Reviewerly/exHarmony huggingface dataset:
A Comprehensive Study of Parameters Analysis for Galactic Swarm Optimization

The galactic swarm optimization algorithm is a metaheuristic approach inspired by the motion and behavior of stars and galaxies.It is a framework that can use basic metaheuristic search methods.The method, which has a two-phase structure, performs exploration in the first phase and exploitation in the second phase.GSO tries to find the best solution in the search space by repeating these two phases for the specified number of times.In this study, the analysis of maximum epoch number (EPmax), the number of iterations in the first phase ( 1 ), and the number of iterations in the second phase ( 2 ) parameters, which determine the exploration and exploitation balance in the GSO method, was performed.15 different parameter sets consisting of different values of these three parameters were created.The methods with 15 different parameter sets were performed at 30 independent runs.The methods were analyzed using 26 benchmark functions.The functions are tested in 30, 60, and 100 dimensions.Detailed results of the analysis were presented in the study, and the results obtained were also evaluated statistically.
Malicious Domain Name Detection Based on Doc2vec and Hybrid Network

Abstract In traditional malicious domain name detection algorithms, machine learning detection methods take a long time to extract features and the extraction methods are more complex; deep learning detection methods are easy to lose the semantic features of the entire domain name, and the detection results of a single neural network for different sample data sets are unstable . In response to the above problems, a malicious domain name detection method based on Doc2vec and hybrid network was proposed, and a new DLR (Doc2vec-LSTM-RNN) detection model was constructed. The word vector is constructed using the Doc2vec algorithm optimized on the basis of the Word2vec algorithm, which retains the semantic information of the domain name as a whole; and mixes the bidirectional LSTM network and the bidirectional RNN network in series to carry out deep-level feature extraction and enhance the robustness of the detection model; finally the Softmax function is used to output the classification result. Experimental data shows that this method has excellent performance in malicious domain name detection. Compared with traditional machine learning and deep learning detection methods, it has higher detection accuracy.
Optimization using Genetic Algorithm in Food Composition

This paper aims to process daily food consumption based on the Guideline to Healthy Food or the principle of Healthy 4 Perfect 5 (H4P5).The Guideline to Healthy Food does not include the portion and size of daily food consumption, therefore it is perfected into the Guidelines to Balanced Nutrition (GBN).GBN is performed by consuming three servings of protein vegetables, three servings of animal protein, eight servings of carbohydrates, five servings of vegetables, five servings of fruits, and eight servings of water.Each part of the portion has a proportional size and dose based on GBN.The portions are measured by food composition using the optimization process using a Genetic Algorithm (GA).Optimization by a using Genetic Algorithm yielded three outputs, namely, first, the initial objective function (0) compared to the generative objective function (1) produces a value of 6.13: 4.72 (there is an increase in chromosomes, because, the smaller the value, the better the chromosome is).Second, probability mutation is 50% of mutation rate, crossover probability is 5%, the maximum generation is 36, the maximum fitness point is 0.240806, and the size of the population is 6.Third, optimization using the Genetic Algorithm in food composition produces one food model, one consumption model, and one composition model.Thus, the result of this research (food model, consumption model, and composition model) can be a recommendation for food consumption patterns (normal condition) and a reference for national food consumption policies.
Optimal Bayesian supervised domain adaptation for RNA sequencing data

Abstract Motivation When learning to subtype complex disease based on next-generation sequencing data, the amount of available data is often limited. Recent works have tried to leverage data from other domains to design better predictors in the target domain of interest with varying degrees of success. But they are either limited to the cases requiring the outcome label correspondence across domains or cannot leverage the label information at all. Moreover, the existing methods cannot usually benefit from other information available a priori such as gene interaction networks. Results In this article, we develop a generative optimal Bayesian supervised domain adaptation (OBSDA) model that can integrate RNA sequencing (RNA-Seq) data from different domains along with their labels for improving prediction accuracy in the target domain. Our model can be applied in cases where different domains share the same labels or have different ones. OBSDA is based on a hierarchical Bayesian negative binomial model with parameter factorization, for which the optimal predictor can be derived by marginalization of likelihood over the posterior of the parameters. We first provide an efficient Gibbs sampler for parameter inference in OBSDA. Then, we leverage the gene-gene network prior information and construct an informed and flexible variational family to infer the posterior distributions of model parameters. Comprehensive experiments on real-world RNA-Seq data demonstrate the superior performance of OBSDA, in terms of accuracy in identifying cancer subtypes by utilizing data from different domains. Moreover, we show that by taking advantage of the prior network information we can further improve the performance. Availability and implementation The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https://github.com/SHBLK/BSDA. Supplementary information Supplementary data are available at Bioinformatics online.
Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model

