Reviewerly/exHarmony huggingface dataset:
Development and Implementation of Kalman Filter for IoT Sensors: Towards a Better Precision Agriculture

In this paper, we present an approach to increase the robustness of the sensors' readings. It is quite troublesome to get noises as IoT sensors need to be installed outdoor. As the problems have to be addressed properly, we decide on implementing Kalman Filter to reduce the noises. Based on the experiments, Kalman Filter serves better sensors' readings. It can reduce the errors due to noises up to 66.49 percents. Therefore, the implementation of Kalman Filter will bring additional values to precision agriculture.
Application Analysis Of Smart Wearable Devices in Students’ Physical Activity Under the Background Of Big Data

Abstract With the rapid development of information technology, big data is gradually being popularized and promoted in various industries. Smart wearable devices have attracted much attention for their objective, accurate and continuous measurement of the amount of exercise. With the continuous improvement of intelligent equipment performance, large data analysis and intelligent wearable device technology can real-time capture the movement of the students in class data: trajectory, heart rate, speed, cost, etc., through data analysis, to monitor and intervene student’s sports activities in the class at the same time, can help the teacher more scientific system of the training plan, a more comprehensive understanding of the students in the class level and performance, enhance the level of student’s physical stamina and physical health and will bring far-reaching influence to sports research.
The Mediating Effect of Job Satisfaction on the Relationship of HR Practices and Employee Job Performance: Empirical Evidence from Higher Education Sector

The aim of this research is to analyze the effect of human resource practices (HR) on employee job performance under the mediating effect of job satisfaction. A total of 300 faculty members from six public sector universities responded to the self-administered questionnaire. The findings come from both descriptive statistics and inferential statistics using cross-sectional data which was performed at the expediency of the scholar. Confirmatory factor analysis (CFA) was performed using structural equational modelling (SEM) to obtain the results of the study. This study has found that HR practices: recruitment and selection, training and development, performance appraisal and compensation have direct and significant effect on employee job performance through job satisfaction among the university faculty members. The impact of these HR practices has widely been studied and their importance has acknowledged. However, the number of studies addressing this issue in public-sector universities is extremely scarce. This study has addressed that gap. The findings of the study, if given serious note by the policy makers in the respective field, are expected to improve employee job performance. The study is expected to have enriched the body of knowledge on the mediating effect of job satisfaction on the relationship of HR practices and employee job performance in the target population and has validated past findings.
Research on Chinese New Word Recognition Method

A major bottleneck in Chinese word segmentation technology is the lack of recognition of OOV, particularly in a specific field. In this paper, an unsupervised method based on the combination of improved PMIn algorithm and entropy is proposed to identify OOV. First of all, this paper used the PMIn algorithm to identify the strings with high mutual information in texts, filtered through stop-word lists and corpora to get the candidate dictionaries. Calculated the adjacency entropy between the candidate dictionaries by setting a threshold value to determine the final OOV. Experiments have proved that the construction field text has a better recognition effect for OOV when using the algorithm proposed in this paper. Compared with the PMI algorithm, the accuracy rate is increased 15.92%, and the recall rate is increased 7.61%.
Aplikasi Asesmen Calon Debitur menggunakan Naive Bayes di Koperasi Mitra Sejahtera SMK Negeri 1 Kota Sukabumi

Cooperatives have an important role in economic development in Indonesia. One of them is the Mitra Sejahtera Cooperative (KMS), which is located in Sukabumi - West Java. The problem that in KMS was the increase in bad credit during the 2015-2019 period which had an impact on decreasing cash circulation flow and income of the KMS. So that in this study focuses on making a prospective debtor assessment application by implementing the Naive Bayes algorithm to provide recommendations on the feasibility of prospective debtors who have the potential for bad credit or not. The training data used are 862 data with parameters of age, gender, loan amount, occupation, income and repayment period. The stages taken include: (1) Research Initiation, (2) Data Selection, (3) Data Pre Processing, (4) System Design, (5) System Implementation, and (6) Program Testing. In system design using structured design, while the implementation of the system uses Microsoft Visual Studio 2012 tools and MySQL database. The test results from the prospective debtor assessment application obtained an accuracy rate of 86%.
WITHDRAWN: An intelligent security based ATM burglary checking framework using internet of thing

