Reviewerly/exHarmony huggingface dataset:
Efficient architecture for improving differential equations based on normal equation method in deep learning

Deep learning has been employed to build applications and greatly promoted the development of the industries from many areas. Among the deep learning algorithms, normal equation method is widely used and is very time-consuming. Thus, it is very urgent to improve normal equation method. First, we propose a systolic Gaussian elimination. Second, we propose a systolic Gauss-Jordan elimination. By integrating other designs, we build an efficient architecture for improving differential equations in normal equation method. We implement our design in the development environment of artificial intelligence, which shows that it is very efficient for deep learning and its applications.
Centralized joint sparse representation for multi-view subspace clustering

Multi-view subspace clustering arises in many computer visional tasks such as object recognition and image segmentation. The basic idea is to measure the same instance with multiple views. In this paper, we proposed two centralized joint sparse representation models, namely, Centralized Global Join t Sparse Representation (CGJSR) and Centralized Local Joint Sparse Representation (CLJSR) for multi-view subspace clustering. CGJSR and CLJSR force the concatenated representation matrix of all views and the representation matrix of each view to be sparse respectively. Both CGJSR and CLJSR allow the sparse coefficient matrix to approach a unified latent structure with an acceptable error. Noises and outliers regularization terms are included in CGJSR and CLJSR to reduce the influence of noises and outliers. Related optimization problems are solved using the alternating direction method of multipliers. Compared with seven state-of-the-art multi-view clustering algorithms, our proposed algorithms can achieve better or comparable results on four real-world datasets.
Prediction of Consumer’s Future Demand in Web Page Personalization System

Utility mining is the recent data science task in the ground of data mining. Utility mining observes profit and quantity of each distinct item present in the transactions, thus it results in productive patterns with high importance in transactional databases. There are many algorithms sketched to trace the entire set of highly productive utility itemsets using user-defined single minimum utility threshold. An efficient framework called high utility itemset mining with automated minimum utility thresholds (HUIM-AMU) is put forward in this research paper. This algorithm uses a condensed tree arrangement called utility pattern tree to store the transactions and a constant value indicating the amount of most profitable itemsets. With very large count of transactions in a database, it is very difficult to identify the importance or productivity of every item. Without the knowledge of items, threshold setting may degrade the effectiveness of the process. In our proposed work, the difficulty in the setting of minimum threshold and the time spent on the analysis of database to set threshold are reduced by automating the threshold setting process. The results clearly indicate that the HUIM-AMU generates only profitable and compact itemsets.
Deriving Scalability Tactics for Development of Data-Intensive Systems

Enforcing scalability quality attribute to an existing developed data-intensive software system is a difficult task. This paper proposes a methodology that reverses the traditional procedure by reframing scalability tactics. The scalability tactics are derived from existing scalable data-intensive system development practices and are presented in the format of a utility tree. These tactics can be analyzed against architectural patterns which are design decessions for software development. This will maximize the hold of scalability attributes in end software product before its actual development. The tactics derived in this paper will help an architect to have the correct design decision for data-intensive system development. Tactics alone may not solve the problem but when contrasted with architectural patterns, they will help in determining if the system is scalable.
The digital media literacy level of the youth in Majene regency and its relationship with the hierarchy of regional development

Abstract Residents of Majene Regency especially young people today like to access information sites on the internet so digital media literacy skills were needed among young people. The research aims (1) to review and describe the digital media literacy level in the Majene Regency and (2) to review and describe the relationship of digital media literacy level and regional hierarchy development of Majene Regency. The research was classified as explanatory research with a quantitative approach. Determination of samples by probability sampling. Data were collected with a questionnaire and document study method. The data were then analyzed using the scoring method with Likert’s scale and scalogram analysis with Gutmann’s scale. The research results indicated that in general digital media literacy’s young level in the Majene Regency was classified as moderate, the capacity of their technical skills was categorized as high, their critical understanding capacity, as well as their communicative abilities, were categorized as moderate. The correlation between the development of the Majene Regency area the literacy level of the youth has a significant correlation. The regions which have a higher hierarchy in digital media literacy of the youth would be categorized as high, while the regions which had a lower hierarchy in digital media literacy would be categorized as low.
Creating Knowledge Model for Handicrafts Promotion in Chiang Mai by Using Semantic Annotation

