method

Curiosity plays an important role in the development of biological cognition, which drives animals to explore unknown environments. Inspired by the research of psychology, this paper proposes a curiosity-based mobile robot path planning method with a mental energy function, which is suitable for robot state evaluation. In our proposed method, a novel mental energy function is designed to guide the learning direction of the robot in navigation task. The experimental results show that the novel method we proposed can effectively guide robot to learn environment information. By changing the simulation environment, robot can reach the end point through re-learning the environment, that reflects the autonomous learning ability and the environmental adaptability of robots.
An Internet and wireless networking‐based water pipes web application for fault reporting

Summary With the advancement of modern technologies such as smart cities, IoT, and e‐governments, people have to participate in their well‐being self‐governing and safe guard their key resources and critical infrastructures. To employ their efforts efficiently, many modern handy applications are created and solutions are proposed. Supported by the Ministry of Water, we designed and developed a free‐of‐charge web application (App.), easy to download from Google play store and operate using simple gadgets. Users can report water and sewage accidents fast and easy and at no cost. By merging Surveying and Geomatics Engineering with Information and Communication Technology (ICT) with Wireless communication, networking technology, and systems, we are able to create a web application to report complaints of accidents directly to the right delegated call centers at the Water Authority using the Internet and a smart mobile phone (simple gadgets). This will automate the complaint process and cancel the old paper work system. Surveying and Geomatics Engineering are concerned with the creation of an accurate GIS to manage the Water Authority data and in the data uploading into Google Maps. The developed computer program (application) works in two ways: first, it connects users (customer or employee) to the water authority electronically, and second, it allows immediate checking about the location of the complaint, momentarily filling using the Authority coordinate system, immediate action delegation (repair) at the water authority, continuous follow up, and confirmation to the user about the case. It performs as well, the needed coordinate transformation to guarantee exact filing and locations. Several software tools and programming languages are used to achieve this work such as ArcGIS, Google earth, Google map, GIS cloud, and Java and C++. As a case study, we present the App. performance for a town “Al‐Fuheis, Jordan” using the Water Authority data for creating the needed Water pipes and Sewage (GIS). Moreover, we presented the performance and the efficiency of the App. even when using Goggle Maps data only without GIS data intervention and it proved to be satisfying as well. As a result. a web App. called “Water Pipes” for the Water Authority is posted on their web to allow users participation in the reporting and the follow up process to better conserve the very scarce water in the country. In addition, it automates the work at the Water Authority from filing to take action and repair electronically in no time.
Box Detection and Positioning based on Mask R-CNN [1] for Container Unloading

Container unloading is a very dangerous job, so how to automate it is very necessary.There are currently machines that can be fully automated and unloaded for specific situations, but the generalization is not good. In order to automatically unload the goods under different situation, it is necessary to find a way to replace the human eye, how to establish a complete identification and positioning Vision system is very important.Traditional methods based on Vision are difficult to achieve end-to-end unloading automation.We use deep learning-based target detection here. The algorithm Mask-RCNN is used to detect specific objects. The second generation Kinect developed by Microsoft can completely re- place the human eye. In the simulation environment, we get the recognition speed of each frame for 0.2 seconds, which can get the three-dimensional coordinates of each box precise. By feeding back the data to the machine, the machine can know their target clearly.This is a good promotion of the development of automatic unloading.
Locally Adaptive Channel Attention-Based Network for Denoising Images

Channel attention has recently been proposed and shown a great improvement in image classification accuracy. In this paper, we show that channel attention can greatly help a low-level vision task, image denoising, as well, and propose channel attention-based networks for image denoising. We provide a thorough analysis on the effect of channel attention on image denoising, which shows that channel attention boosts denoising performance by making the network to focus on informative channels more closely related to noise. We also show that channel attention has an adaptive nature to image contents and noise and propose locally adaptive channel attention for further improving image denoising quality. Experimental results show that our denoising network with global channel attention outperforms existing state-of-the-art methods in both blind and non-blind settings, and our locally adaptive channel attention substantially improves both image quality and computation time.
Research on Power Transaction Information Security of Microgrid Blockchain Network

In order to search for information security of microgrid transaction, the channel mechanism and encryption algorithm of blockchain network are proposed. Aiming at the confidentiality and anti-aggression of microgrid transaction, the security of microgrid power trading information is studied. Firstly, ensure privacy of each transaction based on blockchain alliance and channel isolation mechanism. Then information security encryption algorithm is used to ensure transaction security of both sides in the process of point-to-point transaction. Finally, the transaction consensus is reached by the verified transaction party, and the transaction is completed on the blockchain network. It provides more methods for rebuilding the trust system of microgrid transactions and the order of market management.
Decision Tree-Based Fraud Detection Mechanism by Analyzing Uncertain Data in Banking System

