Reviewerly/exHarmony huggingface dataset:
Chemical Fractionation of Copper and Zinc After Addition of Conocarpous Waste Biochar to A Drill Cutting

Background: Drill Cutting (DC) are large amount of waste generated in gas and oil exploration and production activities that contains toxic substances, especially heavy metals. This study aimed to use Conocarpus Waste (CW) biochar to investigate its effects on changes in chemical forms and stabilization and distribution of Cu and Zn in DC samples of Ahvaz oil field at different incubation times. Methods: In order to study the effects of CW biochar at different rates (0, 2, 5, and 10% w/w) and four incubation times (1, 2, 4, and 8 weeks) for adsorption and chemical fractionation of Copper (Cu) and Zinc (Zn) in DC of Ahvaz oil field in southwestern Iran. An experiment was conducted as a factorial in a completely randomized designing in three replication. Sequential extraction procedure of Tessier was applied for the determination of heavy metals fraction. Results: Application of biochar significantly (p &lt;0.05) increased the pH, soil organic carbon (SOC), electrical conductivity (EC), and cation exchange capacity (CEC) especially at the 10% application rate. After the addition of CW biochar, the exchangeable (EX) and carbonate (CAR) fractions of Cu and Zn, respectively decreased (P≤0.05) significantly while organic matter (OM) bound, oxides (OX) bound, and residual (RES) fraction were increased. Conclusion: The CW biochar can be a low-cost and effective amendment in immobilizing the Cu and Zn, and also effectively to reducing their mobility in DC.
Fault Detection of ICT systems with Deep Learning Model for Missing Data

Fault detection is one of the most important tasks in information and communications technology (ICT) systems. Unsupervised anomaly detection methods, which are based on machine learning for fault detection in the ICT systems, use various kinds of data such as traffic data, memory usage data, CPU usage data, and text log data. The problem of deploying unsupervised anomaly detection methods in real ICT systems is that these data may have missing values. When a record has missing values, existing unsupervised anomaly detection ignores the records or imputes missing values with specific values. However, both operations lead to decreased performance of the anomaly detection methods. In this paper, we propose an unsupervised anomaly detection method that can handle records with missing values without imputation by using a neural network that can process variable length inputs. We experimented with 22 benchmark datasets to evaluate the performance of the proposed method for various kinds of data. The experimental results reveal that the proposed method performs better than existing methods in terms of area under the receiver operating characteristic (AUROC) on average for two cases in which 1) neither training nor test data include incomplete data, and 2) both training and test data include incomplete data. Moreover, we experimented with data from a Wi-Fi service that have missing values. The results show that the proposed method outperformed existing unsupervised anomaly detection methods.
Multi-objective genetic programming for symbolic regression with the adaptive weighted splines representation

Genetic Programming (GP) for symbolic regression often generates over-complex models, which overfit the training data and have poor generalization onto unseen data. One recent work investigated controlling model complexity by using a new GP representation called Adaptive Weighted Splines (AWS), which is a semi-structured representation that can control the model complexity explicitly. This work extends this previous work by incorporating a new parsimony pressure objective to further control the model complexity. Experimental results demonstrate that the new multi-objective GP method consistently obtains superior fronts and produces better generalizing models compared to single-objective GP with both the tree-based and AWS representation as well a multi-objective tree-based GP method with parsimony pressure.
Artificial Intelligence Based University Chatbot using Machine Learning

Today the customer support needs 27/7 and the people faced when some issues related to organization they contact the support center and the support centers are people they are sitting to serve the customer care and they need to full fill the requirement of the customer need and demand this should increase the cost of the people setting by day and night for services and organization need to reduce this cost and desire to automate this process. The proposed study will be the replacement for this used in university chatbot for help and support for peoples which are visiting the office this will also decrease the traffic of the admission office some universities are so far away from the city area and people travel a long distance to reach the university for a very ordinary work this will reduce this and allow the office staff to focus on the other productive activities. The University chatbot (UCB) we build the application using the machine learning models that successfully build the model using the machine learning algorithms, DT, RF, SVM, that can response under the domain of the university related help and support and guide the proper way for the use of university website and referee them the proper links and contact to reduce the traffic from physical office visit of the people. The results show that the Random Forest is outperform for the proposed study.
3D Shape Estimation for Smooth Surfaces Using Double Mirror System

In this paper, a low cost, single camera, double mirror system that can be built in a desktop nail printer will be described. The usage of this system is to capture an image of a fingernail and to generate the 3D shape of the nail. The nail&#x2019;s depth map will be estimated from this rendered 3D nail shape. The paper will describe the camera calibration process and explain the calibration theory for this proposed system. Then a 3D reconstruction method will be introduced, as well. Experimental results will be shown in the paper, which illustrate the accuracy of the system to handle the rendering task.
Aplikasi M-Learning Pada Dongle Melalui Nilai-Nilai Islam

