Reviewerly/exHarmony huggingface dataset:
Application of SVM Algorithm in Teaching Process Evaluation

Data mining is an important data processing technology, which can help us discover useful information and existing rules from a large amount of seemingly unrelated, random data. Data mining is an important data processing technology that can help decision makers make better scientific decisions and predict future development trends. Today, there are relatively many problems in English procedural evaluation, which affect the credibility and effectiveness of English evaluation. Therefore, using data mining to solve English process evaluation has become an important method to solve this problem. This article analyzes and summarizes the current status of English procedural evaluation, analyzes the results obtained by teachers in process evaluation using data mining technology, and then works with teachers Finally, the evaluation results are comprehensively compared to judge whether the teacher’s English procedural evaluation is scientific and reasonable. Therefore, the use of data mining to solve the objective problem of procedural evaluation is promoted, and it will promote the scientific and reasonable English procedural evaluation in the future.
Mobile Expert System Using Forward Chaining for Diagnosing Teak Tree Disease

Abstract The teak trees is a high-quality wood that grows in Indonesia. Teak trees are susceptible to pests and diseases within the first to 12 months of life. The limited number of agricultural agents in each sub-district becomes a reason why we proposed a mobile expert system to detect pests and diseases in teak trees. The method used in this study is forward chaining. The results of this study showed that 9 of disease were successfully detected by a human expert. Whereas by using the mobile expert system only 7 diseases were successfully detected. From this comparison, the accuracy of the mobile expert system using forward chaining method is 77%.
Multiple Feedback Linkage in the Process of Urban River Water Treatment

According to the measurement data of hydrology and water quality, the decision support system in the multiple feedback linkage system is used for calculation, combined with the judgment and decision of the expert system in the water treatment expert assistant decision analysis system, the precise control of the Internet of Things equipment in the water treatment project were realized. an intelligent water body management system was established that combined human intelligence with machine intelligence to realize the process of water body treatment and monitoring technology to collect, analyze and process data, to achieve more efficient water treatment and more accurate water body monitoring. Use the comprehensive water-related data collected to provide decision support for regional industry development.
Artificial bee colony algorithm with rotational invariance based on hypersphere

Abstract In this study, we show the lack of rotational invariance in the Artificial Bee Colony algorithm (ABC) through numerical experiments. After analysis from the viewpoint of rotational invariance, we develop an ABC with rotational invariance using a hypersphere. The performance of the proposed ABC with rotational invariance is verified through numerical experiments for typical separable benchmark functions without and with rotation. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
A Hybrid Particle Swarm Optimization with Crossover and Mutation of Genetic Algorithm for Solving the Wide Constraint Problem

When optimizing the spatial data, a lot of constraints should be handled. Some constraints might be too wide for a metaheuristic algorithm, e.g. particle swarm optimization, to allocate the candidate locations outside a wide constraint. However, particle swarm optimization notably has fast computation characteristic and many researchers used this method for optimizing their spatial data. In the other hand, genetic algorithm has not only better exploitation-characteristic performance in searching but also has mutation and crossover that was proven in this study can be overcome the wide constraint problem. To minimize the drawback of genetic algorithm, i.e. need many computation resources, the hybrid particle swarm optimization with genetic algorithm through the use of crossover and mutation was used. Half of lower fitness values from particle swarm optimization were optimized using crossover and mutation in genetic algorithm. After merging the results of both methods, the optimum location showed that the proposed method was able to allocate the land use in a case study area outside the wide constraint.
Dynamic Programmable Clock Frequency Using Machine Learning Algorithms to Reduce Power Consumption in Wearables

In today’s world, wearable devices have become a key device in monitoring all internal activities of human being with the help of sensors. Humans are provided with a wristband device fabricated with various sensors to detect overall body condition in every day-to-day activity. Most of the wearable devices can be used to monitor the beats per minute (BPM) by heart rate sensor (pressure sensor), sleep and body movement activity by three-axis accelerometer sensor, the respiratory rate by biosensors and electrocardiogram (ECG) by sensors (electrodes). It is well known that soon in the near future, all sensors related to human activity and health monitoring play a major role in human beings; especially, for old age people, the wearable devices with well-packed sensor will soon be reaching out the market. The most wearable devices are able to provide almost all sensors in a single package, but fail to work if the power goes off shortly. The proposed model makes an attempt to reduce the power consumption in the wearable device by introducing the programmable clock frequency with machine learning concept. To reduce the power consumption in wearable devices, a programmable clock frequency of 1 GHz has been designed and monitored on a monthly basis. The proposed work uses different frequencies based on different activities of wearable device with generated data set of machine learning algorithms and is analysed using PYNQ boards. The proposed work concludes based on the multilinear-based regression model to check the power consumed for the given parameters observed that step count consumes 96% power during the complete analysis.
Implementation of incognito method and SHA-3 as an alternative to PIN selection in web login