As of now, enormous electronic information stores are being kept up by banks and other money-related organizations. Information mining advancement gives the region to get to the right information at the right time from massive volumes of information. Data classification is an established issue in AI and information mining. In regular choice (decision) tree investigation, a normal for a tuple is either supreme or partial. The choice tree calculations are utilized for dissecting solid and numerical information of uses. In the surviving techniques, they play out the extended model of choice tree examination to help information tuple having factual characteristics with uncertainty characterized by discretionary pdf. Along these lines, we proposed an improved novel choice tree for the two information, speaking to the development and a framework utilized for AI and information mining to strengthen the requirement of the financial undertaking. This paper expects to evaluate the utilization of strategies for choice trees to aid the trepidation of bank extortion. The choice trees aid this work of choosing the characteristic that will build up a superior exhibition in determining the odds of bank fraud.
Boundary Matching and Interior Connectivity-Based Cluster Validity Anlysis

The evaluation of clustering results plays an important role in clustering analysis. However, the existing validity indices are limited to a specific clustering algorithm, clustering parameter, and assumption in practice. In this paper, we propose a novel validity index to solve the above problems based on two complementary measures: boundary points matching and interior points connectivity. Firstly, when any clustering algorithm is performed on a dataset, we extract all boundary points for the dataset and its partitioned clusters using a nonparametric metric. The measure of boundary points matching is computed. Secondly, the interior points connectivity of both the dataset and all the partitioned clusters are measured. The proposed validity index can evaluate different clustering results on the dataset obtained from different clustering algorithms, which cannot be evaluated by the existing validity indices at all. Experimental results demonstrate that the proposed validity index can evaluate clustering results obtained by using an arbitrary clustering algorithm and find the optimal clustering parameters.
Combining Pose and Trajectory for Skeleton Based Action Recognition using Two-Stream RNN

The changes of skeleton joint positions in skeleton sequences are caused by pose evolution and human body movements in the 3D space over time, which are both essential for action recognition. Most existing methods use relative coordinate system dependent on specific skeleton joint to track the action. However, because of the relative coordinate system,so the movements of action are eliminated. In this paper, the original coordinate system for raw skeleton sequences is transformed into the pose coordinate system and the trajectory coordinate system. Then the salient pose descriptor and trajectory descriptor of action sequence are extracted in the pose coordinate system and the trajectory system, respectively. The salient pose descriptor models the pose evolution over time while the salient trajectory descriptor describes the human body movements in the 3D space. Our method use a two-stream LSTM network to describe the changes of the temporal sequences. The salient pose and trajectory descriptors are fed into pose stream and trajectory stream separately. In order to merge the features from the pose stream and trajectory stream,we concatenate those features and use a fully connected network to de the next step. We evaluated our experiment on two benchmark datasets. Our experiment shows that our method gets good performance on two datasets.
To Coerce or Not to Coerce? A Quantitative Investigation on Cybersecurity and Cybercrime Legislations Towards Large-Scale Vulnerability Notifications

The rise of large-scale vulnerability scanners, which make the detection of vulnerabilities easy, has recently raised the attention to the security community. This leads to multiple research on the effectiveness of large-scale vulnerability notifications as a countermeasure and several studies have examined the different factors that could impact the effectiveness and how they could incite people to apply vulnerability fixing steps. And we contend that using a legal constraint could improve the vulnerability fixing rate. In this paper, we survey the Cybersecurity and Cybercrime Legislations of 156 countries looking for Articles that consider the liability of vulnerable resource owners. We discovered that only two countries required companies, regardless of the industry, to implement cybersecurity measures and conduct vulnerability assessments. This study aims to define a more effective large-scale vulnerability notifications system using Cybersecurity and Cybercrime Laws.
Design of expert system to determine the proper diet using harmony search method

Abstract Excess or lack of nutrients contained in food will cause a problem in the body. To solve this problem easily, a computerized system is needed that can provide some advice on the amount and type of food that needed to be consumed. The aim of this study is to design an expert system which addresses individual diet based on calorie activities and body needs. This study uses the Prototype as a software development methodology and Data Flow Diagram (DFD) as a software model. This expert system uses a harmony search algorithm to determine the amount of food with Harris-Benedict formula to calculate the body’s calorie needs. The results of functional system testing and based on the assessment of nutritionists, this expert system have been worked well for people with normal body metabolism.