Reviewerly/exHarmony huggingface dataset:
Simultaneous Supervised and Unsupervised Classification Modeling for Assessing Cluster Analysis and Improving Results Interpretability

In the unsupervised classification field, the unknown number of clusters and the lack of assessment and interpretability of the final partition by means of inferential tools denote important limitations that could negatively influence the reliability of the final results. In this work, we propose to combine unsupervised classification with supervised methods in order to enhance the assessment and interpretation of the obtained partition. In particular, the approach consists in combining of the clustering method k-means (KM) with logistic regression (LR) modeling to have an algorithm that allows an evaluation of the partition identified through KM, to assess the correct number of clusters, and to verify the selection of the most important variables. An application on real data is presented to better clarify the utility of the proposed approach.
Design and implementation of mapping public waste disposal in jambi city with geographical information system based on android application

Abstract This system is an application that is expected to give the solution of the problems currently faced by the Agency in Jambi City. This system is designed to be as can be used by users to send problems regarding the waste disposal to the Jambi City Agency and can be used to find the location of the public waste disposal that is closest to the user’s location by using Android; can be used by the Jambi City Agency’s staff to make the Jambi City to be cleaner if there is a problem regarding the public waste disposal that sent by the user; and can be used anywhere, because this application is designed and can run on Android-based smartphones.
Application of the Perlin Noise Algorithm as a Track Generator in the Endless Runner Genre Game

Abstract The rapid development of technology has made the game as one of the popular entertainment media ranging from children to adults. Endless Runner games can be interpreted into two things, namely player characters that cannot stop or the game track that continues to run. The Perlin Noise algorithm will generate a Track Generator that continues to run randomly. Procedurally track generators can produce Pseudo-endless tracks, which are commonly used in Endless Runner games. Each track is generated by combining a series of blocks installed with each other, where each block experiences a deformation change in front of it. So that the next block that will be made in accordance with the one behind it thus repeating the process will result in unlimited tracks. Making this Track Generator uses C Sharp programming language (C #) using the Unity 5.5 game engine.
Performance Evaluation of Bio-Inspired based Visual Tracking Algorithm

In computer vision, visual tracking becomes a significant task alongwith it remains critical for various vision applications and challenges. Even though various focuses have been already given over the researches for appearance models, but those attentions are not so much. It is assuming to be a procedure for searching the target. In this paper, we made an attempt to compare the performance of two efficient tracking methods namely BA and firefly algorithm (FA). These two algorithms are discussed and validated on different scenarios in terms of various measures. The comparative outcomes depict which the BA-based tracker exhibit better results than the other trackers. And, it is efficient powerful tracking methodology. The future work will be more proficient in feature selection algorithms with targets tracking with multiple in the BA-based tracking system.
Evaluation of the Classifiers in Multiparameter and Imbalanced Data Sets

The paper discusses the basic problems resulting from the classification of imbalanced data, which are additionally described by a large number of parameters. The paper also presents various optimization methods, including the use of a synthetic indicator that is the product of specificity and the power of sensitivity, which was proposed by the author.
Design of Speed Regulation of Ping-Pong Ball Ejector Based on RBF Neural Network PID Control

In order to keep the height of the table tennis ball machine from being stable, it is difficult to achieve fast and accurate speed regulation in the brushless DC motor control system for the traditional PID control. A method of controlling the RBF neural network PID is proposed. Based on the mathematical model of the brushless DC motor, the self-learning ability of the RBF neural network is used to adjust the parameters of the PID controller in real time to adjust the motor speed so that the table tennis ball-out mechanism is highly stable. The experimental results show that the motor control system based on the RBF neural network PID control strategy has stable start-up, good static and dynamic performance, and strong robustness, which can meet the requirements of high stability of the ball-out machine.
A Novel Multiple Segmented Image Encryption

Image encryption safeguards information against unauthorised users and has numerous applications in military and communicational systems. Guaranteeing the security and robustness of the image has become a vital factor. This paper proposes a new multiple segmented multiple-image encryption framework by utilising secure force algorithm, Multiple Circular Shifting and Pixel Shrinking (MCSPS) and chaotic permutation techniques. The security of the proposed algorithm is enhanced by operating three different techniques to encrypt each segmented image. Several metrices have been used to evaluate the security of the projected framework against statistical and differential analysis. Experimental results validate that the projected algorithm can survive the statistical and differential attacks.
A Sentiment Analysis of Twitter Data Using Bi-Directional Long Short Term Memory