This purpose of study analyzed mobile learning application using by Dangle tool through Islamic values for students of English department at Faculty of Teacher Training and Education in University of Muhammadiyah Sumatera Utara. Research method conducted descriptive qualitative. Population conducted of 136 students 4th semester of English department in 2019/2020 academic year in University of Muhammadiyah Sumatera Utara. Sample of 32 students taken Curriculum Design Material subject of English department at Faculty of Teacher Training and Education. The technique of collecting data used observation and documentation during the learning process. Data collection method carried out using by the spiral model of Kemmis and Mc and Taggart, these were; Action planning, treatment / observation of action, reflection and revision. The results of study showed the students can use the dongle properly and enthusiastically used it in the class of assignment presentation. M-learning application of dongle tool should be carried out in all classes in University of Muhammadiyah Sumatera Utara to advance learning technology.
A Combination Design of Bag, Chair, Charger, Tables Made with Hazardous Waste Raw Materials in Tackling Environmental Pollution in the 4.0 Revolution Era

The design of bags, chairs, chargers and tables is intended for commuter workers who carry out mobility activities every day. Design works that help to support commuter worker activities use a multi-functional system. The multi-function system is indicate by the shape and features attached. This product design method uses several stages including; 1) problem identification; 2) literature study: 3) design sketches, and: 4) design. The design results are turned into chairs, tables for lesehan (sitting on the floor) and bags. In addition, the other things are the storage box to store equipments for commuter workers while doing mobility and a solar cell which functions to capture sunlight which will be converted into electrical energy for energy sources to charge cellphones. Design works can be used in accordance with their respective functions. Design works carry the principle of multifunction, easy use, strong, lightweight and flexible. It is hoped that the design for this multi-functional products can be practical in use in the development of creative industries, and can overcome environmental pollution problems. From a theoretical perspective, it can be a reference for academics and science.
CUSTOMER COMPLAINT INFORMATION SYSTEM AT NATIONAL STANDARDIZATION AGENCY OF INDONESIA

Technological developments changes the flow of information, where anyone can receive information easily. BSN as an Indonesian non-ministerial government institution needs to think about customer satisfaction with the services that provided. Completion of customer complaint is an important thing in fulfillment customer satisfaction. At present, the delivery of information regarding complaints against BSN services is still done in a conventional manner, where each user submits a complaint directly to the Helpdesk. However, in this case the section head is difficult to monitor every complaint that exists to improve the SLA. By proposing an Customer Complaint information system that can monitor customer complaints, it is hoped that it can improve service to any complaints that exist. This research is expected to assist users so that any complaints submitted can be resolved immediately, and are expected to increase SLA.
Applying Similarity Measures to Improve Query Expansion

The huge evolving in the information technologies, especially in the few last decades, has produced an increase in the volume of data on the World Wide Web, which is still growing significantly. Retrieving the relevant information on the Internet or any data source with a query created by a few words has become a big challenge. To override this, query expansion (QE) has an important function in improving the information retrieval (IR), where the original query of user is recreated to a new query by appending new related terms with the same importance. One of the problems of query expansion is the choosing of suitable terms. This problem leads to another challenge of how to retrieve the important documents with high precision, high recall, and high F measure. In this paper, we solve this problem through applying different similarity measures with the use of English WordNet. The obtained results proved that, with a suitable selection method, we are able to take advantage of English WordNet to improve the retrieval efficiency. The work proposed in this paper is extracting the terms from all the documents and query, then applying the following steps: preprocessing, expanding the query based on English WordNet, selecting the best terms, weighting of term, and finally using the cosine similarity and Jaccard similarity to obtain the relevant documents.&#x0D; Our practical results were applied on the DUC2002 dataset that contains 559 documents distributed over several categories. The average precision of cosine (for random queries) = 100% whereas the average precision of Jaccard = 84.4 %, and the average recall of cosine = 86.8% whereas the average recall of Jaccard = 73.4%. The average f-measure of cosine = 92%, whereas the average f-measure of Jaccard = 76%.
Optimistic Analysis of Renewable Energy System in Office Building with HOMER

