Reviewerly/exHarmony huggingface dataset:
SEGMENTASI DAN PERAMALAN PASAR RETAIL MENGGUNAKAN XGBOOST DAN PRINCIPAL COMPONENT ANALYSIS

The growth of the online retail market in Indonesia is an excellent business opportunity. It is predicted that this growth will continue to move upward due to the increasing internet penetration. With greater exposure to brands, products and offerings, consumers become smarter and wiser in their purchasing decisions. Offering goods and services that match the tastes and behavior of consumers is very important to maintain business continuity. So far, the models developed are divided into two major parts, namely the time series approach and machine learning. In this study, segmentation and forecasting of online retail sector sales were carried out using extreme gradient boosting (XGBoost). The data used in this study is an online retail dataset obtained from the UCI repository. The k-means clustering (KMC) method is applied to determine the target or data class. Principal component analysis (PCA) is applied to reduce data dimensions by eliminating irrelevant features. Model evaluation is based on a confusion matrix and macro average ROC curve. Based on the research results, XGBoost can perform retail data classification well, this can be seen through confusion matrix metrics and ROC curves.
Process Evaluation Study of Multiple-Systems Collaborative Child Welfare Approach: A Perspective on Foster Care Service Delivery

It has become essential for those in child welfare policy development to understand models of collaboration processes in foster care programs, if they are to help programs succeed and improve outcomes for children and families in foster care.This paper presents a qualitative evaluation model used by the Local Interagency Network for Children's Services (LINCS), in Shasta County, California, as an example of evaluation of multisystems collaboration in child welfare services.This evaluation of a multisystems service delivery process includes key findings regarding day-to-day operations, financial relationships, and working relationships, as well as a comparison of the prior foster care services delivery system and the LINCS program.Lessons learned, recommendations, and implications for knowledge of the foster care service delivery process and evaluation research of foster care programs are included.
Category-Oriented Self-Learning Graph Embedding for Efficient Image Compact Representation

As one of the ways to acquire efficient image compact representation, graph embedding (GE) based manifold learning has been widely developed over the last two decades. Good graph embedding depends on the construction of graphs concerning intra-class compactness and inter-class separability, which are crucial indicators of the effectiveness of a model in generating discriminative features. Unsupervised approaches are intended to reveal the data structure information from a local or global perspective, but the resulting compact representation often has poorly inter-class margins due to the lack of label information. Moreover, supervised techniques only consider enhancing the adjacency affinity within the class but excluding the affinity of different classes, which results in the inability to fully capture the marginal structure between distributions of different classes. To overcome these issues, we propose a learning framework that implements Category-Oriented Self-Learning Graph Embedding (COSLGE), in which we achieve a flexible low-dimensional compact representation by imposing an adaptive graph learning process across the entire data while examining the inter-class separability of low-dimensional embedding by jointly learning a linear classifier. Besides, our framework can easily be extended to the semi-supervised situation. Extensive experiments on several widely-used benchmark databases demonstrate the effectiveness of the proposed method comparing with some state-of-the-art approaches.
Fracture and Inequality of Digital Skills among Moroccan Ph.D. Students “Hassan II University-Casablanca”, as a Case of Study

At the present time, the acquisition of digital skills or "e-skills", defined as the ability to master Information and Communication Technologies is essential for the majority of personal, educational and professional activities of Moroccan Ph.D students.How well do doctoral students master basic digital skills?Does faculty membership affect this degree of mastery?In this work, we will draw a brief portrait of the inequalities of digital skills between doctoral students from different faculties of Hassan II Casablanca University, by referring to a baseline of basic digital skills developed with the help of university professors.
The Development of Assistant Selection Application of Information and Communication Technology Laboratory

This research aimed to design a support system to facilitate the decisionmaking process in terms of selecting the acceptance of Integrated ICT (Information and Communication Technology) Laboratory assistants using the Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods.The AHP method was used for the process of determining the weight of each criterion determined from the Integrated ICT Laboratory, Budi Luhur University, while the TOPSIS method was used to determine the ranking based on the calculated values.
Data Analysis and Classification of mHealth Shimmer2 Sensor Data sets for Human Physical Activities Recognition

