Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis on Upgraded Cloud Data Security and Scalability

Distributed computing a disseminated system for sharing information over web, fills in as an online information reinforcement with adaptability. The paper depicts different classes of mists relying upon the use of cloud and furthermore on the administrations given by the cloud. Information security is one of the significant difficulties looked by cloud suppliers and cloud clients. Cryptography is recommended as the fitting answer for verifying the cloud information. Survey on a portion of the current strategies for verifying the information and scaling the information put away in the cloud is likewise incorporated into this paper. The data owners can move data on to the cloud; can moreover make approvals on the moved data to control its passageway by various sorts of customers. Cryptographic systems and different techniques are fused alongside the conventional access control approaches truly improves the security and adaptability of information. The investigation on the survey focuses to the insider dangers in cloud security and adaptability as one of the best issues in distributed computing.
Corpus Linguistics in Sweden : Pioneers and their context

This volume presents findings from research on the development of corpus linguistics in Sweden as a scientific innovation. It begins with a presentation of the early international development of co ...
REVENGE AND IGNORANCE AS WEAPONS OF HATE SPEECH: 10.24250/JPE/2/2019/GK/DR/RM/ED/AE/EB

The year 2019, among some positive aspects, reflects some worrying aspects. Among them, the most harmful seems to be hate speech. Present at all levels of society, both in Romania and in the world, hatred brings with it the rejection of difference, negativity, violence and aggression. To tackle psychological sources of hate speech, our project Hateâ€™s Journey, financed by Erasmus+, 2018-2-ES02-KA205-011733 has designed an online questionnaire composed by some single item research questions, general data collection and tests regarding emotional regulation, internet content awareness and helping attitudes. The hypothesis of this research is that the revenge thinking pattern and ignoring attitude towards the negative effects of hate speech are powerful predictors of future online perpetrator pattern of hate speech. Researchâ€™s 206 participants are residents of Latvia in 24.8%, Romania 24.8%, Spain 24.8%, and Turkey 25.7%, with an age mean of m=30 years, 39.8% males and 60.2% females. A multiple linear regression was calculated to predict the online hate speech perpetrator pattern. As results show, revenge thinking pattern (B=0.365, SE=0.082, Beta=0.317, t=4.452 at a p < 0.001) and ignoring attitude towards the negative effects of hate speech (B=0.233, SE=0.076, Beta=0.219, t=3.076 at a p < 0.005) are significant predictors of hate speech perpetrator pattern. Conclusions and implications are discussed.
Preprocessing Method for Training Dataset in Character Recognition using Convolutional Neural Network

Nowadays, image processing technology is used in the logistic industry widely to eliminate a labor shortage and prevent human errors. However, the existing image processing technology cannot be applied on some work processes such as to identify the product name written in characters that printed on an unstable package surface, because the color and font aren’t uniform and the shapes are deformed always. Therefore, we propose a system previously which enabled this task using character recognition technology by Convolutional Neural Network (CNN) with a certain accuracy. In this research, we focus on how to make an optimal training set which can obtains a high accuracy. As a result, the accuracy is improved to 99.8% by adding the diversity of the image's background for learning process.
Library Guides: Research4Life Training: CCREOH Training 2018

This training is supported by the Elsevier Foundation 2014 New Scholars and Innovative Libraries in Developing Countries program
On Planning Distributed Minimal Sensor Networks

Sensor networks are key mechanisms to integrate relevant features from the physical world, and in particular offer a useful layer to assimilate diverse metrics for health promotion. Yet, for a large number of nodes, sensor networks still pose bottleneck problems due to high computational load and energy demands. In this paper, we propose a method to enable the construction of minimal distributed sensor networks, and discuss its potential applications for sensor networks in healthcare.
Cultivating Semantics for Data in Agriculture and Nutrition. Recommendations from the RDA Agrisemantics Working Group.

