Reviewerly/exHarmony huggingface dataset:
Enhanced Kernel-Based Multilayer Fuzzy Weighted Extreme Learning Machines

The high-dimensional and imbalanced data classification appears in many actual applications, but there are many problems encountered in practical operation. To overcome the disadvantages of kernel-based multilayer extreme learning machines (ML-KELM), enhanced kernel-based multilayer fuzzy weighted extreme learning machines (EML-KFWELM) has been proposed in this study. First, ML-KELM ignores imbalance learning, so we embed weighted strategy into ML-KELM to enhance the classification performance of the minority class. Meanwhile, we propose fuzzy membership to eliminate classification error caused by outlier and noise samples. Furthermore, we develop an enhanced grey wolf optimization (EGWO) method to perform the parameters optimization and improve the generalization performance of ML-KELM. In addition, the advantage of EML-KFWELM is that representation learning and classification can be integrated into a single learning process. Finally, computational comparisons with other state-of-the-art methods are performed on various real-world and gene expression data. Experimental results demonstrate that the proposed EML-KFWELM has good stability and can efficiently deal with the high-dimensional and imbalanced data.
Computational Experimental Study on Social Organization Behavior Prediction Problems

With the development of mobile Internet, behavioral trajectories of human life are more and more recorded, which makes it possible to use computer technology to mine organizational behavior patterns. The mining of organizational behavior patterns based on social computing can not only prepare them in a targeted manner but also predict the consequences of possible measures. The organization behavior pattern mining has achieved a series of achievements in the fields of e-commerce and enterprise management. However, the problem of class imbalance and nonconsistent misclassification cost is common in the field of organizational behavior. For this problem, this article compares and analyzes the performance of the organizational behavior prediction model established by four typical cost-sensitive learning methods based on six classifiers, which provides a basis for the appropriate selection of cost-sensitive learning methods in different situations. Among them, the upsampling learning method is a better cost-sensitive learning method. However, there are some shortcomings in the upper sampling method. In order to avoid the possible overfitting problem of the social organization behavior prediction model established by the upper sampling method, this article proposes a new cost-sensitive learning method suitable for the mining of organizational behavior patterns. Based on the cost curve, this article proposes an effective personalized solution to the problem of class disequilibrium and nonconsistent misclassification cost in organizational behavior prediction modeling.
Education for the Multifaith Community of Cybersecurity

The demand for cybersecurity professionals is growing. Many cybersecurity academic and training programmes exist to prepare students and professionals for these jobs. The programmes cover many areas of cybersecurity with considerable overlap, but with different emphases. Some are highly technical and cover little non-technical; others do the opposite. Cybersecurity jobs typically require some technical knowledge, an ability to place security problems in a larger context, and an ability to communicate this information effectively and convincingly. The problem with treating technical and non-technical subjects as silos rather than recognizing the two are tightly related and need to be taught together. This paper shows how seven common cybersecurity frameworks and ten masters’ courses from the UK and US cover both technical and non-technical content. It examines the balance of technical courses, non-technical courses, and courses that mix both technical and non-technical material. It argues that these topics cannot be siloed, and their balance is critical to meeting the goals of the frameworks and programmes.
Teachers’ classroom instruction reinforcement strategies in english language class

In the context of this study, reinforcement is an act of teachers to strengthenvstudents’ positive behaviour in learning English in the classroom. This qualitative case study was a classroom discourse which employed necessary quantification. The aim was to investigate the use of classroom instruction reinforcement strategies by two English language teachers and their perceptions at a state secondary school in Semarang in the academic year of 2019. The two English teachers were observed in five different lessons. The teachers were interviewed to explore their perceptions of using reinforcement in the classroom. The instruments of this study were observation sheet, interview guideline, and audio-video recordings. The findings showed that the teachers reinforced the students in three types of classroom instruction reinforcement. The three types of reinforcement strategies were: praise or other verbal reinforcement, tangible rewards, and token rewards. The reinforcement strategy mostly used was praise, followed by tangible rewards and token rewards. Based on the interviews, the teachers perceived that giving positive reinforcement to the students was important. The findings suggest the teachers implement the reinforcement strategies fairly to increase teacher-students interaction.
A Novel Method for Privacy Preservation of Health Data Stream