Human physical activity recognition is one of the important attributes for well being of human health and activity recogni-tion field. A physical signal with respect to time based directed toward recognizes human activity events is projected, in the view of this work usage wearable accelerometer sens-ing devices were implanted taking place the human body lo-cation on the upper body Chest Sensor (CS), Left Ankle Sen-sor (LAS) and Right Lower Arms Sensor (RLAS). Accelerome-ter feature extracted based acceleration signals with respect to time, physical appearance of the accelerometer x, y, and z dimension values reported/recorded using shimmer2 weara-ble sensor device is recommended at the categorization of the 10 different users was performed 12 different types human activities, including vigorous and moderate activities. User ages between 24 to 29 years and human body weight (HBW) are 53 to 83 Kg=m2. Results were on view a large validity per-formance precision and recall were getting 95 for each human activities. The whole classifiers accuracy results for all combi-nation of the feature set of all sensors is 98%. The considered work could be used to observe the human body motion of different body location of users and to perform data analysis and classification.
Minimum Threshold Determination Method based on Dataset Characteristics in Association Rule Mining

Abstract The process of extracting data to obtain useful information is known as data mining. Furthermore, one of the promising and widely used techniques for this extraction process is association rule mining. This technique is used to identify interesting relationships between sets of items in a dataset and predict associative behavior for new data. The first step in association rule mining is the determination of the frequent item set that will be involved in the rule formation process. In this step, a threshold is used to eliminate items excluded in the frequent itemset which is also known as the minimum support. Furthermore, the threshold provides an important role in determining the number of rules generated. However, setting the wrong threshold leads to the failure of the association rule mining to obtain rules. Currently, the minimum support value is determined by the user. This leads to a challenge that becomes worse for a user that is ignorant of the dataset characteristics. In this study, a method was proposed to determine the minimum support value based on the characteristics of the dataset. Furthermore, this required certain criteria to be used as thresholds which led to more adaptive rules according to the needs of the user. The results of this study showed that 6 from 8 datasets, obtained a rule with lift ratio &gt; 1 using the minimum threshold value that was determined through this method.
A Benchmark for Suitability of Alluxio over Spark

Big data applications play an important role in real time data processing. Apache Spark is a data processing framework with in-memory data engine that quickly processes large data sets. It can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools. Spark’s in-memory processing cannot share data between the applications and hence, the RAM memory will be insufficient for storing petabytes of data. Alluxio is a virtual distributed storage system that leverages memory for data storage and provides faster access to data in different storage systems. Alluxio helps to speed up data intensive Spark applications, with various storage systems. In this work, the performance of applications on Spark as well as Spark running over Alluxio have been studied with respect to several storage formats such as Parquet, ORC, CSV, and JSON; and four types of queries from Star Schema Benchmark (SSB). A benchmark is evolved to suggest the suitability of Spark Alluxio combination for big data applications. It is found that Alluxio is suitable for applications that use databases of size more than 2.6 GB storing data in JSON and CSV formats. Spark is found suitable for applications that use storage formats such as parquet and ORC with database sizes less than 2.6GB.
Gravitational Search Algorithm: A Novel Approach for Structural Test Path Optimizationl

"Gravitational Search Algorithm: A Novel Approach for Structural Test Path Optimizationl." Journal of Interdisciplinary Mathematics, 24(1), p. vThis article refers to:Gravitational search algorithm: A novel approach for structural test path optimization
An Experimental Researchon Outsourced Data Science for File Seeking

Over the year People have become more privacy conscious.Security require constant effort, one simply can’t rely on just some top notch antivirus or firewall software’s.So, best solution is to encrypt the data before sending it to any of the third party storage.Now, in this scenario where user choose to encrypt all the data before outsourcing; what happen is performing regular operations like search becomes a very hectic task; as one has to decrypt all data before actually starting the searching process.In this paper, we are exploring a technique that will help user perform search over encrypted data.Also the main attraction of this technique is even though the user can’t remember the exact keyword to look for the particular encrypted file;still the user will be able to search with partially correct keywords.We will also discuss some string matching algorithms in this paper.
Detecting Emotions through Pitch and Formants for South Kamrupi Dialect of Assamese Language

Speech is a form of voice signal that propagates through atmosphere and human beings communicate primarily through the exchange of speech. Along with their thoughts, human also expresses their emotions while communicating through the speech. Spoken dialects are the most native forms of speech communication and emotion is expressed very effectively through it. Speech is digitized to use it for speech processing. The speech processing task mostly comprises of feature extraction and pattern recognition. Pitch and Formants are two most fundamental speech features through which emotions can be modeled. Modeling emotion is a pattern matching task. This paper describes a successful empirical study of Pitch and Formants based emotion recognition system on the widely spoken, colloquial South Kamrupi dialect of Assamese language.
Steganography Using Android-Based Five Pixel Pair Differencing and LSB Substitution Method

