Reviewerly/exHarmony huggingface dataset:
Using Secret Image Block to Secure LSB Data Steganography

Data steganography is one of the most popular methods of data protection, but most introduced methods are not secure. In this paper research a simplified LSB method of data steganography will produced, this method will easily used to hide any message in any covering image, changing the image or/and changing the message will not require any changes in the algorithm. The proposed method will use a complicated key to select a secret block from the covering image to be used to hide the message. The PK will contain 5 components of double data type, thus the PK will provide a huge key space which is capable to resist any hacking attempt, the algorithm of the proposed method will be very sensitive to the PK values, any changes in these values will be considered as a hacking attempt by producing unexpected outputs. The proposed method will be implemented and tested using various messages, various covering images, and various PK; the obtained results will be analyzed applying sensitivity analysis, quality analysis and security analysis to prove the improvements added to LSB method of data steganography.
Hubungan Motivasi Belajar Dengan Minat Belajar Pelajar Bermasalah Disiplin Di Sebuah Sekolah Menengah

Motivation and interest are the starting points for students in learning that can be used to achieve desired goals. The purpose of this study is to find out whether there is a relationship between learning motivation and learning interest of students with disciplinary problems in a school in kulim. This research was conducted on students aged 14,16 and 17. The sample in this study was 130 students with disciplinary problems who were randomly selected. Data were obtained through questionnaires. The results showed that there was a significant relationship between learning motivation and learning interest of students with disciplinary. The results of multiple regression analysis showed that regression model containing motivation and interest in learning variables can explain significantly 32% of the variance in the variable age f (2,127) = 7.314, k &lt;.05. The learning motivation variable (beta =-. 201), (t = 2.258, k &lt;.05) was a significant predictor of age. The learning interest variable (beta =-. 193), (t = 2.164, k &lt;.05) was an insignificant predictor of age and there was a negative relationship between learning motivation and learning interest of students with disciplinary problems
Application of the K-Means Cluster for the Classification of Disadvantaged Districts/Cities in Maluku Province

Maluku Province is still the 4th poorest province in Indonesia. This is due to the disparity in development between the provincial and district centers, cities and villages as well as government work programs that are not implemented evenly. To overcome and evaluate these problems, it is necessary to plan or study the classification of underdeveloped regions, namely by grouping districts/cities based on indicators of nderdeveloped areas. This research was conducted using secondary data obtained from the Central Statistics Agency (BPS) of Maluku Province. The method used in this study is to use the K-Means Cluster analysis method. The results of the study indicate that there are 2 classifications of underdeveloped and undeveloped areas in Maluku Province. Cluster 1 consists of Tanimbar Islands Regency, Southeast Maluku Regency, Central Maluku Regency, Buru Regency, Aru Islands Regency, West Seram Regency, Eastern Seram Regency, Southwest Maluku Regency, South Buru Regency and Tual City. In Cluster 2 there is only one area, namely Ambon City.
Window Based Min-Max Feature Extraction for Visual Object Tracking

Background/Objectives: Visual object tracking is considered as difficult problem and becomes more challenging because of the various environmental conditions.In order to achieve the efficient result in the area of visual tracking, feature extraction will play the important role.This study demonstrates the Min-max feature extraction method to improve the tracker robustness in the area of visual tracking.Methods: The proposed min-max features along with existing Histogram of Oriented Gradient (HOG), and Convolution Neural Network (CNN) features are given to the Spatial Temporal Regularized Correlation Filter (STRCF) to find the new position of the target and it is successfully solved through Alternative Direction Method of Multipliers (ADMM).By employing both Spatial and temporal regularization methods, without much compromise in the efficiency, the boundary effect is handled.The min-max feature will extract the object's window-based features as foreground and background.The foreground consists of higher color values than the background.As compared to the Color Names (CN) proposed minmax feature method gives accurate features to identify the objects in a video.In order to present the performance, the method is tested on the OTB dataset image sequences and compared with the state-of-the-art tracker and achieved the promising results for all of tested videos.Findings: Our method, using Minmax feature, gives Mean OP and FPS 61.44% &18.27 respectively, which shows improvement in the tracking accuracy along with the computational speed as compared to CN feature.
Cross-domain entity identity association analysis and prediction based on representation learning

