Reviewerly/exHarmony huggingface dataset:
The Studentsâ€™ Values Acquired At School

The trends of introducing and securing quality in education have imposed the need for emphasizing the values that the students acquire at school. We have decided to look this subject closely because students have always been the most obvious indicator for the teacherâ€™s success, the parentsâ€™ success and the schoolâ€™s achievement as a whole. That is why we have started the research about the values which direct the students towards proper functioning and development of their personality. The future of our society depends on the studentâ€™s values, acquired during their schooling. The lessons values in the educational process should be adequately adopted and nurtured. The children have a tremendous power of observing and their feelings are deeply rooted. They always observe their parents and teachers, who are a source of inspiration. Moreover, young people represent a great value to our society, and it is needed a lot of attention and encouragement for further met cognition. Therefore, the most important thing for students is to begin the process of acquiring values from an early age, which would be a solid foundation for their further development and living.
Rainfall Forecasting Using Neural Network With Bee Colony Optimization

The rainy season is only available in tropical climates. One of the problem that occurs during the rainy season is flooding. Hence forecasting is needed to predict rainfall. This research proposes a rainfall forecasting using backpropagation method with Bee colony optimization. One problem with the backpropagation method is the initialization of initial weights and random biases. Where the backpropagation weight value can be optimized using the bee colony algorithm. The initial weight can be optimized by the bee colony method. The bee colony is included in a swarm intelligence that can be used to optimize weight and bias form backpropagation method. Expected results from the backpropagation method could be improved with bee colony optimization. Results of the study shows that the learning rate of 0.3 MSE with 0.0111 results. From the previous results with the best learning rate of 0.3, then optimization of weight and bias with bee colony algorithm. Many foods sources and the number of bees as much as 3 MSE with 0.00939 results. Therefore, it is concluded that bee colony can optimize the weight and biases of backpropagation to get minimal errors with learning $\mathbf{rate} =3$ and number of $\mathbf{bees} =3$ .
Image Quality Improvement of Underwater Images in Ideal HSI Color Space

In the underwater circumstance, the light attenuates, and its attenuation rate depends on the wavelength. Long wavelength light is easily absorbed. Also, the light attenuation rate of blue and green component changes according to the water quality, thus, the white balance of images is lost. The attenuation of the light reduces colorfulness and contrast of images, furthermore, causes blurring. When photographing, the illumination becomes uneven for the subject, thus, brightness unevenness occurs in the acquired captured images. In this paper, an underwater image signal is converted to the ideal HSI color space and apply processing suitable for various deterioration factors to saturation and intensity independently. High quality improvement of underwater image was realized.
Comparison of Regression Algorithm for Vanishing Point Estimation based on Car Distribution

In order to improve an application from the viewpoint of helping blind people waiting for the bus, classification of the vanishing view is required. The vanishing point from a perspective view is a feature that may improve the performance of viewpoint classification, especially in the case of congested traffic. The idea proposed by this study bases on the vanishing point estimation following car distribution on the road. In essence, the cars are detected using a You Only Look Once (YOLO) technique which extracts all the to the center point of a squared boundary. Normalization of the data points are then computed in percentages. Next is the calculation of the nineteen features of the data points of the car distribution. Finally, the vanishing point is estimated by supervised regression. This experiment compares the estimated results among five different regressions consisting of Linear, k-Nearest Neighbor, Decision Tree, Support Vector, and Multi-layer Per-ceptron regression. The obtain results show that the k-Nearest Neighbor regression computed the lowest distance error of 10.53, compared to the other regression algorithms.
Preliminary Study on Visual Attention Maps of Experts and Nonexperts When Examining Pathological Microscopic Images

Pathological microscopic image is regarded as a gold standard for the diagnosis of disease, and eye tracking technology is considered as a very effective tool for medical education. It will be very interesting if we use the eye tracking to predict where pathologists or doctors and persons with no or little experience look at the pathological microscopic image. In the current work, we first establish a pathological microscopic image database with the eye movement data of experts and nonexperts (PMIDE), including a total of 425 pathological microscopic images. The statistical analysis is afterwards conducted on PMIDE to analyze the difference in eye movement behavior between experts and nonexperts. The results show that although there is no significant difference in general, the experts focus on a broader scope than nonexperts. This inspires us to respectively develop saliency models for experts and nonexperts. Furthermore, the existing 10 saliency models are tested on PMIDE, and the performance of these models are all unacceptable with AUC, CC, NSS and SAUC below 0.73, 0.47, 0.78 and 0.52, respectively. This study indicates that the saliency models specific to pathological microscopic images urgent need to be developed using our database—PMIDE or the other related databases.
When IoT Meets Blockchain: Challenges in Distributed Consensus

