Reviewerly/exHarmony huggingface dataset:
“What’s happening over there?”: a study of the current state of services, challenges, and prospects in Nigerian medical libraries

The authors examined the current state of service delivery, collections, and challenges in Nigerian medical libraries.We employed a descriptive mixed method research design using a cross-sectional quantitative survey of Nigerian medical librarians and qualitative interviews with heads of selected Nigerian medical libraries.Respondents indicated that the US National Library of Medicine classification scheme is most commonly used to organize the resources of medical libraries in Nigeria. Respondents indicated that library users have a high understanding about the library but exhibit low usage of library services. Nigerian medical libraries have social media accounts but use them infrequently. Most medical librarians do not provide specialized services to health care professionals, and monographs are the major information resources in their collections. Most medical librarians in Nigeria have beginner-level knowledge of systematic reviews and evidence-based medicine and rarely organize training for library users.Our findings show that services offered by medical libraries in Nigeria are still evolving. Identified skill deficits among medical librarians need to be addressed. The country's library associations and international programs in developing countries should focus on providing continuing education and training of Nigerian medical librarians to enhance their support for medical education and practice in Nigeria.
An Efficient Algorithm for Extracting High-Utility Hierarchical Sequential Patterns

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, where utility is used to measure the importance or weight of a sequence. However, the underlying informative knowledge of hierarchical relation between different items is ignored in HUSPM, which makes HUSPM unable to extract more interesting patterns. In this paper, we incorporate the hierarchical relation of items into HUSPM and propose a two-phase algorithm MHUH, the first algorithm for high-utility hierarchical sequential pattern mining (HUHSPM). In the first phase named Extension, we use the existing algorithm FHUSpan which we proposed earlier to efficiently mine the general high-utility sequences (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>g</mml:mi></mml:math>-sequences); in the second phase named Replacement, we mine the special high-utility sequences with the hierarchical relation (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>s</mml:mi></mml:math>-sequences) as high-utility hierarchical sequential patterns from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>g</mml:mi></mml:math>-sequences. For further improvements of efficiency, MHUH takes several strategies such as Reduction, FGS, and PBS and a novel upper bounder TSWU, which will be able to greatly reduce the search space. Substantial experiments were conducted on both real and synthetic datasets to assess the performance of the two-phase algorithm MHUH in terms of runtime, number of patterns, and scalability. Conclusion can be drawn from the experiment that MHUH extracts more interesting patterns with underlying informative knowledge efficiently in HUHSPM.
Continuity of natural science education as a condition for training specialists of the agrarian sector of the economy

This article is devoted to the study and construction of a model of continuous natural science education. The article considers the concept of “continuouslearning”inthefield ofnaturalscience.Specialemphasismade on thespecificityandamountofcontentatdifferentlevelsofeducation:pre-school, primary, secondary general and secondary professional, additional for training personnel in the agricultural sector. The foreign and domestic experience of lifelong learning training practices is analyzed in detail. The social effect of the results of the implementation of the projects is characterized by the creation of a modern industry of continuing education for children and new forms of lifelong education, creates a space for tests, forms the choice of life path and professional development of staff for the agriculturalsector.
The Study of the Theoretical Size and Node Probability of the Loop Cutset in Bayesian Networks

Pearl’s conditioning method is one of the basic algorithms of Bayesian inference, and the loop cutset is crucial for the implementation of conditioning. There are many numerical algorithms for solving the loop cutset, but theoretical research on the characteristics of the loop cutset is lacking. In this paper, theoretical insights into the size and node probability of the loop cutset are obtained based on graph theory and probability theory. It is proven that when the loop cutset in a p-complete graph has a size of p − 2 , the upper bound of the size can be determined by the number of nodes. Furthermore, the probability that a node belongs to the loop cutset is proven to be positively correlated with its degree. Numerical simulations show that the application of the theoretical results can facilitate the prediction and verification of the loop cutset problem. This work is helpful in evaluating the performance of Bayesian networks.
Motivation for Learning English Early Childhood Through Storytelling Method Using e-Big Book media