The purpose of this study was to conduct optimal renewable energy analysis of the freezer and boiler application of a typical office building using a Homer (Hybrid Optimization Model for Electric Renewables) program developed by the NREL (National Renewable Energy Laboratory) program.The Stand Alone Model and Grid-Connected MODEL were use to derive the optimum combination of NPC (Total Net Present Cost).The results indicated that the optimum value for the Stand Alone Model is 30 kW PV, 50 KW FC, 30 KW Battery, and the GRID-Connected Model was derived to be optimal to use only GRID.Since there is no renewable energy that has not yet reached Grid Parity, this was considered a predictable result, which further analyzed the optimal combination according to the change in electricity prices and gas prices.The result was optimal (GRID + PV combination) when the current electric dermal is about 2.5 times (gas prices).In conclusion, when the new renewable energy is applied, the PV was more economical than other renewable energy sources, and even if the gas struggle (if the electricity price of $1.2/kWh) is constantly rising, gas prices were not a major factor in the selection of energy combinations.
Perceptions of adults with obesity about multiprofessional remote monitoring at the beginning of the COVID-19 pandemic/ Percepciones de adultos con obesidad bajo telemonitorización multiprofesional en el inicio de la pandemia de COVID-19/ Percepções de adultos com obesidade sobre monitoramento remoto multiprofissional no início da pandemia de COVID-19

ABSTRACT Objective: To understand the perception of adults with obesity about remote monitoring at the beginning of the pandemic of COVID-19. Methods: A descriptive-exploratory, qualitative study with participants in remote monitoring of a multi-professional treatment program in southern Brazil. Data was collected through digital platforms with interviews in written or audio-recorded records. The results were submitted to content analysis, thematic modality. Results: 26 women participated, emerging the thematic category Difficulties and potentialities in adherence to lifestyle changes during the pandemic. Among the difficulties are the change of routine and increased anxiety; and, among the potentialities are the multi-professional accompaniment, decreased tension, and weight control. Final considerations: The participants' perception showed considerable weaknesses in adhering to the program, caused by social distance. Studies with remote interventions are essential to improve the quality of this type of health care.
Privacy Threats of Acoustic Covert Communication among Smart Mobile Devices

The emerging, overclocking signal-based acoustic covert communication technique allows smart devices to communicate (without users’ consent) utilizing their microphones and speakers in ultrasonic side channels, which offers users imperceptible and convenient personalized services, e.g., cross-device authentication and media tracking. However, microphones and speakers could be maliciously used and pose severe privacy threats to users. In this paper, we propose a novel high-frequency filtering- (HFF-) based protection model, named UltraFilter, which protects user privacy by enabling users to selectively filter out high-frequency signals from the metadata received by the device. We also analyze the feasibility of using audio frequencies (i.e., ≤18 kHz) to the acoustic covert communication and carry out the acoustic covert communication system by introducing the auditory masking effect. Experiments show that UltraFilter can prevent users’ private information from leaking and reduce system load and that the audio frequencies can pose threats to user privacy.
Upper Bound on Privacy-Utility Tradeoff Allowing Positive Excess Distortion Probability

This letter investigates the information-theoretic privacy-utility tradeoff. We analyze the minimum information leakage (f-leakage) under the utility constraint that the excess distortion probability is allowed up to ε∈[0, 1). The derived upper bound is characterized by the ε-cutoff random transformation and a distortion ball.
A Hazard Assessment of Chemicals in Liquid Synthetic Detergent Using GreenScreen

The purpose of this study was to present a method for self-identification and management of the hazards of chemical substances in consumer chemical products using a Green Screen model. In this study, the GreenScreen method was used to evaluate the hazards of chemical substances in each use in the consumer chemical product, and a hypothetical consumer chemical product was selected as the target product. The hazard management method was proposed through the hazard assessment of the target substances. As a result of hypothesis evaluation of hypothetical consumer chemical products, various results were drawn from benchmarks 1 to 4, and in the case of liquid synthetic detergents, surfactants, preservatives and fragrances was classified as a level to manage the hazard. GreenScreen method recommends that the chemicals in the product should be managed to reduce the hazards for those with a benchmark of 2 or higher, and if these databases are built into a structured inventory, companies will be able to review the substances that need to be replaced by considering their functions.
A Robust Distributed Clustering of Large Data Sets on a Grid of Commodity Machines

Distributed clustering algorithms have proven to be effective in dramatically reducing execution time. However, distributed environments are characterized by a high rate of failure. Nodes can easily become unreachable. Furthermore, it is not guaranteed that messages are delivered to their destination. As a result, fault tolerance mechanisms are of paramount importance to achieve resiliency and guarantee continuous progress. In this paper, a fault-tolerant distributed k-means algorithm is proposed on a grid of commodity machines. Machines in such an environment are connected in a peer-to-peer fashion and managed by a gossip protocol with the actor model used as the concurrency model. The fact that no synchronization is needed makes it a good fit for parallel processing. Using the passive replication technique for the leader node and the active replication technique for the workers, the system exhibited robustness against failures. The results showed that the distributed k-means algorithm with no fault-tolerant mechanisms achieved up to a 34% improvement over the Hadoop-based k-means algorithm, while the robust one achieved up to a 12% improvement. The experiments also showed that the overhead, using such techniques, was negligible. Moreover, the results indicated that losing up to 10% of the messages had no real impact on the overall performance.
Credit Card Fraud Detection using Machine Learning

