Reviewerly/exHarmony huggingface dataset:
Identification of Suitable Contrast Enhancement Technique for Improving the Quality of Astrocytoma Histopathological Images.

Contrast enhancement plays an important part in image processing. In histology, the application of a contrast enhancement technique is necessary since it can help pathologists in diagnosing the sample slides by increasing the visibility of the morphological and features of cells in an image. Various techniques have been proposed to enhance the contrast of microscopic images. Thus, this paper aimed to study the effectiveness of contrast enhancement techniques in enhancing the Ki67 images of astrocytoma. Three contrast enhancement techniques consist of contrast stretching, histogram equalization, and CLAHE techniques were proposed to enhance the sample images. The performance of each technique was compared by computing seven quantitative measures. The CLAHE technique was preferred for enhancing the contrast of the astrocytoma images. This technique produces good results especially in contrast enhancement, edge conservation and enhancement, brightness preservation, and minimum distortions to the enhanced images.
Measuring Biases of Word Embeddings: What Similarity Measures and Descriptive Statistics to Use?

Word embeddings are widely used in Natural Language Processing (NLP) for a vast range of applications.However, it has been consistently proven that these embeddings reflect the same human biases that exist in the data used to train them.Most of the introduced bias indicators to reveal word embeddings' bias are average-based indicators based on the cosine similarity measure.In this study, we examine the impacts of different similarity measures as well as other descriptive techniques than averaging in measuring the biases of contextual and non-contextual word embeddings.We show that the extent of revealed biases in word embeddings depends on the descriptive statistics and similarity measures used to measure the bias.We found that over the ten categories of word embedding association tests, Mahalanobis distance reveals the smallest bias, and Euclidean distance reveals the largest bias in word embeddings.In addition, the contextual models reveal less severe biases than the noncontextual word embedding models with GPT showing the fewest number of WEAT biases.
Proposing an Innovative Model Based on the Sierpinski Triangle for Forecasting EUR/USD Direction Changes

Proposing an Innovative Model Based on the Sierpinski Triangle for Forecasting EUR/USD Direction Changes
Choice of ideal sunshine hour based model to predict global solar radiation in India

Solar radiation is the key energy source for most of the energy conversion systems, whether it is biological or mechanical. It is also the most fundamental energy source for future energy demand. Like most of the developing countries, India also lacks sufficient instrument facilities to measure global solar radiation (GSR) at recommended spatial interval and alternative approaches must be used to generate GSR data. In the present study, six well known empirical models were tested to estimate the GSR over twelve major cities of India using long-term global solar radiation and bright sunshine hour data. The empirical coefficients have been calculated for all the models and each location using regression analysis method. Daily GSR are then calculated using those regression constants along with statistical analysis. Results reveal that all the models shows close estimation with low mean bias error (MBE), root mean square error (RMSE) and mean percentage error (MPE) values. Among all models, linear exponential and linear logarithmic models are highly recommended for prediction of GSR throughout the country, except Shillong, where Bakircilinear exponential model is recommended. Significance tests i.e., t-test also confirms that this two model produce most significant results than others.
A Survey of Low Visibility Image Enhancement Based on MSRCR Algorithm

With the development of computer vision systems in the fields of traffic and safety monitoring, image enhancement has become an important research direction of computer vision.After development, the Retinex algorithm has a better enhancement effect, and the MSRCR algorithm has now developed into one of the important methods in image enhancement.This article introduces the development of the Retinex algorithm, and focuses on the main process and steps of the MSRCR algorithm, and uses the algorithm to enhance the low-visibility images of haze and night in multiple scenes.The actual results show that the MSRCR algorithm has a better enhancement effect and has a wide range of application values.
A SURVEY OF THE HOST HYPERVISOR SECURITY ISSUES PRESENTED IN PUBLIC IAAS ENVIRONMENTS AND THEIR SOLUTIONS

The use of virtualization can be attributed to the success of cloud computing. However, usage of a hypervisor in a shared environment among mistrusting users presents significant challenges. This paper surveys the works on host hypervisor security issues presented in cloud computing, performing a short review of current literature on the subject. Addressing several key topics, namely threats and known attacks against the hypervisor or a virtual machine (vm) that exist in a shared environment. This paper also contains a thorough review and comparison of the current solutions and proposed mitigations for the known attacks and identifies any potential gaps. Aiming to uncover if a hypervisor can provide the level of confidentiality, integrity and availability expected by cloud consumers. Research is critically analyzed and consideration for each solutions suitability of implementation in an Infrastructure as a Service (IaaS) environment is applied, including the impact on performance, if any.
Disaster preparedness behaviors in biology education: Knowledge of environmental disaster mitigation