The dynamic data mining is used to extract the hidden knowledge of dynamic data which can be extensively used in many research communities such as security, healthcare and smart home creation. In this research work, the Sentiment Analysis (SA) on social medias such as Twitter, face-book are classified to find the public opinion on various domains. The early work identified the overall sentiment than the details of sentiment, and mostly focused on document-level which leads poor analysis in sentiment. The characteristics of text is noise and sparsity in which the traditional methods are unsuitable for social network data with limited length. In this research work, the Bidirectional Long Short Term Memory (Bi-LSTM) are used for tweets classification. Usually, the noises such as URLs, positive and negative emoji, stop words are reduced from raw tweets in the pre-processing stage. The twitter sentiment analysis (TSA) is done by feature extraction approach to pre-processed twitter data for extracting useful information. The Bi-LSTM obtained the three types of results such as positive, negative and neutral to validate the TSA for Sanders dataset. The proposed Bi-LSTM approach achieved 90.04% accuracy, 88.12% precision, 92.31% recall and 90.17% F-Measure when compared with existing methods such as Support Vector Machine (SVM), Neural Network (NN).
Search Engine and Performance Optimization of Drupal-based Websites

Abstract In view of incomplete and in-depth of research on search engine and performance optimization of Drupal-based websites, using the method of documentation, the common solutions are summarized. Their implementation methods are described and put into practice. Reference and guidance are provided for the search engine and performance optimization of Drupal-based websites.
CASECOP: A Drone Case for Mobile

The Casecop is a mobile drone case that is used for mobile safety. The mobile with drone case wherever dropped automatically the mobile flying and detecting the user who has a drone controller and coming to near that user easily. These drone case help to many users to protect mobile. Our proposed system provides waterproof for the mobile through the sensors which are placed into the mobile case. When the sensor detects the water flows, it will automatically close the areas like charger port, headphone port, etc.
Thyroid nodule diagnosis system based on the densely connected convolutional network

Thyroid cancer, which is one of the top ten most prevalent cancer in Taiwan, can be diagnosed by traditional fine-needle aspiration biopsy or ultrasonic imaging technology cooperated with physicians' clinical experience. Recently, the computer aided diagnosis (CAD) system based on ultrasonic technology is well adopted by the hospitals. However, based on ultrasonic image and human experience, the cancer can only be diagnosed approximately in 80%, the rest thus have to return to invasive biopsy. What is worse, there are still remaining 20% uncertainness after biopsy. In order to increase the detection rate in CAD system, an approach based on shear wave ultrasonic image with computer vision and deep learning technology is developed. Three methods, namely, texture analysis, traditional convolutional neural network (CNN), and densely connected convolutional network (DenseNet), are used for study and comparison. With manual ROI selection, the method based on the DenseNet achieves 88% accuracy, 90.9% sensitivity as well as 96.5% specificity on our testing data, and is thus selected as the kernel used in our thyroid nodule diagnosis system for benign/malignant classification. Furthermore, a semi-automatic user interface has been built, which can diagnose thyroid nodule in real-time clinically and thus improve the physicians' diagnosis accuracy as well as reduce the probability of invasive biopsy.
An Overview on the URV Model-Based Approach to Cluster Mixed-Type Data

In this paper, we provide an overview on the underlying response variable (URV) model-based approach to cluster and, optionally, simultaneously reduce ordinal and, optionally, continuous variables. We summarize and compare its main features discussing some key issues. An example of application to real data is illustrated comparing and discussing clustering performances.
Analysis A parallel combination between the Number Theorists aRe Us, Riverst Shamir Adleman and Triple Data Encryption Standard methods for measuring the speed of document security

Abstract Computer security is a very important thing in an information system. The strength of the NTRU algorithm is the difficulty of finding a short vector of a lattice (a discrete subgroup of a collection of vectors that includes the entire vector environment) of a random polynomial that has a large degree. The strength of the RSA algorithm lies in the level of difficulty in factoring non-prime numbers into the primary factor. As long as no efficient algorithm has been found to find the prime factors of large integers, the RSA algorithm is highly recommended for message encryption. And finally this is the TRIPEL DES algorithm designed using a 56-bit key, and that size is enough to run a secure encryption technique. This algorithm provides a simple solution that is running the DES Algorithm 3 times for data blocks. The results of our study of measuring speed on encryption and decryption using the same file are superior to the RSA algorithm compared to the NTRU and TRIPLE DES algorithms.
The Corporation Lawsuit Prediction based on Guiding Learning and Collaborative Filtering Recommendation

