Reviewerly/exHarmony huggingface dataset:
Proposal of a Method to Detect Obstacle Using Projector and Camera

In recent years, the introduction of business and service robots has progressed in Japan, and the number of unmanned transfer robots is increasing. Therefore, I will focus on obstacle detection, which is an indispensable function for automated guided vehicles. Conventional methods for detecting obstacles include ultrasonic sensors, PSD sensors, and 2D-LIDAR, but they have disadvantages such as a narrow measurement range, being easily affected by disturbances, and having a long mechanical life. Therefore, as a previous research, our laboratory has been developing an obstacle detection system using a line laser and a camera in order to improve these disadvantages. In this method, a line laser is used to irradiate the floor surface in front of the robot with a horizontal line, and obstacles are detected from changes in the horizontal line. This method improves the disadvantages of the conventional method and enables the detection of obstacles with a long life that is not easily affected by disturbances over a wide area. However, there are disadvantages such as being able to detect only obstacles on the straight line of the laser and being able to detect only part of the obstacles. Therefore, I propose an obstacle detection system using a projector and a camera for the purpose of improving the disadvantages of the conventional method and the disadvantages of previous research. This method is a system in which a projector irradiates the floor surface in front of the robot with multiple vertical and horizontal lines and detects obstacles from the characteristic changes in the lines. In this study, we conducted three experiments to verify the superiority of this study when compared with the previous studies. As a result of the experiment, it was confirmed that this study improved the disadvantages of the conventional method and the previous study
Use of Blockchain Technology for Electronic Prescriptions

Distributed ledger technology can be used as a transparent, shareable ledger, that can record transactions between two parties efficiently and in a more secure, verifiable, and permanent way than the current electronic prescribing systems. We studied the use of a distributed ledger electronic prescribing programme, Prescription Abuse Greatly Reduced (PAGR) Prescriptions, to examine the effect of blockchain on provider prescribing efficiency at three family medicine clinics.The PAGR was installed side-by-side to the electronic health record at three family medicine practice clinics in middle Tennessee. A prospective, convenience sample of patients at all three clinics was used for analysis. Trained observers were used in each clinic to document the side-by-side use of current prescribing practice versus the use of the PAGR electronic prescribing system by the individual providers.The primary outcome was total time to write the prescription. Secondary metrics included compliance with checking the state's Physician Drug Monitoring Program (PDMP.) , accuracy of medicine reconciliation, use of patient's eligibility on insurance, prescription benefits, and change in prescription caused by benefits analysis or drug-interactions. Provider satisfaction was measure on a 4-point Likert scale.Data were analysed using two-tailed, paired Student T-tests with alpha set at 0.05. A sample size of 107 patients was calculated to have a power of 80% to detect a 50% change in the prescription writing time.The primary outcome of total prescription writing time was 171 ± 41 sec for current prescribing practice versus 63 ± 15 sec for the PAGR system (p = 0.0006). All providers were extremely satisfied with the use of the PAGR programme.Use of the PAGR electronic prescription programme significantly saved a mean of 1 min 48 sec per written prescription at the three Family Medicine Clinics. The PAGR also provided accurate medicine reconciliation and complete PDMP checks for controlled substance prescriptions. The patient real-time benefits check and drug-drug and allergy-drug reviews resulted in the provider changing the prescription 28% of the time, enhancing safety and out-of-pocket patient expenses. Future enhancements include expanding the insurance benefits analysis and developing provider notifications when patients are non-compliant with filling their prescriptions.
Cyber Threat Intelligence in Risk Management

Cyber Threat Information (CTI) has emerged to help cybersecurity professionals keep abreast of and respond to rising cyber threats (CT) in real-time.This paper aims to study the impact of cyber threat intelligence on risk management in Saudi universities in mitigating cyber risks.In this survey, a comprehensive review of CTI concepts and challenges, as well as risk management and practices in higher education, is presented.Previous literature was reviewed from theses, reviews, and books on the factors influencing the increase of cyber threats and CTI as well as risk management in higher education.A brief discussion of previous studies and their contribution to the current paper and the impact of CTI on risk management to reduce risk.An extensive search of more than 65 research papers was conducted and 28 were cited in this survey.Cyber threats are changing in addition to the huge flow of information about them and dealing with these threats on time requires advance and deep information about the nature of these threats and how to take appropriate defensive measures, and this is what defines the concept of CTI.The use of cyber threat information in risk management enhances the ability of defenders to mitigate growing cyber threats.
OPTIMALISASI PENERAPAN TEKNOLOGI VIRTUAL REALITY TOUR KAMPUS UNIVERSITAS MAJALENGKA MENGGUNAKAN METODE MULTIMEDIA DEVELOPMENT LIFE CYCLE