The poster reports on results of the activities carried within the Agrisemantics Working Group of the Research Data Alliance (RDA) (https://www.rd-alliance.org/groups/agrisemantics-wg.html). The group gathers together researchers and practitioners at the intersection between semantic technologies and agriculture. As a highly interdisciplinary domain, agriculture needs semantics as a possible response to data findability and interoperability. Yet, semantics remain unevenly adopted in this community partly because of unawareness, skill gaps, and lack of tooling usable by non-semantics experts to edit, map, and use semantic resources. The Agrisemantics WG has recently produced a set of recommendations to facilitate the use of semantics for Data on Agriculture and Nutrition, with the ultimate goal of improving data interoperability, discoverability and reusability. Based on an open dialog with a community of researchers and practitioners of semantic technologies, we have identified four basic points to address in order to make their use more widespread and effective. First, foster the development of a generic web-based framework to work with semantic resources. Such a framework should be adaptable according to: the task to perform, the domain of interest, the coverage within the domain, and the user competencies. Second, promote initiatives that support the reuse of existing resources, or their alignment. Third, promote the adoption of common metadata models for the description of semantic resources and alignments, and the use of global identifiers to facilitate reuse, usage tracking, and proper citation. Finally, promote courses on semantics and integrate them into curricula in all relevant disciplines-ie. computer sciences, data & information management and analytics, agronomy, bioinformatics, etc. These high level recommendations are further specified and translated into specific recommendation related to roles and activities in: data stewardship (or data management), software development, semantics professionals, and policy makers. Many of our recommendations either address or indirectly contribute to the implementation of FAIR principles. Our poster will show higher level recommendations together with the specific recommendations.
Security Considerations Regarding Compression Dictionaries

Dictionary-based compression enables better performance, but bringsstate into the process of compression, with all the complexities thatfollow. This document explores the security implications of thistechnique in the context of internet protocols and enumerates knownrisks and mitigations.
Web-Based College Student Assignment File Collection Application Using Google Drive API

The use of internet media, especially cloud storage as a practical solution in information and communication should be able to be applied in academics, especially in the scope of teaching and learning. By using cloud storage as a communication medium, especially in sending data in the form of assignments files can facilitate lecturers and students in carrying out one of the mandatory activities in lectures, namely the collection of assignments. The main problem that often occurs is the storage capacity provided by hosting services is very limited, so storing large amounts of files will require large costs as well. Google Drive provides an API driven file storage service that makes it easy for users to create applications that can communicate and interact with 15 GB free storage services. The free service is expected to facilitate teaching and learning activities in lectures, especially in the collection of student assignments
Development of Modified Block Cifer Algorithm TEA, Free from Vulnerability of “Connected Keys Attack”

In the framework of this article, general information on the block cipher TEA was reviewed, including the history of its creation, advantages and disadvantages, as well as details of the process of encryption and decryption. Three main vulnerabilities of the cipher are described: differential cryptanalysis, the presence of equivalent keys, and in most detail - the “related-key” attack. Next, we consider the algorithm for generating the DES round keys and its modification, which makes it possible to integrate this algorithm into the TEA.
LibGuides: Library Resource Access Issues: Troubleshooting Steps

Information to help troubleshoot access to library resources Steps to help troubleshoot access to library resources
INFORMATION LITERACY IN EARLY CHILDHOOD EDUCATION

&#x0D; &#x0D; Literacy could be a supporter of the forefront of education. Teachers have a crucial role in filtering and remodeling data on their students. The utilization of knowledge skill has to be trained by sorting and selecting the proper media for learning whose main purpose is to extend students' intelligence. Skill coaching is a way to boost the flexibility to use learning media, particularly Early Childhood Education and Kindergartens in Lubuk Begalung, Padang. The objectives to be achieved include: to increase the insight of Early Childhood Education and Kindergarten teachers about media, and the ability of media information literacy in learning. Training methods i.e. lectures, discussions, questions, and answers, and case simulations. The conclusion obtained from this activity is that early childhood and kindergarten teachers can choose the media responsibly and able to criticize media content more intelligent in the classroom.&#x0D; &#x0D; &#x0D;
A QoS-aware resource allocation framework in virtualised cloud environments

In cloud platforms, resource allocation service plays an important role for running user applications efficiently. However, current allocation mechanism in many cloud platforms only provides best-effort service for user's jobs, which means that user's quality-of-service (QoS) requirements can not be well guaranteed. In this paper, we present a novel QoS-aware resource allocation framework, which applies feedback control technique to achieve the goal of fair resource allocation between multiple virtual machine (VM) instances. Theoretical analysis of the proposed control model has proven that it can meet the feasibility and stability requirements. Experimental results conducted on a real-world cloud platform show that the proposed resource allocation framework can significantly improve the effective resource utilisation as well as the overall task execution efficiency. In addition, the proposed framework also shows better robustness when the cloud platform is in presence of highly dynamic workload.
A Survey of Recent Advances in Efficient Parsing for Graph Grammars