Abstract Currently, the PIN is still used to verify identity on web-based applications. PIN is widely used because it is easy authentication. However, a PIN has a potential security risk that is vulnerable to shoulder surfing attacks. The way to reduce shoulder surfing attacks is to create an interface that is difficult to attack with shoulder surfing attack techniques. One interface that can be applied is the incognito method. In this research, the application of the incognito method is implemented as a web login application. The application is built based on the web using the SHA3-256 algorithm to hash the PIN. The results of the stufy prove that the application built is resistant to shoulder surfing attack by attackers.
Computational Intelligence in the Reversible Data-Hiding Processes Using Radon Transform

Data hiding has emerged as a major research area due to the phenomenal growth in internet and multimedia technologies. Securing data transmitted over the internet becomes a challenging issue caused in digitization and networking over the past decade. Data hiding schemes have been adopted to protect digital media content which involves confidential data such as text, video, audio, images, and compression coding. A good reversible data hiding scheme is characterized by the possession of attributes like reversible, imperceptible, high payload capacity, and robustness. By reversible, it's meant that the extraction of the payload as well as the restoration of the host image perfectly from the stego image. Secondly, the imperceptible stego image resemblance against the cover/host image. Finally, robustness counts for the ability to sustain the secret payload against both intentional and unintentional attacks; it has been observed that all the proposed algorithms are more robust and reversible against various attacks in lower bit error rate and higher normalization coefficient.
Reexamining the principle of mean-variance preservation for neural network initialization

This paper examines neural network parameter initialization schemes by distinguishing between two sorts of randomness: randomness in network parameters and randomness in network inputs. The authors show that in higher layers of a deep network fluctuations arising from input randomness can decay to zero while the scale of fluctuations arising from parameter randomness remains constant.
Use of Artificial Intelligence for Health Insurance Claims Automation

The research focuses on the automation of health insurance claims by using artificial intelligence, more specifically, machine learning. In today’s world, where every bit of a record collected is considered information and where every bit of this information plays a vital role in the future decisions, traditional, and manual methods of determining whether a claim made are authentic or fake, of deciding whether to accept or reject that claim in the health insurance business are no longer viable. On the other hand, artificial intelligence the ability of machines to perform equal or more than a human mind is taking over the world. Applying those abilities in place of traditional claim assessment, we get a system that is not only efficient and fast, but it also finds the trends and patterns in the data that were previously not known to exist. The motivation behind this project is to save time, be efficient, avoid tedious manual work, and most importantly human error. The implemented system will help to distinguish between absolutely deserved and undeserved health insurance claims.
Developing a Framework to Identify Potential Firms for Job Assignment

Nowadays, people are searching for companies via the internet to assign different types of software-related tasks. With the growing number of software firms, a problem is arising about which software firm is best suited to perform a particular job. It is not easy for a certain individual to determine the best software firm for a particular task according to his requirements manually. Therefore, by building a system to recommend the best-suited software firms for a particular job and can minimize the complexity of this important process. In this paper, a recommendation system is introduced which helps any individual or organization by suggesting them the most suited software firm according to his needs. Our system takes input job description and after processing it, suggest the user the best-suited firms according to the job description. The job description is processed by removing stop word, stemming, and extracting keywords. Porter Stemming algorithm is used to stem the job description. From the extracted keywords the job category is identified. From the software firms which have experience in working in the identified category and similar firms to these firms, some decision parameters are calculated. These decision parameters are used to identify the best-suited software firms working in the identified category.
Fuzzy Logic Method as Determining of Internet Network VSAT performance in Black Spot Region at District of Enrekang