The blockchain is seen as a beneficial development for IoT (Internet of Things), as it offers vital answers for a package which can fix security and trust checks, major help expenses, etc.  Blockchain's decentralization will be mainly due to  the consensus mechanism which awards specific trade during a distributed path without the ties between others. It begins with the basic message of blockchain and explains why the accepted component recognizes a particular career during a blockchainIoT process. Two basic ideas of commended mechanisms such as Proof of Stake (PoS) and Proof of Work (PoW) are addressed and reports are demanded in IoT. The Direct Acyclic Graphs(DAG) is used to show why it is reactive than IoTPoS and PoW structure.  Hashgraph and Tangle are mainly based on consensus mechanisms.
The Dark Age of Online Civil Society

&#x0D; &#x0D; &#x0D; Online civil society is vulnerable to various malicious actors who conduct cyberattacks against individuals, companies, and governments. Victims of these attacks are using strike backs, hack backs, and defensive cyber actions as a means to send a message to the attacker, we are not a victim (Neal, 2019). My research presents one model of “revenge attacks” whereby the individual defends themselves online without the assistance of government.&#x0D; &#x0D; &#x0D;
Secure and efficient identity-based proxy signature scheme with message recovery

Digital signature with proxy delegation, which is a secure ownership enforcement tool, allows an original signer to delegate signature rights to a third party called proxy, so that the proxy can sign messages on behalf of the original signer. In today’s modern society, many applications use this mechanism. Several types of delegations are quite prevalent and the delegation of signing authority is one of them. In a traditional digital signature scheme, signer transmits signature along with message for verification, which leads to additional communication, computation cost and requires extra bandwidth. To resolve these issues, in this paper, we present an efficient ID-based proxy signature scheme with message recovery using bilinear pairings. Because of the message recovery feature, the proxy signer need not send the message to the verifier, so that the proposed scheme reduces the bandwidth requirement and communication cost. Our proposed scheme is proven secure against existential forgery under adaptively chosen message and identity attacks in the random oracle model (ROM) with the assumption that the Computational Diffie-Hellman Problem (CDHP) is intractable. We compare our scheme with related schemes. The efficiency analysis shows that the scheme is computationally efficient. Thus the proposed ID-based proxy signature scheme is secure and efficient both in terms of computation and communication costs than the related existing schemes.
Error Rate Analysis of State-of-the-Art Algorithms for Face Detection

Face recognition is an easy task for humans but it is tedious and complex for computers. Currently, Eigenfaces, Local Binary Pattern Histograms (LBPH) and Fisherfaces algorithms are considered as state-of-the-art and are widely used algorithms for face detection. Eigenfaces and Fisherfaces uses Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) respectively while LBPH uses histogram to recognize the face in the image. In this paper we have analyzed and compared the Eigenfaces, Local Binary Pattern Histograms (LBPH) and Fisherfaces algorithms for different scenarios where there is high probability of getting errors. From the simulation results it has been observed that for these algorithms there is increases in the error rate whenever there is a change in the environment, lighting condition, hair and moustache change etc. in the face. Moreover, from the results it has been found that Eigenfaces and Fisherfaces are more prone to the errors than the LBPH.
Post Processing of Image Segmentation using Conditional Random Fields

The output of image the segmentation process is usually not very clear due to low quality features of Satellite images. The purpose of this study is to find a suitable Conditional Random Field (CRF) to achieve better clarity in a segmented image. We started with different types of CRFs and studied them as to why they are or are not suitable for our purpose. We evaluated our approach on two different datasets - Satellite imagery having low quality features and high quality Aerial photographs. During the study we experimented with various CRFs to find which CRF gives the best results on images and compared our results on these datasets to show the pitfalls and potentials of different approaches.
Unsupervised Learning of Monocular Depth from Videos

To estimate single-view depth, a novel unsupervised framework is designed in this paper. In addition, the proposed model outputs optical flow and camera motion from monocular video sequences simultaneously. Without any additional extra annotations, the three components are predicted jointly from their individual networks. In our work, view synthesis losses are employed as primary supervision for training the three components. Moreover, to handle the existences of moving objects in the scene, we segment the scene into static and dynamic parts by comparing the full optical flow and 2D rigid flow resulted from camera ego-motion. Only static regions is considered when computing the view synthesis losses. Experimental results on KITTI dataset reveal that proposed algorithm in this paper excels previous unsupervised approaches, and perform comparbaly with supervised ones.
Navigation System for Mobile Robot using RGBD Sensor based on Probabilistic Roadmaps