At present days ATM has become prominent technology for banking sectors. It can do things like depositing of money, with drawling of money, fund transferring and other banking services. It is one of the crucial aspects of our financial activities. The growth rate of ATM units is exponentially high because of their multifunctional tasks. As we all know with the increase of ATM units the thefts occurred in the ATM units are also increased. So, it needs to be secure by technologically and physically. The Banking sector has already secured from the software related thefts. But ATM is the place where it is vulnerable to the physical attacks from the culprits. The proposed system provides security to the ATM as well as customers from the physical attacks and it is designed on the idea of to catch the thief inside the ATM centre only. The system is integrated with microcontroller unit, pressure sensor, and temperature sensor. Whenever any irrelevant things happened to the ATM unit from the burglars then immediately the sensors send signal to the microcontroller unit. Once the micro controller receives the signal from the sensors it do simultaneous things like closing the door, leakage of gas using solenoid valve, sending alert message to the higher authorities and police station by using GSM and GPS technology, activating the buzzer and led section for visual warnings. The closed door will not open until unless the correct password is given to the system with the help of keypad. One emergency button is fixed on the top of ATM to protect the user from the blackmailers.
An improved combination modeling method for free lime content in cement clinker

The free lime (fCaO) content in cement clinker is an important quality index of cement production process and main cause of cement instability. Aiming at the difficulty in real-time measuring of fCaO content, building the fCaO content model is particularly important. In this work, the ameliorated quantum- behaved particle swarm optimization (A-QPSO) algorithm, which local search area is modified with the number of iterations, is proposed based on using the sequential quadratic programming algorithm to enhance the local search ability. The A-QPSO is adopted to adjust the parameters of multiple kernel extreme learning machine. The improved combination modeling method is also employed to build the fCaO content model. Compared with other methods, the method proposed in this research performs favorably in terms of both regression accuracy and generalization ability according to the experimental results.
Selection of quantum computing architecture using a decision tree approach

Richard Feynman proposed the idea of quantum computing for the first time in 1982 (R. P. Feynman, 1982) [1]. In the last few years, various industries have started accepting quantum solutions to their business problems. Universal quantum computers are being built by Industry tech giants to solve problems in business, engineering, and science. A variety of quantum software projects are being developed using different quantum devices, algorithms, frameworks, and quantum programming languages. To correctly identify the problems that show promise for quantum advantage, to develop proof-of-concept implementations of quantum solutions, and to actually develop software implementing these solutions is a challenging feat. This paper helps ease the decision-making process in choosing the right software architecture based on the type of the quantum project.
A Literature Review on Facial Age Estimation Researches

Face is the unique characteristic of humans that distinguishes between individuals. Every feature on the face can be analyzed to produce demographic information such as gender, age, race, etc. Any such information can be estimated through an automated process, including age. Age estimation through facial images is one of the areas that is still being pursued to deliver accurate values. To better understand facial age estimation, this paper will discuss several things regarding age estimation. First, the factors that can affect the accuracy of predictions, second, the methods used to estimate the age, and then various databases of facial images and information of age at which the picture was taken, and also the parameters that commonly used to measure the accuracy of the estimation results. This review also provides a summary regarding some researches already conducted in age estimation. In the conclusion section, there is a brief about future work that could be done, along with the reason why such research needs to do.
Multi-Task Decomposition Architecture based Deep Reinforcement Learning for Obstacle Avoidance

Obstacle avoidance is a basic skill of mobile robots. Currently, various Deep Reinforcement Learning (DRL) based approaches have been proposed to enable the robot to navigate in complex environments. However, these existing approaches merely employ collision-related reward to guide the learning of deep models, and thus fail to capture good domain knowledge for obstacle avoidance policy. Actually, practical applications also have strict requirements on speed and energy consumption, except for safety. In addition, the learning efficiency of the above DRL-based approaches is low or even unstable. To handle the above challenges, in this paper, we propose a Multi-task Decomposition Architecture (MDA) based Deep Reinforcement Learning for robot moving policy. This method decomposes robot motion control into two related sub-tasks, including speed control as well as orientation control, with obstacle avoidance inserted into each sub-task. Each sub-task is associated with one single reward and is solved using Dueling Double Q-learning (D3QN) algorithm. Q-values from two different sub-tasks are fused through aggregator to derive final Q-values which are used for selecting actions. Experiments indicate this low dimensional representation makes learning more effective, including better security and control over speed and direction. Moreover, robots can be widely used in new environments, even dynamic ones.
Evaluation of Empirical Models for the Assessment of Global Solar Radiation in Baramati Region, Maharashtra