Cross-domain identity association of network entities is a significant research challenge and a vital issue of practical value in relationship discovery and service recommendation between things in the Internet of things, cyberspace resources surveying mapping, threat tracking, and intelligent recommendation. This task usually adds additional difficulty to the research in practical applications due to the need to link across multiple platforms. The existing entity identity association methods in cross-domain networks mainly use the attribute information, generated content, and network structure information of network user entities but do not fully use the inherent strong positioning characteristics of active nodes in the network. In this article, we analyzed the structural characteristics of existing relational networks. We found that the hub node has the role of identity association positioning, and the importance of identity association reflected by different nodes is different. Moreover, we creatively designed a network representation learning method. We proposed a supervised learning identity association model combined with a representation learning method. Experiments on the public data set show that using the identity association method proposed in this article, the ranking accuracy of user entity association similarity is about 30% and 25% higher than the existing two typical methods.
Telepresence Technology to Facilitate Remote Working with Human Physicality

Remote work allows people to free themselves from geographical and temporal constraints, and there are calls for it to be made available as an option for essential workers for whom it is not readily applicable.This article introduces our efforts to develop telepresence technology with which a person can use an artificial body or robot to sense the situation at a remote location and interact with other people and the environment in the same way as when working in the field.Thus, they can carry out their required tasks and engage in face-to-face communication without any discomfort or hindrance.
Failure Mode Analysis (FMA) for Visual-Based Navigation for UAVs in Urban Environment

Visual-based navigation systems for Unmanned Aerial vehicles (UAVs) have become an interesting research area focused on improving robustness and accuracy in the urban environment.However, a lack of integrity can damage UAVs, limiting their potential usage in civil applications.For safety reasons, integrity performance requirements must be met.In literature, such systems require significant attention for their ability to perform fault analysis, referred to as failure mode.In this paper, we have conducted a failure mode analysis in urban environments for UAVs to identify threats and faults presented in existing Visual-inertial Navigation Systems.In addition, we propose a federated-filter-based fault detection and execution system to improve navigation performance under faulted conditions.
An End-to-End Task Allocation Framework for Autonomous Mobile Systems

This work aims to unravel the problem of task allocation and planning for multi-agent systems with a particular interest in promoting adaptability.We proposed a novel end-toend task allocation framework employing reinforcement learning methods to replace the handcrafted heuristics used in previous works.The proposed framework achieves high adaptability and also explores more competitive results.Learning experiences from the feedback help to reach the advantages.The systematic objectives are adjustable and responsive to the reward design intuitively.The framework is validated in a set of tests with various parameter settings, where adaptability and performance are demonstrated.
A New Spectral Compression Method based on the Minimization of the Color Difference and Root Mean Square Error

This paper describes two new interim connection spaces (ICSs), P-2OC and P-3OC, for spectral image compression and reconstruction. For this type of ICS, the weighting table or compression matrix H is modelled as a variable. The associated reconstruction matrix N is chosen as the Weiner estimation matrix. The objective function f(H) is the combination of the averages of the CIEDE2000 color difference (&#x394;E00) and the root mean square error (RMSE) between the original and reconstructed reflectance values. Hence, the compression matrix H is determined by solving the nonlinear minimization problem based on the Munsell training dataset. The proposed ICSs, P-2OC and P-3OC, were tested and compared, respectively, with ICS-2SI and ICS-3SI developed by Zhang et&#xA0;al.&#xA0;(JOSAA, Vol. 29, pp. 1027&#x2013;1034) in 2012 using the NCS dataset, and 2 spectral images. Performance tests showed that the proposed P-2OC and P-3OC ICSs are better than the ICS-2SI and ICS-3SI ICSs, respectively, in terms of RMSE, goodness of fit coefficient (GFC), and &#x394;E00 under CIE illuminants D65, A, C and F11. Therefore, it is expected that the P-2OC and P-3OC ICSs can find applications in spectral image compression and cross-media reproduction.
Mobile Finger Print Verification and Automatic Log in Platform Using Blockchain

Abstract: Biometric identification has become increasingly popular in recent years. With the development of cloud computing, database owners are motivated to outsource the large size of biometric data and identification tasks to the cloud to get rid of the expensive storage and computation costs, which however brings potential threats to users’ privacy. In this paper, we propose an efficient and privacy-preserving biometric identification outsourcing scheme. Specifically, the biometric data is encrypted and outsourced using Block Chain. To execute a biometric identification, the database owner encrypts the query data and submits it to the cloud. The Block Chain performs identification operations over the encrypted database and returns the result to the database owner. A thorough security analysis indicates the proposed scheme is secure even if attackers can forge identification requests and collude with the cloud
Experience of college students with ASD on participation in college class