Environmental disaster was important issue to be solved. Current situation disasters such as flood, earthquake, tsunami, volcanoes and others could be happened. This study aimed to obtain information about the relationship between knowledge of disaster and preparedness behaviors and disaster preparedness behaviors. This research was a Sequential Explanatory combination research consisting of 2 variables, namely knowledge about disaster mitigation as an independent variable (X) and disaster preparedness behaviors as a dependent variable (Y). This research was conducted in January-June 2019. The population in this study was all students of Grade XI MIPA in MAN in Bogor City in the academic year 2018/2019 with a total sample of 207 respondents. Research data were obtained using multiple choice test and observation sheet. Quantitative data analysis with descriptive statistical calculations, prerequisite tests using the Liliefors test and the Bartlett test. The data hypotheses testing used the Pearson Product Moment formula. Analysis of qualitative data carried out by reducing data, presenting data, and drawing conclusions. The results showed that there was a positive relationship between knowledge of disaster mitigation and disaster preparedness behaviors (t-value 3.56 &gt; t-statistic 0.05, r=0.234 &amp; r2=5.51%) with a low interpretation value. The conclusion of this research there were low relationship between knowledge of disaster and preparedness behaviors.
Classification of Imbalanced Data of Medical Diagnosis using Sampling Techniques

When there is gigantic difference between the ratio of two classes in the classification algorithms, then the classifier may tend to favor the instances of majority class whereas, it becomes difficult for the classifier to learn the minority class samples.Either, undersampling is used or oversampling is used for this imbalance but, most of the undersampling techniques does not consider distribution of information among the classes while the oversampling technique leads overfitting of the trained model.So, to resolve this issue integration of undersampling as well as oversampling technique can be done.Majority class samples can be undersampled using a new approach, namely, MLP-based undersampling technique (MLPUS).Majority Weighted Minority Oversampling Technique (MWMOTE) can be used for generating the synthetic samples for minority class.The main objective is to handle the imbalance classification problem occurring in the medical diagnosis of rare diseases and combines the benefits of both undersampling and oversampling Experiments are performed on 7 real world data sets for the evaluation of proposed framework's performance.
PENGEMBANGAN MEDIA PEMBELAJARAN INTERAKTIF MENGGUNAKAN LECTORA PADA MATERI HUKUM NEWTION TENTANG GERAK

Learning media is a form of innovation in the learning process. The purpose of this research is to develop interactive learning media using the Lectora application as a media for learning physics in schools and to find out the assessment of material experts, media experts and students of interactive learning media using the Lectora application as a learning medium so that this product is suitable for use in the learning process. This research uses research and development methods or often called Research and Development (R&amp;D). Model adapted from the ADDIE development model (Analysis, Design, Develop, Implement, and Evaluate. The ADDIE model is used to be the basis for developing a device or infrastructure for an effective training program. This model uses 5 stages, namely the Analysis stage. , Design, Development, Implementation, and Evaluation After going through the process of the stages carried out, the results obtained are that the interactive physics learning media using the Lectora application has met the criteria as a physics learning media that is suitable for use or used in the learning process at school.
Identity-Based Proxy Re-encryption over NTRU Lattices for Cloud Computing

Identity-based proxy re-encryption (IB-PRE) is a kind of public key cryptography that allows a proxy to convert the ciphertext based on Alice’s identity into another ciphertext of the same message for Bob. It has important applications in cloud computing, distributed systems and secure e-mail forwarding. So far, the existing lattice-based IB-PRE schemes are designed on the general lattice and their security is based on the learning with errors (LWE) assumption. In this paper, we propose an IB-PRE scheme over NTRU lattices with the desirable properties of anonymity, unidirectionality, backward collusion safeness and multi-hop. Our IB-PRE scheme is provable secure under adaptive chosen-plaintext and adaptive chosen-identity attacks. And, in the random oracle model, the security of our scheme is based on the decisional Ring-LWE assumption. Besides, the key and ciphertext sizes of our IB-PRE scheme are short, only one or two ring elements, and our scheme could encrypt n bits in each encryption process.
Verification of Robustness against Noise and Moving Speed in Self-localization Method for Soccer Robot

We use a white line on the MSL field for self-localization.Our proposed self-localization method generates the search space based on a model-based matching using white line information.The method recognizes the robot position by optimizing the fitness
Digital Civilization Information Culture: Forecasts And Reality