Multiview representation learning of data can help construct coherent and contextualized users' representations on social media. This paper suggests a joint embedding model, incorporating users' social and textual information to learn contextualized user representations used for understanding their lifestyle choices. We apply our model to tweets related to two lifestyle activities, `Yoga' and `Keto diet' and use it to analyze users' activity type and motivation. We explain the data collection and annotation process in detail and provide an in-depth analysis of users from different classes based on their Twitter content. Our experiments show that our model results in performance improvements in both domains.
The Purview of Blockchain Appositeness in Computing Paradigms: A Survey

Blockchain technology is getting more and more pertinent to solve most of the digital problems that we face today. Blockchain is notable for its prominent features like immutability, decentralization, consensus, privacy, and security. However, blockchain is still suffering from different barriers like quantum attacks, scalability problems, integration problems, incompetence to face bigdata, storage problems, and so on. The main aim of this study was to find out the scope, various problems raised, and the applicability of blockchain technology when integrated with different computing paradigms like cloud computing, edge computing, fog computing, osmotic computing, big data computing, and quantum computing. To conduct this study, we have surveyed different research articles in the combination of blockchain technology and computing paradigms. Based on this survey, we have mentioned the contemporary research works, challenges, and a list of possible research opportunities and solutions.
A Dashboard for Mitigating the COVID-19 Misinfodemic

This paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of, and effective interventions against the COVID-19 misinfodemic on Twitter.Specifically, it introduces a public dashboard which, in addition to displaying case counts in an interactive map and a navigational panel, also provides some unique features not found in other places.Particularly, the dashboard uses a curated catalog of COVID-19 related facts and debunks of misinformation, and it displays the most prevalent information from the catalog among Twitter users in user-selected U.S. geographic regions.The paper explains how to use BERT-based models to match tweets with the facts and misinformation and to detect their stance towards such information.The paper also discusses the results of preliminary experiments on analyzing the spatiotemporal spread of misinformation.
E-mail Spam Filtering using Genetic Algorithm based on Probabilistic Weights and Words Count

Spam email filtering is a hot area of research, as they are growing with time. Most of the spam mails are promotional in nature. Therefore, spam mails are not harmful for the computers, but these mails are annoying for user. Spam mails can be filtered using spam filtering methods like Bayes and Naive Bayes classifications. Classification is done on the basis of content of the mail, or in particular on words and probability is calculated of finding a word from spam and ham classifier words. There are few words which can be found in both spam and ham mails, thus threshold based mechanism is desirable for correct classification. For correct classification using Bayes and Naive Bayes dataset should be huge ideally number of mails should be infinite. But in real applications a scheme is desired which is adaptive in nature and can provide good results with a few mails. In the similar direction, in this paper a genetic algorithm based spam detection method is detailed which is very simple and provide good results with limited dataset.
A Game Theoretical Pricing Scheme for Vehicles in Vehicular Edge Computing

Vehicular edge computing (VEC) brings the computing resources to the edge of the networks and thus provisions better computing services to the vehicles in terms of response latency. Meanwhile, the edge server can earn their revenues by leasing the computing resources. However, a higher price does not always bring forth more benefits for the edge server in VEC, since it may discourage vehicles from renting more computing resources from VEC. To the best of our knowledge, few of previous works have focused on the real-time pricing problem for VEC. We investigate in this paper the pricing problem from the viewpoints of both vehicles and the edge server, so as to optimize the utility values and revenues of vehicles and the edge server, respectively. We resort to the Stackelberg game for modeling the interactions between vehicles and edge server, and a distributed algorithm for this pricing problem is proposed in the paper. Experimental results have displayed the efficiency and effectiveness of the proposed algorithm.
STUDENTS’ DIFFICULTIES IN ENGLISH SPEAKING CLASS

This study aim to answer: (1) What are of student’s difficulties and possible solution in English speaking at the ninth years grade of SMPI AL-KAROMAH SAMPANG? (2) What factors are causing student’s difficulties in English speaking at the ninth years grade of SMPI AL-KAROMAH SAMPANG?. The research design of this study is descriptive qualitative. The writer uses interview and observation as instrument of data. In this research, the researcher uses non participant observation, the writer only becomes an observer. The writer had taken part in class being observed. While participating as an observer, the writer observes what kinds of students’difficulties in English speaking and what are the causing factors students’ difficulties in English speaking. From the result of the observation, interview, and questionnaire the researcher concluded that most of the student at the ninth grade of SMPI Al-Karomah Sampang got difficulties in English speaking class here was caused by fear of mistakes, shyness, lack of motivation, and lack of confidence. And some of these factors are related to the learners themselves, the teaching strategies, the curriculum, and the environment.
SECURE FILE STORAGE ON CLOUD USING HYBRID CRYPTOGRAPHY