Objectives In this study, we listened to the experiences and specific stories of ASD students' participation in classes during college life, and based on this, we tried to find out the meaning of ASD college students' participation in classes. Methods For this purpose, five ASD students enrolled in a four-year university in Daegu Metropolitan City were individually interviewed from the perspective of the person concerned about their participation in class during their school life experiences. The collected data were analyzed and categorized according to the qualitative interview analysis procedure, and their inner story was derived. Results Through this study, the following key themes were derived. 1) To solve on my own, 2) A desire to do better, 3) Help that suits me, 4) A stepping stone for difficult growth, 5) A group task that is sometimes alone together, 6) A situation that is contrary to the motivational situation. Conclusions The results of this study suggest that many improvements are required for class participation by ASD college students, and it was found that ASD college students especially need support from close colleagues, direct seniors, and professors as well as systemic support.
Interactive English E-Learning Based on Cloud Speech-to-Text API

Good English skills will be a competitive asset in the world of work and education. Mastery of English is essential to be prepared early on. Schools allocate study time and learn how to use textbooks, which sometimes makes students bored and bored. Students are expected to be more motivated in learning and practicing English language skills. So we need a suggestion for other learning media that is more applicable. This study aims to develop mobile interactive e-learning media based on Cloud Speech-To-Text API at PGRI Pasir Sakti vocational high school. The method used is adopting the waterfall type system development life cycle. The material feasibility test was carried out based on expert validation by two English teachers, which resulted in an agreed category. Meanwhile, the ISO 25010 standard testing covers four aspects: functional suitability, performance efficiency, portability, and usability, which resulted in strongly agreed categories.
Technique, Experience and the Social Function of Techno Music: A Comparative Analysis of Theodor Adorno and Robert Fink

The aim of this article is to deepen reflections on the links between the technical aspects of techno music, the experience of listening to it and its social function.In order to achieve that goal, we carry out a comparative analysis between Theodor Adorno and Robert Fink, which allows us to construct an interpretation of the social dimensions of the techno technique, specifically from the analysis of its teleology and time.This work acknowledges the irreconcilable aspects of the authors; where Fink finds a liberating possibility of manifestation and creation of desire or an expression of an emerging new subjectivity, Adorno finds the innermost fibres of a mechanism of social alienation.These contradictory interpretations, complexified by contributions from other authors (such as Rick Snoman, Mark Butler and Diedrich Diederichsen, Ragnhild Solberg and Juliane Rebentisch), allow us to build a detailed description of the particularities of techno aesthetics.
Automatic Detection System of Substation Relay Protection Device Based on Failure Density Function

Because the traditional substation relay protection device automatic detection system has the problem of inaccurate detection results, the substation relay protection device automatic detection system based on the failure density function is designed. The hardware structure of the system is designed by the high-performance C8051F040 single chip microcomputer and the insulation detection sensor. On the basis of the system hardware design, the system software function is optimized. According to the relay protection current setting calculation flow, the genetic algorithm is derived to construct the failure density function, and the automatic detection of the relay protection device in the substation is carried out, Through the hardware design and software design of the system, the design of automatic detection system of substation relay protection device based on genetic algorithm is completed. The experimental results show that the designed system can detect the insulation voltage of positive and negative bus more accurately and has high practicability in the practical application process, which fully meets the research requirements.
Substation Telecontrol Fast Stable Point-to-Point System Based on Naive Bayesian Algorithm

In order to better guarantee the operation effect of substation, a substation telecontrol fast point-to-point system based on Naive Bayesian algorithm is designed. The hardware structure configuration of the system is designed, the system software function is improved, and the fast point-to-point steps of substation telecontrol are simplified. And the experiment proves that the performance of the substation telecontrol fast point-to-point system based on Naive Bayesian algorithm is significantly improved, which can better ensure the operation safety of the substation.
TDH: An Efficient One-stop Enterprise-level Big Data Platform

Big data technology is rapidly changing IT industry. Among those technologies, Hadoop is the best-known one and keeps growing its popularity. Transwarp Data Hub (shortened to TDH) is an enterprise-level big data platform developed by Transwarp Technology (Shanghai) Co., Ltd. The last five years have witnessed rapid development in its growth, and it has gained experience from the deployment and implementation in the fields such as postal service, transportation, and finance. Moreover, the company has been engaged in the exploration of the newborn big data technology. Transwarp Data Hub provides five major products: Analytical Database (Transwarp Inceptor and Transwarp ArgoDB), Real-time Streaming Engine (Transwarp Slipstream), Knowledge Database (Transwarp Search and Transwarp StellarDB), Operational Database (Transwarp Hyperbase), and Data Science Platform (Transwarp Discover). Enterprises can leverage data to build core business systems more effectively and accelerate business innovation by deploying, installing, and using TDH.
Deep Neural Network Based Electro-Mechanical Optimization of Electric Motors