Abstract Internet networks using VSAT have some disadvantages caused by the relative distance of the satellite and the earth which is a significant propagation delay. To determine the optimal quality of VSAT internet network, a study of VSAT internet network performance was performed using Fuzzy Logic method, based on the parameters of bandwidth, delay, packet loss, and throughput conducted for 2 days in the morning, afternoon and night in 2 villages in Enrekang Regency, namely Singki Village and Tindalun Village. Retrieval of data parameters at these locations using a wireshark application software which is then inputted into fuzzy logic so as to produce three categories of performance, good or sufficient, and bad network quality. It is said that performance is good if the results on fuzzy logic are 0, sufficient performance compilation of results from fuzzy logic is 0.5 and performance is poor if the results of fuzzy logic are valued 1. The VSAT internet network requests optimal compilation parameters of network quality such as 6 mbps bandwidth, delays below 1,000 ms, packet loss less than 50% and throughput above 50 kbps which has been designed in fuzzy logic. From the results of the study, obtained a value for the village of Tindalun 0.5 which means enough. Whereas, for performance in Singki Village, 2 different values were obtained, while compilation using Wifi was valued at 0.5, which was sufficient, and compilation using WireLAN was 0 which meant good.
Planning information systems Kuliah Kerja Nyata (KKN) Institut Pendidikan Indonesia Garut

Abstract Kuliah Kerja Nyata (KKN) is an intraculicular education activity multidisciplinary is carried out at outside campus including the practice of Higher Education Tri Dharma namely community service and carried out routinely every year. With the complex problem of KKN it is necessary to design a governing information system kuliah kerja nyata activities so that it runs well and smoothly, starting from planning activities, surveys, registration, grouping, monitoring, evaluation and reporting. Lembaga Pengabdian kepada Masyarakat (LPM) as a manager of KKN experiencing many problems, by making an analysis of system design system design real job lecture information is a solution to the problem the implementation of Kuliah Kerja Nyata runs effectively and efficiently, because with a real job lecture information system all activities can well organized, although at present there is an information system Kuliah Kerja Nyata running for registration and finance, but it is not yet comprehensive for other activities and is still conventional in nature. The focus of the objectives of this study is to produce a design planning analysis information system in the form of information needs with the required entity making it easier to develop KKN information systems with an approach Geographic Information System (GIS) with the application of progressive Web App for easier implementation. The purpose of this study to make the analysis of real working class information system to improve administrative services better.
Zero-Order Optimization Techniques

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
Justice on Demand: True Crime in the Digital Streaming Era, Tanya Horeck (2019)

Review of: Justice on Demand: True Crime in the Digital Streaming Era , Tanya Horeck (2019) Detroit: Wayne State University Press, 240 pp., ISBN 978-0-81434-063-9, p/bk, $34.99
Anonymous Block Chain, Electronic Voting Using Linkable Ring Signatures

Electronic voting is the use of electronic means to cast and count votes. The system must be able to offer fairness, trust, transparency, privacy, integrity, and availability of votes. The application of block chain in electronic voting seems to match the above qualities of an electronic voting system. However, recent researches have shown that block chain technology lacks anonymity. Transactions are pseudonymous and not anonymous. For this cause, a proposed method to use linkable ring signatures in block chain electronic voting system provides anonymity. With this system, a voter can verify votes with the highest level of anonymity from other voters. Voters can also tally the votes without the use of centralized third party. The proposed system will be implemented using Ethereum protocol. It uses elliptic curve cryptography to provide authentication and non-repudiation, immutability of vote records through hashing algorithms, transparency, and accessibility due to its decentralized nature as well as voter anonymity.
Employing Mark Knowledge Management Model to Improve Agile Team Productivity: An Extension of the Scrum Process

The current competitive arena has required Agile software companies to adopt the proper solutions in order to improve productivity like other companies, but focusing on the governing Agile principles and values has led to the dominance of tacit/implicit knowledge over explicit one in the development teams. Tacit knowledge is accompanied by uncertainty, which is among the most important factors affecting productivity. In such a situation, the adoption of a modern managerial approach is essential in order to stabilize the existing tacit knowledge. Therefore, the current research proposed a combined framework of knowledge management based on Scrum development cycle adaptation to Mark knowledge management as a model that has the potential to manage tacit knowledge. Employing the proposed framework in a case study revealed significant results in practice. The results indicated an increase in the total average of productivity by 3%, and a reduction in the number of change requests by 3, and a reduction in the number of rework tasks by 8 in the team understudy.
A Multiagent Learning Approach for Distributed Control of Address Randomization in Communication Destination Anonymization