Campus introduction media in general still use billboards and brochures or advertisements, therefore the use of Virtual Reality technology is very good to use. Optimizing the application of this technology is made to build an interactive application that can visualize the facilities and infrastructure at University Majalengka in the form of 360 images and videos to make it easier for users to digitally view the facilities and infrastructure of the University Majalengka Campus. The method used in this research is MDLC (Multimedia Development Life Cycle). This method can optimize the manufacturing process and application of technology used in a smartphone media that is integrated with other media or 3D media. MDLC rule pattern itself by prioritizing interactive rests on the user. This application was built using the C # programming language with Unity 3D software. The final result of this research is the creation of a virtual tour application of the University Majalengka Campus which can be used by users to view interactive digital facilities and infrastructure on campus with a 360 degree angle using a smartphone that has a Gyroscope sensor which can be integrated with the paired virtual reality technology. into virtual reality glasses.
Effectiveness of stego images pre-noising with fractional noise for digital image steganalysis

Counteraction to sensitive data leakage in cyber-physical systems is topical task today. Solving of the task is complicated to widely usage by attackers of novel steganographic methods for sensitive data embeddinginto innocuous (cover)files, such as digital images. Feature of these embedding methods isminimization of cover image’s parameters alterations during message hiding. This negatively affectsdetection accuracy of formed stego images bystate-of-the-art statistical stegdetectors. Therefore, advanced methods for detection and amplification of cover image’s parameters abnormal changes caused by data embedding are needed.The novel approach for solving of mentioned task is applying of image pre-processing (calibration) methods. These methods are aimed at estimation parameters either of cover, or stego images from current analysed image.The majority of known calibration methods are based on cover image content suppression by utilization of extensive set of high-pass filters. This makes possible close to state-of-the-art detection accuracy by the costof time consuming preselectionof appropriate filters. Therefore, this approach may be inappropriate in real cases, when fast re-train stegdetector for revealing of stego images formed by unknown embedding methodsis required.For overcoming this limitation, we proposed to calibrate an image by amplification of alterations caused by message hiding. This can be realized by data re-embedding into images or their pre-noising.The effectiveness of such approach was proved for wide range of modern embedding methods in the case of message re-embedding. The paper is aimed at performance analysis of image calibration by pre-noising, namely by using of non-stationary fraction noise.The performance analysis of proposed solution was performed for novel HUGO and MG adaptive embedding methods on standard VISION dataset. According to obtained results, we may conclude that applying of proposed solution allows achieving close to state-of-the-art detection accuracy for HUGO embedding method and low (less than 10%) cover image payload.Also, low computation complexity of proposed solution makes it an attractive alternative to novel cover rich models based stegdetectors. Nevertheless, solution’s performance concedes effectiveness of novel stegdetectorsfor medium(less than 20%) and high (more 25%) cover image payloadfor MG embedding method
SMART CV FOR LIFELONG QUALIFICATIONS CERTIFICATION BASED ON BLOCKCHAIN

There is a need for fast, automatic, and trusted verification of a person’s qualifications. This paper proposes the Smart CV (Curriculum Vita) that contains links to Blockchain-based certifications of the person’s qualifications. Also, the paper proposes an architecture for the Blockchain-based Smart CV consisting of five layers: 1) Trusted certificate Issuers; 2) Trusted Distributed Ledgers; 3) Trusted management; 4) Smart CV; 5) Users. Educational institutes, accreditation organizations, public authorities, employers and others can cooperate to issue and accept these Blockchain-based certificates presented on a person’s Smart CV.
Access Control and File Distribution Management for Electronic Diploma and Transcript using Ethereum Smart Contract and IPFS