In this contribution the authors use a Deep Neural Network based approach for the optimization of an electric motor, taking into account both electromagnetic and mechanical constraints, i.e. approaching the problem from the multiphysics point of view.
Image Encryption Using Modulated Chaotic Maps

Digital image is a data format used in different areas today. However, transmission of images via unprotected channels increases risks of attacks and threats. Due to distinctive features of images, stronger algorithms are needed for encryption. Typically, symmetrical stream encryption algorithms work faster than other methods developed so far. However, it requires a strong pseudo-random number generator. In this study, a novel secure image encryption algorithm by using chaotic mapping functions has been suggested. It is strong against decryption as well as being fast, flexible and having expandable key length capability.
Growth of Youth-Owned Micro and Small Enterprises in Kigali City, Rwanda: What is the Role of Government Entrepreneurial Interventions?

Micro and Small Enterprises are major players in the wealth of nations, and therefore various entrepreneurial interventions were launched for boosting the economies and standards of living by creating employment opportunities and profits. However, these MSEs have experienced some problems which lessen their growth. The objective of the research was to investigate the effect of government interventions on the growth of youth-owned MSEs in Kigali City, Rwanda. The study was anchored on the resource-based view, Gibb’s support theory, adoption theory, and growth of a firm theory/ Explanatory and descriptive research designs were adopted. The sample was 154 respondents drawn from three districts of Kigali city with a population of 252 youth-owned MSEs. Data was collected using a questionnaire and analyzed using multiple linear regression analysis. The study concludes that entrepreneurial training, access to credit, technology-based online social media, and market access effectively influenced the growth of MSEs in Kigali City, Rwanda. The study recommended that government entrepreneurial interventions should be effectively factored into policy-making and program implementation for sufficient growth of MSEs. MSEs should adopt technology-based online social media to market their products to the global market beyond the Rwanda boundary. Moreover it’s an effective management tool for customer relations for online business.
Increasing the Human Development Index Through the Rural Community Literacy Improvement Program

The Human Development Index (HDI) can be increased in various ways, one of which is the community literacy improvement program. Literacy and literacy, cultural and civic literacy, numeracy literacy, financial literacy, digital literacy and scientific literacy are proven and highly effective in promoting the efforts to increase the Human Development Index. The six basic literacy are not only effectively applied to the world of formal education, but can be actualized in society in general. This study involved 56 communities representing each hamlet in one village determined by purposive sampling technique with certain criteria. The results of this study indicate that improving literacy, cultural and civic literacy, numeracy literacy, financial literacy, digital literacy and scientific literacy are proven to increase the Community Development Index. Improving literacy skills is carried out by providing various kinds of basic training according to community needs. The basic training provided can stimulate the community to be more concerned with developments in the surrounding environment.
Evolutionary approach for dynamic constrained optimization problems

The number of research works on dynamic constrained optimization problems has been increasing rapidly over the past two decades. In this domain, many real-life decision problems need to be solved repeatedly with changing data and parameters. However, no research on dynamic problems with changes in the coefficients of the constraint functions has been reported. In this paper, to deal with such problems, a new evolutionary framework with multiple novel mechanisms is proposed. The new mechanisms are for (1) dealing with both linear and non-linear components in the constraint functions, (2) identifying the rate of change in the coefficients of the variables and (3) updating the population efficiently after every change occurs in the problem. To evaluate the performance of the proposed algorithm, we designed a new set of 13 dynamic benchmark problems, each of which consists of 20 dynamic changes and 3 different scenarios. The results demonstrate that the proposed algorithm significantly contributes in achieving good quality solutions, high feasibility rates and fast convergence in rapidly changing environments. In addition, the framework shows its capability of using different meta-heuristics to solve dynamic problems.
The Effectiveness of Using a Digital Learning System (DLS) Through a Talking Stick Type Cooperative Learning Model in Social Studies Subjects for Class V Elementary School

Following a breakthrough in the development of learning technology, the Digital Learning System (DLS) is a breakthrough in developing learning technology where students can take advantage of technology in the form of software or hardware, online or offline, that is packaged attractively and interactively. This study aimed to determine the effectiveness of using DLS through the Talking Stick in terms of learning motivation and student learning outcomes. This research is descriptive analysis research with a quantitative approach; namely, the data obtained is quantitative data that will be analyzed descriptively. The sample used in this study is fifth-grade elementary school students, as many as 14 students in social studies subjects. The research was conducted at SD Negeri 124405 Pematang Siantar. The results showed that 78.58% of students already had learning motivation, and 85.71% of student learning outcomes passed the KKM. Based on these data, using the Digital Learning System Cooperative learning model Talking Stick has been said to be effective in motivation and student learning outcomes in class V SD Negeri 124405 Pematang Siantar.