English learning motivation is a condition that arises in a person to carry out learning activities, especially English, to increase vocabulary and achieve learning goals, but the learning process was still focused on the teacher and used lecturing method. Therefore, this research aimed to analyze the use of storytelling methods based on e-big book media. This research was a quasi-experimental research with nonequivalent control group design. The population of this research was group B which was consisted of 546 children. The samples were taken by a cluster random sampling technique. The sample consisted of 25 students in the experimental group and 22 students in the control group. Data collection was done by using a non-test with an observation method. The data were analyzed by using t-test. The results of data analysis were t-count = 10,73 at the 5 % significance level and dk = 45, t-table = 2,003 t-count = 10,73&gt; t-table = 2,003. So, the use of storytelling methods based on e-big book media would have an impact on English learning motivation. This research helps teachers to add references to learning media that can be used.
Bridging Over from Learning Videos to Learning Resources Through Automatic Keyword Extraction

The presented system and approach facilitate intelligent, contextualized information access for learners based on automatic learning video analysis. The underlying workflow starts with automatically extracting keywords from learning videos followed by the generation of recommendations of learning materials. The approach has been implemented and investigated in a user study in a real-world VET setting. The study investigated the acceptance, perceived quality and relevance of automatically extracted keywords and automatically generated learning resource recommendations in the context of a set of learning videos related to chemistry and chemical engineering. The results indicate that such extracted keywords are in line with user-generated keywords and summarize the content of videos quite well. Also, they can be used as search key to find relevant learning resources.
Pengembangan Buku Penuntun Praktikum Sistem Peredaran Darah Manusia dengan Model Argument-Driven Inquiry (ADI)

This study aims to produce a practical guidebook of human circulatory system with Argument-Driven Inquiry (ADI) model for students at junior high schools. The design of this research was R&amp;D with 4-D model including define, design, develop, and disseminate. However, this research was only up to the develop step. The result of this study is in form of a practical guidebook of human circulatory system and using language that is easily understood and in accordance with EYD. Then, the practical worksheet consists of student identities, title of practices, basic theories, objectives, research questions, tools, materials, work steps, argumentation schemes, argumentation sessions, and reports. Next, the results of validation of experts and practitioners as well as the results of the readability test of the students obtain "Excellent" category. Moreover, the results of the implemented tests of all practices obtain "Almost All Activities Implemented" category. As a result, the practical guidebook of the human circulatory system with ADI model is valid and practical.
Borobudur: Commodification Within A Poor Knowledge Conservation

This paper takes Borobudur as a showcase in the promotion of an artifact with poor knowledge management. It indeed, one of the UNESCO World Heritage Sites since 1991, but it has been presented merely as a physical appearance. What makes it interesting is its grandeur as the largest Buddhist temple in the world during the glory of the Sailendra Kingdom in Java for five centuries, instead of the landmark of science and technological advance which should have advanced Indonesia’s achievement by now. The commodification has led Indonesia to turn Borobudur as a magnet for foreign tourists. There are numerous Buddhist sites in Indonesia which are part of the tourist destinations to trace Buddhist civilization in Indonesia, such as Buddhist temple in Jambi (Sumatra), Sleeping Buddha Statue in Mojokerto (East Java), and other Buddhist temples in Java and Bali. Yet, Borobudur has not been presented as the trail of Buddhist Civilization given the absence of knowledge conservation, let alone knowledge reproduction. Borobudur signifies the fact that Buddhism is the earliest religion that heavily influenced the incoming dominant religion later on. Much of its intangible aspect of the heritage has lost and forgotten given the poor knowledge management in the country. Ministry of Education needs to recover and reinvent the lost knowledge to make the commodification go along with meaning-making.
Point at the Triple: Generation of Text Summaries from Knowledge Base Triples (Extended Abstract)

We investigate the problem of generating natural language summaries from knowledge base triples. Our approach is based on a pointer-generator network, which, in addition to generating regular words from a fixed target vocabulary, is able to verbalise triples in several ways. We undertake an automatic and a human evaluation on single and open-domain summaries generation tasks. Both show that our approach significantly outperforms other data-driven baselines.
System Identification Using Monotonic Fuzzy Models

Fuzzy models became very popular tool for identification of nonlinear systems from input-output data. Unfortunately, their behaviour is considerably deteriorated in the regions where the data is sparse or contaminated by a noise. The paper shows that when a nonlinear function describing the systems is monotonic then a fuzzy system guaranteeing monotonicity significantly improves the performance of the fuzzy model. Furthermore, sufficient conditions for monotonicity of Takagi-Sugeno fuzzy systems with Gaussian membership functions are derived.
Identification of Cancer Disease Using Image Processing Approahes