The estimation of the solar potential is the key requirement for the maximum utilization of solar energy. These estimation helps in the policy making, renewable energy generation and consumption and sustainable development. The estimation of daily global solar radiation (GSR) has evaluated in this study. Diverse empirical models comprising of numerous climatological constraints sunshine hour, temperature, relative humidity was chosen and their applicability was tested for the Baramati region (Maharashtra). The regression constants of the developed and proposed model were computed and the performance analysis was evaluated. The comparison among various models was done by using metrics RMSE, MBE, MPE, Correlation Coefficient. It is observed that the proposed model H/H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sub> = -0.0001 + + 0.3014 (ss/ss) - 0.3927 (ss/ss) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + 0.0032RH +0.0175T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> out performs better than the other available models with lowest RMSE and MPE valued 1.795 and 0.295 respectively.
Greedy algorithm for local heating problem

This paper studies a planning problem for heating water. Hereby, boilers (e.g. gas or electric boilers, heat pumps or microCHPs) are used to heat the water and store it for domestic demands. We consider a simple boiler which is either turned on or turned off and has a buffer of limited capacity. The energy needed to run the boiler has to be bought on a day-ahead market, so we are interested in a planning which minimizes the cost to supply the boiler with energy. We present a greedy algorithm whose time complexity is O(Tα(T)) where T is the number of time intervals and α is the inverse of Ackermann function.
Cooperative FPA-ATS Algorithm for Global Optimization

This paper presents the novel cooperative metaheuristic algorithm including the flower pollination algorithm (FPA) and the adaptive tabu search named the cooperative FPA-ATS. The proposed cooperative FPA-ATS possesses two states. Firstly, it starts the search for the feasible solutions over entire search space by using the FPA’s explorative property. Secondly, it searches for the global solution by using the ATS’s exploitative property. The cooperative FPA-ATS are tested against ten multimodal benchmark functions for global minimum finding in order to perform its search performance. By comparison with the FPA and ATS, it was found that the cooperative FPA-ATS is more efficient than the FPA and ATS, significantly.
Domain Adaptation from Public Dataset to Robotic Perception Based on Deep Neural Network

A robot needs to understand the environment in a human-like manner in order to provide good service for us. However, the working environment changes from time to time for reasons such as light and layout. As a result, the robot needs to adapt existing knowledge to fit current environment. Deep neural network has shown its advantages in feature extraction which could be used in later processing. In this paper, we use a framework of deep neural network, Mask R-CNN, to endow a robot with the capability of adapting knowledge from public dataset to current environment instead of training from scratch. The effectiveness of our proposed adaptive method is evaluated on an actual humanoid NAO robot's environment, where the robot could intelligently perceive the classification and localization of objects, as well as their spatial layout even they are placed randomly.
A Planning Model Based on Swarm Intelligence Evolution and Semi-supervised Clustering Algorithm

Swarm evolution learning is to design machine learning algorithms based on optimization tools provided by evolutionary algorithms. Evolutionary learning is also called evolutionary algorithms. It is an algorithm cluster inspired by the biological evolution of nature. With the impact of global warming and climate change in recent years, a large number of cold-water fishes have been migrating, and the planning and prediction of migration paths has become a major research hot-spot. In this paper, a semi-supervised clustering neural network is used to build a model to predict the most likely migration direction of fish in Scotland, and a simulated annealing algorithm is used to calculate the maximum profit. This study will have certain implications for the development of fisheries and the allocation and planning of future marine resources reference value.
Breast Tumour Detection by using New Expectation Maximization Algorithm

Breast Cancer is one of the most far-reaching disease, especially in ladies. Breast disease is a malignant growth that develops from the breast tissue. Indications of breast malignant growth may remember an irregularity for the breast. By change of its shape, dimpling of the skin, liquid originates from the nipple. Breast Cancer Starts when cells in the breast start to develop crazy. These cells for the most part from a tumour. That can regularly be seen on an X-Ray or felt as a knot. Breast diseases differentiate from various pieces of the breast. May breast malignant growth start in the ducks that convey milt to the Nipple? The brief number of malignancy starts with different tissues in the breast. while they are called sarcomas and lymphomas. A disease would increase when the malignant growth cells enter into the blood lymphomas system, and it's spread to outstanding pieces of the body. In this procedure of division preprocessing is applied to wipe out non-cortical tissues using skull stripping with district developing technique for a superior spotlight on cortical tissues. The proposed strategy shows the best cortical tissue division contrast and the current methods. The equivalent drew nearer can likewise be utilized for effective fragment in uproarious conditions utilizing non-direct Adaptive Wiener channel.
An Improved SMOTE Algorithm Using Clustering