Keeping anonymity of communication destination in networking is one of the important issues to be improved since sniffing packets can still be a major threat especially on a local network system. In 2017, U-TRI has been proposed by Wang et al. as one of the approaches to provide better anonymity in such a context with acceptable overheads. However, as they mentioned, U-TRI still suffers from the issues that allow attackers to utilize their observed traffic trends. In this paper, we present an approach to solve this issue by introducing a multi-agent learning for autonomously coordinating multiple end-hosts and a simulation environment to analyze it.
A flexible model for edge representation in digital imagery

In this paper, we propose a flexible model for edge representation. The model is able to capture important characteristics of edge profiles in digital imagery such as the blur level of the edges and the amounts of gutters and humps around the edges. First, the model for edge profiles is developed based on the Dolph-Chebyshev function and the procedure of estimating the edge model parameters under a least - square framework is presented. Then, the validity of the proposed model is proved experimentally using edge profiles extracted from images with various blur and noise levels. The model is compared with the single and double Gaussian-based models and it is observed that the proposed model consistently produces the lowest root-mean-square-error (RMSE) on all tested edge profiles. Finally, the procedure for edge detection in two-dimensional images using the proposed model is derived and it is shown that our edge detector performs better than the single and double Gaussian-based edge detectors in terms of F-measure.
Designing an augmented reality learning environment for visuospatial thinking in geometry

The PhD research work targets at designing a technopedagogical learning environment in classrooms using Augmented Reality (AR) technology that incorporates the components of visuospatial thinking (VST) to enhance this skill. In doing so, two topics of Geometry i.e. (1) Visualizing 3D Solids and (2) Lines and Angles have been taken to create the modules of the AR Learning Environment (ARLE) for middle school students. Several pilot studies are being conducted to identify the best possible factors in defining the framework that highlights the essential components for designing such an ARLE for middle school students. Moreover, the research looks into establishing how, why and where AR technology can help in enhancing the pedagogical aspect of learning VST skills.
Automatic Detection of Sensitive Attribute in Privacy-Preserved Hadoop Environment Using Data Mining Techniques

Privacy-preserving data mining is one of the evolving fields in today’s technical era as the data is generated in rapid amount due to the presence of automated devices. This study has been focused on the automatic detection of sensitive attributes using data mining technique in Hadoop environment. We establish privacy-preserved Hadoop environment (PPHE) that automatically detects sensitive attribute using data mining techniques. The proposed PPHE considers Twitter to enable user to post messages. The content of the posted tweets is wide ranging and contains private information such as email addresses, mobile numbers, physical addresses, and date of birth. The study comprises of five major processes that includes authentication, preprocessing, feature extraction, classification, and data deduplication. The major purpose of this study is in three folds. At first, we authenticate each Twitter user by identifying their identities. Key generation center (KGC) produces public key and private key for each Twitter user using the combination of RSA and Elgamal algorithm which preserves the privacy for registered users. In the second, we classify the tweets into private and non-private attributes by using type-2 fuzzy logic system. In the third, we pursue the data suppression technique to compress private tweets. Finally, we evaluate cosine similarity to share user’s content. The proposed method was assessed based on performance parameters such as accuracy, precision, and F-measure.
Medical Spirometer for Diagnosing COPD Base On The Measurement of FVC and FEV1

Abstract Lung is one of the vital respiratory organs in the human body. Where during respiration, there is a process of gas exchange in the lungs by taking oxygen (O2) from the air and releasing carbon dioxide (CO2) into the air. Because in the process of respiration involves air in the surrounding environment, the more contamination of air is inhaled, can cause various lung diseases. If the lung diagnosis is done early, then lung disease can get health care earlier. Chronic Obstructive Pulmonary Disease (COPD) is a type of disease that blocks the flow of air into the lungs due to swelling and mucus or phlegm, so that the sufferer has difficulty breathing. In making this research, we want to make a design tool to detect COPD and determine its severity using medspiro (medical spirometer). COPD can be diagnosed by measuring the value of Forced Expiratory Volume in One Second (FEV1), Forced Vital Capacity (FVC) and comparing it with predictive values of FVC and FEV1. The results obtained from the manufacture of spirometers have FVC measurement errors of 5.8950%, and FEV1 measurement errors of 10.5030%.