With the development of economy, tourism is becoming the global industry and it is growing rapidly. Chiang Mai as a famous destination, tourism industry is the fountain of wealth which plays an important role in the economic development. According to Tourism of Thailand, Chiang Mai office, Chinese tourists remain the largest crowds of Chiang Mai tourism which contribute a lot to tourism income. As national income goes up, tourism purchase has become a kind of very popular activities for Chinese tourists when they travel to a new place. Chiang Mai as the most effective area of the "One Tambon One Products" cultivation program of Thailand, Chiang Mai has so many different kinds of handicrafts which can be a good choice for Chinese tourists. For the sake of improving Chinese tourists' purchase intention to handicrafts, promotion is required. In this research, knowledge model will be designed for guiding the handicrafts' promotion by following knowledge engineering methodology. Marketing mix will be applied as the framework of knowledge acquisition to extract tacit and explicit knowledge from the experts and literature. Semantic annotation will be used to analysis and model the knowledge and make it become understandable.
An Efficient Node Priority and Threshold-Based Partitioning Algorithm for Graph Processing

Partitioning algorithms play vital role while processing large graph data. It is not possible to process such large graphs using a single machine; the graphs are usually partitioned into different clusters. These partitioned clusters can then be used as distributed memory clusters for processing by systems like Pregel. This research work proposed a node priority and threshold-based partition algorithm for graphs. The proposed method clusters the nodes in the graphs into four partitions based on their reachability either as incoming or outgoing. The initial partitions are generated by considering both inward as well as outward strategies. Final partitions are created by combining the merits of both strategies. Incompatible partitions are eliminated, and refined partitions are the results due to this analysis. The entire work is subdivided into four phases. The proposed method is implemented in Java platform. The partitions generated from two real-time datasets are analyzed, and the outcomes are presented in this paper. The datasets considered for the research work is large enough to address the issues of the existing works. The time taken for partitioning is the metric used to assess the proposed algorithm.
Technology: Memes (Units of Culture)

Memes, or units of culture (ideas, processes, products), are tools for solving problems and are thus a kind of technology. The domain of Memetics as a science of cultural evolution was in a Kuhnian pre-paradigm state from 1976 until 2016 when the hard domain problems of the structure, unit, and criterion in Memetics were resolved. This article summarizes that new scientific theoretical model of bioculture, including the Creative Practice Theory stage model of creativity; the discovery of the holon/parton structure of the meme, the unit of culture; and some related empirical investigations in the study of cultural evolution and creativity.
Network Traffic Compression With Side Information

There is a high degree of correlation among packets traversing the Internet. As these packets are often routed through different paths, eliminating such correlation requires to process them individually. Traditional universal compression solutions would not perform well over a single short packet because of the insufficient data available for learning the unknown source parameters. In this paper, we define a notion of correlation between information sources and characterize the average redundancy in universal compression when side information from a correlated source is available. We show that the presence of side information provides at least 50% traffic reduction over traditional universal compression when applied to network packet data providing theoretical evidence for previous empirical studies.
Patient Bayesian Inference: Cloud-Based Healthcare Data Analysis Using Constraint-Based Adaptive Boost Algorithm

Cloud-based healthcare data are a form of distributed data over the internet.The internet has become the most vulnerable part of critical healthcare infrastructures.Healthcare data are considered to be sensitive information, which can reveal a lot about a patient.For healthcare data, apart from confidentiality, privacy and protection of data are very sensitive issues.Proactive measures such as early warning are required to reduce the risk of patient's data violation.This chapter investigates the ability of Patient Bayesian Inference (PBI) for network scenario analysis with violation of patient data to produce early warning.The Bayesian inference allows modeling the uncertainties that come with the problem of dealing with missing data, allows integrating data from remote nodes, and explicitly indicates dependence and independence.The use of constraint-based adaptive boost algorithm can demonstrate the patient's Bayesian inference performance in the real-world datasets from healthcare data.
How to avoid mistakes when preparing a journal to submit to Scopus

To submit a journal for indexing in Scopus editors or publishers of journals must fill in a form, specially designed by the Scopus team and experts for this purpose. The submission form is available at specially prepared for this purpose website. The form is rather voluminous, and during the journal’s preparation for evaluation and when submitting it is important to envisage some details to avoid rejection at the first step of checking towards minimum criteria. It is important to fill in correctly the certain information about the journal in the submission form so that when preparing by the Scopus team the journal for evaluation all information about the journal for further review is presented correctly. Also during preparation for submitting the journal the typical mistakes, which will hamper passing the journal in the expert system in the future, should be avoided. The article focuses on the mistakes that are often made by specialists who are responsible for submission and gives advice on how to avoid them.
Disambiguation of features for improving target class detection from social media text