The proposed model is liable to meet the required security needs of data centred of cloud. AES, DES and RSA are used for the encryption of file slices takes minimum time and has maximum throughput for encryption and decryption from other symmetric algorithms. The idea of splitting and merging adds on to meet the principle of data security. The hybrid approach when deployed in cloud environment makes the remote server more secure and thus, helps the cloud providers to fetch more trust of their users. For data security and privacy protection issues, the fundamental challenge of separation of sensitive data and access control is fulfilled. Cryptography technique translates original data into unreadable form. Cryptography technique is divided into symmetric key cryptography and public key cryptography. This technique uses keys for translate data into unreadable form. So only authorized person can access data from cloud server. Cipher text data is visible for all people.
Sine cosine algorithm for underwater multilevel thresholding image segmentation

As an important and effective method, the multilevel thresholding not only attractes the attention of many scholars in recent years, but also is widely used to solve the image segmentation problem. However, the computational complexity becomes large when the threshold levels increase. To overcome this shortcoming, the sine cosine algorithm (SCA) based on Kapur's entropy method is proposed to solve the underwater multilevel thresholding image segmentation problem, which can balance exploration and exploitation to obtain the global optimal solution. To verify the effectiveness and feasibility of the SCA, the segmentation results are compared with other algorithms including bat algorithm (BA), flower pollination algorithm (FPA), particle swarm optimization (PSO), whale optimization algorithm (WOA) by maximizing fitness value of Kapur's entropy method. The fitness value, execution time, peak signal to noise ratio (PSNR), structure similarity index (SSIM) and Wilcoxon's rank-sum test are important evaluation indicators. The experimental results indicate that the SCA has a shorter execution time, higher segmentation accuracy and stronger robustness.
BlockCare: SDN-Enabled Blockchain Framework for Securing Decentralized Healthcare and Precision Medicine Applications

The growing importance and maturity of Internet of Things (IoT) and wearable computing are revolutionizing healthcare diagnosis and body treatment by providing access to meaningful healthcare data and improving the effectiveness of medical services. In this context, personal health information must be exchanged via trusted transactions that provide secure and encrypted sensitive data of the patient. Moreover, healthcare smart devices need flexible, programmable, and agile networks to allow on-demand configuration and management to enable scalable and interoperable healthcare applications. Two complementary trends show promise in meeting these needs. First, blockchain is emerging as a transparent, immutable, and validated-by-design technology that offers a potential solution to address the key security challenges in healthcare domains by providing secure and pseudo-anonymous transactions in a fully distributed and decentralized manner. Second, software-defined networking (SDN) offers a significant promise in meeting the healthcare communication needs by providing a flexible and programmable environment to support customized security policies and services in a dynamic, software-based fashion. To that end, we present our ideas on SDN-enabled blockchains that can be used to develop and deploy privacy-preserving healthcare applications. First, we present a survey of the emerging trends and prospects, followed by an in-depth discussion of major challenges in this area. Second, we introduce a fog computing architecture that interconnects various IoT elements, SDN networking, and blockchain computing components that control and manage patients’ health-related parameters. Third, we validate our architecture in the context of three use cases involving smart health care, precision medicine, and pharmaceutical supply chain. Finally, we discuss open issues that need significant new research investigations.
Comparing the Performance of CNNs and Shallow Models for Language Identification

In this work we compare the performance of convolutional neural networks and shallow models on three out of the four language identification shared tasks proposed in the VarDial Evaluation Campaign 2021. In our experiments, convolutional neural networks and shallow models yielded comparable performance in the Romanian Dialect Identification (RDI) and the Dravidian Language Identification (DLI) shared tasks, after the training data was augmented, while an ensemble of support vector machines and Naive Bayes models was the best performing model in the Uralic Language Identification (ULI) task. While the deep learning models did not achieve state-of-the-art performance at the tasks and tended to overfit the data, the ensemble method was one of two methods that beat the existing baseline for the first track of the ULI shared task.
Research on Settlement Prediction of Small Water Conservancy Project based on ELM Model Optimized by Genetic Algorithm