It is meaningful to use data mining technology to predict the type of lawsuit which a company may receive so that enterprises can avoid lawsuit risks. So we propose a corporation lawsuit prediction algorithm based on guiding learning and collaborative filtering recommendation. Firstly, we use the adaptive synthetic sampling approach (ADASYN) to generate more synthetic data for different minority classes according to their different level of difficulty in learning, so that the training would focus on these minority classes that are difficulty to learn and reduce the learning bias introduced by the imbalance of data distribution. Secondly, for the sake of solving the problem that the insufficient samples make it difficult for the model to learn enough knowledge resulting in a large fluctuation of final scores during the training and poor model stability, we use guiding learning to integrate the basic knowledge of all types of lawsuit a company may receive in the future obtained by the multi-label classification model into the training process of TOP-1 and TOP-2 predictive models. Finally, in order to further improve the prediction accuracy, we use the collaborative filtering recommendation algorithm (CFRA) to select the most similar sample with each test sample from the training set, and the lawsuit type of the selected sample is directly used as the predicted lawsuit type of the corresponding test sample, thereby improving the total prediction accuracy. The experimental results show that the proposed algorithm can effectively predict the most probable lawsuit types of the Top2 for corporations.
Implementation and impact of CSR practices in sustainable social projects of Islamic development bank the case study of Abu-Halima projects in Sudan

The paper aims to investigate the implementation and impact of Abu-Halima projects which classified as CSR intervention funded by IDB. A case analysis of Abu-Halima project in Sudan illustrates the implementation, management, and impact of CSR to make consideration attention of sustainable projects of IDB funds executed by Bank of Khartoum as a local partner. The focus is on the impact of CSR practices in sustainable social projects. Personal structured interviews are performed with 125 borrowers from Abu-Halima project supported with semi-structured interviews for 5 managers of Islamic Development Bank (IDB). Quantitative analysis was used for this paper supported with qualitative results. There is enhancing in the standard of livings of borrowers who benefit from Abu-Halima project, and there is increasing in the expenditure of borrowers after IDB's loan compared to before. The concern of this study is to investigate the critical factors that ensure the sustainability of the program to meet its social mission through successfully managing and monitoring the shareholders and stakeholders as well. Therefore, the following objective was developed to bridge this gap in the Islamic Development Bank to monitor and assess the result and impact. The paper presents a bank that has long financial interventions for working with social projects. The paper demonstrates how the actual impact of sustainable social projects in CSR practices determined by beneficiaries.
A Novel Environment Perception Algorithm Combining Biologically Inspired Features and Brain-like Memory Pattern

Abstract In this paper, we proposed a novel framework for environment perception. The proposed model combines the physio-logical mechanisms of human vision and psychological principle of cognitive development. Our algorithm consists of two major components, a gist module and a memory module. The gist module simulates the cognition process associated with visual attention. The memory module is simulated by the Incremental Hierarchical Discriminant Regression (IHDR) tree, which ensures the memories of the world are distributed over the hierarchy. Experimental results on the USC dataset demonstrated that the proposed algorithm is robust and efficient.
Development of Control System Based on the Fuzzy Controller

The development and application of a multi-modular data store and control system are presented in this paper. In order to realize intelligent control alike engineering works, a Fuzzy Controller has been developed. The main application of the system is to realise opportunity of programming various software algorithms applicable to powerful inverter converters such a DC-DC or DC-AC. There is a software developed for personal computer, based on Windows OS on a DELPHI Object Pascal and for the used microcontrollers coded in C. To the system is added four peripheral subsystems, based on microcontrollers of the ATMega32 type.
Long-term stability of the wavelength method of height scale calibration for interference microscopy

An important characterization for surface topography instruments is a traceable calibration of the height scale. We calibrate our coherence scanning interference microscopes using a natural spectral emission line in place of a sequence of material measures such as step-height specimens. The uncertainty budget for our approach includes estimates for several error sources associated with long term drift. Here we summarize results collected over 3 years’ experience from our laboratories to provide statistical support for confirming and refining these uncertainty contributions. We find that the source wavelength stability is &lt; 0.005% RMS and the stability of the height scaling factor (the amplification coefficient) is &lt; 0.02% RMS over 900 days. Both values are better than our original estimates. We also show &lt; 0.13% RMS reproducibility of the complete traceable process using acceptance test data for over 100 manufactured instruments. Finally, we report results of 3 years of experience in certifying step-height specimens using the traceable wavelength method.
Architecting for scale

Most start-ups aspire to become non-start-ups someday. One would argue then that architecting for scale means doing it right the first time. However, start-ups usually start with a single idea in search of market fit. Taking time to design and implement a scalable architecture is time that is not spent responding to customer demands and is usually not a priority. This short-term vision may lead start-ups to accumulate technical debt. This work is geared towards understanding how start-ups find a viable trade-off between customer demands and long term goals. We conduct expert interviews at start-ups and scale-ups with the hypothesis that reusing software for the management of technical debt would allow a start-up to quickly respond to the demanding needs of the market in the long run.
On the preservers of maximally entangled states