The research of autonomous mobile has always been a popular field and navigation system is one of the key topic. To achieve autonomous capability, a well-behave navigation system is required. Currently, there are many different kinds of path planning methods, each with its own advantages and disadvantages. This paper presents an indoor navigation system for mobile robot using a RBGD sensor base on probabilistic roadmaps(PRM). In this approach, the indoor environment is identified with the use of RGBD SLAM algorithm and a cloud point map is generated. Then the point cloud map is converted to Octo-map that can be used to create occupancy grid map. In the end, a collision-free path is produced for mobile robot based on modified PRM algorithm. The experiments are conducted with robot operating system (ROS), mobile robot and Kinect v2. The result shows that the navigation system has good performance on mobile robot within an indoor environment.
GIS-based mineral system approach for prospectivity mapping of iron-oxide apatite-bearing mineralisation in Bergslagen, Sweden

Bergslagen is one of the richest mineral districts in Sweden for base and precious/critical minerals and metals. In this work a mineral system approach for targeting of iron-oxide apatite-bearing m ...
Sistem Informasi Monitoring Proses Pembelajaran di STMIK STIKOM Indonesia

The Lecture Program Unit (SAP) consists of determining learning objectives, the material to be discussed, how to present it, supporting media and learning resources that can be used. At the end of each teaching and learning activity, for the purposes of monitoring and evaluation, teaching staff are required to write SAP realization in a teaching journal. At STMIK STIKOM Indonesia, the process of writing this teaching journal is done manually. The lecturer filled out the material given in class in a teaching journal. The process is done manually resulting in the process of monitoring and evaluation can not be done regularly. In this research, a Web-based monitoring system for teaching and learning process was developed in STMIK STIKOM Indonesia. From the test results obtained, the system built can facilitate lecturer coordinator lecturers to monitor the course of the teaching and learning process. In addition, the system built can also facilitate lecturer coordinator lecturers in conducting the recapitulation process of the suitability of SAP with the realization of SAP written by lecturers supporting courses
“Duolingo: An Alternative Application for Learning English Grammar and Vocabulary”

This article explores about the using an already existing language learning app, Duolingo, as an alternative application for learning English grammar and vocabulary. Duolingo is a platform that provide a language-learning website and app. The app and the website are free, so everyone can use it without giving any extra fee. The aim of this application is to teach vocabulary and grammar, which some exercise series that can be chosen by the user. In this study, writer discuss about the using of Duolingo in moblie version. The writer consider that almost student in this era are using mobile phone. Hopefully by using this application, will motivate student in learning English, especially grammar and vocabulary.
Wavelet DB44 and MBB Algorithm for Sasak Vowels Recognition

Ancient manuscripts are original handwriting that is at least 50 years old and has significance for civilization, history, culture, and science. In the preservation of ancient manuscripts, an attempt was made to digitize ancient documents. The hope is that by carrying out this process, the old documents can be used in historical learning and increase the interest of the younger generation in studying history. So far, the attempt to digitize ancient manuscripts is still limited to the storage of manuscripts in the form of digital documents, generally in the way of image files. So, the existing digital manuscripts are still static, while each manuscript has specificity in how to read them. Therefore, this research is a preliminary study for digitizing ancient documents, especially voice-based Sasak language. The vowel sound of Sasaknese speakers used in this research. Wavelet db44 and WPCC used as the feature extractor, and FCM implemented to build the feature reference/model. The accuracy of the recognition is evaluated using the DTW algorithm. From the evaluation obtained that the average recognition accuracy of the system using training dataset is 50% while using testing dataset is 27.14%.
Data Embedding Technique in Encrypted Image Using Hamming Coding

Image encryption is a solution to prevent the personal privacy leakage in images. While the encrypted images increasingly stored in could sever, it is difficult to get the exact image because the encrypted image are meaningless. To conquer the difficulty, the queried indices are embedded into the encrypted images. After extracting hidden indices, the image can be found by searching the extracted indices.
Financial Development and Economic Growth in Sub-Saharan Africa: The Role of Institutions