Most of the conventional privacy preservation methods focus on static datasets.These methods cannot be applied as it is on real-world datasets; which dynamically modify data.If one tuple in the dataset get modified, statistics of complete dataset gets changed.Privacy preservation measures must be applied after modification of dataset.Such re-anonymization of complete dataset is incompetent when large datasets are often changed.When dynamic stream data is considered, we have to apply different privacy techniques which can apply privacy on each tuple.Although several studies have addressed data privacy for static, dynamic and stream data, they are not adequate for avoiding similarity attack and reducing data loss in privacy preservation.Even in the big streaming data privacy preservation, repetition of L-diverse group can take place.This repetition can cause re-identification of individual.Therefore, we identified limitations of data-privacy preservation for stream data and developed more efficient L-diversity algorithm for preserving privacy of data streams.We used hash values of L-diverse groups to find similarity among the groups.Human disease ontology is used to find synonyms of disease terms.Experimental results demonstrated that, our proposed anonymization algorithm reduced data loss of L-diverse group.
The rationale for relevance development of substrate-attribute model of information culture in the framework of the philosophy of culture

Introduction. The information society changes many familiar phenomena of traditional society. These include, first of all, culture, which acts as an indicator that civilization is moving to a new stage of development. In the information age, significant changes are taking place in the culture. In the works of scientists, a categorical apparatus is developed that allows describing and studying new phenomena. The category "information culture" is one of such innovations in the scientific literature. However, research shows that a unified approach to understanding the essence of this phenomenon has not yet developed. In the study of information culture, the issues of its relationship with society and the individual are well developed, and the possible social consequences of its development and implementation in all spheres of human life and activity are presented. However, the weak point or "white spot" remains the study of its nature, essential, attribute, and substrate properties. The purpose of this article is to substantiate the relevance of developing a substratum-attribute model of information culture within the framework of the philosophy of culture. Materials and Methods. The main research methods used were analytical review, analysis, grouping, generalization, comparison, and substrate approach. Results: the analysis of works devoted to the philosophical and cultural research of information culture has shown that researchers of the nature and essence of information culture as a category of cultural philosophy pay insufficient attention to the development of its substratum-attribute model. There are practically no complete system studies that result in the construction of a substratum-attribute model of information culture. Discussion and Conclusions. The studied models of information culture do not fully reflect its nature, essential features and qualities that allow us to understand the causes and mechanisms of its influence on the course of civilizational, social and anthropological changes. Therefore, the development of a substratum-attribute model of information culture is due to the need to identify the causes of possible changes in the generic qualities of a person that occur under its influence as a socio-cultural phenomenon.
Redundant Data Normalization using the Novel Data Mining Algorithms

Redundant detection is a process of identifying set of words which relates to corresponding real world object in database documents.A database implies a group constituent parts linked data which is controlled & recovered efficiently.The thought of the more extensive database is high system database which resolution include of a great number of records can be collected within the database that know as great quantity regards data may collected under database scheme.In unusual statements, similar data warehousing, information digging or information integration data necessity be refined as a pre-processing tread to assure the variety of data & the performance of uses.An essential task in information purification is redundant data normalization.Existing LCS (Least Cost Methods) refer to various data paradigms & record types.Trustworthy problems along those inquiries are however to be defeated.This writing introduces a (GFS) Greedy Forward Search based approach to redundant record detection to find duplicate words in large dataset.The outcomes show that the system has a significant improvement in design prediction accuracy moreover robustness.
Comp4Text Checker: An Automatic and Visual Evaluation Tool to Check the Readability of Spanish Web Pages

One important requirement for a web page to be accessible for all, according to the current international recommendations from the W3C Accessibility Initiative is that the text should be readable and understandable to the broadest audience possible. Nowadays, unfortunately, the information included in the web pages are not easy to read and understand to everybody. This paper introduces the Comp4Text online readability evaluation tool, which is able to calculate the readability level of a web page based on classical linguistic measures (sentence to sentence) and detect unusual words and abbreviations. Moreover, it provides recommendations to solve the readability problems and show everything in a very visual way. Thanks to this tool, the web page designers and writers could improve their sites, being easier to be read and understand for all. Currently, Comp4Text is based on the Spanish language, but it can be easily extended to other languages if the readability metrics and easy-to-read rules are well-known.
Malaysian researchers on open data: The first national survey on awareness, practices and attitudes