We characterize the linear maps that preserve maximally entangled states in L(X⊗Y) in the case where dim⁡(X) divides dim⁡(Y).
Performance Evaluation of Chaotic Maps &amp; Attractors in Image Encryption

With the rapid development in the field of communication, the confidential transfer of information become vital as they are passing through an unsecure public channel. The contidentiality can be greatly achieved by means of encryption. As images are the most important multimedia content need to be transmitted, the concept of image encryption emerged and raise as a hot topic in the research area as well as for real time applications. In this proposed work, the performance analysis of various chaotic maps such as Logistic, Sinus, Circle and Chebyshev and attractors namely Lorenz and Lu are carried out and the results evaluated the strength of each map and attractors for image encryption. From the results, it is evidenced that the chaotic map such as chebyshev and circle map outperforms the logistic and sinus map in terms of correlation, entropy and keyspace wherein the Lorenz attractor outperforms the Lu attractor. Near zero correlation and the entropy close to 8 are the important parameters for good image encryption.
Entanglement enhanced distinguishability of coherence-breaking channels

Originated from the superposition principle in quantum mechanics, coherence has been extensively studied as a kind important resource in quantum information processing. We investigate the distinguishability of coherence-breaking channels with the help of quantum entanglement. By explicitly computing the minimal error probability of channel discrimination, it is shown that entanglement can enhance the capacity of coherence-breaking channel distinguishability with same types for some cases while cannot enhanced for some other cases. For coherence-breaking channels with different types, the channel distinguishability cannot be enhanced via entanglement.
Implementation of MD Heuristic Method for Classifying Numerical Data In Data Preprocessing

Abstract Implementation of Data Mining with various methods will generate new knowledge and information in the form of rule which is very useful, but in implementation of data mining experience some weakness in data processing process. One weakness is that not all variable data can be easily processed, if the data is processed has a numerical value or data in the form of numbers then the division of classification is very difficult to do. If the classification of data is so many variables then the branch on the decision tree is also very much and result in less accurate results of rules or knowledge generated. MD Heuristic method can be used to classify numerical data or numbers that have a large range of data. By implementing the MD Heuristic Method in classifying the data in numerical form it can easily classify the data to make the data classification more precise and accurate, by calculating and averaging between the upper and lower bounds of the large data set. The use of MD Heuristic can facilitate preprocessing data with decision tree algorithm C4.5. The result of this research is detainee data at Labuhan Deli Detention Center, that is the data of age of detainee can be classified with more accurate to facilitate in process of data and more shorten rule or knowledge in result of decision tree image.
Face Recognition using Invariant Feature Vectors and Ensemble of Classifiers

In this paper, we have proposed an efficient and computationally inexpensive approach toward two mainstreams of image recognition, i.e, face recognition and person identification. Our proposed model is invariant to pose, expression, scale, illumination, and translation withGarg, Harsh the application of different techniques and implementation of their algorithms. For the purpose of making the model illumination invariant, we have preprocessed the image with linear transformation. Features are extracted from preprocessed facial images using a Modified-Local Difference Binary descriptor which incorporates gradient knowledge from a nonlinear scale space. The extracted facial features are then quantized to a vector. Subsequently, an extreme gradient boosting algorithm is used, resulting in fast and high-performance classification. The proposed method has been experimented on three benchmark datasets like GRIMACE, FACES95, FACES96 producing significant results in terms of speed, accuracy, and efficiency. We extended our proposed method and tested it on Raspberry Pi 3 to conclude that it is fast on limited processor and memory settings. This pipeline has resulted in a faster and efficient face recognition approach with decrease in error rate around +8 to +10%.
SPORES: A new distributed heuristic inspired by fungi reproduction

Heuristic methods are essential tools in solving complex optimization problems. There are many ways to design and implement heuristic algorithms, some based on systematic exploration, others on mimicking natural phenomena and organisms. Each approach has its own advantages and shortcomings, depending on the nature of the problem and the available computing resources. In this paper, we introduce a new heuristic, called Spores Algorithm (SPA). Inspired by asexual fungi reproduction, the algorithm makes use of distributed computing to enhance response time without penalizing the solution's quality. This heuristic has been tested on benchmark optimization functions as well as a case study using the Travelling Salesman Problem (TSP). Test results show high potential and great added value in comparison with other heuristics.