Cancer, also called malignancy, is an abnormal growth of cells.There are more than 100 types of cancer, including breast cancer, skin cancer, lung cancer, colon cancer, prostate cancer, and lymphoma.Symptoms vary depending on the type.Cancer treatment may include chemotherapy, radiation, and/or surgery.According to American Cancer Society America will be encountering 1,806,950 new cases of cancer in the year 2020 causing 606,520 deaths.Cancer is the leading cause of death in the world.Cancer can be classified into two main categories malignant and benign.Early detection of cancer is the key to the successful treatment of cancer.There are various methodologies for the detection of cancer some include manual procedures, Manual identification is time-consuming and unreliable therefore computer-aided detection came into the research.Computer-aided detection involves image processing for feature extraction and classification techniques for the recognition of cancer type and stages.In this paper, several different algorithms have been discussed such as SVM, KNN, DT, etc. for the classification of the different cancers.This paper also presents a comparative analysis of the researches done in the past.
Complicações neurológicas associadas ao SARS-CoV-2 (COVID-19) no Brasil: Organização do grupo NEUROCOVID-RIO e achados preliminares

We live in moments of uncertainty due to the serious pandemic promoted by the Sars-CoV-2 virus. Furthermore, in our country, other viruses are there, such as H1N1, Dengue, Zika, Chikungunya, Measles, Yellow Fever, which often leave us perplexed by the differential diagnosis of different neurological syndromes that may, eventually, be associated. We created the NEUROCOVID-RIO Group comprising currently 44 health experts from several areas of the neurological sciences from different hospitals and laboratories from various institutions, including public and private universities. A database in which mathematical instruments are applied, including artificial intelligence, was organized. Preliminary findings show that well defined neurological syndromes seems to be more associated to Zika and Chikungunia viruses than with Sars-Cov-2.
Rise of Evolutionary Multi-Criterion Optimization: A Brief History of Time with Key Contributions

One of the main success stories in the evolutionary computation (EC) field is the use of EC framework to solve multi-criterion optimization problems. These problems give rise to a set of trade-off Pareto-optimal solutions, instead of a single optimal solution; hence a population-based EC framework is a natural choice for solving them. Starting in the early nineties with a few parameter-dependent algorithms, the research and application of evolutionary multi-criterion optimization (EMO) algorithms has become a field of its own, by attracting mathematicians, computer scientists, engineers, economists, managers, and entrepreneurs. In this chapter, we provide a chronological account of key contributions which kept the field alive, useful, and vibrant. A bibliometric study of published materials on the topic is also provided to paint a picture of the rise and the popularity of the field.
Lagrangian Decomposition for Classical Planning (Extended Abstract)

Optimal cost partitioning of classical planning heuristics has been shown to lead to excellent heuristic values but is often prohibitively expensive to compute. We analyze the application of Lagrangian decomposition, a classical tool in mathematical programming, to cost partitioning of operator-counting heuristics. This allows us to view the computation as an iterative process that can be seeded with any cost partitioning and that improves over time. In the case of non-negative cost partitioning of abstraction heuristics the computation reduces to independent shortest path problems and does not require an LP solver.
J48-Based Algorithm in Predicting the Success Rate in the Board Examination

J48 algorithm was utilized in this study to predict the performance rate in the Board Examinations for Teachers Education program (LET) of the secondary teacher education graduates.To develop the model, the raw scores in the college entrance examination as well as the general average (GA) in the core subjects, professional subjects, and common subjects were considered.Likewise, the performance in the LET review and in the actual LET Board, whether pass or fail were also taken into consideration.A total of 348 examples was deliberate came from 2012 to 2017 list of graduates.The pruned tree summed up to 16 utilizing 10 leaves.The Kappa value was established to be 0.8195 meaning an almost perfect agreement.In other words, all of the categorized instances by the researcher-developed J48-based academic analytics model had closely matched the actual count data.Hence, predicted failures, could be promptly given appropriate intervention programs by the dean and the professors in order to enhance the scores and GA of students.By so doing, the success rates in the LET will be improved.The J48 algorithm was tested at Aklan State University (ASU) [1] due to the absence of a data-mining system that could predict success in the LET.Therefore, the sets of data used in this study were from ASU specifically in Kalibo Campus.In its entirety, this study might help in determining the success rates of graduates in the LET that could eventually help in the accreditation of teacher education programs.
Face Recognition for Payment and Information Sharing