The study investigates the awareness, practices and attitudes of researchers in regard to open data – i.e. the sharing and reuse of research data – which is part of a larger study that concentrated on the scholarly communication readiness of Malaysian researchers in Open Science. The data were gathered by means of a survey which obtained 135 responses from researchers based in five research universities in Malaysia. The main conclusions are: (a) the researchers are aware of open data, yet, they are not practising it as shown in the mean scores, as well as in their responses towards the statements asked; (b) unclear information on data privacy policy, misuse of data, and the fear of losing publication opportunity are part of disincentives for data sharing. The requisite for open data understanding, practices and attitudinal change is needed for these may impact research practices, government policies and scientific knowledge, leading to research transparency and accountability, social benefit and economic growth. This paper concludes with a discussion that policies incentivizing the sharing and reuse of open data, as well as tools and guidance to support data sharing, and a strong incentives and rewards to implement open data among researchers, should be encouraged. Future studies should look into the importance of rewards for data sharing among researchers’ institutions. Studies bridging the gap between policy and practices of open data should be examined, if true openness in research is to be established in Malaysia.
Containerization for shipping Scientific Workloads in Cloud

In Cloud computing environment, researchers are actively looking for opting containerization technology using various tools to achieve better performance in High Performance Computing (HPC) applications execution.In virtualization world, containers are getting more popular and found suitable in comparison of virtual machines as they are giving better performance.Along with agility, support micro services and integrated easily with management and monitoring tools.To have containerization in place, Docker is one of the most suitable open source platforms with Operating System (OS) level virtualization.Singularity is also one of the popular solutions to work with HPC applications.Before directly start using the technologies and tools, they must be analyzed, explored and have a proof of concept for performance first.This paper presents (1) Containers evaluation using open source platform Singularity and Docker (2) monitoring of containers using orchestration and monitoring system Kubernetes (3) feature analysis of scientific workloads using Containers in cloud.Feature analysis reports having performance result sets are primarily aimed for helping the DevOps on making the decisions for choosing the right technology and tools to run their parallel and high performance computing applications.
Learning to count: A deep learning framework for graphlet count estimation

Abstract Graphlet counting is a widely explored problem in network analysis and has been successfully applied to a variety of applications in many domains, most notatbly bioinformatics, social science, and infrastructure network studies. Efficiently computing graphlet counts remains challenging due to the combinatorial explosion, where a naive enumeration algorithm needs O( N k ) time for k -node graphlets in a network of size N . Recently, many works introduced carefully designed combinatorial and sampling methods with encouraging results. However, the existing methods ignore the fact that graphlet counts and the graph structural information are correlated. They always consider a graph as a new input and repeat the tedious counting procedure on a regular basis even if it is similar or exactly isomorphic to previously studied graphs. This provides an opportunity to speed up the graphlet count estimation procedure by exploiting this correlation via learning methods. In this paper, we raise a novel graphlet count learning (GCL) problem: given a set of historical graphs with known graphlet counts, how to learn to estimate/predict graphlet count for unseen graphs coming from the same (or similar) underlying distribution. We develop a deep learning framework which contains two convolutional neural network models and a series of data preprocessing techniques to solve the GCL problem. Extensive experiments are conducted on three types of synthetic random graphs and three types of real-world graphs for all 3-, 4-, and 5-node graphlets to demonstrate the accuracy, efficiency, and generalizability of our framework. Compared with state-of-the-art exact/sampling methods, our framework shows great potential, which can offer up to two orders of magnitude speedup on synthetic graphs and achieve on par speed on real-world graphs with competitive accuracy.
Reservoir Memory Machines as Neural Computers

Differentiable neural computers (DNCs) extend artificial neural networks with an explicit memory without interference, thus enabling the model to perform classic computation tasks, such as graph traversal. However, such models are difficult to train, requiring long training times and large datasets. In this work, we achieve some of the computational capabilities of DNCs with a model that can be trained very efficiently, namely, an echo state network with an explicit memory without interference. This extension enables echo state networks to recognize all regular languages, including those that contractive echo state networks provably cannot recognize. Furthermore, we demonstrate experimentally that our model performs comparably to its fully trained deep version on several typical benchmark tasks for DNCs.
Person Re-Identification Using Additive Distance Constraint With Similar Labels Loss