Context-free graph grammars, in particular hyperedge replacement graph grammars, look back on over 30 years of history.They share many of the good properties of contextfree string languages.Unfortunately, the complexity of parsing is the big exception: early results in the field showed that even for fixed grammars, the membership problem can be NP-complete.Moreover, the known results about polynomial parsing that were obtained afterwards, while constituting nice theoretical work, seemed to be of limited practical value.This is because they were either based on very "impractical" restrictions, or the degree of the polynomial running time depended on the grammar and could thus become large.In the current decade, the question received renewed interest because hyperedge replacement is one of the candidate formalisms for specifying semantic graphs in natural language processing.Using graph grammars in this area requires parsing algorithms that are not only polynomial in theory, but efficient in practice.Preferably, the degree of the polynomial bounding their running time should be a (small) constant independent of the grammar, or else it should depend on parameters not likely to be large.The talk will present an overview of results towards this goal, discussing their requirements, advantages, and disadvantages as well as a few possible directions for future work.
L'acquisizione del lessico L2 in un programma study abroad : Uno studio longitudinale di apprendenti svedesi di italiano come lingua seconda

This study investigates whetherthe context of learning contributes to the vocabulary acquisitionin asecond language. Swedish L2 learnersof Italian studying at Stockholm universityare compared to Sw ...
Melanoma and Breast Cancer Detection using Deep Learning Techniques

In this survey paper for the above titled project we will arrange melanoma pictures and breast disease information into benign and malignant. The undertaking is completed utilizing Breast Cancer Wisconsin Dataset and ISBI 2016 challenge in melanoma classification dataset. The breast cancer malignancy characterization is finished utilizing artificial neural system (ANN). Melanoma classification is finished utilizing Convolutional Neural Network (CNN).
Visual Coin-Tracking: Tracking of Planar Double-Sided Objects

We introduce a new video analysis problem – tracking of rigid planar objects in sequences where both their sides are visible. Such coin-like objects often rotate fast with respect to an arbitrary axis producing unique challenges, such as fast incident light and aspect ratio change and rotational motion blur. Despite being common, neither tracking sequences containing coin-like objects nor suitable algorithm have been published. As a second contribution, we present a novel coin-tracking benchmark containing 17 video sequences annotated with object segmentation masks. Experiments show that the sequences differ significantly from the ones encountered in standard tracking datasets. We propose a baseline coin-tracking method based on convolutional neural network segmentation and explicit pose modeling. Its performance confirms that coin-tracking is an open and challenging problem.
UPAYA MENINGKATKAN EFEKTIVITAS PENGELOLAAN WAKTU DALAM PEMBELAJARAN MELALUI PENGGUNAKAN CEKLIS MURID PADA GURU SDN SE KECAMATAN SOREANG KOTA PAREPARE TAHUN PELAJARAN 2015/2016

The purpose of this school action research is to improve the effectiveness of learning management by using the Student checklist on SDN Teachers throughout the sub-district of Parepare in the 2015/2016 Academic Year. The objectives of this study can be formulated as follows.The results obtained by students in the action research activities of this school experienced an increase as follows in the first cycle of Indonesian language subjects on average UH score 58.86 in the second cycle scored 66.71. Mathematics in the first cycle the average value of UH 57.57 in the second cycle the average value of 66.43. English in the first cycle, the average UH value is 58.71 in the second cycle 66.43. Thus there is an increase: Indonesian language is 7.81%, mathematics is 8.46% and English is 7.38%. The value of UTS when compared to UTS last year in the same students while in class V also increased. The average UTS score before the school action research is carried out as follows. The average score of UTS in Indonesian is 51.70. the average math UTS score is 50.61, and the average English UTS score is 53.26. The average value of UTS after school action research is carried out as follows. The average Indonesian UTS score is 66.00 mathematics 67.71, English 67.61. With the results of these studies experienced an increase as follows. Indonesian language increased 14.30%, mathematics increased 17.10%, and English increased 14, 27%
An efficient data hiding method in images

&lt;p class="Abstract"&gt;Data Hiding has a huge range of applications in the medical field for transmission. It is helpful in securing the documentation of the patients from the violator with good storage space. The medical images of different modalities like CT, MRI, and PET with the digitized clinical information can be sent to the doctors across the world for the treatment. Due to the bandwidth and storage constraints, medical images along with the clinical information must be compressed before transmission and storage. This paper gives a technique for hiding the digitized clinical information along with the DICOM images in Complex Contourlet Transform (CCT) Domain. It also analyses the compression method by using an Entropy Encoder method. Hence, this work suggests that the data hiding method based on Complex Contourlet Transform (CCT) is efficient and also it has a high hiding capacity. The improved value of Compression Ratio (CR), Space Saving (SS), Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) shows that the new method satisfies the properties of the data hiding method.&lt;/p&gt;
Bio-inspired algorithms for diagnosis of breast cancer