The study aims to determine the relationship between financial development and economic growth with respect to the state of institutional quality on 27 Sub-Saharan Africa countries using a five-year averaged dataset over the period 1982-2016. The findings established evidence of a U-shaped relationship between financial development and growth which entails that more (less) finance drives (retards) growth in the region. The turning point beyond which financial development begins to contribute positively on economic growth ranges between 33% and 37%. Even though the connection is not strong, the selected institutional variables also showed both a direct and an indirect positive impact on economic growth. Financial development is positive and significant when embedded in selected well-developed institutions, which implies a complementary relationship of institutions and financial development on growth. It is therefore recommended that policy makers and responsible authorities take stringent measures to ensure the development of institutions and the financial sector for sustainable economic growth in the region. JEL codes: G20, O43
Analysis of substantial growth on Data Center in virtualization local area network applications

In emerging IT industry, data is increasing highly and because of which data center is evolving around due to the limitations and challenges faced in maintenance. Data Center started its transformation from server connectivity to cloud data center, main goal is to achieve high data availability. In this paper, the survey of data center architecture, operations, evolution and challenges faced are discussed.
Underwater Image Denoising via AMP with Renyi Entropy

Approximate message passing (AMP) in wavelet domain has been widely used in image denoising. However, the AMP-based denoising method is not robust to the 'shot noise', which is dynamic and short-period in the wavelet domain. First, a preprocessing filter is designed with Entropy to combat the restlessness of noise component in spatial domain. Then, a shot noise filter is designed via AMP with Renyi Entropy in wavelet domain. By using the two-step denoising method, we can 'individualized' denoise both in spatial and wavelet domains. The experimental results show that, with preprocessing filtering, our denoising method outperform the design via AMP with wavelet thresholds.
Considering the Queer Disabled/Debilitated Body: An Introduction of Queer Cripping

Research Article| October 01 2019 Considering the Queer Disabled/Debilitated Body: An Introduction of Queer Cripping Andrew R. Spieldenner Andrew R. Spieldenner Andrew R. Spieldenner, PhD, is assistant professor of communication at California State University-San Marcos and vice-chair of the United States People living with HIV Caucus, the most representative national network of people living with HIV in the country. He has been appointed North America NGO delegate to UNAIDS, the UN joint program on HIV. Search for other works by this author on: This Site Google QED: A Journal in GLBTQ Worldmaking (2019) 6 (3): 76–80. https://doi.org/10.14321/qed.6.3.0076 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Andrew R. Spieldenner; Considering the Queer Disabled/Debilitated Body: An Introduction of Queer Cripping. QED: A Journal in GLBTQ Worldmaking 1 October 2019; 6 (3): 76–80. doi: https://doi.org/10.14321/qed.6.3.0076 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveMichigan State University PressQED: A Journal in GLBTQ Worldmaking Search Advanced Search The text of this article is only available as a PDF. © 2019 Michigan State University2019 Article PDF first page preview Close Modal You do not currently have access to this content.
The Study on the Level of Acquisition of Information Structure-Focused on Expletive there, Passive and Inversion Sentences

The purpose of this paper is to show whether and how university learners show any significant difference in the level of acquisition of information structure after a learning process. In order to fulfill the purpose, 17 participants answered some survey and research questions before and after learning the basic notion of information structure focusing on expletive, passive and inversion sentences. The result shows that there is no significant difference in the acquisition level on expletives but there is on passive and inversion sentences. It means that what should be more focused on when learning or teaching information structure is expletive there sentences. Also, the main reason for the low level of acquisition on three sentence types before learning is not because the notion is difficult but the students did not learn about them based on information structure.
Load Balancing Based on Statistical Model in Expert Cloud

Expert Cloud is a new class of cloud computing which enables the users to achieve their requirements from a collection containing experts and skills created by the Human Resources (HRs). The acquisition of these skills and experts from this collection is possible by using the internet and cloud computing concepts without consideration of the HRs location. The load balancing in cloud computing means equal load distribution among resources, Virtual Human Resources (VHRs) and servers. The effective load distribution in a heterogeneous environment such as cloud is an important challenge. The increase in the number of users, the differences of request types and also different resources capabilities and capacities cause that some resources become overload and some others become idle. This paper presents a dynamic load balanced task scheduling algorithm in expert cloud. In this method, we utilize the Genetic Algorithm (GA) as a ranking for making distinction among the VHRs capabilities. In the proposed method, interval estimation and specification matrix are used to allocate the VHRs and also to determine the service rate. The load balancing and mapping process are modeled based on Simple Exponential Smoothing and Probability Theory. This statistical load balancing model allows allocating the VHRs based on service rate and Poisson model. Thus, each task is delivered to the VHR; which is capable to execute it. The simulation results have shown that the expert cloud could reduce the execution and tardiness time and improve VHR utilization. The cost of using resources as an effective factor is also observed.