The forecasts of the development of information civilization given by famous scientists, philosophers, futurologists until the middle of the 20th century are quite well-known and many provisions formed the basis of the concepts of the information society.These forecasts were corrected at the end of the twentieth century.Since that time, after more than 20 years, information civilization gets new names -virtual, technogenic, digital, etc.An analysis of the predictions made 20 years ago about the alleged development of information culture and civilization and their implementation in the context of digital civilization is of interest today.The authors studied the most popular concepts developed at the end of the previous century by Russian scientists for the development of the information society and compared their forecasts with the real situation of the state of modern digital civilization.The study showed that the main concerns of scientists about the consequences of the development of information culture in the era of digital civilization are justified: information culture leads to the transformation of social systems, social relations and institutions; digital civilization is characterized by the informational inequality of social actors, which increases social tension in society; there is a unification of cultures, leading to a violation of sustainable development and the destruction of cultural self-identification; there is an artificial evolution of a man, ahead of the pace of natural evolution.
Using Confidential Data for Domain Adaptation of Neural Machine Translation

We study the problem of domain adaptation in Neural Machine Translation (NMT) when domain-specific data cannot be shared due to confidentiality or copyright issues.As a first step, we propose to fragment data into phrase pairs and use a random sample to fine-tune a generic NMT model instead of the full sentences.Despite the loss of long segments for the sake of confidentiality protection, we find that NMT quality can considerably benefit from this adaptation, and that further gains can be obtained with a simple tagging technique.
Evaluating and Explaining Natural Language Generation with GenX

Current methods for evaluation of natural language generation models focus on measuring text quality but fail to probe the model creativity, i.e., its ability to generate novel but coherent text sequences not seen in the training corpus.We present the GenX tool which is designed to enable interactive exploration and explanation of natural language generation outputs with a focus on the detection of memorization.We demonstrate the tool on two domainconditioned generation use cases -phishing emails and ACL abstracts.
The Influence of Hello Book Media on Early Childhood Language Skills

Children who have difficulty expressing their wants and needs are more likely to be helped and fail to gain independence. Providing stimulation to foster communication skills starting from an early age through play and media activities. This study aims to analyze the effect of the Hello Book media on early childhood language skills. This type of research is a Quasi Experiment (quasi-experiment). The population in this study were students of group B of two Kindergarten Institutions. The data collection technique uses observation to determine the level of development of language skills. The hypothesis test used is the Independent t-test parametric statistical test. According to the calculation results obtained t = -4,303 with a sig value. = 0.0001. The difference in the mean value between the experimental group and the control group was 2,150. Thus, it can be concluded that the use of Hello Book media in learning has a positive and significant effect on children's language skills in Kindergarten.
Google Traffic sebagai Masukan Kebijakan Transportasi Perkotaan (Studi Kasus: Kota Bandar Lampung)

Congestion in urban areas is vulnerable because of the impact of the growth of activities and an increasingly specialized and complex economy. For this reason, it is necessary to formulate a good urban transportation policy to accommodate increased urban transportation needs. Google Traffic is a feature found on Google Maps to see the level of congestion in an area. Google Traffic can detect an area with red, yellow or green indications through the principle of Real Time Data using data from the Global Positioning System (GPS). This study wanted to see how the use of the Google Traffic Feature as input to urban transportation policies by looking at existing urban transportation policies, analyzing the movement system model on Google Traffic, and analyzing the development of Google Traffic model policies, which would be useful for the development of Urban Transportation Policies, especially Bandar Lampung City as a case study.
First Person and Third Person Perspective in Virtual Reality: Analysis of Cybersickness Symptoms

Advances in technology makes it easier to gain access to the virtual world. This has led to more and more application and games being targeted towards the virtual world. But with the growing popularity of the virtual world, cybersickness has grown in popularity as well. This study aims to evaluate the factors affecting cybersickness in the Virtual Reality (VR) environment. There are few factors causing the effect of cybersickness in VR like duration, field of view, speed, habituation, and susceptibility of said user. Those factors affect differently in first person perspective(1pp) and third person perspective(3pp). To measure the cybersickness, a Virtual Reality Questionnaire (VRSQ) measurement index is utilized. The experiment was conducted with the following settings. The participants consisted of 20 males and 4 females who never used VR before. They performed task using short games. It consisted in total of 4 tasks (2 types of game (action and adventure) x 2 perspective (1pp and 3pp) = 4 tasks). The Latin Square design was used to minimize the effect of order. Then, a questionnaire was conducted after each treatment. Paired Dependent T-Tests was performed to check if there are differences in oculomotor, disorientation and VRSQ total score. There was a significant difference in 1pp and 3pp in both games. It is recommended to use third person perspective to reduce the cybersickness in VR environment.
Distance Functions Study in Fuzzy C-Means Core and Reduct Clustering