FPPD and LSB Substitution modified method are development of FPPD and LSB Substitution method.The modification of FPPD and LSB Substitution is located at the pixel block that is going to be used to insert the secret message.The pixel block being used is 2x2.The LSB Substitution method is a method to insert the data by changing the last bit of the pixel into the byte of the hidden message.FPPD uses data blocks and calculates the capacity of each blocks, then inserts the hidden message in accordance to the capacity of each blocks.The development of this method is expected to increase the capacity of the plain text without reducing the quality of the data produced.To help users in hiding and sending data, a steganoskrip application using improved PFFD and LSB Substitution method will be developed on Android smartphone.
The Potential of Blockchain Technology for Health Information Exchange: Experimental Study From Patients’ Perspectives (Preprint)

<sec> <title>BACKGROUND</title> Nowadays, a number of mechanisms and tools are being used by health care organizations and physicians to electronically exchange the personal health information of patients. The main objectives of different methods of health information exchange (HIE) are to reduce health care costs, minimize medical errors, and improve the coordination of interorganizational information exchange across health care entities. The main challenges associated with the common HIE systems are privacy concerns, security risks, low visibility of system transparency, and lack of patient control. Blockchain technology is likely to disrupt the current information exchange models utilized in the health care industry. </sec> <sec> <title>OBJECTIVE</title> Little is known about patients’ perceptions and attitudes toward the implementation of blockchain-enabled HIE networks, and it is still not clear if patients (as one of the main HIE stakeholders) are likely to opt in to the applications of this technology in HIE initiatives. Thus, this study aimed at exploring the core value of blockchain technology in the health care industry from health care consumers’ views. </sec> <sec> <title>METHODS</title> To recognize the potential applications of blockchain technology in health care practices, we designed 16 information exchange scenarios for controlled Web-based experiments. Overall, 2013 respondents participated in 16 Web-based experiments. Each experiment described an information exchange condition characterized by 4 exchange mechanisms (ie, direct, lookup, patient-centered, and blockchain), 2 types of health information (ie, sensitive vs nonsensitive), and 2 types of privacy policy (weak vs strong). </sec> <sec> <title>RESULTS</title> The findings show that there are significant differences in patients’ perceptions of various exchange mechanisms with regard to patient privacy concern, trust in competency and integrity, opt-in intention, and willingness to share information. Interestingly, participants hold a favorable attitude toward the implementation of blockchain-based exchange mechanisms for privacy protection, coordination, and information exchange purposes. This study proposed the potentials and limitations of a blockchain-based attempt in the HIE context. </sec> <sec> <title>CONCLUSIONS</title> The results of this research should be of interest to both academics and practitioners. The findings propose potential limitations of a blockchain-based HIE that should be addressed by health care organizations to exchange personal health information in a secure and private manner. This study can contribute to the research in the blockchain area and enrich the literature on the use of blockchain in HIE efforts. Practitioners can also identify how to leverage the benefit of blockchain to promote HIE initiatives nationwide. </sec>
Non-frontal facial expression recognition based on salient facial patches

Abstract Methods using salient facial patches (SFP) play a significant role in research on facial expression recognition. However, most SFP methods use only frontal face images or videos for recognition, and do not consider variations of head position. In our view, SFP can also be a good choice to recognize facial expression under different head rotations, and thus we propose an algorithm for this purpose, called Profile Salient Facial Patches (PSFP). First, in order to detect the facial landmarks from profile face images, the tree-structured part model is used for pose-free landmark localization; this approach excels at detecting facial landmarks and estimating head poses. Second, to obtain the salient facial patches from profile face images, the facial patches are selected using the detected facial landmarks, while avoiding overlap with each other or going beyond the range of the actual face. For the purpose of analyzing the recognition performance of PSFP, three classical approaches for local feature extraction-histogram of oriented Gradients (HOG), local binary pattern (LBP), and Gabor were applied to extract profile facial expression features. Experimental results on radboud faces database show that PSFP with HOG features can achieve higher accuracies under the most head rotations.
Foreign language teachers’ preparing for a mobile educational future