The problem of unbalanced data classification has received widespread attention. Due to the poor classification of minority classes in the dataset, an improved SMOTE using clustering method is proposed in this paper. Firstly, the noise samples in the dataset are identified and removed according to the KNN algorithm; then the minority samples in the dataset are divided into different sub-clusters according to the K-means, and the sample density of the different sub-clusters respectively is calculated, and the sub-clusters with relatively low sample density are given higher sampling weight. Finally, new minority samples are synthesized between the cluster sample of each sub-cluster and the sub-cluster center. Seven groups of KEEL datasets are selected for comparative experiments, and using random forest classifier to classify the oversampling balanced datasets. The F-measure, Recall, and G-mean are selected as evaluation indicators. The improved algorithm has better classification effects.
RSA encrypted FSK RF transmission powered by an innovative microwave technique for invulnerable security

Ensuring the secure communication is the prime concern for all kinds of data communication infrastructure. Several data encryption techniques are in use for secure communication. There is a scope for further improvement in the security level, as well as for the received signal quality. The selection of a proper combination of encryption and modulation techniques can improve the overall effectiveness of both the security level and the received signal quality. The combination of asymmetric cryptography (here use RSA (Rivest–Shamir–Adleman) algorithm) algorithm with the proposed reflex klystron-based frequency shift keying (FSK) modulation technique provides a high level of secure communication system. The performance of the proposed technique is experimentally verified. The conventional FSK modulation technique operates in the low-frequency band, but the proposed FSK modulation technique is powered by high-power microwave devices, which operate in the X band. The experimental result has been demonstrated and successfully verified for secured communication using the X band.
Behavior-driven development (BDD) Cucumber Katalon for Automation GUI testing case CURA and Swag Labs

Graphical user interface (GUI) is widely used in software applications. About 50% of the application code is for GUI implementation. Because labor costs are high and demands for high-quality software are increasing, automation GUI testing will be a good investment for any software company. We will apply the Behavior-driven development (BDD) method for automation GUI testing with the Cucumber framework on the CURA and Swag Labs web applications. The BDD methodology used is based on the NLP (natural language program) concept. Where the information of semi-automatically produce a step definition and code framework. The scenarios are given on the natural language and then applied to Software Testing. Class diagrams and sequence diagrams are also applied to each represent a code framework automatically. The author uses Katalon Studio software. It was found that the results of the test case automation GUI testing on the CURA website with 210 steps passed, 3 step warnings with average responses times in the test case were 1.92 minutes. The results of the test case automation GUI testing on the Swag Labs web with 87 where 36 steps failed, 4 step warning and 44 step passed with an average responses times in the test case are 2,122 minutes.
Decision-making and computational linguistic tools application for overall estimation of the level of social tension

Abstract The study of social tension, its causes and laws of variation, as well as establishing its level is crucial during crises, epidemics and wars. The purpose of this research is to develop the system for determining the level of tension in society on the basis of data from social networks using decision-making and computational linguistic tools. The impact of various factors on the growth of social tensions is determined with the help of knowledge bases built by means of decision support systems. To analyse the emotionality of comment on news publications the TF-IDF and Word2vec methods are used. Within the framework of the method of goal dynamic estimating of alternatives, the level of social tension is determined as the degree of achievement of the main goal, the ratings of news publications and the ratings of news events that contributed to the increase in social tension are calculated. The study presents the efficient method of determining the level of social tension and defining the main factors that have the greatest impact on the growth of social tension. Further studies are needed to improve the estimates of factors and the overall estimate of social tension.
Similarity Preserving Unsupervised Feature Selection based on Sparse Learning

Various feature selection methods have been recently proposed on different applications to reduce the computational burden of machine learning algorithms as well as the complexity of learned models. Preserving sample similarities and selecting discriminative features are two major factors should be satisfied, especially by unsupervised feature selection methods. This paper aims to propose a novel unsupervised feature selection approach which employs an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm regularization model to preserve global and local similarities by minimizing an objective function. Cluster analysis is also incorporated in this framework to take the inherent structure of the data into account. The experimental results show the strength of the proposed approach as compared with the earlier well-known methods on a variety of standard datasets.