The rise of social media has led to an abundance of textual data, as well as the rise of unhealthy behaviours targeted at others (e.g. bullying, hate speech) or at oneself (e.g. suicide). In recent years, machine learning approaches have been employed to detect such behaviours, which tend to constitute a small portion of the social media content and need to be distinguished from other discourse on social media that may discuss such behaviours without displaying that behaviour, e.g. social media posts about helping people who may be at risk of suicide, thus, making this a very challenging task. In the context of machine learning, such behaviours are referred to as target classes, i.e. the main behaviours to be detected. In this paper we proposed an approach for disambiguation of features in relation to their membership to the target class vs. non-target class(es). We validate our approach with a case study on suicide detection and our results show that the proposed disambiguation approach leads to a better detection rate of suicide.
Library: Illustration: Additional Help

Illustration is a type of art that is a visual explanation or interpretation of a written text.
1C:Automated scheduling system. School: Experience of the implementation according regional features

The article presents the results of the process of implementing the software product 1C:Automated scheduling system. School at school 61 of the city Cheboksary (the Chuvash Republic), taking into account regional characteristics and using new functionality. The curator of the project was 1C, the developer was Big Numbers company, and the regional partner was Leader Soft company. During the implementation of the automation project, the following main stages were carried out: an agreement was signed; roadmap and schedule were developed; the project working group was approved (curators from the school, regional partner, 1C firm, developer); the program was installed; training was provided; requirements for the implementation of processes were defined. The results of the project were: freeing up time for the deputy director in scheduling; creating schedule templates in manual, automatic, mixed modes; operational schedule adjustment; building reports on the lessons and the effectiveness of the use of the premises.
SR-SYBA: A Scale and Rotation Invariant Synthetic Basis Feature Descriptor with Low Memory Usage

Feature description has an important role in image matching and is widely used for a variety of computer vision applications. As an efficient synthetic basis feature descriptor, SYnthetic BAsis (SYBA) requires low computational complexity and provides accurate matching results. However, the number of matched feature points generated by SYBA suffers from large image scaling and rotation variations. In this paper, we improve SYBA’s scale and rotation invariance by adding an efficient pre-processing operation. The proposed algorithm, SR-SYBA, represents the scale of the feature region with the location of maximum gradient response along the radial direction in Log-polar coordinate system. Based on this scale representation, it normalizes all feature regions to the same reference scale to provide scale invariance. The orientation of the feature region is represented as the orientation of the vector from the center of the feature region to its intensity centroid. Based on this orientation representation, all feature regions are rotated to the same reference orientation to provide rotation invariance. The original SYBA descriptor is then applied to the scale and orientation normalized feature regions for description and matching. Experiment results show that SR-SYBA greatly improves SYBA for image matching applications with scaling and rotation variations. SR-SYBA obtains comparable or better performance in terms of matching rate compared to the mainstream algorithms while still maintains its advantages of using much less storage and simpler computations. SR-SYBA is applied to a vision-based measurement application to demonstrate its performance for image matching.
Assessment of National Crime Reporting System: Detailed Analysis of Communication

Dominican Republic is proposing and adopting measures to reduce the existent crime rate in the country, and to provide better protection to its citizens. One of the adopted measures is a secure communication system between users and police in which citizens can report suspicious activities. This research aims to comprehend how communication works between the client application and the police desktop and to study how the system, including reporting system and desktop application, is able to handle malicious code sent by a bad-intentioned user.
Video 360 and Augmented Reality

The advent of all types of eXtended Reality (XR)—VR, AR, MR—raises serious questions, both technological and pedagogical. The setup of campus services around XR is only the prelude to the more complex and expensive project of creating learning content using XR. In 2018, the authors started a limited proof-of-concept augmented reality (AR) project for a library tour. Building on their previous research and experience creating a virtual reality (VR) library tour, they sought a scalable introduction of XR services and content for the campus community. The AR library tour aimed to start us toward a matrix for similar services for the entire campus. They also explored the attitudes of students, faculty, and staff toward this new technology and its incorporation in education, as well as its potential and limitations toward the creation of a “smart” library.
Looking Also From Another Perspective: Exploring the Benefits of Alternative Views for Alignment Tasks