Fraudulent transactions using credit card has been a growing concern with far reaching among various such as including government, corporate organizations, finance industry. Internet business is the most helpful answer for grow the client base and accomplish the biggest stage with a little venture. The fast development in the E-Commerce has significantly expanded Visas use for online buys and it actuated blow-up in the Credit card misrepresentation. For both online just as ordinary buy Credit card turned into the most well-known method of instalment, extortion cases associated with it are additionally emerging. The false exchanges are mistaken for certified exchanges and the basic example coordinating methods are not frequently enough to identify those cheats precisely. Effective location misrepresentation framework execution wound up basic to limit their misfortunes for all credit card issuing banks. Present day strategies dependent on Artificial Intelligence, Data mining, Fuzzy rationale, Machine learning, Sequence Alignment, Genetic Programming and so forth., are developed in distinguishing different Visa deceitful exchanges. When credit card transactions become a common mode of payment, machine learning has been based on handling the credit card fraud problem. This paper investigates naïve bayesian, k-nearest neighbor's performance on highly skewed credit card fraud based on genetic and optimization algorithm to determine the fraudulent transaction using credit card. Logistic Regression is a supervised classification technique which returns the probability of binary dependent variable predicted from the independent dataset variable that is logistic regression predicts the probability of different outcomes that have two values either yes or no and false or true. The Proposed System have been applied with genetic and optimization algorithm to find out the fraudualent transaction using credit card.
Leader Election in a Cluster using Zookeeper

This paper presents an algorithm for flexible and fast leader election in distributed systems using Apache Zookeeper for configuration management.&#x0D; The algorithm proposed in this paper is designed for applications that do not use symmetric nodes so they need a specialized election process or for applications that require a more flexible approach in the leader election process. The algorithm proposes a different approach as it allows assigning prioritizations for servers in the cluster that are candidates to become a leader. The algorithm is flexible as it takes into consideration during the leader election process of the different server settings and roles, network properties, communication latency or specific application requirements.
Architecture and Architectural Patterns for Mobile Augmented Reality

A software architecture codifies the design choices of software developers, which defines a modularorganizational spine for the design of a software artefact. Different architectures may bespecified for different types of artefacts, a real-time interactive artefact, for example, wouldhave markedly different requirements to those of a batch based transactional system. The use ofsoftware architecture becomes increasingly important as the complexity ofartefacts increases. Augmented Reality blends the real world observed through a computerinterface, with a computer generated virtual world. With the advent ofpowerful mobile devices, Mobile Augmented Reality (MAR)applications have become increasingly feasible, however the increased power hasled to increased complexity. Most MAR research has been directed towardstechnologies and not design resulting in a dearth of architecture and design literature for MAR. This research is targeted at addressing this void. The main requirement that a MAR architecture must meet isidentified as being the efficient real-time processing of data streams such asvideo frames and sensor data. A set of highly parallelised architecturalpatterns are documented within the context of MAR that meet thisrequirement. The contribution of this research is a software architecture, codifiedas architectural patterns, for MAR.
A Computer-Aided Detection System for Breast Cancer Detection and Classification

Breast cancer is a dangerous disease and considered the second cause of death for women globally. Reading breast cancer images requires experienced radiologists. Radiologists may have a problem with their visual decision about breast cancer. Therefore, a computer-aided detection (CAD) system is needed to help radiologists in their decisions. The early detection of breast cancer using computer vision systems, such as image processing, increases the success of treatment. Developing a well-designed CAD system is still a challenging problem because of the low rate of accuracy performance. In this paper, an improved CAD system is introduced for classifying breast cancer tumors into normal and abnormal classes.  In this CAD system, a region-based segmentation approach, namely region growing, is used. Discrete wavelet transform is used for the histogram and texture-based feature extraction. The 120 candidate features were ranked and selected according to two criteria which are the interclass separation and classification accuracy criterion. Four different classifiers, Linear Discriminant Analysis, Artificial Neural Network, Decision Tree, and Support Vector Machine, were used for classification. The results are obtained using a 10-Fold cross-validation technique on the MIAS data set.  The highest accuracy achieved was 93.6% by Support Vector Machine classifier using the best 69 features from the interclass separation method. The sensitivity and specificity achieved were 89.2% and 99.0%, respectively.  The results show improved accuracy compared to previous works selected from the literature review.