Despite the promising progress made in recent years, person re-identification (Re-ID) remains a challenging task due to the intra-class variations. Most of the current studies used the traditional Softmax loss for solutions, but its discriminative capability encounters a bottleneck. Therefore, how to improve person Re-ID performance is still a challenging task. To address this problem, we proposed a novel loss function, namely additive distance constraint with similar labels loss (ADCSLL). Specifically, we reformulated the Softmax loss by adding a distance constraint to the ground truth label, based on which similar labels were introduced to enhance the learned features to be much more stable and centralized. Experimental evaluations were conducted on two popular datasets (Market-1501 and DukeMTMC-reID) to examine the effectiveness of our proposed method. The results showed that our proposed ADCSLL was more discriminative than most of the other compared state-of-the-art methods. The rank-1 accuracy and the mAP on Market-1501 were 95.0% and 87.0%, respectively. The numbers were 88.6% and 77.2% on DukeMTMC-reID, respectively.
A Relaxation-Based Approach for Mining Diverse Closed Patterns

In recent years, pattern mining has moved from a slow-moving repeated three-step process to a much more agile iterative/user-centric mining model. A vital ingredient of this framework is the ability to quickly present a set of diverse patterns to the user. In this paper, we use constraint programming (well-suited to user-centric mining due to its rich constraint language) to efficiently mine a diverse set of closed patterns. Diversity is controlled through a threshold on the Jaccard similarity of pattern occurrences. We show that the Jaccard measure has no monotonicity property, which prevents usual pruning techniques and makes classical pattern mining unworkable. This is why we propose anti-monotonic lower and upper bound relaxations, which allow effective pruning, with an efficient branching rule, boosting the whole search process. We show experimentally that our approach significantly reduces the number of patterns and is very efficient in terms of running times, particularly on dense data sets.
A Novel Algorithm for Dividing Uzbek Language Words into Syllables for Concatenative Text-to-Speech Synthesizer

Demand for multimedia systems, including speech synthesis systems, is growing rapidly to simplify and facilitate the use of rapidly evolving modern technologies.Designing text-to-speech systems capable of producing natural-sounding speech segments in the Uzbek language is a challenging and open problem.In this paper, we propose an algorithm based on concatenative methods for dividing Uzbek language words into syllables for text-to-speech synthesis.First, the electronic dictionary of the Uzbek word and syllables was developed based on the proposed algorithm.Then, the structure and characteristics of these words and syllables were analyzed.This electronic dictionary has more than 31.5 thousand words and 3 thousand syllables.
Sugeno Fuzzy Inference Method and MatLab Application Program for Simulation of Student Performance Evaluation in the Elementary Mathematics Learning Process

The purpose of this study is to make a simulation for the assessment of student performance in elementary mathematics learning process by using fuzzy logic to overcome the problem of the assessment process of student performance.Besides that there is no special system that can optimize in providing support for teachers in conducting evaluations that are still manual calculations.One way of determining the calculation of student evaluation results can be facilitated by using the help of Artificial Intelligence (AI) considerations as an optimization.In consideration of the performance evaluation (performance) of these students using fuzzy logic with the sugeno inference system method.This sugeno method is a fuzzy inference method for rules which is represented in the form of IF-THEN, where the system output is not a fuzzy set, but in the form of a linear equation.The criteria used in the assessment of student performance include very unsuccesusful, unsuccessful, average, successful, and very successful.In this simulation the results displayed with manual calculations and MatLab calculations as a comparison of the results of the manual calculation result value of 45 while in the MatLab calculation the result value is 48,5.So it can be concluded that the difference caused by the level of accuracy of the results of the inference rule in manual calculations is less effective and sometimes even a lot of inference rules must be adjusted.
A new method based on stacked auto-encoders to identify abnormal weather radar echo images

Abstract It is not denied that real-time monitoring of radar products is an important part in actual meteorological operations. But the weather radar often brings out abnormal radar echoes due to various factors, such as climate and hardware failure. So it is of great practical significance and research value to realize automatic identification of radar anomaly products. However, the traditional algorithms to identify anomalies of weather radar echo images are not the most accurate and efficient. In order to improve the efficiency of the anomaly identification, a novel method combining the theory of classical image processing and deep learning was proposed. The proposed method mainly includes three parts: coordinate transformation, integral projection, and classification using deep learning. Furthermore, extensive experiments have been done to validate the performance of the new algorithm. The results show that the recognition rate of the proposed method can reach up to more than 95%, which can successfully achieve the goal of screening abnormal radar echo images; also, the computation speed of it is fairly satisfactory.