Object alignment is an activity frequently performed in several domains. Traditional AR/VR methods provide virtual guides such as text, arrows, or animations to assist users during alignment. This work explores the feasibility of using virtual cameras and mirrors instead. We conducted a study where participants aligned objects assisted by virtual cameras and mirrors in VR. Data regarding alignment, time to completion, users’ distance traveled, average head velocity, usability, and mental effort was collected. Results show that virtual helpers reduce mental effort and distance traveled and increase acceptance without negatively affecting alignment accuracy.
Algebraic cryptanalysis and new security enhancements

We briefly discuss linear decomposition and nonlinear decomposition attacks using polynomial-time deterministic algorithms that recover the secret shared keys from public data in many schemes of algebraic cryptography. We show that in this case, contrary to common opinion, typical computational security assumptions are not very relevant to the security of the schemes; i.e., one can break the schemes without solving the algorithmic problems on which the assumptions are based. Also we present another and in some points similar approach, which was established by Tsaban et al. Before demonstrating the applicability of these two methods to two well-known noncommutative protocols, we cryptanalyze two new cryptographic schemes that have not yet been analyzed. Further, we introduce a novel method of construction of systems resistant against attacks via linear algebra. In particular, we propose improved versions of the well-known Diffie–Hellman-type (DH) and Anshel–Anshel–Goldfeld (AAG) algebraic cryptographic key-exchange protocols.
Real and Virtual Environment Mismatching Induces Arousal and Alters Movement Behavior

This paper examines a common problem found in a number of virtual reality setups—mismatches between real and virtual environments. Specifically, this paper investigates whether the mismatching between a real and a virtual environment in terms of appearance and physical constraints can affect the arousal (electrodermal activity) and movement behavior in the participants. For this study, one baseline condition and four mismatch conditions that examine different mismatching types were developed and tested in a between-group study design. The participants were immersed in a virtual environment and were asked to walk in a direction given to them along a provided path. During that time, electrodermal activity and the walking motion of participants were captured to assess potential alterations in their arousal and movement behavior respectively. Results obtained from this study indicate significant differences in the electrodermal activity and movement behavior of participants, especially when walking in a virtual environment that is mismatched both in appearance and physical constraints. Even though to a lesser degree, evidence was also found that correlates electrodermal activity with movement behavior. Limitations and future research directions are discussed.
A Dictionary based Compression Scheme for Natural Language Text with Reduced Bit Encoding

Data compression, also called compaction, the process of reducing the amount of data needed for the storage or transmission of a given piece of information, typically by the use of encoding techniques. Character encoding is genuinely related to data compression which represents characters with a type of encoding technique. Encoding characterizes the way toward putting a movement of characters into a specific arrangement for incredible transmission or point of confinement. Compression of data covers a goliath space of employments including data correspondence, data securing and database improvement. For the most part two surely understood compression procedures named Huffman and LZW are really utilized for text compression. In this paper, we propose an effective and straightforward compression techniques for huge common text by a 5 bit encoding scheme which can convert 8 bit characters to 5 bit named 5 Bit Encoding Scheme (5BE). It can most likely beat Huffman and LZW regarding compression proportion. This plan gives an encoding calculation changing over any 8 bit characters in English and Bangla by 5 bit by using a look up table. The look up table is created by utilizing Zipf dissemination which is a discrete circulation of generally utilized characters in various dialects. In the wake of changing over the characters into 5 bit, we consistently ascertain a k-Series scheme to build a database dictionary. With the penalty of storage for the dictionary, we compress a natural text by 87%. This dictionary will be used by the compression and decompression algorithms and to be employed in the client side. Therefore, constructed only once. Hence the facilities provided by the compression technique will be found without interruption. The reverse algorithm to recuperate the genuine data is additionally illustrated. We compare our algorithm to both the known Huffman and LZW technique. Promising efficiency is exhibited by our experimental result.
Distraction at Mobile Learning Environments

This chapter focuses on distraction at mobile learning environments from different perspectives. It is hoped that this chapter will contribute to the academic discussion on this topic by exploring whether the term ‘distraction' accurately describes learners' tendencies to leave the formal, standard, or prescribed content and access the non-standard applications and websites during the academic attempts. This chapter will also introduce a more in-depth discussion on the root causes that drive learners' deviation to the non-standard applications and websites. Furthermore, it will propose and discuss some emerging arguments such as converting ‘learning distractions' into ‘learning attractions' in a way that might enhance the academic discussion on theory and practice of mobile learning research. Finally, it will introduce three effective techniques and strategies that have the potential to control distractions in mobile learning environments.