Fuzzy C-Means is a distance-based clustering process which applied by fuzzy logic concept. Clustering process worked in linear to the iteration process to minimizing the objective function. The objective function is an addition of the multiplication between the coordinates distance towards their closest cluster centroid and their membership degree. The more the iteration process, the objective function should get lower and lower. The objective of this research is to observe whether the distances which usually applied are able to fulfill the aforementioned hypothesis for determining the most suitable distance for Fuzzy C-Means clustering application. Few distance function was applied in the same dataset. 5 standard datasets and 2 random datasets were used to test the fuzzy c-means clustering performance with the 7 different distance function. Accuracy, purity, and Rand Index also applied to measure the quality of the resulted cluster. The observation result depicted that the distance function which resulted in the best quality of clusters are Euclidean, Average, Manhattan, Minkowski, Minkowski-Chebisev, and Canberra distance. These 6 distances were able to fulfill the basic hypothesis of the objective function behavior on Fuzzy C-Means Clustering method. The only distance who were not able to fulfill the basic hypothesis is Chebisev distance.
Reduction Of A Term In A Scientific Theory

The subject of this research is the concept of reduction in the logic and methodology of science. On the one hand, reduction is understood as a relationship between a term and its defining expression within a scientific theory, on the other hand, as a relationship between two theories. Since the expansion of the theory occurs due to the introduction of new terms into its vocabulary with the help of nominal definitions, reduction is an operation opposite to the definition: due to reduction, terms are removed from the dictionary of the theory. Moreover, the theory itself is defined in accordance with the set-theoretic approach as a class of sentences that are closed with respect to derivability. The novelty of the research lies in the fact that it examines the semantic and epistemological aspects of the formal definition of reduction. In particular, the explication of the reduction relation between the two theories is based on the concept of functional equivalence of theories. It has been established that the list of basic terms of the theory can only be specified conventionally. All terms introduced with the help of nominal definitions turn out to be reducible. Consequently, a distinctive feature of a theoretical term is the possibility of its reduction.
The Problems of Meta-Subject Outcomes Assessment in General Education

The article is devoted to the problem of meta-subject outcomes evaluation in general education. The need to consider this issue is justified by the contradictions between the existing approaches to evaluating new results and the lack of approved, accepted diagnostic tools. In addition, there are clear gaps between the activity-based learning framework of the Federal State Educational Standard and the traditional (knowledge-information) system for evaluating educational results. In the course of comparative and problem analysis, a number of benchmarks is identified. This allows to distinguish between approaches, concepts and their bases. Thus, the reference points are the educational system itself and the methods of teaching in which the school and the teacher work (developmental training, Pedagogy of mental and action approach, competence-based approach, etc.). For example, the basis for evaluating the meta-subject results of education in the activity-based approach are the stages of forming mental actions from simple to complex. The criteria basis for evaluating the meta-subject results of education are the concepts of “thinking”, “activity” and “result”, as the indicators of personal development level. The assessment of meta-subject skills in the competence-based approach is focused on “outcome results”. The description of theoretical approaches to the assessment of meta-subject results of education gives grounds for proper selection and application of the necessary assessment tools, thereby contributing to the improvement of general education quality. Keywords: evaluation, diagnostics, activity approach, meta-subject outcomes, educational outcomes
A Hybrid Between TOA and Lévy Flight Trajectory for Solving Different Cluster Problems

In data analysis applications for extraction of useful knowledge, clustering plays an important role. The major shortcoming of traditional clustering algorithms is exhibiting poor performance in solving complex data cluster problems. This research paper introduces a novel hybrid optimization technique based clustering approach. This paper is designed with two main objectives: designing efficient function optimization algorithm and developing advanced data clustering approach. In achieving the first objective, the standard TOA is first enhanced by hybridizing with Lévy flight trajectory and benchmarked on 23 functions. A new clustering approach is developed by conjoining k-means algorithm and Lévy flight TOA. Tested the numerical complexity of the proposed novel clustering approach on 10 UCI clustering datasets and 4 web document cluster problems. Conducted several simulation experiments and done an analysis of the results. The obtained graphical and statistical analysis reveals that the proposed novel clustering approach yields better quality clusters.
Research on the Export Porcelain Pattern of Changsha Kiln in the Belitung Shipwreck

Taking the Belitung shipwreck in the Tang Dynasty as a starting point, this article classifies the patterns of Changsha kiln porcelain in the Belitung shipwreck and analyzes the causes of different patterns.As a medium for carrying cultural symbols, Changsha kiln porcelain reflects the diversity of world culture and the attitude of the ancestors of the Tang Dynasty towards tolerance of foreign cultures.