Most commonly found cancer among women is breast cancer. Roughly 12% of women grow breast cancer during their lifetime. It is the second prominent fatal cancer among women. Breast cancer diagnosis is necessary during its initial phase for the proper treatment of the patients to lead constructive lives for an extensive period. Many different algorithms are introduced to improve the diagnosis of breast cancer, but many have less efficiency. In this work, we have compared different bio-inspired algorithms including artificial bee colony optimisation, particle swarm optimisation, ant colony optimisation and firefly algorithm. The performances on these algorithms have been measured for UCI Dataset of Wisconsin Diagnostic Breast Cancer, and the results have been calculated using different classifiers on the selected features. After the experiment, it is seen that BPSO has shown maximum accuracy of 96.45% and BFA has shown considerable results of 95.81% with six features which is minimum of all algorithms.
Deep Neural Networks for Corrupted Labels

The success of deep convolutional networks on image and text classification and recognition tasks depends on the availability of large, correctly labeled training datasets, but obtaining the correct labels for these gigantic datasets is very difficult task. To deal with this problem, we describe an approach for learning deep networks from datasets corrupted by unknown label noise. We append a nonlinear noise model to a standard deep network, which is learned in tandem with the parameters of the network. Further, we train the network using a loss function that encourages the clustering of training images. We argue that the non-linear noise model, while not rigorous as a probabilistic model, results in a more effective denoising operator during backpropagation. We evaluate the performance of proposed approach on image classification task with artificially injected label noise to MNIST, CIFAR-10, CIFAR-100 and ImageNet datasets and on a large-scale Clothing 1M dataset with inherent label noise. Further, we show that with the different initialization and the regularization of the noise model, we can apply this learning procedure to text classification tasks as well. We evaluate the performance of modified approach on TREC text classification dataset. On all these datasets, the proposed approach provides significantly improved classification performance over the state of the art and is robust to the amount of label noise and the training samples. This approach is computationally fast, completely parallelizable, and easily implemented with existing machine learning libraries.
Towards Robust Inference of Biomedical Signals in Energy-Efficient Neuromorphic Networks

Computation capability characteristics of neuromorphic analog/mixed-signal spiking neural networks offer capable platform for implementation of cognitive tasks on resource-limited embedded platforms. In this paper, we derive stochastic model of spiking neural processing systems for energy-efficient recognition and inference of biomedical systems. We examine imperfections in the network dynamics and noise-induced information processing, influence of the uncertainty on the behavior of the emulated networks, and impact on the clustering accuracy of cardiac arrhythmia. Experimental results indicate that stochasticity at networks connections is a adequate resource for deep learning machines.
Design of Experiments as a Prerequisite for Development of Fuzzy Models?

Design of experiments is an approach where the experiments for measurement acquisition should be planned very carefully in order to obtain high-informative data. This paper tackles a problem of constructing mathematical models of complex nonlinear problems based on measurement data of poor information content. Two practical examples are dealt with. The first one is a textile wastewater decolourisation process, and the second one an atmospheric corrosion of structural metal materials. In both cases the measurement data were not adequate for constructing a very accurate model. In such cases it is necessary to make a trade-off between model complexity and model accuracy.
Diagnosis of Breast Cancer using Machine Learning Algorithms

Breast cancer is one on the most well-known kind of malignant growth influencing ladies from all around the globe. If not diagnosed at an early stage it can lead to many complications or even death. This disease can affect an individual due to genes, environmental affects and many other factors. In today’s world with very advanced technologies and surgical procedures, the treatment of cancer has somewhat become easier. Machine learning and artificial intelligence techniques has played a vital role in detection, analysis and visualization of cancer cells in the human body. In this paper a review about the various machine learning algorithms used for the detection of cancerous cells have been presented. Some of the algorithms reviewed are K-nearest neighbors, random forest, and support vector machine etc., for detection of cancerous tumor.
Summarizing Test Grades Using Descriptive Statistical Tools

In this paper, several Educational Statistical Tools for summarizing a test marks are discussed.The mean, variance, 5number summary, Difficulty index and Discrimination index are discussed in details.The tools are simple, so that they can be understood by almost all instructors regardless of their backgrounds in statistics.The contents of the paper can be very useful for users of statistics at different areas and in particular, teachers.