This reserach build access control and file distribution management system for electronic diploma and transcript using ethereum smart contract and InterPlanetary File System (IPFS). The falsification of diplomas/transcripts is one of the problems in education. In Indonesia, falsification of diplomas/transcripts is a form of criminal act of falsifying letters. In addition, diplomas/transcripts that have not been digitalized make them easily damaged, lost, and difficult to manage. Therefore, this research developed digital diploma/transcript as digital twin from the hardcopy of diploma/tramscript. This research used IPFS to store data in a distributed system and Smart Contracts Blockchain to store and protect the digital diploma/transcript. The system also comes with access control to create and give approval for diplomas or transcripts to be published and saved into the system. Access control settings will be saved using the blockchain. This research using Quality of Service test method for measurethroughput, packet loss, and delay. Beside that, tis research also analysis the usage of Central Processing Unit and Random Access Memory from the system. Based on the test that has been done, the fake diploma/transcript detection system can be run properly by using 1 node to 5 nodes. The best throughput value during the process of making and validating the diploma/transcript is to use 1 node. The value of packet loss in the process of making and validating the certificate/transcript has a very good category. The value of delay in the process of making and validating the diploma/transcript has a very good category.
Hybrid Neural – Based Model for Predicting the Construction Project Performance of High – Rise Building Projects in Metro Manila, Philippines

Assessing construction project performance through adapting an innovative approach can multiply the production of high-quality project outputs which is a catalyst to the socio-economic progress of a country. Preliminary data collection was employed through a meta-cognitive analysis of reviewing related literature which directs to the backbone of qualitative information that is relevant to the periodically experienced construction performance-influencing factors and to develop an assessment questionnaire about the influencing factors affecting the project performance. The IBM SPSS program was used to verify the reliability and consistency of the fundamental statistics of the questionnaire responses of cost, time and quality performance ratings. A predictive mathematical model was developed for forecasting cost, time, and quality performance rating employing Levenberg-Marquardt training algorithm with Hyperbolic Tangent Sigmoid function. The prediction model result shows a highly satisfying performance on its variance from the substantive values and suggests a high correlation between these values. The relative importance of the factors affecting the cost, time, and quality performance rating was calculated via sensitivity analysis through connection weights using Garson’s Algorithm to view the order of influence of the parameters that have great significance to the success of a project.
Determination of the Effects of Copper (Cu) and Lead (Pb) Heavy Metals on Soil Carbon and Nitrogen Mineralization

Heavy metal (HM) pollution has become one of the most important environmental problems of the present day, as a result of the developing industrial activities. Accordingly, it is important to understand microorganism activities in soil ecosystems that have been exposed to HMs for a long time. The aim of this study was to show the potential effects of ores on soil carbon and nitrogen mineralizations which were taken from copper (Cu) and lead (Pb) mines in Balıkesir-Balya and Kastamonu-Küre districts in Turkey. The carbon (C) and nitrogen (N) mineralizations were determined by using the CO2 respiration method (30 days) and the Parnas Wagner method (42 days) under the controlled laboratory conditions (28 °C, 80% of field capacity), respectively. It was observed that carbon mineralization decreased depending on the dose increase. 250 mg kg-1 treatment with Pb was lower than the control and there was a significant difference between them (P &lt; 0.001). In terms of nitrogen mineralization rate (%), there was no significant difference among all treatments. According to the results, Pb affected microorganisms more negatively; however, the presence of Cu slightly decreased its negative effect. It is possible to conclude that carbon mineralization can be indicator for HM pollution in the soil. However, nitrogen mineralization was not a determining factor at HM pollution in this study.
Home Security using Face Recognition Technology

Face is the easiest way to penetrate each other's personal identity. Face recognition is a method of personal identification using the personal characteristics of an individual to decide the identification of a person. The method of human face recognition consists basically of two levels, namely face detection and face recognition. There are three types of methods that are currently popular in the developed face recognition pattern, those are Eigen faces algorithm, Fisher faces algorithm and CNN neural network for face recognition
Towards a Generic Neural Network Architecture for Approximating Tone Mapping Algorithms

Tone curves are a key feature in any image processing pipeline, and are used to change the pixel values of an input image to find an output image that looks better.Perhaps the most widely deployed tone curve algorithm is Contrast Limited Histogram Equalisation (CLHE).CLHE is an iterative algorithm that tone maps an input image so that the histogram of the output is (approximately) maximally uniform subject to the constraint that the tone curve has bounded slope (neither too large or too small).In this paper, we build upon a neural network framework [1] that was recently developed to deliver CLHE in fewer iterations (each layer in the neural network is analogous to a single iteration of CLHE, but the network has fewer layers than the number of iterations needed by CLHE).The key contribution of this paper is to show that the same network architecture can be used to implement a more complex (and more powerful) tone mapping algorithm.Experiments validate our method.
Web Page Recommendation using Random Forest with Fire Fly Algorithm in Web Mining