To find suitable for small water conservancy engineering standard method for prediction of subsidence. This paper based on the genetic algorithm GA optimization extreme learning machine, three different ELM model activation function. From this, six computational models are obtained. According to the input of groundwater dynamic changes, precipitation, temperature and soil four indicators of the two kinds of input combinations, a total of 12 kinds of model input. It’s concluded that the optimal settlement prediction model, the results showed that: Ga-ELM sin model shows high accuracy, and genetic algorithm can improve the calculation accuracy of ELM model. Groundwater dynamics is the main factor affecting settlement.
Plataformas algorítmicas: interpelação, perfilamento e performatividade (Algorithmic Platforms: Interpellation, Profiling and Performativity)

Portuguese Abstract: Este trabalho de articulacao teorica, ancorado em pesquisa bibliografica, considera como plataformas algoritmicas as redes de acesso controlado nas quais o usuario e governado via algoritmos e sua identidade e redefinida continuamente com base em dados fornecidos por ele ou extraidos de sua atividade. Para entender as circunstâncias em torno da interacao do usuario com essas plataformas, o artigo propoe e desenvolve categorias que adaptam e reformulam elementos conceituais de diversas origens. A interpelacao refere-se a abordagem do usuario pelo algoritmo, que e recalibrada a cada iteracao e cuja agencia pode distribuir-se em instâncias de alteridade representadas pelo algoritmo ou por outros atores. Tal abordagem opera por meio da definicao ad hoc de perfis a partir da combinacao de tracos do usuario, o perfilamento. Este, por sua vez, consolida-se em virtude de sua assuncao pelo usuario, que corresponde a performatividade.English Abstract: This work of theoretical articulation, anchored on bibliographic research, considers as algorithmic platforms those networks with controlled access in which the user is governed by algorithms and his identity is continually redefined based on data provided by him or extracted from his activity. In order to understand the circumstances surrounding the user’s interaction with these platforms, the article proposes and develops categories that adapt and reformulate conceptual elements from a variety of sources. Interpellation is the algorithm approach to the user, which is recalibrated at each iteration and whose agency can be distributed in instances of alterity represented by the algorithm or other actors. This approach operates through profiling, i.e., the ad hoc definition of profiles grounded on the combination of user traits. Profiling, in turn, is consolidated by virtue of its assumption by the user, which corresponds to performativity.
Cultural Competence as a Factor Successful Professional Activity

Article analyzes the problems of cultural competence as the main factor of successful professional activity of applicants for higher education in economic specialties, since the formation of personality culture as the most important condition for its self – organization and self-development is one of the leading tasks of modern education. A generally accepted and important characteristic of a specialist is a certain set of professional knowledge and skills, which may not coincide in the composition, structure, and severity of certain qualities in representatives of different types of activity. The article analyzes cultural competence and as a result of the study, it turns out that where emotional intelligence is powerless, a person with a well-developed emotional intelligence understands what all people have in common and what distinguishes each of us from the other. A culturally competent person, while also being a specialist in economic specialties, is able to determine which properties of a particular person are characteristic of all people, which are only for him, and which are neither universal nor unique. Therefore, he adapts quite easily to the Customs and traditions of the new environment: he gets into the habit of observing and consciously adapting to the new environment. The findings state that cultural competence is the result of a development process that requires long-term commitment. This is not a specific end product that can be achieved based on the results of a two-hour master class, it requires an active learning and practice process for a long time. It’s easier to talk about becoming culturally competent than to do it. Professionals who work with different ethnic and cultural groups and can therefore become more culturally competent by advancing through three main stages: developing awareness, acquiring knowledge, and developing and maintaining cross-cultural skills.
Artificial intelligence and machine learning: a perspective on integrated systems opportunities and challenges for multi-domain operations

This paper provides a perspective on historical background, innovation and applications of Artificial Intelligence (AI) and Machine Learning (ML), data successes and systems challenges, national security interests, and mission opportunities for system problems. AI and ML today are used interchangeably, or together as AI/ML, and are ubiquitous among many industries and applications. The recent explosion, based on a confluence of new ML algorithms, large data sets, and fast and cheap computing, has demonstrated impressive results in classification and regression and used for prediction, and decision-making. Yet, AI/ML today lacks a precise definition, and as a technical discipline, it has grown beyond its origins in computer science. Even though there are impressive feats, primarily of ML, there still is much work needed in order to see the systems benefits of AI, such as perception, reasoning, planning, acting, learning, communicating, and abstraction. Recent national security interests in AI/ML have focused on problems including multidomain operations (MDO), and this has renewed the focus on a systems view of AI/ML. This paper will address the solutions for systems from an AI/ML perspective and that these solutions will draw from methods in AI and ML, as well as computational methods in control, estimation, communication, and information theory, as in the early days of cybernetics. Along with the focus on developing technology, this paper will also address the challenges of integrating these AI/ML systems for warfare.