Theoretical and practical fundamentals of foreign language teachers' preparing for a mobile educational future are substantiated in the study.Benefits, limitations and risks of using mobile applications in the process of teaching are identified.The scientists' outlooks on the given issue are analyzed.The models for future foreign language teacher training in new technologies Content and Language Integrated Learning (CLIL) and Technological Pedagogical Content Knowledge (TPCK) are outlined.Some recommendations for using mobile technologies in education are highlighted.
Various Methods for Object Detection Based on Deep Learning

The growing technology in the world made-up the deep learning method, which classifies different vehicles from a video. In deep learning technology use different strategies such as RCNN, Fast RCNN, RPN, faster RCNN, YOLO, SSD. All methods offer various accuracy of the identification of the vehicle. The convolutional natural network determining an object detection task exploitation in deep learning. Object detection is very important in AI as well as in videos using pc vision. Through this paper demystifies the important role of deep learning supported by CNN for object detection. And the methodology offers additional correct result. Deep learning techniques shows the development of object detection in various area and the different technics are assessed during this paper.
Implementation of a Secured Authentication System using a Policy Generator with Email Notifications

For securing the login, passwords of users from intruders and hackers, the website owners and administrators are providing certain guidelines to the users to create secure and strong passwords using a mechanism called Password Checkers. These guidelines which are provided helps the users to create strong passwords, these guidelines are also becoming the raw input for the hackers as they clearly show based on which policy the password was generated which increases the risk for brute force attacking with more ease. There by increasing the success rate probability for the brute force attackers. To overcome and to decrease the success probability for brute force attacking the Dynamic Password Policy Generator is being devised.The profiles of users are built and maintained by the system automatically bases on the interaction with the monitored database in training phase. This DBSAFE system will help both the administrator as well as the users to feel secured in terms with their data security. Also whenever, an unsuccessful attempts leaving a notification through an email will always add a extra layer of security to the system. When the system’s critical files were all under watch and someone try to access those, concerned people will be intimated to verify the system security keeping the system and database safe and healthy.
FAST: An extension of the Wavelet Synchrosqueezed Transform

&lt;p&gt;Extracting frequency domain information from signals usually requires conversion from the time domain using methods such as Fourier, wavelet, or Hilbert transforms. Each method of transformation is subject to a theoretical limit on resolution due to Heisenberg’s uncertainty principle. Different methods of transformation approach this limit through different trade-offs in resolution along the frequency and time axes in the frequency domain representation. One of the better and more versatile methods of transformation is the wavelet transform, which makes a closer approach to the limit of resolution using a technique called synchrosqueezing. While this produces clearer results than the conventional wavelet transforms, it does not address a few critical areas. In complex signals that are com-posed of multiple independent components, frequency domain representation via synchrosqueezed wavelet transformation may show artifacts at the instants where components are not well separated in frequency. These artifacts significantly obscure the frequency distribution. In this paper, we present a technique that improves upon this aspect of the wavelet synchrosqueezed transform and improves resolution of the transformation. This is achieved through bypassing the limit on resolution using multiple sources of information as opposed to a single transform.&lt;/p&gt;
Advanced Machine Learning Models to Handle Unifying Attacks in Images

Critical advancement has been made with profound neural systems as of late. Sharing prepared models of profound neural systems has been a significant in the fast advancement of innovative work of these frameworks. In digital environment, there are different types of applications face security related attack sequences from third parties. Most of the machine learning related approaches was introduced to describe security in wind and vulnerable attack sequences. Digital Watermarking is one of the approach to handle adversary related security approach to handle attacks appeared in digital environment. But it has some limitations to describe efficient security behind the web related applications appeared in real time environment. So that in this paper, we propose and implement advanced machine learning approach i.e Neural Network based Click Prediction (NNBCP) to handle web related attack sequences in real time environment. It uses Integrated CAPTCHA procedure to provide machine learning based captcha generation for user login and registration to handle different types of attacks in digital systems.
Analisis Aliran Air Bawah Permukaan Dengan Menggunakan Metode Geolistrik

Cean water is needed in a human community. The need for clean water can be taken from subsurface water through a pumping process. Efforts to get clean water that has sustainability can be done by knowing the direction of the aquifer flow. The direction of the aquifer flow can be known through subsurface modeling. This study uses the geoelectric method in modeling the subsurface layer to determine the position of the aquifer. Based on the subsurface model, the average depth of an aquifer is between 30-100 meters. The subsurface model shows the type of aquifer depressed with a clay layer as the upper and lower limits of the aquifer. The subsurface water is estimated to have a flow direction from East to West in a confined aquifer.