Nowadays, internet has become the easiest way to obtain more information from the web and millions of users search internet to find out the information. The continuous growth of web pages and users interest to search more information about various topics increases the complexity of recommendation. The user's behavior is extracted by using the web mining techniques, which are used in web server log. The main aim of this research study is to identify the navigation pattern of users from the log files. There are three major steps in the web mining process namely pre-processing the data, classification of pattern and users discovery. In recent periods, the web page articles are classified by the researchers before recommending the requested page to users. However, every category size is too large or manual labors are often needed for classification tasks. A high time complexity issues are faced by some existing clustering methods or according to the initial parameters, these techniques provides the iterative computing that leads to insufficient results. To address the above issues, a recommendation for web page is developed by initializing the margin parameters of classification techniques which considers both effectiveness and efficiency. This research work initializes the Random Forest's (RF) margin parameters by using the FireFly Algorithm (FFA) for reducing the processing time to speed up the process. A large volume of user's interest data is processed by these margin parameters, which provides a better recommendation than existing techniques. The experimental results show that RF-FFA method achieved 41.89% accuracy and recall values, when compared with other heuristic algorithms.
THE OBJECTS OF PATENT LAW

In the circumstances of development and functioning of the innovative model of economy, and necessity of creation and introducing of new technics and advanced technologies in manufacture the patent law is of a great importance in the intellectual property law system. The issue of patent law has repeatedly been the subject of researches in the national civil law science, but the question of its objects still remains unresolved. Therefore, the task of this article is to determine the range of the objects protected by patent law, as well as to describe their features. The article draws attention to the existence of different approaches to understanding the concept of «patent» in national legislation and international acts. The invention is analyzed as the most important object of patent-law protection. The inexpediency of defining the invention and its objects by pointing to all fields of technology is emphasized. Regarding to the utility model, the latest legislative changes in the definition of its objects are analyzed, as well as, some considerations are expressed regarding the prospects of introducing the criterion of inventive step for it. As for the industrial design, taking into account the latest changes in the legislation in this area, it is stated that it is inexpedient to refer it to the objects of patent law. It is argued that a plant variety and a breed of animals as the results of selection cannot be attributed to the scope of patent law. It is emphasized that patent law should not be equated with industrial property law – the latter is a broader concept, which, in addition to the objects of patent law, also includes other categories of objects (industrial designs, trademarks, geographical indications, etc.). It is concluded that the objects of patent law are inventions and utility models. In this regard, it is proposed in the process of recoding the rules on industrial designs to be allocated in a separate chapter of the Civil Code of Ukraine.
Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film

The Support Vector Machine (SVM) method is a method that is widely used in the classification process. The success of the classification of the SVM method depends on the soft margin coefficient C, as well as the parameter  of the kernel function. The SVM parameters are usually obtained by trial and error, but this method takes a long time because they have to try every combination of SVM parameters, therefore the purpose of this study is to find the optimal SVM parameter value based on accuracy. This study uses the Firefly Algorithm (FA) as a method for optimizing SVM parameters. The data set used in this study is data on public opinion on several films. Class labels used in data classification are positive class labels and negative class labels. The amount of data used in this study is 2179 data, with the distribution of 436 data as test data and 1743 data as training data. Based on this data, an evaluation process was carried out on the Firefly Algorithm-Support Vector Machine (FA-SVM). The results of this study indicate that the Firefly Algorithm can obtain the optimal combination of SVM parameters based on accuracy, so there is no need for trial and error to get that value. This is evidenced by the results of the FA-SVM evaluation using a value range of C=1.0-3.0 and =0.1-1.0 resulting in the highest accuracy of 87.84%. The next evaluation using a range of values ​​C=1.0-3.0 and =1.0-2.0 resulted in the highest accuracy of 87.15%.
Popularity Based Recommendation System

A Recommendation engine recommends the most relevant items to the user by using different algorithms to filter the data. A Recommendation system is more useful in the context of data extraction relating to applications of big data and machine learning. As the name indicates Popularity based recommendation system works with the current vogue. It basically uses the items which are in swing at present. This is the most basic recommendation system which provides generalized recommendation to every user depending on the popularity. Whatever is more popular among the general public that is more likely to be recommended to new customers. The generalized recommendation not personalized is based on the count. In this paper I am going to use a class that we created which includes the methods to create recommendations and to recommend the item to the user. Next I will load the data of Comma Separated Value (CSV). After that sort the sound name based on the how many users have listened to the sound name. After the collection of data code splits the dataset into training and the test dataset using 80–20 ratio. This creates an instance of popularity based recommenders class. At last I will use the popularity model to make the predictions.