We have seen the emergence of various payment and information sharing methods with the rapid digitization of such services. Payment methods like NFC, Credit/Debit card, and QR code-based have become very common. These methods were intended to provide secure, safe and faster transactions. Although these have succeeded in their intentions up to some extent, there are various factors that are posing problems like confusion, time consumption, security threat, fraud, and theft. When it comes to sharing contact information and social media handles, visiting cards are still preferred but this is not the most reliable method as one either tends to lose these cards or has to store this information manually on their phone. This paper provides a comprehensive survey of the various available methods for making mobile payments and sending contact details as well as the challenges faced. Furthermore, we discuss and compare alternative available technologies like face recognition that can be implemented.
Multiple Layers of Fuzzy Logic to Quantify Vulnerabilities in IoT

Quantifying vulnerabilities of network systems has been a highly controversial issue in the fields of network security and IoT.Much research has been conducted on this purpose; however, these have many ambiguities and uncertainties.In this paper, we investigate the quantification of vulnerability in the Department of Transportation (DOT) as our proof of concept.We initiate the analysis of security requirements, using Security Quality Requirements Engineering (SQUARE) for security requirements elicitation.Then we apply published security standards such as NIST SP-800 and ISO 27001 to map our security factors and sub-factors.Finally, we propose our Multi-layered Fuzzy Logic (MFL) approach based on Goal question Metrics (GQM) to quantify network security and IoT (Mobile Devices) vulnerability in DOT.
Learning spatial–temporal features via a pose-flow relational model for action recognition

Pose-based action recognition has always been an important research field in computer vision. However, most existing pose-based methods are built upon human skeleton data, which cannot be used to exploit the feature of the motion-related object, i.e., a crucial clue of discriminating human actions. To address this issue, we propose a novel pose-flow relational model, which can benefit from both pose dynamics and optical flow. First, we introduce a pose estimation module to extract the skeleton data of the key person from the raw video. Second, a hierarchical pose-based network is proposed to effectively explore the rich spatial–temporal features of human skeleton positions. Third, we embed an inflated 3D network to capture the subtle cues of the motion-related object from optical flow. Additionally, we evaluate our model on four popular action recognition benchmarks (HMDB-51, JHMDB, sub-JHMDB, and SYSU 3D). Experimental results demonstrate that the proposed model outperforms the existing pose-based methods in human action recognition.
APPLYING EMPIRICAL THRESHOLDING ALGORITHM FOR A KEYSTROKE DYNAMICS BASED AUTHENTICATION SYSTEM

Through the application of a password-based authentication technique, users are granted permission to access a secure system when the username and password matches with that logged in database of the system.Furthermore, anyone who provides the correct username and password of a valid user will be able to log in to the secure network.In current circumstances, impostors can hack the system to obtain a user's password, while it has also been easy to find out a person's private password.Thus, the existing structure is exceptionally flawed.One way to strengthen the password-based authentication technique, is by keystroke dynamics.In the proposed keystroke dynamics based authentication system, despite the password match, the similarity between the typing pattern of the typed password and password samples in the training database are verified.The timing features of the user's keystroke dynamics are collected to calculate the threshold values.In this paper, a novel algorithm is proposed to authenticate the legal users based on the empirical threshold values.The first step involves the extraction of timing features from the typed password samples.The password training database for each user is constructed using the extracted features.Moreover, the empirical threshold limits are calculated from the timing features in the database.The second step involves user authentication by applying these threshold values.The experimental analyses are carried out in MATLAB simulation, and the results indicate a significant reduction in false rejection rate and false acceptance rate.The proposed methodology yields very low equal error rate of 0.5% and the authentication accuracy of 99.5%, which are considered suitable and efficient for real-time implementation.The proposed method can be a useful resource for identifying illegal invasion and is valuable in securing the system as a correlative or substitute form of client validation.
“Fear in 280 characters”

This chapter focuses on the phenomenon of fear of terrorist attacks in cyberspace through the analysis of linguistically encoded emotional responses of online social media (tweets). The analysis sample is tweets related to the attacks on Charlie Hebdo, Nice, and Barcelona between 2015 and 2017. The main objective is to analyze the evolution of emotivity in tweets during the first 24 hours after each of the attacks through automatized sentiment analysis. A method to quantify the fear of terrorist attacks in this environment through linguistic sentiment analysis is proposed. The main results indicate that the emotional profile of tweets after terrorist events changes as a function of elapsed time after the attack, the hashtag, and the event in question. This study follows the recent critical trend of traditional methodologies for approaching the phenomenon of fear based on self-reported measures, offering an alternative that combines Big Data, real-time data collection, and linguistic sentiment analysis in text-mediated communication.